Life expectancy gaps related to income/education

I’m currently writing a topic on ‘the causal effect of education on health’, so this is a topic I’ve looked at a bit – consider this post a ‘workblog’-post, even though it’s only tangentially related to what I’m working on.

This kind of stuff – health disparities related to education and income – pops up in the public debate every now and then, see e.g. this recent article (in Danish), or this analysis by AE-rådet (also in Danish). This is ‘politics’ to some extent (see the previous post), but it’s also a question about what’s actually going on in the world, and the latter type of question is the type of question I tend to be interested in answering. I’d like to make some general points here which are sometimes overlooked:

i. People with lower education are fatter. And being fat is bad for your health.

ii. People with lower levels of education smoke more: “Well-documented declines in smoking prevalence over time have not occurred evenly throughout society (12, 13). They have been most substantial among the most educated. Thus, the least educated form increasing proportions of those who remain smokers.” Regarding alcohol the picture is more complicated (as I’ve talked about before), however it should be noted that if the variance of the quantity consumed by the highly educated is lower than for the lower educated groups, as they claim in the article I link to at the beginning of this paragraph, then it would make sense if the highly educated people who die from alcohol-related diseases die later and lose fewer years of their life to the alcohol than does the group with low education (‘the uneducated alcoholic loses 20 years, the educated alcoholic loses five…’). Either way alcohol matters much less than smoking, and the differences aren’t that big in the former case. Incidentally the causal pathways of the smoking link are still unclear: “The causal pathways between education and smoking are both complicated and contested in the literature.” (link)

iii. Lifestyle differences among different educational groups make up a big part of the difference in health outcomes: “the mediating effects of health behaviors – measured by smoking, drinking, exercising and the body mass index – account in the short run for 17% to 31% and in the long run for 23% to 45% of the entire effect of education on health, depending on gender.”

iv. An additional point related to point iii.: I haven’t looked for studies on this because it’s obvious, but the health gradient is more sensitive to stuff like income level and employment status in countries like the US than it is in Denmark. So international (non-Scandinavian?) estimates of the magnitude of educational effects and income effects on health outcomes are likely to be biased upwards, compared to what the magnitude would be in a country like Denmark where ability to pay for medical services problems are unlikely to have much influence on life expectancy at this point.

v. I’ll spell out this point even though it should be obvious by now: Many of the reasons why people with a low education on average die too soon relate to the fact that they on average make poorer choices when it comes to their health. And the stuff mentioned above is just a small part of what’s going on; you also have related stuff like information channels and compliance differences, on top of stuff like ‘likelihood of seeking proper medical attention conditional on you actually needing it, and ability to verbalize complaints so that the doctor makes the correct inferences’ (e.g. a lot of T2 diabetics don’t get diagnosed, and this lowers their life expectancy significantly).

vi. Note that whereas it’s true that some jobs are still more unhealthy than others (a traditional mechanism most people think of when they’re thinking about these things), if the connection between type of work and health risks is known people employed in such jobs would be expected to earn a risk premium – this is not super relevant when you look at education and health, but it is something to have in mind when analyzing health and income stuff.

vii. It should be noted that if you get better over time at treating people for stuff that isn’t lifestyle-related and so stop a lot of people from dying early on of other causes, then lifestyle-stuff is going to become a big driver of health disparities.

The Cardiovascular System

I just finished the book, which is published by Britannica Educational Publishing and edited by Kara Rogers.

It’s a little bit repetitive, but it’s really quite good. I knew a lot about the subject already, but this is my first textbook dealing specifically with this topic and there were a few places where I had ‘aha-moments’ and suddenly understood everything a lot better – I really enjoy reading books that give me such experiences.

I should point out that Khan Academy has a lot of good stuff on this subject, and the videos there go into a lot more detail than does the book – I haven’t seen all those videos, but I’ve seen enough of them to know that this is mostly good stuff. I should perhaps also point out that each link above is to a topic covered at Khan Academy, each with multiple videos of coverage. Wikipedia also has some stuff on this subject.

As an intro textbook to the subject I think the book is a decent choice, though the illustrations are somewhat lacking. All concepts are properly introduced and defined, and definitions will sometimes be repeated other places in the book (which is part of what makes it repetitive) so you don’t necessarily need to memorize everything to keep track of what’s going on. My main points of criticism would be the unnecessary amount of repetition and the fact that it doesn’t actually go into much detail. The latter point of criticism can however also be considered a plus if you don’t know very much about the subjects covered, and of course the somewhat superficial treatment of the material also means that this is by no means a hard textbook to read.

I found it hard to blog stuff from the book, because most of it is just definitions, ‘how does it all work?’, ‘what can go wrong and how does it go wrong?’, disease progression, treatment options, etc. Not a lot of numbers in there, or a lot of stuff that can easily be quoted ‘out of context’. But I figured I couldn’t blog the book without at least posting a few bits from the book, so below a few quotes (none of these are ‘old numbers’; the book was published in 2011):

“of those likely to die during the first two weeks after a major heart attack, nearly half will die within one hour of the onset of
symptoms.”

“less than half of the persons who die from heart attacks each year in the United States survive long enough to reach the hospital.”

“While life expectancy following a heart transplant is difficult to predict, the average recipient will live 8 to 10 years.” [...] The survival rate at one year is now about 84 percent and at three years about 77 percent.”

“The renal arteries deliver to the kidneys of a normal person at rest 1.2 litres (2.5 pints) of blood per minute, a volume equivalent to approximately one-quarter of the heart’s output. Thus, a volume of blood equal to all that found in the body of an adult human is processed by the kidneys once every four to five minutes.”

“In general, the rate of heartbeat varies inversely with the size of the animal. In elephants it averages 25 beats per minute, in canaries about 1,000. In humans the rate diminishes progressively from birth (when it averages 130) to adolescence but increases slightly in old age. The average adult rate is 70 beats at rest.”

A big part of the book is available at the link.

Making Choices in Health: WHO Guide to Cost-Effectiveness Analysis

You can buy the book here, though I should note that I’m certain that free versions of the book are also available online. I started reading it yesterday and I completed it today.

The book consists of two parts: Part one deals with “Methods for Generalized Cost-Effectiveness Analysis” and part two consists of “Background Papers and Applications”. If you’re weird, like me, (or if you’re a researcher in the field…) you’ll want to read both parts. They write in the introduction that: “The main objective of this Guide is to provide policy-makers and researchers with a clear understanding of the concepts and benefits of GCEA [generalized cost-effectiveness analysis]. It provides guidance on how to undertake studies using this form of analysis and how to interpret the results.” As mentioned the book has two parts. It’s very clear that part one is written mainly for the politicians and that part two is written for the researchers – and good luck finding a politician who’ll actually read part 2 (/or part 1..?). I like to think that part one can be read and understood by most people, including certainly most readers of this blog, and I do not believe it requires a lot of knowledge about statistics or mathematics; some papers in part 2 on the other hand require math beyond the level I’ve taken for the reader to understand all the steps taken (here are a few wikipedia articles I had a look at while reading this part of the book). They repeat themselves a bit here and there, but it’s not hard to just skim passages containing stuff you’ve already dealt with elsewhere.

It should be noted that although some of it is a bit technical, there’s some good stuff in part 2 as well – for instance I really liked this table (from the fourth study in part 2, Econometric estimation of country-specific hospital costs):

Click to view full size. The obvious conclusion to draw here is that costs do not vary much across countries – no, they definitely do not… Actually I was very surprised to learn that there’s a huge amount of variation even within countries – in the same article they note that: “it must be emphasized that there is wide variation in the unit costs estimated from studies within a particular country [...] These differences are sometimes of an order of magnitude, and cannot always be attributed to different methods. This implies that analysts cannot simply take the cost estimates from a single study in a country to guide their assessment of the cost-effectiveness of interventions, or the costs of scaling-up. In some cases, they could be wrong by an order of magnitude.”

In the first chapter they state that:

“It appears that the field can develop in two distinct directions, towards increasingly contextualized analyses or towards more generalized assessments. Cost-effectiveness studies and the sectoral application of CEA [cost effectiveness analyses] to a wide range of interventions can become increasingly context specific—at the individual study level by directly incorporating other social concerns such as distributional weights or a priority to treat the sick and at the sectoral level by developing complex resource allocation models that capture the full range of resource, ethical and political constraints facing decision-makers.
We fear that this direction will lead ultimately to less use of costeffectiveness information in the health policy dialogue. Highly contextualized analyses must by definition be undertaken in each context; the cost and time involved as well as the inevitable complexity of the resource allocation models will limit their practical use. The other direction for sectoral cost-effectiveness, the direction that WHO is promoting [...] is to focus on the general assessment of the costs and health benefits of different interventions in the absence of various highly variable local decision constraints. A generalized league table of the cost-effectiveness of interventions for a group of populations with comparable health systems and epidemiological profiles can make the most powerful component of CEA readily available to inform health policy debates. Relative judgements on cost-effectiveness—e.g. treating tuberculosis with the DOTS strategy is highly cost-effective and providing liver transplants in cases of alcoholic cirrhosis is highly cost-ineffective—can have wide ranging influence and, as one input to an informed policy debate, can enhance allocative efficiency of many health systems.”

I’m not a health economist so I have no idea which way the field has developed since the book was written. The book isn’t exactly brand new (it’s from 2003) and so I figured one way to probe whether the recommendations have been followed in the years after the book was published was to try to figure out the extent to which one of the big ideas here, the use of Stochastic League Tables in CEAs, has been implemented. So I went to google scholar and searched for the term – and it gave me 7400+ results (and 589 since 2012). It seems to me that the use of these things at least have caught on. I incidentally have no idea to which extent researchers have now moved towards the use of GCEAs and away from the previously (?) widely used ‘incremental approach’ studies when performing these analyses. I posted the long quote above also to caution people unfamiliar with the literature against complaining about CEAs which are ‘not specific enough’ (a complaint I’ve made myself in the past…) – it may make a lot of sense to not make a CEA too specific, in order to make it more potentially useful to decisionmakers. A related point is that the idea of using CEAs in a formulaic way to decide which health interventions ‘pass the bar’ and which do not, and thus base decisions such as which health interventions should receive government support only on the outcome of CEAs, do not have much support in the field – as they put it in Murray, Lauer et al. (study 7 in the second part):

“The results of cost-effectiveness analysis should not be used in a formulaic way—starting with the intervention that has the lowest cost-effectiveness ratio, choosing the next most attractive intervention, and continuing until all resources have been used (10). There is generally too much uncertainty surrounding estimates for this approach; moreover, there are other goals of health policy in addition to improving population health. The tool is most powerful when it is used to classify interventions into broad categories such as those we used. This approach provides decision-makers with information on which interventions are low-cost ways of improving population health and which improve health at a much higher cost. This information enters the policy debate to be weighed against the effect of the interventions on other goals of health policy.”

(They also emphasize this aspect in the first part of the book). I could quote a lot of stuff from the book, but if you’re interested you’ll read it and if you’re not you’d probably not read my quotes either. If you’re interested in cost-effectiveness analyses, I think you should probably read this book – or at least the first part which is relatively easy and does not take that long to read. If you’re not interested in this stuff you should definitely stay away from it. But I think the book is a good starting point if you seek to understand some of the main concepts, issues, and tradeoffs involved when doing and interpreting CEAs.

One last thing I should note, primarily to the people who will not read the book: Many people think of the people doing stuff like cost-effectiveness analyses in this field as the bad guys. That’s because they’re the ones who keep reminding us that we can’t afford everything. When it comes to health care we don’t like to be reminded of this fact, because sometimes when it’s been decided by decisionmakers that public money should not be spent on X it means that someone will die. What I’d like to remind you of is that resource constraints don’t go away just because people prefer to ignore them; rather, when people disregard cost-effectiveness it may just mean that fewer people will be helped and more people will die than if a different course of action, perhaps the one suggested by a CEA, had been taken. CEAs may not provide the complete answer to how we should do these things and they have some limitations, but we should all keep in mind that it matters how we spend our money on this stuff, and that completely ignoring the resource constraint isn’t really a solution to the problems we face when dealing with these matters.

Adult development and aging: Biopsychosocial Perspectives, 4th edition (IV)

I’ve now finished the book. I must say that I’m a bit disappointed but thinking about it this is likely mostly due to the huge variation in the quality of the material here; some of it is really great (I’ve tried to cover that stuff here), some of it is downright awful. If you’re interested in this kind of stuff, you may also like this previous post of mine (I liked that book better).

Below I’ve tried to pick out the good stuff from chapters 10-14 (there’s quite a bit of not-very-good-stuff as well). As always, you can click on the figures/tables to see them in a higher resolution:

“Looking at the intrinsic–extrinsic dimension of vocational satisfaction, researchers have found that people with high neuroticism scores are less likely to feel that their jobs are intrinsically rewarding. Perhaps for this reason, neuroticism is negatively related to job satisfaction; by contrast, people high in the traits of conscientiousness and extraversion are more satisfied in their jobs (Furnham, Eracleous, & Chamorro-Premuzic, 2009; Judge, Heller, & Mount, 2002; Seibert & Kraimer, 2001).
The relationship between personality and job satisfaction works both ways. In one longitudinal study of adults in Australia, although personality changes predicted changes in work satisfaction, changes in personality were also found to result from higher job satisfaction. Over time, workers who were more satisfied with their jobs became more extraverted (Scollon & Diener, 2006).
People’s affect can also have an impact on the extent to which they perceive that there is a good fit between their work-related needs and the characteristics of the job. People who tend to have a positive approach to life in general will approach their work in a more positive manner, which in turn will lead to a better person–environment fit (Yu, 2009). [...]

The Whitehall II Study, a longitudinal investigation of health in more than 10,300 civil employees in Great Britain, provides compelling data to show the links between workrelated stress and the risk of metabolic syndrome (Chandola, Brunner, & Marmot, 2006). Carried out over five phases from 1985 to 1997, the study included measurements of stress, social class, intake of fruits and vegetables, alcohol consumption, smoking, exercise, and obesity status at the start of the study. Holding all other factors constant and excluding participants who were initially obese, men under high levels of work stress over the course of the study had twice the risk of subsequently developing metabolic syndrome. Women with high levels of stress had over five times the risk of developing this condition.

More recent research suggests that Whitehall II men who reported higher justice at work (such as perceived job fairness) had a far lower risk of metabolic syndrome compared with men who experienced lower work justice (Gimeno et al., 2010). For women, stress encountered at work independently predicted Type 2 diabetes, even after controlling for socioeconomic position and stressors unrelated to work (Heraclides, Chandola, Witte, & Brunner, 2009). [...]

When work–family conflict does occur, it takes its toll on the individual’s physical and mental health, causing emotional strain, fatigue, perception of overload, and stress (van Hooff et al., 2005). There are variations in the extent and impact of work–family conflict, however, and not all workers feel the same degree of conflict. Conflict is most likely to occur among mothers of young children, dual-career couples, and those who are highly involved with their jobs.Workers who devote a great deal of time to their jobs at the expense of their families ultimately pay the price in terms of experiencing a lower overall quality of life (Greenhaus, Collins, & Shaw, 2003). There are higher levels of work-family conflict among those employed in the private sector than those employed in the public sector (Dolcos & Daley, 2009).
Age also plays into the work–family conflict equation. Younger workers (under age 45) typically experience more conflict than older workers (46 and older); though when older workers experience conflict the effects seem to be stronger (Matthews, Bulger, & Barnes-Farrell, 2010). [...]

Overall, workers over the age of 55 are nearly half as likely to suffer a nonfatal injury as those who are 35 years and younger, and about half as likely to suffer death due to a work-related injury. However, when older workers (55–64) must miss work due to injury or illness, they spend twice as many days away from work (12) per year than do younger workers (25–34) (Bureau of Labor Statistics, 2010c). [...]

Few retirees show a ‘‘crisp’’ pattern of leaving the workplace in a single, unreversed, clear-cut exit. Most experience a ‘‘blurred’’ exit in which they exit and reenter the workplace several times. They may have retired from a long-term job to accept bridge employment, such as an insurance agent who retires from the insurance business but works as a crossing guard or server at a fast-food restaurant. Other workers may retire from one job in a company and accept another job performing another role in the same company.
Workers who have a long, continuous history of employment in private sector jobs tend not to seek bridge employment because they typically have sufficient financial resources (Davis, 2003). In general, involvement in bridge employment is strongly related to financial need. [...]

about 17% of the 65 and older population are still considered to be in the labor force, meaning that they are either working or actively seeking employment. Virtually all of those 75 years and older (93%) have ended their full-time participation in the nation’s workforce (Bureau of Labor Statistics, 2010b). However, many remain employed on a part-time basis; nearly half of all men and 61% of all women 70 years and older engage in some paid work (He et al., 2005). [...]

Retirement is in many ways a 20th-century phenomenon (Sterns & Gray, 1999). Throughout the 1700s andmid-1800s very few people retired, a trend that continued into the 1900s; in 1900 about 70% of all men over 65 years were still in the labor force. [...] Attitudes toward retirement were largely negative in the United States until the mid-1960s because lack of employment was associated with poverty. People did not want to retire because their financial security would be placed at risk. However, with increases in earnings and Social Security benefits, retirement began to gain more acceptance. [...]

The transition itself from work to retirement seems to take its toll on marital satisfaction when partners have high levels of conflict. The greatest conflict is observed when one partner is working while the other has retired. Eventually, however, these problems seem to subside, and after about 2 years of retirement for both partners, levels of marital satisfaction once again rise (Moen, Kim, & Hofmeister, 2001). [So large spousal age differences would seem to predict higher levels of conflict, US...] [...]

[...]

Approximately 90% of adults who complete suicide have a diagnosable psychiatric disorder. The most frequent diagnoses of suicidal individuals are major depressive disorder, alcohol abuse or dependence, and schizophrenia. Among suicidal adults of all ages, the rates of psychiatric disorders are very high, ranging from 71% to over 90%.

Each year, approximately 33,000 people in the U.S. population as a whole die of suicide. The majority are ages 25 to 54 (Xu et al., 2010). The age-adjusted suicide rate in the United States of all age, race, and sex groups is highest for all demographic categories among White males aged 85 and older at about 48 suicide deaths per 100,000 in the population (Centers for Disease Control and Prevention, 2010f). [...]

Typically, nursing homes are thought of as permanent residences for the older adults who enter them, but about 30% of residents are discharged and able to move back into the community after being treated for the condition that required their admission. About one quarter of people admitted to nursing homes die there, and another 36% move to another facility (Sahyoun, Pratt, Lentzner, Dey, & Robinson, 2001). [I found this to be very surprising and would love to see some Danish numbers..., US] [...] As of 2008, there were approximately 15,700 nursing homes in the Unites States with a total of over 1.7million beds, 83% of which were occupied (National Center for Health Statistics, 2009). [...]

In 2008 [Medicaid] provided health care assistance amounting to $344.3 billion. Nursing homes received $56.3 billion from Medicaid in 2008. Together Medicare and Medicaid (federal and state) financed $813.5 billion in health care services in 2008, which was 34% of the nation’s total health care bill of $2.3 trillion (private and public funding combined) and 82% of all federal spending on health (Center for Medicare and Medicaid Services, 2010b). [...]

deficiencies in nursing homes remain a significant problem, limiting severely the quality of care that many residents receive. Continued reporting of these deficiencies, monitoring by government agencies, and involvement of family members advocating for residents are important safeguards. If you have a relative in a nursing home, it is important for you to be aware of these problems and vigilant for ways to prevent them from affecting your relatives. [...] Although there is a relatively small percentage overall of people 65 and older living in nursing homes, the percentage of older adults who are institutionalized increases dramatically with age. As of 2004 (the most recent date available), the percentages rise from 0.9% for persons 65 to 74 years to 3.6% for persons 75 to 84 years and 13.9% for persons 85+ (Federal Interagency Forum on Age-Related Statistics, 2009). [...]

Alzheimer’s disease is found in nearly half of all nursing home residents (45% in 2008) [...] 56.8% of nursing home residents are chairbound, meaning that they are restricted to a wheelchair. Despite the large number of residents with Alzheimer’s disease, only 5% of nursing homes have special care units devoted specifically to their care (Harrington, Carrillo, & Blank, 2009). [...] Nearly two thirds (65.2%) of residents receive psychotropic medications, including antidepressants, antianxiety drugs, sedatives and hypnotics, and antipsychotics (Harrington et al., 2009). [...] A study of the daily life of residents conducted in 2002 revealed that, as was the case in the 1960s, residents spend almost two thirds of the time in their room, doing nothing at all (Ice, 2002). Thus, for many residents, there are simply not enough activities in the average nursing home (Martin et al., 2002). [...]

In a dying person, the symptoms that death is imminent include being asleep most of the time, being disoriented, breathing irregularly, having visual and auditory hallucinations, being less able to see, producing less urine, and having mottled skin, cool hands and feet, an overly warm trunk, and excessive secretions of bodily fluids (Gavrin & Chapman, 1995). An older adult who is close to death is likely to be unable to walk or eat, recognize family members, in constant pain, and finds breathing to be difficult. A common syndrome observed at the end of life is the anorexia-cachexia syndrome, in which the individual loses appetite (anorexia) and muscle mass (cachexia). The majority of cancer patients experience cachexia, a condition also found commonly in patients who have AIDS and dementia. In addition to the symptoms already mentioned, patients who are dying are likely to experience nausea, difficulty swallowing, bowel problems, dry mouth, and edema, or the accumulation of liquid in the abdomen and extremities that leads to bloating. [...]

Marital status and education are two significant predictors of mortality. The age-adjusted death rate for those who never married is substantially higher than for those who were ever married, even taking into account the higher mortality of those who are widowed and divorced. The advantage holds for both men and women across all age groups of adults ages 15 and older (Xu et al., 2010). Educational status is also related to mortality rate. In all age groups, those with a college education or better have lower mortality rates. [...] Not only the level of occupation, but also the pattern of jobs people hold throughout adulthood, are related to mortality rates. The risk of mortality is lower in men who move up from manual to professional or managerial-level occupations (House, Kessler, Herzog, & Mero, 1990; Moore & Hayward, 1990). Men who hold a string of unrelated jobs have higher rates of early mortality than those with stable career progressions (Pavalko, Elder, & Clipp, 1993). [...]

Across all countries studied by the World Health Organization, the poor are over four times as likely to die between the ages of 15 and 59 as are the nonpoor (World Health Organization, 2009). [...]

The majority of patients in SUPPORT ['Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments' - US] stated that they preferred to die at home; nonetheless, most of the deaths occurred in the hospital (Pritchard et al., 1998). Furthermore, the percentage of SUPPORT patients who died in the hospital varied by more than double across the five hospitals in the study (from 29 to 66%). The primary factor accounting for the probability of a patient dying in the hospital rather than at home was the availability of hospital beds. Later studies in countries such as Great Britain, Belgium, and the Netherlands have confirmed that place of death varies according to availability of hospital beds rather than any specific characteristics of patients or wishes of their families (Houttekier et al., 2010). [...]

[...]

Identity processes may provide a means of maintaining high levels of well-being in the face of less than satisfactory circumstances. Through identity assimilation, people may place a positive interpretation on what might otherwise cause them to feel that they are not accomplishing their desired objectives. The process of the life story, through which people develop a narrative view of the past that emphasizes the positive, is an example of identity assimilation as it alters the way that people interpret events that might otherwise detract from self-esteem (Whitbourne et al., 2002). For instance, older psychiatric patients minimized and in some cases denied the potentially distressing experience of having spent a significant part of their lives within a state mental hospital. Therefore, they were not distressed in thinking back on their lives and past experiences (Whitbourne & Sherry, 1991). People can maintain their sense of subjective well-being and can portray their identity in a positive light, even when their actual experiences would support less favorable interpretations.”

Adult development and aging: Biopsychosocial Perspectives, 4th edition (II)

The first post about the book is here. Below some stuff from chapters 4 and 5, which I liked a lot better than the first ones because they had a lot more data:

“The overall pattern of body weight in adulthood shows an upside-down U-shaped trend reflecting the fact that most people increase in their weight from the 20s until the mid-50s, after which their weight decreases. Most of the weight gain that occurs through the years of middle adulthood is due to an increase in BMI (Ding, Cicuttini, Blizzard, Scott, & Jones, 2007), which is manifested mainly as the accumulation of body fat around the waist and hips (commonly referred to as the ‘‘middle-aged spread’’). The loss of body weight in the later years of adulthood is not, however, due to a loss of this accumulated fat and so does not mean that older adults necessarily become healthier or more fit. Instead, older adults lose pounds because they suffer a reduction of FFM [fat-free mass] due to loss of muscle mass, even if they maintain high levels of activity (Manini et al., 2009).
At the other end of the spectrum, some older adults continue to gain weight to the point of developing a BMI that places them in the overweight or obese categories. Between the mid-1990s and mid-2000s, the percent of older adults classified as overweight increased from 60 to 69% and as obese from 22 to 31% (Houston, Nicklas, & Zizza, 2009). [...]

You are able to move around in your environment due to the actions of the structures that support this movement, including the bones, joints, tendons, and ligaments that connect the muscles to the bones, and the muscles that control flexion and extension. In the average person, all these structures undergo age-related changes that compromise their ability to function effectively. Beginning in the 40s (or earlier in the case of injury), each component of mobility undergoes significant age-related losses. Consequently, a gradual reduction of walking speed occurs (Shumway-Cook et al., 2007). [...] The adult years are characterized by a progressive age-related loss of muscle tissue, a process known as sarcopenia. There is a reduction in the number and size of muscle fibers, especially the fast-twitch fibers involved in speed and strength. As indicated by research from cross-sectional studies, muscle strength (as measured by maximum force) reaches a peak in the 20s and 30s, remains at a plateau until the 40s to 50s, and then declines at a faster rate of 12 to 15% per decade (Kostka, 2005), with more pronounced decreases, at least cross-sectionally, for men. Muscular endurance (as measured by isometric strength) is, however, generally maintained throughout adulthood (Lavender & Nosaka, 2007). [...] The loss of muscle mass brings with it a set of negative consequences including increased risk of falling, limitations in mobility, and reduced quality of everyday life. Unfortunately, sarcopenia can become part of a vicious cycle because the greater the loss of muscle mass, the greater the difficulty in undertaking exercise, causing an exacerbation of muscle loss and further weakening (Lang et al., 2009). [...]

Bone is living tissue that constantly reconstructs itself through a process of bone remodeling in which old cells are destroyed and replaced by new cells. The general pattern of bone development in adulthood involves an increase in the rate of bone destruction compared to renewal and greater porosity of the calcium matrix, leading to loss of bone mineral content. [...] Estimates of the decrease in bone mineral content over adulthood are about .5% per year for men and 1% per year for women (Emaus, Berntsen, Joakimsen, & Fonnebo, 2006). Further weakening occurs due to microcracks that develop in response to stress placed on the bones (Diab, Condon, Burr, & Vashishth, 2006). Part of the older bone’s increased susceptibility to fracture can be accounted for by a loss of collagen, which reduces the bone’s flexibility when pressure is put upon it (Saito & Marumo, 2009). [...]

Cardiovascular efficiency is indexed by aerobic capacity, the maximum amount of oxygen that can be delivered through the blood, and cardiac output, the amount of blood that the heart pumps perminute. Both indices decline consistently at a rate of about 10% per decade from age 25 and up so that the average 65-year-old has 40% lower cardiovascular efficiency than the young adult (Betik & Hepple, 2008). The decline is more pronounced in males than females (Goldspink et al., 2009). Maximum heart rate, the heart rate achieved at the point of maximum oxygen consumption, also shows a linear decrease across the years of adulthood. Declines in aerobic capacity occur even in highly trained athletes, but those who continue to exercise at a high level of intensitymaintain their aerobic capacity longer than non-athletes (Tanaka&Seals, 2003). [...] With regard to aerobic functioning, exercise is one of the best ways you can slow down the rate of your body’s aging process. [...]

Approximately 30% of all adults 65 and older suffer from urge incontinence, a form of urinary incontinence in which the individual experiences a sudden need to urinate, and often results in urine leakage. Stress incontinence involves loss of urine experienced during exertion. The prevalence of daily incontinence ranges from 12% in women 60 to 64 years old to 21% in women 85 years old or older [...] A variety of treatments are available to counteract incontinence, but because people often mistakenly assume that bladder dysfunction is a normal part of aging, they are less likely to seek active treatment. In one study of more than 7.2 million patients diagnosed with overactive bladder, 76% went untreated (Helfand, Evans,&McVary, 2009). Medications such as tolderodine (Detrol LA) are becoming increasingly available to help control bladder problems. [...]

Although men do not experience a loss of sexual function comparable to the menopause (despite what you might hear about the ‘‘male menopause’’), men undergo andropause, which refers to age-related declines in the male sex hormone testosterone. The decline in testosterone is equal to 1% per year after the age of 40, a decrease observed in longitudinal as well as cross-sectional studies (Feldman et al., 2002). The term ‘‘late-onset hypogonadism’’ or ‘‘age-associated hypogonadism’’ has begun to replace the term andropause, although all three terms are currently in use. [...] Erectile dysfunction (ED), a condition in which a man is unable to achieve an erection sustainable for intercourse, is estimated to increase with age in adulthood, from a rate of 31% among men 57–65 to 44% of those 65 and older. ED is related to health problems in older men, including metabolic syndrome (Borges et al., 2009). [...]

Normal aging seems to have major effects on the prefrontal cortex, the area of the brain most involved in planning and the encoding of information into long-term memory, as well as in the temporal cortex, involved in auditory processing (Fjell et al., 2009). The hippocampus, the structure in the brain responsible for consolidating memories, becomes smaller with increasing age, although this decline is more pronounced in abnormal aging such as in Alzheimer’s disease (Zhang et al., 2010). [...]

Most people require some form of corrective lenses by the time they reach their 50s or 60s. Presbyopia, or loss of the ability to focus vision on near objects, is the primary culprit for the need for reading glasses, and is the visual change that most affects people in midlife and beyond.
Presbyopia is caused by a thickening and hardening of the lens, the focusing mechanism of the eye (Sharma & Santhoshkumar, 2009). As a result, the lens cannot adapt its shape when needed to see objects up close to the face. By the age of 50, presbyopia affects the entire population. Treatment for the cause of presbyopia does not exist, and although bifocals were the only correction since the time of Benjamin Franklin (who invented them) newer multifocal contact lenses are increasingly becoming available on the market (Woods, Woods, & Fonn, 2009). Though you cannot cure presbyopia, you may be able to alter its onset because lifestyle habits seem to affect the rate at which the presbyopic aging process occurs. For example, smoking accelerates the aging of the lens (Kessel, Jorgensen, Glumer, & Larsen, 2006).
Older adults are also likely to experience the loss of visual acuity, or the ability to see details at a distance. The level of acuity in an 85-yearold individual is approximately 80% less than that of a person in their 40s. [...]

Loss of balance is one of the main factors responsible for falls in older adults (Dickin, Brown, & Doan, 2006). In 2007 alone, more than 15,800 people 65 and older were known to have died directly from injuries related to falls (Kung, Hoyert, Xu, & Murphy, 2008); 1.8 million were treated in emergency departments for fall-related nonfatal injuries, and about 460,000 of these people were hospitalized (Stevens, Ryan, & Kresnow, 2006). [...]

Smell and taste belong to the chemical sensing system referred to as chemosensation. The sensory receptors in these systems are triggered when molecules released by certain substances stimulate special cells in the nose, mouth, or throat. Despite the fact that the olfactory receptors constantly replace themselves, the area of the olfactory epithelium shrinks with age, and ultimately the total number of receptors becomes reduced throughout the adult years. At birth, the olfactory epithelium covers a wide area of the upper nasal cavities, but by the 20s and 30s, its area has started to shrink noticeably.
Approximately one third of all older adults suffer some form of olfactory impairment (Shu et al., 2009) with almost half of those 80 years and older having virtually no ability to smell at all (Lafreniere & Mann, 2009). The loss of olfactory receptors reflects intrinsic changes associated with the aging process, as well as damage caused by disease, injury, and exposure to toxins. Research suggests that these environmental toxins may play a larger role in olfactory impairment than changes due to the aging process. [...]

A sedentary lifestyle is the first major risk factor for heart disease. The relationship between leisure activity and heart disease is well established (Yung et al., 2009), with estimates ranging from a 24% reduction in the risk of myocardial infarction among non-strenuous exercisers to a 47% reduced risk among individuals engaging in a regular pattern of strenuous exercise (Lovasi et al., 2007). As it happens, the majority of adults at highest risk for heart disease (i.e., those 75 and older) are the least likely to exercise. Only about 36% of people 65 to 74 and 16% of those 75 and older engage in vigorous leisure activity (National Health Interview Survey, 2009). [...] Approximately one fifth of all adults in the United States are current smokers. The rates of current smokers decrease across age groups of adults to 10% of those 65 and older (National Health Interview Survey, 2009). [...]

In 2009, it was estimated that nearly 1.5 million Americans received a diagnosis of cancer (not including skin cancer or noninvasive cancers) and that about 10.5 million are living with the disease. The lifetime risk of developing cancer is about 1 in 2 for men and 1 in 3 for women (American Cancer Society, 2009). [...] All cancer is genetically caused in the sense that it reflects damage to the genes that control cell replication. [...this is actually, I think, a very good way to put it.] [...]

A nationwide study of over 900,000 adults in the United States who were studied prospectively (before they had cancer) from 1982 to 1998 played an important role in identifying the role of diet. During this period of time, there were more than 57,000 deaths within the sample from cancer. The people with the highest BMIs had death rates from cancer that were 52% higher for men and 62% higher for women compared with men and women of normal BMI. The types of cancer associated with higher BMIs included cancer of the esophagus, colon and rectum, liver, gallbladder, pancreas, and kidney. Significant trends of increasing risk with higher BMIs were observed for death from cancers of the stomach and prostate in men and for death from cancers of the breast, uterus, cervix, and ovary in women (Calle, Rodriguez, Walker-Thurmond, & Thun, 2003). We can conclude from this research that maintaining a low BMI is a critical preventive step in lowering your risk of cancer.

In addition to BMI, eating specific foods seems to play a role in cancer prevention. Stomach cancer is more common in parts of the world—such as Japan, Korea, parts of Eastern Europe, and Latin America—in which people eat foods that are preserved by drying, smoking, salting, or pickling. By contrast, fresh foods, especially fresh fruits and vegetables, may help protect against stomach cancer. Similarly, the risk of developing colon cancer is thought to be higher in people whose diet is high in fat, low in fruits and vegetables, and low in highfiber foods such as whole-grain breads and cereals. [...]

It is estimated that 8 million women and 2 million men in the United States suffer from osteoporosis (Sweet, Sweet, Jeremiah, & Galazka, 2009). Women are at higher risk than men because they have lower bone mass in general but nevertheless, osteoporosis is a significant health problem in men. Rates of osteoporosis-related bone fracture are equivalent to the rates of myocardial infarction (Binkley, 2009). Women vary by race and ethnicity in their risk of developing osteoporosis; White and Asian women have the highest risk, whereas Blacks and Hispanics the lowest. In addition, women who have small bone structures and are underweight have a higher risk for osteoporosis than heavier women. [...]

According to the World Health Organization, the number of people suffering from diabetes worldwide is approximately 171 million in 2010, a number that will double by 2030. [...]

Approximately 20% of cases of dementia are due to cerebrovascular disease (Knopman, 2007). [...] In vascular dementia, progressive loss of cognitive functioning occurs as the result of damage to the arteries supplying the brain. Dementia can follow a stroke, in which case it is called acute onset vascular dementia, but the most common form of vascular dementia is multi-infarct dementia or MID, caused by transient ischemic attacks. In this case, a number of minor strokes (‘‘infarcts’’) occur in which blood flow to the brain is interrupted by a clogged or burst artery. Each infarct is too small to be noticed, but over time, the progressive damage caused by the infarcts leads the individual to lose cognitive abilities. There are important differences between MID and Alzheimer’s disease. The development of MID tends to be more rapid than Alzheimer’s disease, and personality changes are less pronounced. The higher the number of infarcts, the greater the decline in cognitive functioning (Saczynski et al., 2009). [...]

People who develop Parkinson’s disease show a variety of motor disturbances, including tremors (shaking at rest), speech impediments, slowing of movement, muscular rigidity, shuffling gait, and postural instability or the inability to maintain balance. Dementia can develop during the later stages of the disease, and some people with Alzheimer’s disease develop symptoms of Parkinson’s disease. Patients typically survive 10 to 15 years after symptoms appear.”

Adult development and aging: Biopsychosocial Perspectives, 4th edition

“Everyone ages. This very fact should be enough to draw you into the subject matter of this course, whether you are the student or the instructor. Yet, for many people, it is difficult to imagine the future in 50, 40, or even 10 years from now. The goal of our book is to help you imagine your future and the future of your family, your friends, and your society. We have brought together the latest scientific findings about aging with a more personal approach to encourage you to take this imaginative journey into your future. [...]

Our goal is to engage you by presenting you with information that is of both personal and professional interest. We will explore the variety of ways individuals can affect their own aging process, such as through incorporating behaviors and activities designed to maintain high levels of functioning well into the later decades of life.”

From the introduction and first part of chapter 1. You can find the book here. I thought the subject would be interesting to read about, and apparently this is the kind of stuff that’s available. I’m not super impressed at this point as there’s a lot of ‘talk’ included in the first chapters of the book – they tend to use many words to say very little. And quite a bit of the talk stuff is just unscientific theorizing without data. But there’s some interesting stuff here as well. Below some stuff from the first 3 chapters (click to view figures in a higher resolution):

“In 1900, the number of Americans over the age of 65 years made up about 4% of the population [...] People 65 and older now represent 12.3% of the total U.S. population [...] In 1990, an estimated 37,306 people over the age of 100 lived in the United States. By 2004 this number increased 73% to 64,658, and by 2050 there will be over 1.1 million of these exceptionally aged individuals.”

“Women over the age of 65 currently outnumber men, amounting to approximately 58% of the total over-65 population [in the US]. [...] In 2010, there were 531 million people worldwide over the age of 65. Predictions suggest that this number will triple to 1.53 billion by the year 2050 (U.S. Bureau of the Census, 2010c). China currently has the largest number of older adults (106 million), but Japan has the highest percentage of people 65 and older (20%) (Kinsella & He, 2009). [...]

[...]

“The most compelling attempts to explain aging through genetics are based on the principle of replicative senescence, or the loss of the ability of cells to reproduce. Scientists have long known that there are a finite number of times (about 50) that normal human cells can proliferate in culture before they become terminally incapable of further division (Hayflick, 1994).

Until relatively recently, scientists did not know why cells had a limited number of divisions. It was only when the technology needed to look closely at the chromosome developed that researchers uncovered some of the mystery behind this process.

As we saw in Figure 2.6, the chromosome is made up largely of DNA. However, at either end of the chromosomes are telomeres, repeating sequences of proteins that contain no genetic information (see Figure 2.8). The primary function of the telomere is to protect the chromosome from damage. With each cell division, the telomeres become shorter, ultimately altering patterns of gene expression affecting the functioning of the cell and the organ system in which it operates. Once telomeres shorten to the point of no longer being able to protect the chromosome, adjacent chromosomes fuse, the cell cycle is halted, and ultimately the cell dies (Shin, Hong, Solomon, & Lee, 2006). Evidence linking telomere length to mortality in humans suggests that the telomeres may ultimately hold the key to understanding the aging process (Cluett & Melzer, 2009).

However, biology does not completely explain the loss of telomeres over the course of life. Supporting the idea of biopsychosocial interactions in development, researchers have linked telomere length to social factors. Analyzing blood samples from more than 1,500 female twins, researchers in the United Kingdom determined that telomere length was shorter in women from lower socioeconomic classes (Cherkas et al., 2006). There was a difference of seven ‘‘biological years’’ (measured in terms of telomeres) between twins with manual jobs and their co-twins in higher-ranking occupations. The researchers attributed this difference to the stress of being in a lower-level occupation in which people have less control over their day-to-day activities. Body mass index, smoking, and lack of exercise were additional factors influencing telomere length. A subsequent study on this sample provided further research of the important role of lifestyle factors. Even after the researchers adjusted for such factors as age, socioeconomic status, smoking, and body mass index, people who engaged in higher levels of physical activity had longer telomeres than those who did not (Cherkas et al., 2008). [...]

Random error theories are based on the assumption that aging reflects unplanned changes in an organism over time. The wear and tear theory of aging is one that many people implicitly refer to when they say they feel that they are ‘‘falling apart’’ as they get older. According to this view, the body, like a car, acquires more and more damage as it is exposed to daily wear and tear from weather, use, accidents, and mechanical insults. Programmed aging theories, in contrast, would suggest that the car was not ‘‘built to last,’’ but rather was meant to deteriorate over time in a systematic fashion.  [...]

The free radical theory, or oxidative stress theory (Sohal, 2002), focuses on a set of unstable compounds known as free radicals, produced when certain molecules in cells react with oxygen. The primary goal of a free radical is to seek out and bind to other molecules. When this occurs, the molecule attacked by the free radical loses functioning. Although oxidation caused by free radicals is a process associated with increasing age, researchers have questioned the utility of this approach as a general theory of aging (Perez et al., 2009).”

Chapter 3 has some stuff on problems with making causal claims in this area of research and some stuff on longitudinal studies and cross-sectional studies in this area, including pros and cons of the two types of studies. After that they note that:

“considerable progress in some areas of research has been made through the application of sequential designs. These designs consist of different combinations of the variables age, cohort, and time of measurement. Simply put, a sequential design involves a ‘‘sequence’’ of studies, such as a cross-sectional study carried out twice (two sequences) over a span of 10 years. The sequential nature of these designs is what makes them superior to the truly descriptive designs conducted on one sample, followed over time (longitudinal design) or on different-aged samples, tested on one occasion (cross-sectional design). Not only do sequential studies automatically provide an element of replication, but when they are carried out as intended, statistical analyses can permit remarkably strong inferences to be drawn about the effect of age as distinct from cohort or time of measurement.”

Much of the stuff covered in chapter 3 on research methods should be known stuff to people reading a blog like this, because aging research isn’t that different from other types of research. I skimmed over some of this stuff because much of it is (a wordier and less formalized way to deal with) known stuff from introductionary statistics classes in my past.

Pathophysiology of Disease: An Introduction to Clinical Medicine (2nd edition) (II)

My first post about the book can be found here. In this post I’ll talk a little bit the chapters 4-5, which deal with infectious diseases and neoplasia.

When reading chapter 4 – on infectious diseases – it was a great help to have read chapter 3 first – that chapter had a lot of stuff on how the body defends itself against the kind of stuff they talk about in chapter 4, and even though they recap a bit of that stuff in chapter 4 it’s probably smart to read those two chapters in the order they appear in the book. I did not find chapter 4 particularly hard to read, in part probably because this stuff is closely related to the microbiology stuff I read in the past, which dealt in much more detail with the microorganisms causing these diseases. There’s a lot of important concepts covered: transmission mechanisms; factors impacting disease susceptibility; the normal microbial flora and how it relates to this topic at hand; constitutive defences the body (‘defence mechanisms which do not require prior contact with the microorganism – such as physical and chemical barriers to colonization, inflammatory response, the complement system, and phagocytosis); disease progression (to cause disease all microorganisms must go through four stages: they must encounter the host, gain entry, multiply and spread, and cause host tissue injury. The course of an infection may vary from asymptomatic to life threatening; an important distinction is between acute (and sub-acute) and chronic infections). Chronic infection is not the only outcome of a long-term colonization; an individual may also enter a carrier state, or the infection may become latent until reactivation.).

After the ‘general stuff’ has been covered in chapter 4, they deal with the pathophysiology of some examples of infectious diseases; infective endocarditis (bacterial or fungal infection of the interior of the heart), meningitis, pneumonia, infectious diarrhea, and sepsis/sepsis syndrome/septic shock. I’ll not go into much detail about these diseases, but I should probably note here that the names we use to describe infectious diseases like these may cause people to misunderstand how they work: To be clear, there isn’t just one ‘endocarditis bacterium’ or a specific ‘meningitis bacterium’. When specific sites/organ systems are invaded by microorganisms which spread and cause tissue damage (damage which can be caused both by the toxins released by the invading microorganisms and by the host response to the invasion), we have a name for that – but many different microorganisms may cause symptoms by invading the specific site or organ system in question, though some are more likely to affect specific sites than others. Sometimes the names of the microorganisms may even add to this confusion; for example one of the most common causes of bacterial meningitis (infection of the meninges) in children aged 2 months to 15 years is H Influenzae (which kind of sounds like, well…).

Chapter 5 deals with neoplasia. If you dont’ feel like reading this stuff, at least read Mukherjee. The chapter deals briefly with colon carcinoma as an example of an epithelial neoplasia; this stuff from Khan Academy is another great resource on this subject – it also deals a bit with cancer development more generally, and it’s a lot more accessible than is this chapter.

Anyway, the chapter… It starts out with a bit of a downer: “The recognition of overt malignancy by physical examination or imaging requires the presence in the body of about 1 billion malignant cells.” It goes on to note that: “A preclinical phase may sometimes be recognized” but even so, “More commonly, the preclinical phase goes undetected until invasive cancer, occasionally with regional or distant metastases, is already present.” That’s the way it is. The chapter then goes on to talk about many of the same things Mukherjee covers in the latter half of his book, like the role of tumor suppressor genes and oncogenes, the role of environmental triggers (carcinogens), inheritance, … In the field of oncology there seems to be a big focus on the role of genetic changes taking place in the cell(s), and: “A paradigm for sequential genetic alterations has been proposed as a necessary set of events leading to tumorigenesis.” Mukherjee also has more on this, if you’re interested. There are a lot of oncogenes and tumor suppressor genes that play a role in human cancers. In terms of the phenotypic changes they cause, this table is relevant (click to view full size):

A good related quote from the first part of the chapter:

“Molecular and cellular changes in tumor cells are, in a sense, a modification of normal physiology that benefits their growth and spread. The initial alterations may be “preprogrammed” in rare inherited malignancies, or they may be acquired as a consequence of mutations brought about by environmental exposure or occuring by chance during normal cell division. In a process akin to evolution, albeit in a fast time frame, additional genetic changes occur that favor further growth, invasion, and spread. Evasion of the host’s immune system, enhanced proliferative and invasive potential, and resistance to therapy are examples of early, middle, and late changes in the progression of neoplasia.”

The simple way to think about cancer is this: Cancer cells outcompete the surrounding cells because they’re better at growing and spreading, and they cause disease because the reason why they’re better at growing and spreading is that they’re no longer doing what they’re supposed to be doing, and because they’re taking up space and nutrients from the cells that still do their jobs.

The chapter also has some stuff on breast cancer and talks a bit about the BRCA mutations. After that they talk about mesenchymal, neuroendocrine and germ cell neoplasias, which are types of neoplasias the pathophysiology of which “can be described in terms of the embryonic tissue of origin.” The examples they include are carcinoid tumors, testicular cancer, and sarcomas. Again the naming of these diseases may be a bit confusing – tissue will often migrate during development and you can actually end up with, say, a testicular neoplasm which is not located anywhere near your testes (for example, the testicular tissue may have migrated to your chest..). Then they cover hematologic neoplasms (blood, bone marrow, or lymph nodes), and here they’ve included some stuff on lymphomas (“uncontrolled proliferation and potential dissemination of lymphocytes“) and AML, a type of leukemia (Mukherjee has a lot more stuff on that). I found it surprising that they did not spend more time on the last part, the systemic effects of neoplasia – basically they only spend one page on that stuff, though they do also include a few tables to illustrate these aspects of the diseases. I’ve included one of the figures below (click to view in a higher resolution):

Indirect systemic effects of cancer which are not caused by the local presence of cancer cells are what’s called pareneoplastic syndromes.

Sexually Transmitted Diseases (4th edition) (II)

My initial plan was to read the book from cover to cover. Today I realized that the book has more than 2000 pages, and so I probably will not read all of it.

Anyway, some stuff from part 2 (chapters 6-9) of the book, on Social and Psychological Dimensions of Sexuality, which I read today:

i.

(click to view full size. In Denmark 50% of the females have had their sexual debut at age 16 (Danish link). The Norwegian number is similar (link).)

ii. “Network epidemiology offers a comprehensive way of thinking about individual sexual behavior and its consequences for STI. Unlike other health-related behaviors (e.g., smoking and seat belts), behaviors that transmit STI directly involve at least two people, and the links either of these persons might have to others. Understanding this process requires moving beyond the standard, individual-centered research paradigm. This has important implications for the analytic framework, data collection, and intervention planning. [...] the network perspective changes the way we think about targeting concepts such as “risk groups” and “risk behaviors.” The inadequacy of these concepts became clear as HIV prevalence rose among groups that do not engage in individually risky behavior, for example, monogamous married women.², ³, ⁴, ⁵ By the same token, a group of persons with extremely “risky” individual behavior may have little actual risk of STI exposure if their partners are uninfected, and not linked to the rest of the partnership network. It is not only individuals’ behaviors that define their risk, it is their partners’ behavior and (ultimately) their positions in a network.

The network perspective also changes the way we think about population-level risk factors: the key issue is not simply the mean number of partners but the connectivity of the network, and connectivity can be established even in low density networks. One of the primary ways in which this happens is through concurrent partnerships. Serial monogamy in sexual partnerships creates a highly segmented network with no links between each pair of persons at any moment in time. Relax this constraint, allowing people to have more than one partner concurrently, and the network can become much more connected. The result is a large increase in the potential spread of STI, even at low levels of partnership formation.

Finally, the network perspective changes the way we think about behavior change. Because the relevant behavior occurs in the context of a partnership, individual knowledge, attitudes, and beliefs do not affect behavior directly. Instead, the impact of these individual-level variables is mediated by the relationship between the partners. A young woman who knows that condoms help prevent the sexual spread of HIV may be unable to convince her male partner to use one. It is not her knowledge that is deficient, but her control over joint behavior.

Networks thus determine the level of individual exposure, the population dynamics of spread, and the interactional context that constrains behavioral change. Taking this seriously represents a paradigm shift in the study of STI.”

iii. “Using network analysis, researchers have identified two basic behavioral patterns that have a large impact on the STI transmission network: selective mixing and partnership timing. Selective mixing is about how we choose partners: the population comprises several subgroups and the question is how many partnerships form within and between groups. Partnership timing is about the dynamics of relationships: monogamy requires partnerships to be strictly sequential, concurrency allows a new partnership to begin while an existing partnership is still active. Both are guided by norms that influence individual behavior, which in turn create partnership network structures that leave distinctive signatures on transmission dynamics and prevalence.

Partnership networks also have other structural features that can be important for STI spread. One example is closed cycles, e.g., the triangles and odd-numbered cycles that can emerge in same sex networks, and larger even-numbered cycles for heterosexual networks. Closed cycles have the effect of sequestering an infection and preventing further spread outside the cycle.”

iv. “Age mixing is generally assortative, but shows an asymmetry among heterosexual couples, with males typically older than their female partners. Age is also an attribute that changes over time. The net impact on transmission dynamics depends on whether the STI is curable or incurable. For curable STIs, prevalence will typically peak among youth, since rates of partner change are high in this group and partners are typically of similar ages, so the STI circulates rapidly within group. In the United States, for example, about 70% of all chlamydia cases and 60% of all gonorrhea cases are found among persons 15-24 years of age.⁴⁸ Age matching will lead to higher prevalence among youth in this case, as it intensifies the spread within this group. Incurable STI, by contrast, will accumulate with years of exposure, so higher prevalence should be found among older groups for these STIs. For example, in the United States, only 11% of persons living with HIV are in the 15-24-year-old age group.⁵⁵ In this case, assortative age mixing will (all else equal) protect youth by lowering their exposure to higher prevalence older partners.⁵⁶“

(click to view full size)

v. “In the earliest studies of partnership sequencing effects, researchers focused on monogamy and the duration of monogamous partnerships. Long-term monogamous pair formation slows down the rate of disease transmission, as concordant pairs provide no opportunity for spread, and discordant pairs remain together after transmission has occurred. Analytic findings support this intuition: increasing partnership duration raises the number of contacts needed to reach the reproductive threshold, lowers the peak number infected, and increases the time to peak infection.⁸⁹, ⁹⁰, ⁹¹, ⁹²

Attention then turned to the impact of concurrency, to understand the impact of relaxing the rule of monogamy.⁹³, ⁹⁴, ⁹⁵ Concurrency has several consequences that lead to amplified transmission. First, as the earlier research showed, concurrency reduces the time between transmissions: the pathogen is not trapped in a partnership since there is another partner available for immediate subsequent transmission. Second, concurrency removes the protective effect of sequence. Under serial monogamy, earlier partners in the sequence are not exposed to infections that the index case acquires from later partners. Under concurrency, earlier partners lose this protection. In Fig. 7-2, partner 1 is indirectly exposed to partner 2, and partner 3 is exposed to partner 4. Not only does this expose two additional persons, it creates two new potential chains of infection from these persons to others.⁹⁶ Third, concurrent partnerships link individuals together to create large connected “components” in a network—if you have more than one partner, then your partner may have more than one partner, and so on. Such connected components function like a well-designed road network—they allow a pathogen to travel rapidly and efficiently to many destinations.

Concurrency increases the speed of STI transmission through a population. [...] a substantial number of studies have examined concurrency, and in general the findings have confirmed its importance for STI transmission.”

vi. “Given the high prevalence of HIV among MSM [men who have sex with men, US] in many large cities, averaging about 25% in the United States but ranging by city from 18 to 40%,⁶⁶ HIV infection per se has become a very salient issue for MSM partnerships. The likelihood of being in a partnership, albeit of short or long duration, with someone having HIV infection is much more likely for MSM than it is for heterosexuals. This makes discussion of HIV status a particularly important dynamic for MSM within their partnerships. Disclosure of HIV status remains a challenge for HIV-positive MSM; among those reporting unprotected sex, almost half⁶⁷,⁶⁸ reported not disclosing their HIV status to prospective sex partners prior to having unprotected sex, and even fewer report disclosing within their casual partnerships.⁶⁹ Within more intimate partnerships there is a tension between facing fear of rejection from a partner and wanting to share the information with that partner, often resulting in a delay of the discussion. ⁷⁰“

vii. “Hart²³ has argued not only that there is increasing recognition of venereal disease as a behavioral disease, but that psychological variables implicated in venereal disease may be primarily related to the personality of the individual. He reported for his heterosexual sample that an increase in extroversion, and to a lesser extent neuroticism as measured by the Eysenck Personality Inventory,²⁴ were associated with increased STI. Similar findings are reported by other researchers: Eysenck found that extroverts will have intercourse earlier, more frequently, with more different partners and in more different positions than introverts: they will also engage in more varied sexual behavior outside intercourse and engage in longer foreplay.²⁵ [...]

Eysenck²⁵ found that high psychoticism scorers (those who tend to be isolated, affectless, and aggressive) were also more sexually curious, more accepting of premarital sex, more promiscuous, and more hostile. Extroverts scored as more promiscuous and less sexually nervous, while high scorers on the neuroticism scale had significantly lower scores on sexual satisfaction and significantly higher scores on excitement, nervousness, sexual hostility, sexual guilt, and sexual inhibition.

The association between romantic love, conceptualized as a possibly biologically programmed urge to fall in love, that intellectually blinds the individual, and STD acquisition, is reviewed by Goldmeier and Richardson.³⁵ They see romantic love as akin to an obsessional condition in which euphoric mental states override the rational aspects of a decision to have sex or safer sex. Goldmeier and Richardson note research³⁶ shows that people “in love” differed from controls in having reduced serotonin transporter sites (measured in platelets, and perhaps reflecting a putative “altered serotonergic tone,”) and that being “in love” is associated with raised cortisol levels until the initial throes of love dissipate in 12-24 months. They argue that these data support the contention that romantic love produces a “deterministic and nonlogical response to have sex and thus acquire an STI” and that biological states produced by being “in love” may drive some STI-related risk behaviors.”

viii. “There is a large literature on the response to genital herpes infection, reviewed by Longo and Koehn.⁶⁹ [...] For people who have had genital herpes for less than a year, negative life events, depression, anxiety, anger, and social alienation predict herpes simplex virus (HSV) recurrences; after a year, high levels of depression and low self-esteem are consistently associated with more frequent HSV recurrences. It is important to note that responses to infection may lead to these states, thus setting up a cycle of response and recurrence. [...] Carney et al.,⁷¹ in a longitudinal study, found that the first episode of genital herpes had a substantially negative psychological impact, with over 60% meeting screening criteria for being a psychiatric case as measured by the General Health Questionnaire. However, two-thirds of these became noncases if there were no recurrences of disease: if there were recurrences, the level of psychiatric case classification stayed high. A clinical study⁷² found that the majority of people with genital HSV report that infection made them less capable of physical warmth and intimacy, enjoy sex less, and feel less sexually desirable. This extended outside sexual contacts: all reported that work performance was also hampered. A majority reported disturbance of affect, feeling that genital HSV is incompatible with happiness, and feeling pessimistic about the future course of the illness. Depression was also reported by 84%. Sexual dysfunctions including reduced interest, reduced ability to achieve orgasm, avoidance of intimacy, and reduced enjoyment of sex, as well as feeling repugnant to others [...]

Psychological complications of HIV infection may be exogenous or endogenous. Exogenous complications arise from the psychosocial stresses resulting from negative societal and interpersonal reactions to AIDS. Faulstich⁸⁷ notes that the “worried well” (whether infected or not) may exhibit generalized anxiety and panic attacks, along with excessive somatic preoccupation and fear of the disease. On diagnosis of HIV infection or AIDS, individuals may exhibit disbelief and denial, followed by depressive and anxiety symptoms. Emotional distress may commonly lead to adjustment disorders with depressed mood or major depression. Recurrent psychological themes include uncertainty about disease progression, social isolation (imposed or adopted), dealing with terminal illness, and guilt or blame over lifestyle. Suicidal ideation may be present. The advent of HAART may have lowered the intensity of the psychological impact of HIV infection in places where HAART is accessible, as can medical feedback about success or failure of treatment regimens.⁸⁸

Endogenous complications result from the neuropsychiatric sequelae of HIV infection, either from the direct effect of HIV infection, on the central nervous system (CNS), opportunistic CNS infections, or CNS neoplasia. Up to half of patients with AIDS in the absence of HAART may present signs and symptoms of CNS infection, including subacute encephalitis characterized by malaise, social withdrawal, lethargy, and reduced sexual drive (these may also be signs and symptoms of depressed mood, or of systemic disease).

Subsequently, signs of progressive dementia may appear. Neuropsychiatric deficits resulting from HIV may typically involve impaired language, memory and integrative abilities, and occasionally depressed mood, and their insidious onset makes it important to maintain a high index of suspicion that psychological symptoms may indicate onset of CNS involvement. Although rarer, tertiary syphilis may also involve the CNS and include psychological symptoms. [...]

Nilsson Schönnesson and Ross⁹⁰ found that psychological adaptation occurs but as the disease enters each new phase (asymptomatic, mild symptomatic, severe, and terminal), psychologic symptoms reoccur. Mood states in the asymptomatic and mild symptomatic phase typically included anger, whereas disappointment, sense of violation, and feelings of aloneness characterized the terminal phase, with powerlessness and helplessness being expressed in all phases.”

Sexually Transmitted Diseases (4th edition)

…by King Holmes, P. Sparling, Walter Stamm, Peter Piot, Judith Wasserheit, Lawrence Corey, Myron Cohen (…and many others: “This edition welcomes new editors Myron Cohen, Larry Corey, and Heather Watts, and 119 new authors”).

I thought that since I brought up my recent doctor’s appointment (not STD-related in any way…) in my last post, I should update you on that stuff here before getting to the book blogging. It was good news all around: There was nothing unusual about the EKG, I do not have microalbuminuria and my Hba-1c was 0.070 (/53) [relevant link to Danish readers]. HDL cholesterol was higher- and LDL and total cholesterol levels, as well as triglycerides, were much lower than required, and the BP was 123/82. I’m always a little concerned about the BP values because they’re sort of the ‘weakest link’ when it comes to my regular test results, but it’s nowhere near high enough to justify any kind of pharmacological intervention at this point.

Back to the book: I’ve read roughly the first 100 pages (Introduction and Overview as well as Part 1 – i.e. the first 5 chapters), and I like it so far. Some good stuff from the first part of the book:

1. “The prevalence, of persistent vaginal and cervical infections are remarkably high in young women; and the incidence, and prevalence, of the chronic STIs are exceptionally high in adults, with seroprevalence increasing steadily with advancing age for infections caused by HIV, syphilis, hepatitis B, and especially, HSV-2 and the genital types of HPV. It is therefore, undoubtedly true that a very large proportion of patients seen by clinicians of all disciplines—perhaps the majority of all adults in the world—have one or more STIs.”

2. “Cohort studies demonstrate condom effectiveness against STI acquisition, not only vs. HIV, but also vs. HSV, gonorrhea, and chlamydial and vaginal infections, and specifically against HPV infection—refuting earlier concerns that condoms did not prevent HPV acquisition.” (that condoms do seem to offer protection against HPV was news to me. Later on in the book the protection offered is made more explicit: “Even for human papilloma virus, which can be transmitted without exposure of mucosal surfaces, condoms have been found to reduce the risk of acquisition by 70%.²⁸“)

3. One major effect of the introduction of penicillin [...] was loss of public health interest in STD control. Public spending on STD control declined throughout the world, and these diseases became a low priority.²⁴ For example, India developed the capacity to manufacture its own penicillin in 1954, after which the state governments of India turned their attention to other health problems.⁴⁰

One significant exception to this trend was China. Partly because the Chinese had blamed STDs on foreign occupation of China and foreign cultural decadence, the Communist government adopted STD control as one of its major policy initiatives immediately after its 1949 political victory. In a campaign that included widespread public relations efforts through plays, radio programs, and small discussion groups, the government undertook a massive screening and treatment program including vocational rehabilitation for former female sex workers. By 1964, the government claimed to have eliminated STDs, a statement that is impossible to verify but widely accepted as a general indication of a very low Chinese prevalence rate. The long-term effects of the campaign are, however, less clear. Because STDs were represented as a social evil and sign of decadence, Chinese patients tried to avoid public hospitals, which charged STD patients to punish them for their having acquired these diseases. Social stigma became a major problem. Furthermore, the medical specialty of venereology was no longer practiced and taught after 1960s. With the liberalization of employment policies in 1989 and the subsequent development of an enormous migrant labor population (between 50 and 120 million people), rates of STDs began to increase, with insufficient medical resources and ability to respond.⁴¹“

4.”Historically, prevention is the neglected aspect of STD control programs. Moral reformers have often asserted their control over prevention efforts by defining STD prevention as a problem of morality. Whether led by church groups themselves or by charitable organizations, these efforts focused on fear-based messages about the consequences of immorality (death, disfigurement, infertility, shame) along with representations of happy family life with abundant, healthy offspring as a consequence of correct moral choices..²⁴

This approach seldom focused on the structural factors which influence sexual behavior, such as long-term labor migration which keeps spouses separated, population displacement, and lack of economic opportunities for young females. [...] Not until the threat of HIV/AIDS emerged during the 1980s, when a fatal STD with no cure threatened the lives of millions, did governments begin to invest substantial resources into systematically studying behavioral science approaches to changing behavior.”

5. “STI/HIV are not spread randomly. Unprotected sex with an infected partner is by far the most important risk factor for STI/HIV infection.¹,⁹ This in turn is influenced by prevalence and distribution of infection in a population, as well as the behavior of an individual and his/her partners.

Economic deprivation, low education, economic inequality, and economically driven migration and mobility have all been found to be associated with the risk of STI/HIV infection.¹⁰, ¹¹, ¹² [...] processes associated with development such as increases in disposable income and increases in mobility among certain groups and not others are associated with increased risk.¹⁰,¹⁸ Professions involving high mobility and extended periods away from families, such as migrant labor, serving in the military, driving trucks, or working as sailors are also associated with augmented risk.”

6. “The primary mechanism through which STI contribute to mortality is through mortality associated with HIV. And with an estimated median survival time, a little above 9 years from HIV infection to death in developing countries in the absence of antiretroviral therapy, HIV has had a dramatic impact on adult mortality.²⁵,³⁷ [I've written about these numbers before here on the blog, but the 9 year time frame was news to me. Note that it's age-dependant: "most infected [at birth or as a result of breast-feeding] children, in absence of antiretroviral therapy, will develop AIDS and die before their fifth birthday”. There’s a lot more in the book about this stuff if you’re interested.] [...]

7. “Data on cost-effectiveness [of interventions] are extremely limited and a function of the scarcity of both effectiveness and cost data. The best available data are for health facility-based interventions such as syndromic STI management, screening of blood for transfusion, and prevention of MTCT. The data on cost-effectiveness of behavioral, community, and structural interventions are far weaker. [...] the position taken in the current chapter is that estimates of the cost-effectiveness of STI interventions are highly variable, reflecting both the great heterogeneity in environments as well as the great heterogeneity in the efficiency of service delivery:

The health benefit in terms of numbers of disability-adjusted, discounted, healthy life years saved by curing or preventing a case of syphilis varies from 3 years in a person who has ceased all sexual activity to as many as 161 years in a sex worker with two partners a day. The cost of treating that prostitute for syphilis varies from US$ 5 to US$ 100. Thus the cost per disability adjusted life year (DALY) of syphilis treatment can range from 100/3 or US$ 33 per DALY to 5/161 or less than a US$ 0.05 per DALY. As we learn more about the complexities of delivering STI treatment services and take into account the diversity of risk behavior, the ease with which STI interventions can be ascribed a simple cost-effectiveness ratio has declined. [...]

Almost by definition, there is more to be gained by changing the behavior of people with high levels of risk behavior than by changing that of an equivalent number of people with lower levels of risk behavior. However, the difference in the effectiveness between the two falls as epidemics become more generalized, such that in heavily affected countries prevention interventions are likely to become extremely cost-effective even when targeted to individuals with relatively low levels of risk behavior. Consequently, countries with low-level and concentrated epidemics should emphasize interventions that are targeted to individuals at especially high risk of becoming infected or transmitting the virus, whereas countries with generalized epidemics should also invest heavily in interventions that target entire populations or population subgroups. Thus, any determination of the likely effectiveness and cost-effectiveness of specific interventions in particular circumstances requires an accurate understanding of the stage and nature of the national epidemic.”

8. “The natural history of an infection is the relationship between that infection and disease and associated patterns of infectiousness. In understanding this natural history, individuals can be divided between mutually exclusive categories and the flows between them illustrated schematically in flow diagrams. Figure 3-1 shows the assumptions frequently made about a range of the key STIs in such flow diagrams.”

(click to view full size)

9. “Those with many sexual partners can drive the incidence of an STI in the population and have been described as a “core group”.⁴⁸ Axiomatically, for a sexually transmitted disease (STD) to exist there must be individuals with sufficient sexual partners to transmit infection to more than one other person.⁴⁹ If interventions could reliably prevent infection in these individuals, the STI could be eliminated. Studies of risk behaviors and the distribution of STIs have attempted to identify the characteristics of those within the core group as a target for interventions. [...]

The lower the incidence of an STI in a population, the more it will be concentrated in those with higher risk behaviors. If the behaviors placing individuals at risk are similar then those most at risk of one STI would be the same as those most at risk of another infection. We would expect infections with a higher combined transmission probability and duration to not only be more widespread, but to also be found in those with STIs with a lower combined reproductive potential.⁵² If this is not the case as has been suggested in some observations,⁵³ potential explanations include the acquisition of immunity against one infection, different likelihoods of receiving treatment, and different risk behaviors placing individuals at risk. [...]

The choice of sexual partners of an individual will have a large influence on whether or not they are exposed to someone infected. The choice of sexual partners will depend upon the contexts in which potential couples meet, for instance, schools, church groups, beer halls, and family gatherings and how they relate. Studies show that individuals tend to choose sexual partners, particularly spouses, who are similar with respect to social and demographic variables such as age, education and income.⁴⁷,⁴⁹ Such a choice will lead to assortative (like-with-like) sexual mixing within the population with respect to the specific variables. [...] Assortative mixing restricts the spread of STIs but helps maintain chains of infection within high-risk groups. Thus if mixing were assortative, an STI would be more likely to invade rapidly and persist within a population but would also be less likely to spread widely. In contrast, random mixing would spread infection from high- to low-risk individuals who are dead ends for the infection.”

10. “The basic reproductive number (R₀) is a measure of the potential for the spread of an infection and can be defined for STIs as the average number of infections caused by one infectious individual entering an entirely susceptible population.⁶⁹ The key components determining the value of the basic reproductive number are those discussed above: the transmission likelihood (β), the contact rate (c ) and the duration of infectiousness (D), with, in a simple, illustrative model, the product of these three being the basic reproductive number: R₀ = βcD.⁶⁹ The value of R₀ determines: the chances of an epidemic when an infection enters a population; the rate of spread of the epidemic; the endemic level of infection, and the effort required to bring the infection under control. An important distinction has to be drawn between the basic reproductive number, R₀, which measures the potential for spread in a naive population, and the effective reproductive number, R_t, which changes depending on the experience of infection in the population.⁷⁰ This effective reproductive number is the number of new infections caused by an average infection at a given time, t, which at time zero equals the basic reproductive number. Once some contacts are already infected or immune, the effective reproductive number is reduced and is the product of the basic reproductive number and the fraction of contacts remaining susceptible. When an infection successfully invades a population its prevalence will initially grow exponentially, until it saturates and the effective reproductive number falls. [...] the greater the value of R₀, the higher predicted prevalence of infection and immunity. In the case of STIs, where there is heterogeneity of risk, contacts are concentrated in a small fraction of the population and infection saturates long before it would in a homogenous population. [...]

The pattern of spread of the epidemic and its subsequent progress to an endemic level depends upon the duration of infection and the role of acquired immunity [...] For a short-lived infection with no acquired immunity, such as gonorrhea, we can expect a steady state to be reached quickly. If death or acquired immunity reduces the susceptible pool, the prevalence of infection can fall until the resupply of susceptibles through newly susceptible individuals entering the population either balances the losses or builds up over time to cause new epidemics. The associated declines in prevalence could be confused for the impact of interventions but are the natural course of the epidemic. In the case of syphilis, acquired or concomitant immunity can explain the long-term cycles in incidence observed in US case reports.¹⁰ In the case of HIV, declines in prevalence can reflect earlier declines in incidence caused by saturation.^73″ 

11. “To reduce the incidence of STI infection interventions must alter the reproductive potential of the infection. Shortening the infectious period, reducing the contact rate, or reducing the transmission probability, all reduce the basic reproductive number of infection, while introducing artificial immunity through vaccination would reduce the proportion of the population susceptible and thereby reduce the effective reproductive number. Reducing the basic reproductive number has a nonlinear impact on the endemic prevalence of infection [...] heterogeneity in risk plays a key role in the epidemiology of STIs. In populations with a distribution of risk, small reductions in risk can have a large impact in a lower risk group while having little impact in higher risk groups.⁷⁵ Thus, initially interventions can have a large impact, but as their intensity is increased it generates diminishing returns, as infection is removed from low-risk sections of the population and becomes more concentrated. [...]

The relative success of different STIs is likely to have changed in response to treatment, with chancroid, syphilis, and gonorrhea becoming relatively less common if their symptoms are more likely to receive attention. Similarly within microbial populations, treatment is likely to provide a selective advantage to organisms that generate negligible symptoms, more so than organisms that have partial drug resistance. Over time, we might expect the pathogenicity of curable STIs to decline unless there is a correlation between symptoms and transmissibility, which is hypothetically likely if disease is associated with larger bacterial colonies and transmission depends upon the infectious dose of bacteria. However, if interventions through screening target both asymptomatic and symptomatic infections, then selection is likely to favor organisms that transmit more readily, with a concomitant shorter duration of infection in the absence of treatment.⁷⁷ Similarly, drug resistance becomes a better adaptive strategy if both symptomatic and asymptomatic infections are rapidly treated through active screening.”

12. “An estimated 1.9 million people (1.3-2.6 million) are living with HIV in North America and in Western and Central Europe.

In high-income countries, where the great majority of people who need antiretroviral treatment do have access to it, people living with HIV are staying healthy and surviving longer than infected people elsewhere. Widespread access to life-extending antiretroviral treatment kept the number of AIDS deaths at between 19,000 and 42,000 in 2005. However, prevention efforts are not keeping pace with the changing epidemics in several countries. Sex between men is the most common route of infection in Australia, Canada, Denmark, Germany, Greece, and the United States. Patterns of HIV transmission are changing with an increasing proportion of people becoming infected through unprotected heterosexual intercourse. In Belgium, Norway, and the UK, the increase in heterosexually transmitted infections is dominated by people from countries with generalized epidemics, predominantly sub-Saharan Africa. In the United States, about half of newly reported infections are among African Americans who represent 12% of the population. [...] Drug injecting accounted for more than 10% of all reported HIV infections in Western Europe in 2002 (in Portugal it was responsible for over 50% of cases). In Canada and the United States, about 25% of HIV infections are attributed to drug injecting.”

13. “The incidence, prevalence, and population distribution of sexually transmitted infections (STIs) are largely determined by the complex interplay of dynamically changing demographic, economic, social, and behavioral forces and the response of the health system to emergent STI morbidity patterns. Over the past 3 decades, overall incidence and prevalence of bacterial STI, in particular gonorrhea, syphilis, chancroid, and chlamydial infections have declined in the United States, Western Europe, and many developing countries, to their lowest levels since World War II. Declines in bacterial STI in developing countries are attributed to the widespread implementation of syndromic management and to a large-scale shift to safer sexual behaviors in response to the HIV epidemic. Despite such remarkable declines, rates of some bacterial STI are still high and/or increasing in some subpopulations [...] During the past decade prevalences of viral STI, particularly genital herpes infections (HSV), appear to have increased in many countries [...] Diagnosis, management, and control of viral STIs have changed drastically over the past decade. The introduction of new diagnostic technologies has increased recognition of viral STI, improved sensitivity in identification of bacterial STI, and expanded the repertoire of usable specimens. The use of urine and vaginal swabs has greatly expanded coverage of screening services and has led to the availability of true population-based estimates of the prevalences of STIs.¹ [...] In all societies, for many reasons discussed in this chapter, STIs tend to concentrate in certain populations including urban, poor, and minority populations, with highest rates among sexually active adolescent females followed by adolescent and young adult men. This pattern is particularly pronounced in western industrialized countries where effective prevention and control efforts result in concentrated STI morbidity. During the past decade commercial sex has become an increasingly important factor in STI transmission⁶,⁷ in many areas of the world including the United States and Western Europe.”

14. “Trajectories whereby STI epidemics evolve differ for different types of population-pathogen interactions.³⁰, ³¹, ³² Whereas highly infectious, short duration bacterial STIs—for instance, gonorrhea—depend on the presence of core groups marked by multiple sex partnerships (often of short duration) for their spread, less infectious, long duration viral STIs—for example, herpes simplex virus (HSV) or human papillomavirus (HPV) infections—are less dependent on multiple partnerships of short duration or on short gaps between partnerships. Thus, the pattern of spatial and population distribution of various STIs differs markedly. Syphilis and gonorrhea tend to be concentrated in individuals with multiple partnerships and in populations with highly connected sexual networks; whereas genital chlamydial infections, genital herpes, and genital HPV infections are much more ecumenically, widely distributed across the entire population.³³

15. “The most recent updated estimates for prevalence and incidence of STIs globally are provided by the WHO.⁴⁷ These estimates suggest that of 340 million new cases of gonorrhea, syphilis, chlamydial infection, chancroid, and trichomoniasis STIs in 1999 under 10% occurred in North America and Western Europe; over 90% of new infections were in developing countries (Table 5-1). In 1999, the overall estimated number of new cases of chlamydia, gonorrhea, and syphilis infections among 15-49-year-old men and women totaled over 166 million with close to 92 million cases of chlamydial infection, 62.35 million cases of gonorrhea, and 11.76 million cases of syphilis (Table 5-2). In addition, there were an estimated 173.46 million cases of trichomoniasis.

In developing countries, passive surveillance of STI morbidity is particularly inadequate. However, in recent years the epidemiology of STIs in sub-Saharan Africa is better defined based on large population-based prevalence surveys. The results of these surveys have confirmed the high prevalences of STIs even in rural populations, for example, syphilis (5-10% of adults infected), vaginal trichomoniasis (20-30% of women), and bacterial vaginosis (up to 50% of women). Syphilis has been estimated to cause 490,000 stillbirths and neonatal deaths per year in Africa—a figure similar to the number of children dying of HIV/AIDS worldwide.⁴⁸

16. “Data on gonococcal antimicrobial resistance across the EU are not comprehensive. Plasmid-mediated resistance to penicillin and tetracycline had increased in Europe during the early 1990s. Sporadic resistance to fluoroquinolones was also documented in the early 1990s, mainly imported from South East Asia.⁵² [...] Recently, increases in fluoroquinolone resistance have been reported in many countries in Europe. In Denmark, the laboratory-confirmed percentage of gonococci with fluoroquinolone resistance increased from 0% to 27% in 1999, 17% of the strains were resistant to both penicillin and fluoroquinolones.⁵² [...] By early 2004, fluoroquinolones were no longer recommended in the United States as first-line treatment for MSM, and by early 2007, were no longer recommended as first-line treatment of gonorrhea in any group.⁸⁶

17. “Chlamydia trachomatis is still the most prevalent sexually transmitted bacterial infection in North America and Europe.⁵²,⁵⁴ It is difficult to describe temporal trends in the incidence of chlamydial infection because of the large proportion of asymptomatic infections; the increasing use of increasingly sensitive diagnostic tests, with expansion of chlamydia screening activities in Europe and the United States; the increased emphasis on case reporting by providers; and the improvements in the information systems for reporting. In many European countries, case reporting of genital chlamydial infections is not mandatory; consequently, relatively little information is available from national surveillance sources. [...]

In a recent study,¹²² U.S. women aged 14-49 participating in the National Health and Examination Survey (NHANES) cycles 2001-2004 provided self-collected vaginal swabs; vaginal fluids extracted from the swabs were evaluated for Trichomonas vaginalis using polymerase chain reaction (PCR). The overall prevalence of T. vaginalis was 3.1%; it was highest among non-Hispanic blacks (13.3%) and lower among Mexican Americans (1.8%) and non-Hispanic whites (1.3%). [...]

Viral STIs are not notifiable in most European countries and relatively limited temporal trend data have been published.⁵² Genital HSV infection is the most common ulcerative STI in the UK and the United States. However, many patients with genital herpes do not perceive or recognize symptoms of the infection, and clinical case-reports grossly underrepresent the true incidence of genital herpes as reflected by serologic testing for antibody to HSV-2. [...] HSV-2 prevalence appears to be higher in Northern Europe and in North America than in Western and Southern Europe. The highest prevalence of HSV-2 infection was found among women in Greenland, reaching 57% among 20-26-year olds and 74% in 25-39-year olds. In Scandinavia, HSV-2 prevalence was relatively higher than in other areas of Europe—15-35% among women between 25 and 35 years of age.¹²⁴ [...] The most recent data on HSV-2 seroprevalence in the United States were collected in a stratified random sample of the United States population through the NHANES during 1988 through 1994 and 1999 through 2004.¹²⁶ Persons between ages 14 and 49 were included in the analyses. The overall age-adjusted HSV-2 seroprevalence was to 21.0% in the period 1988-1994, decreasing to 17.0% in 1999-2004, representing a relative decline of 19% between the two surveys. [...] The seroprevalence of HSV-1 also decreased from 62% to 57.7% between the two surveys—a relative decrease of 6.9%. [...]

Genital HPV infections are the most prevalent STIs in the United States and in the world. HPV infections other than those causing genital warts (usually types 6 and 11) are nearly always subclinical, not recognized by the infected individual. By screening for HPV DNA every 3 months, using PCR amplification tests, the cumulative incidence of genital HPV infections in one study was 43% over a 3-year period in one study of sexually active female University students¹²⁷ and 32% over a 2-year period in another.¹²⁸ [...] A recent pooled analysis showed the age standardized prevalence of all types of HPV infection to vary 20-fold among different regions of the world.¹³⁰ The prevalence of high risk types of the virus was 18% in sub-Saharan Africa, 5% in Asia, 10% in South America, and 4% in Europe. The prevalence of HPV infection is highest among young women and appears to drop-off with increasing age.¹³¹ [...] Risk factors for HPV infection include increased number of sex partners, increased number of male partners’ lifetime partners, a short-time interval between meeting a partner and engaging in sexual intercourse, increased age difference between partners, and current smoking.¹²⁸ [...] Based on these preliminary findings from cohort studies, and together with data from national surveys of sexual behavior [...] it is not unlikely that the majority of adults in the United States, perhaps three-quarters, have been infected with one or more types of genital HPV.”

18. “Worldwide, more men than women report multiple partnerships except in some industrialized countries, where the proportions of men and women who report multiple partnerships are similar.¹⁰ The mean age difference between married men and women is lower in industrialized countries (1.9 in Australia and 2.2 in United States) than in developing countries; data are not available on age differences between sex partners. According to a recent review¹⁵³ of estimates of lifetime, prevalence of men having had sexual intercourse with other men is lower in industrialized countries (6% in the UK and 5% in France) than in most other regions of the world. Rates of condom use are generally higher in industrialized countries than in developing countries, especially in women.¹⁰ The increase in condom use in recent years has also often been more substantial in industrialized countries; the only exception to this pattern is France where women have reported declining condom use in more recent years.”

19. “Historically, the predominant focus of STI epidemiology has been on the attributes and behaviors of individuals, and on the risk of acquiring, rather than of transmitting infection. This approach is consistent with the approaches of clinical medicine, chronic disease epidemiology, and psychology. However, when considered as the “sole” or “main” focus, it appears to be inconsistent with STI transmission dynamics¹⁹⁰ and it has been increasingly challenged in recent years. The new paradigm includes at least three principles: that one person’s health outcome is highly dependent on other person’s health outcomes;¹⁹¹,¹⁹² that transmission of infection and its prevention is at least as important and perhaps more important than acquisition of infection and its prevention—thus focusing attention on infected individuals and the role they play in the spread of infection; that characteristics of sex partners and partner selection processes are an important component of risk determination—thereby focusing on behaviors of sex partners as well.¹⁸⁶

20. “The inadequacy of the STD health service infrastructure and the resulting preventable increment in duration of infectiousness is a major reason why the United States has the highest rates of STIs among developed countries.”

21. “In the light of all these considerations, it is obvious that in evaluating behavioral interventions to prevent STIs, and HIV, data from randomized controlled trials are particularly important, the choice of outcome measure is critical, and the outcome measure of choice is the appropriate biomedical measure of the STI or STIs of interest.²⁶⁴,²⁷⁰

Most evaluations of behavioral interventions to date have employed less rigorous study designs and behavioral outcome measures. A systematic review of computerized abstracts from International AIDS conferences between 1989 and 1992 showed that only 10 of 15,946 abstracts reported on randomized controlled trials of behavioral interventions.²⁶⁴ Two subsequent critical reviews of behavioral interventions in general and behavioral interventions for young people reported similar findings.²⁷¹,²⁷² These reviews also indicated that many behavioral intervention studies focused only on determinants of behavior such as knowledge, beliefs, and attitudes as outcome measures. [...] In the past 2 decades a number of behavioral intervention trials have been conducted including those mentioned above. Many of these studies showed efficacy in reducing risky behaviors, and a smaller number showed efficacy in reducing incidence of bacterial STI in study subjects. Interestingly, to date, no cluster randomized trial of behavioral interventions (where at least one arm of the study represented a behavioral intervention) has showed significant impact at the population level.”

Stuff

i. American nuclear guinea pigs : three decades of radiation experiments on U.S. citizens.

“SUMMARY AND CONCLUSIONS

Documents provided by the Department of Energy reveal the frequent and systematic use of human subjects as guinea pigs for radiation experiments. Some experiments were conducted in the 1940s at the dawn of the nuclear age, and might be attributed to an ignorance of the long term effects of radiation exposure, or to the atomic hubris that accompanied the making of the first nuclear bombs. But other experiments were conducted during the supposedly more enlightened 1960s and 1970s. In either event, such experiments cannot be excused.
These experiments were conducted under the sponsorship of the Manhattan Project, the Atomic Energy Commission, or the Energy Research and Development Administration, all predecessor agencies of the Department of Energy. These experiments spanned roughly thirty years. This report presents the findings of the Subcommittee staff on this project.
Literally hundreds of individuals were exposed to radiation in experiments which provided little or no medical benefit to the subjects. The chief objectives of these experiments were to directly measure the biological effects of redioactive material; to measure doses from injected, ingested, or inhaled redioactive substances; or to measure the time it took radioactive substances to pass through the human body. American citizens thus became nuclear calibration devices.
In many cases, subjects willingly participated in experiments, but they became willing guinea pigs nonetheless. In some cases, the human subjects were captive audiences or populations that experimenters might frighteningly have considered “expendable”: the elderly, prisoners, hospital patients suffering from terminal diseases or who might not have retained their full faculties for informed consent. For some human subjects, informed consent was not obtained or there is no evidence that informed consent was granted. For a number of these same subjects, the government covered up the nature of the experiments and deceived the families of deceased victims as to what had transpired. In many experiments, subjects received doses that approached or even exceeded presently recognized limits for occupational radiation exposure. Doses were as great as 98 times the body burden recognized at the time the experiments were conducted.”

It seems that the Tuskegee syphilis experiment wasn’t quite as unique as I’d thought.

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ii. Diuretic Treatment of Hypertension. Interesting, lots of stuff there I didn’t know.

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iii. Income disparity and Risk of death: The Importance of Health Behaviors and Other Mediating Factors

“After adjusting for age, sex, education, and race/ethnicity, risk of death was higher in low-income than high-income group for both all-cause mortality (Hazard ratio [HR], 1.98; 95% confidence interval [CI]: 1.37, 2.85) and cardiovascular disease (CVD)/diabetes mortality (HR, 3.68; 95% CI: 1.64, 8.27). The combination of the four pathways attenuated 58% of the association between income and all-cause mortality and 35% of that of CVD/diabetes mortality. Health behaviors attenuated the risk of all-cause and CVD/diabetes mortality by 30% and 21%, respectively, in the low-income group. Health status attenuated 39% of all-cause mortality and 18% of CVD/diabetes mortality, whereas, health insurance and inflammation accounted for only a small portion of the income-associated mortality (≤6%).

Conclusion

Excess mortality associated with lower income can be largely accounted for by poor health status and unhealthy behaviors. Future studies should address behavioral modification, as well as possible strategies to improve health status in low-income people.”

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iv. Influence of Opinion Dynamics on the Evolution of Games. I’ve only just skimmed this, but it looks interesting. Here’s the abstract:

“Under certain circumstances such as lack of information or bounded rationality, human players can take decisions on which strategy to choose in a game on the basis of simple opinions. These opinions can be modified after each round by observing own or others payoff results but can be also modified after interchanging impressions with other players. In this way, the update of the strategies can become a question that goes beyond simple evolutionary rules based on fitness and become a social issue. In this work, we explore this scenario by coupling a game with an opinion dynamics model. The opinion is represented by a continuous variable that corresponds to the certainty of the agents respect to which strategy is best. The opinions transform into actions by making the selection of an strategy a stochastic event with a probability regulated by the opinion. A certain regard for the previous round payoff is included but the main update rules of the opinion are given by a model inspired in social interchanges. We find that the fixed points of the dynamics of the coupled model are different from those of the evolutionary game or the opinion models alone. Furthermore, new features emerge such as the independence of the fraction of cooperators with respect to the topology of the social interaction network or the presence of a small fraction of extremist players.”

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v. This is awesome.

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vi. Are elder siblings helpers or competitors? Antagonistic fitness effects of sibling interactions in humans.

“Determining the fitness consequences of sibling interactions is pivotal for understanding the evolution of family living, but studies investigating them across lifetime are lacking. We used a large demographic dataset on preindustrial humans from Finland to study the effect of elder siblings on key life-history traits. The presence of elder siblings improved the chances of younger siblings surviving to sexual maturity, suggesting that despite a competition for parental resources, they may help rearing their younger siblings. After reaching sexual maturity however, same-sex elder siblings’ presence was associated with reduced reproductive success in the focal individual, indicating the existence of competition among same-sex siblings. Overall, lifetime fitness was reduced by same-sex elder siblings’ presence and increased by opposite-sex elder siblings’ presence. Our study shows opposite effects of sibling interactions depending on the life-history stage, and highlights the need for using long-term fitness measures to understand the selection pressures acting on sibling interactions.”

Where did they get their data? Well, it was hard for people living in the 17th and 18th century to avoid death or taxes too:

“The demographic dataset from historical Finnish populations was compiled from records of the Lutheran church, which was obliged by law to document all dates of births, marriages and deaths in the population for tax purposes [25–29]. As migration events were relatively rare and the migration records maintained by the church allowed us to follow dispersers in the majority of the cases, these records provide us with relatively accurate information on individual survival and reproductive histories [28] (e.g. 91% of individuals with known birth date were followed to sexual maturity at age 15 years). Our study period is limited to the eighteenth and nineteenth centuries, before the transition to reduced birth and mortality rates [30].”

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vii. I’ve posted about this topic before, here’s a new study on cancer screening procedures: Effect of Three Decades of Screening Mammography on Breast-Cancer Incidence. I think the results are depressing:

“The introduction of screening mammography in the United States has been associated with a doubling in the number of cases of early-stage breast cancer that are detected each year, from 112 to 234 cases per 100,000 women — an absolute increase of 122 cases per 100,000 women. Concomitantly, the rate at which women present with late-stage cancer has decreased by 8%, from 102 to 94 cases per 100,000 women — an absolute decrease of 8 cases per 100,000 women. With the assumption of a constant underlying disease burden, only 8 of the 122 additional early-stage cancers diagnosed were expected to progress to advanced disease. After excluding the transient excess incidence associated with hormone-replacement therapy and adjusting for trends in the incidence of breast cancer among women younger than 40 years of age, we estimated that breast cancer was overdiagnosed (i.e., tumors were detected on screening that would never have led to clinical symptoms) in 1.3 million U.S. women in the past 30 years. We estimated that in 2008, breast cancer was overdiagnosed in more than 70,000 women; this accounted for 31% of all breast cancers diagnosed.

Conclusions

Despite substantial increases in the number of cases of early-stage breast cancer detected, screening mammography has only marginally reduced the rate at which women present with advanced cancer. Although it is not certain which women have been affected, the imbalance suggests that there is substantial overdiagnosis, accounting for nearly a third of all newly diagnosed breast cancers, and that screening is having, at best, only a small effect on the rate of death from breast cancer.”

Absolute Risk of Suicide After First Hospital Contact in Mental Disorder

This new article is rather awesome, if for no other reason then because it involves so many people and follow them over such a long time-frame:

“Objective  To estimate, in a national cohort, the absolute risk of suicide within 36 years after the first psychiatric contact.

Design  Prospective study of incident cases followed up for as long as 36 years. Median follow-up was 18 years.

Setting  Individual data drawn from Danish longitudinal registers.

Participants  A total of 176 347 persons born from January 1, 1955, through December 31, 1991, were followed up from their first contact with secondary mental health services after 15 years of age until death, emigration, disappearance, or the end of 2006. For each participant, 5 matched control individuals were included.”

176.347 people followed for roughly two decades on average. That’s a lot of data. What did they find? Some of the main results:

“Results  Among men, the absolute risk of suicide (95% confidence interval [CI]) was highest for bipolar disorder, (7.77%; 6.01%-10.05%), followed by unipolar affective disorder (6.67%; 5.72%-7.78%) and schizophrenia (6.55%; 5.85%-7.34%). Among women, the highest risk was found among women with schizophrenia (4.91%; 95% CI, 4.03%-5.98%), followed by bipolar disorder (4.78%; 3.48%-6.56%). In the nonpsychiatric population, the risk was 0.72% (95% CI, 0.61%-0.86%) for men and 0.26% (0.20%-0.35%) for women. Comorbid substance abuse and comorbid unipolar affective disorder significantly increased the risk. The co-occurrence of deliberate self-harm increased the risk approximately 2-fold. Men with bipolar disorder and deliberate self-harm had the highest risk (17.08%; 95% CI, 11.19%-26.07%).”

As mentioned they of course they didn’t just limit themselves to following ‘the sick people’ – they also needed people to compare them with… So:

“To estimate the cumulative incidence of suicide among people with no history of mental illness, we adopted a slightly alternative strategy. For each person with a history of any mental illness (as defined in the“Assessment of Suicide and Mental Illness” subsection), we randomly selected 5 people of the same sex and same birth date who had no history of mental illness (time matched). Using the described strategy, we followed up this healthy population (881 735 persons) to provide absolute suicide risks. Because this healthy population was selected at random among all 2.46 million people included in the study population, the estimates obtained represent the absolute risk of suicide among all 2.46 million people without a mental disorder.”

Again, that’s a lot of data – representativeness really is unlikely to be an issue here (at least when dealing with the situation in Denmark). As they put it in the paper: “This is the first analysis of the absolute risk of suicide in a total national cohort of individuals followed up from the first psychiatric contact, and it represents, to our knowledge, the hitherto largest sample with the longest and most complete follow-up.”

Results in a bit more detail:

(click to view full size). I’ve previously seen it argued in papers on anorexia that it’s the phychiatric disorder with the highest mortality rate, so I was a bit surprised by the relatively low numbers here. On the other hand that may be related to the fact that they tend to starve themselves to death rather than take their own lives in the traditional sense, which means that a lot of those excess deaths are not considered suicides. Note that a big majority of all suicides committed are committed by people with a mental illness and that the risk increase from a diagnosis is really quite significant; given the estimates, females with a mental illness are more than 8 times as likely to kill themselves than females without a mental illness, and males are 6 times more likely. Schizophrenic females are almost 20 times as likely to commit suicide than are females without a mental illness. Add substance abuse as well and these females are more than 30 times as likely to commit suicide (the absolute risk is around 7% in that case). The risk is substantially increased for almost all groups when you add substance abuse.

Do also note that not all people in the ‘mental illness’ group are actually people with a mental illness; personality disorders are not usually considered mental illnesses by health professionals, but the study includes in the group of people with mental illnesses people with: “any mental illness (any ICD-8 or ICD-10 code) if they had been admitted to a psychiatric hospital or had been in outpatient care with one of these diagnoses.” (The “any ICD-8 or ICD-10 code” means that people with personality disorders are included in the group as well). This is probably ‘fair enough’ given that at least some of these groups clearly have elevated suicide levels, but it’s worth having in mind that it should change the interpretation slightly. How about people who’ve attempted suicide?

The deliberate self-harm/attempted suicide group is obviously a high-risk group. The follow-up period is shorter than for the other estimates (30 years, rather than 36) so these estimates are perhaps best thought of as lower bounds. There’s some uncertainty regarding the estimates because the sample sizes aren’t that big (which is a good thing I think…), but roughly 1 in 6 Danish males with bipolar affective disorder killed themselves during the period. The absolute risks here are substantial; for the ‘any mental illness’ group, one in 12 committed suicide during the period. Although the female numbers are substantially lower for the group as a whole, for some illnesses the absolute risk is comparable to that of the males (and the excess risk much, much higher). More than one in ten females with schizophrenia and a suicide attempt in the past committed suicide during the follow-up period.

I should perhaps mention here that there may be some significant tail risk unaccounted for in the data, despite the long follow-up period which might lead you to think these are good estimates of the ‘lifetime probability of suicide’. The suicide-rate of Danish males above the age of 85 is the highest of all age groups, and it’s five times as high as the suicide risk of males at the age of 25-29 (Danish link). This is not just a Danish thing – similar dynamics have been observed elsewhere. Age matters a lot here, but people tend to care less when old people kill themselves than when young people do.

Some data on life expectancy variation

From this WHO paper. It has 254 pages and I haven’t read them all – neither should you, a lot of them are just pages of data. Anyway, some more stuff from the paper (click to view graphs and tables in full size):

“37 of the 40 countries with the lowest life expectancy are in Sub-Saharan Africa. HIV/AIDS is a major cause of the poor performance of many Africa countries in terms of health gains over the last decade or so. Overall, life expectancy in Sub-Saharan Africa has declined by 3-5 years in the 1990s due to increasing mortality from HIV/AIDS, with the estimated loss reaching 15-20 years in countries such as Botswana, Zimbabwe and Zambia.” [my emphasis] [...]

“Of the 10.5 million deaths below age 5 estimated to have occurred in 1999, 99% of them were in developing regions (3). The probability of child death (5qo) is typically less than 1% in industrialized countries classified into the A Regional Strata (and 0.5% in Japan), but rises to 300-350 per 1000 in Niger and Sierra Leone. Levels of child mortality well in excess of 10% (100 per 1000) are still common throughout Africa and in parts of Asia (Mongolia, Cambodia, Laos, Afghanistan, Bhutan, Myanmar, Bangladesh and Nepal).

However, perhaps the widest disparities in mortality occur at the adult ages 15-59 years. In some Southern African countries such as Zimbabwe, Zambia and Botswana, where HIV/AIDS is now a major public health problem, 70% or more of adults who survive to age 15 can be expected to die before age 60 on current mortality rates [in the late 80es, the number for Zimbabwe was 15-20%, see p.25 - US]. In several others (e.g. Malawi, Namibia and Uganda) the risk exceeds 60%. The dramatic increase in 45q15 in South Africa is also noteworthy, with estimated levels of 601 per 1000 and 533 per 1000 for males and females respectively in 1999. At the other extreme, 45q15 levels of 90-100 per 1000 are common in most developed countries for men, with risks as low as half this again for women. [...] HIV/AIDS was the cause of about 2.2 million deaths in Africa in 1999, making it by far the leading cause of death on the continent.”

There’s a lot of variation in mortality rates:

…and Africa is not the only region that’s doing badly: “The extraordinary risks of premature adult death among men in Eastern Europe is also clear from the Figure, (EUR C Region) with more than 1 in 3 who survive to age 15 in this Region likely to die before reaching age 60, at current risks compared with 10-12% in Western Europe, Japan and Australia.”

…

“Globally, some 56 million people are estimated to have died in 1999, 10.5 million below age five years. More males (29million) then females (27million) died, reflecting the systematically higher death rates for males at all ages in almost all countries. [...] Worldwide, deaths at ages 15-59 in 1999 amounted to an estimated 15.5 million, (9 million males, 6.5 million females), but with wide uncertainty. By any definition, these deaths (28% of the total over all ages) must be considered premature.”

…

The Danish life tables are at page 112 and I decided to post them below. The US life tables are at page 245. More fine-grained and newer US data are also available here.

Which variables are reported above? Well: “For each age, estimates of central death rates (nMx), the probability of dying (nqx), number of survivors (lx), and expectation of life (ex) are shown.” (p. 19) I didn’t have a clue what the ‘central death rate’ is but luckily one can look that kind of stuff up:

“For a given population or cohort, the central death rate at age x during a given period of 12 months is found by dividing the number of people who died during this period while aged x (that is, after they had reached the exact age x but before reached the exact age x+1) by the average number who were living in that age group during the period.”

Do remember when looking at numbers such as these that it’s not just about how long you live – how you die matters a great deal.

Cancer statistics, 2011

“Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths expected in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival based on incidence data from the National Cancer Institute, the Centers for Disease Control and Prevention, and the North American Association of Central Cancer Registries and mortality data from the National Center for Health Statistics. A total of 1,596,670 new cancer cases and 571,950 deaths from cancer are projected to occur in the United States in 2011. Overall cancer incidence rates were stable in men in the most recent time period after decreasing by 1.9% per year from 2001 to 2005; in women, incidence rates have been declining by 0.6% annually since 1998. Overall cancer death rates decreased in all racial/ethnic groups in both men and women from 1998 through 2007, with the exception of American Indian/Alaska Native women, in whom rates were stable. African American and Hispanic men showed the largest annual decreases in cancer death rates during this time period (2.6% and 2.5%, respectively). Lung cancer death rates showed a significant decline in women after continuously increasing since the 1930s. The reduction in the overall cancer death rates since 1990 in men and 1991 in women translates to the avoidance of about 898,000 deaths from cancer. However, this progress has not benefitted all segments of the population equally; cancer death rates for individuals with the least education are more than twice those of the most educated.”

Link to the publication. Some more data (click to view full size):

Sex differences matter a lot for some types of cancers and the differences between the genders are quite significant. Breast cancer cases make up ~30% of all new cancer cases for women and prostate cancer cases make up a similar proportion  of new male cases. Note that it makes a lot of sense to report the ‘new cases’-metric rather than the ‘total people afflicted’ if you want to know about the risk of getting the disease; some types of cancers are much more aggressive than others and death rates vary a lot, and so if you looked at a metric like ‘people afflicted’, relatively harmless cancers (e.g. (some types of) prostate cancer) would in some sense be ‘overrepresented’. As the report puts it, “the lifetime probability of being diagnosed with an invasive cancer is higher for men (44%) than women (38%) [...] However, because of the earlier median age of diagnosis for breast cancer compared with other major cancers, women have a slightly higher probability of developing cancer before age 60 years.”

Looking just at the death rate, there’s some variation here; spanning from Utah’s very low death rate of just 135.7 (most likely caused by environmental factors – smoking and drinking in particular) to Kentucky’s 216.5 – the report mentions specifically later on that “lung cancer shows by far the largest geographic variation in cancer occurrence”, which I do not find surprising. Even though the two states mentioned have very different death rates, far most states are in the 170+ range so most inter-state differences aren’t that large especially considering how many different factors impact a variable like this.

When looking at the next table remember to look at the actual percentages as the proportions given are only very rough measures. I think it’s interesting that they included the latter, but it’s probably not a bad idea; to a lot of math-challenged individuals such a fraction may convey significantly more information than do the percentage estimates, and the seemingly much greater degree of ‘precision’ of the probability estimates should not make us forget that these are in fact just that, estimates:

Again, recall that these are averages and averaging can hide important variation in the data. For example, the 6-7% lifetime risk of lung- and bronchial cancers is an measure which both includes heavy smokers and non-smokers. A smoker should rationally assume his risk to be significantly higher than that, and a non-smoker would on the other hand probably get a more accurate risk assessment if he assumes that his/her risk of getting that type of cancer is quite a bit lower than the full-sample estimate.

“Cancer replaced heart disease as the leading cause of death among men and women aged younger than 85 years in 1999 (Fig. 6). The overall cancer death rate decreased by 1.9% per year from 2001 through 2007 in males and by 1.5% in females from 2002 through 2007, compared with smaller declines of 1.5% per year in males from 1993 through 2001 and 0.8% per year in females from 1994 through 2002 (Table 5). Notably, the lung cancer mortality rate in women has begun to decline for the first time in recorded history and more than a decade later than the decline began in men.”

…

There’s more at the link.

Chronic diseases, a few numbers

“It is well established that NCDs [noncommunicable diseases] are the leading cause of death in the world, responsible for 63% of the 57 million deaths that occurred in 2008 (2). The majority of these deaths – 36 million – were attributed to cardiovascular diseases and diabetes, cancers and chronic respiratory diseases. [...] In most middle- and high-income countries1 NCDs were responsible for more deaths than all other causes of death combined, with almost all high-income countries reporting the proportion of NCD deaths to total deaths to be more than 70%. [...]

Low- and lower-middle-income countries have the highest proportion of deaths under 60 years from NCDs. Premature deaths under 60 years for high-income countries were 13% and 25% for upper-middle-income countries. In lower-middle-income countries the proportion of premature NCD deaths under 60 years rose to 28%, more than double the proportion in high-income countries. In low-income countries the proportion of premature NCD deaths under 60 years was 41%, three times the proportion in high-income countries.”

From this WHO publication. Males are more likely to die early on from NCDs than are females:

A little more:

“In 2008, the age-standardized adult diabetes prevalence was 9.8% among men and 9.2% among women, reflecting an increase from 8.3% in men and 7.5% in women in 1980 (5). The number of people with diabetes increased from 153 million in 1980 to 347 million in 2008 (5). For raised blood glucose/diabetes, the estimated prevalence of diabetes was relatively consistent across all country income groups.

The prevalence of raised body mass index (BMI) generally increased with rising income level of countries, and rose across all income groups over the three decades. The prevalence of overweight in high-income and upper-middle-income countries was more than double that of low- and lower-middle-income countries.

More than half of adults in high-income countries were overweight and just over one fifth of were obese. In upper-middle-income countries, more than half of adults were overweight and a quarter were obese.

In lower-middle- and low-income countries the increase in prevalence of overweight and obesity over three decades was greater than in upper-middle and high-income countries, with rates of obesity doubling over the three decades between 1980 and 2008 (6). In lowermiddle-income countries obesity doubled during this period from 3-6%, and in low-income countries from 2-4%. Overweight increased from 15-24% in lower-middle-income countries during this period, among low-income countries it rose from 10-16%. In low-income countries women’s overweight and obesity showed the most dramatic increases and in 2008 were double those of men. In these low-income countries women’s overweight doubled from 9% in 1980 to 18% in 2008 and obesity more than doubled from 2-5%.”

The publication contains some country-specific data for most countries in the world. A few Danish data: The publication estimates that NCDs account for 90 % of all deaths. Here’s a slightly more detailed version:

Do notice how little risk there is of dying of a communicable disease – in a historical context, that number is just incredibly low!

The estimated proportion of overweight males in Denmark is 57.8%, and 45.6% of males are estimated to have elevated blood pressure (‘aged 25+ with systolic BP ≥ 140 mmHg and/or diastolic BP ≥90 mmHg or on medication to lower blood pressure’). So yeah, a clear majority of Danish males in general are overweight. The numbers are better for females (46.2% and 36.7% respectively). Blood pressure used to be even higher and it has decreased significantly over the last 30 years; on the other hand both mean blood glucose and BMI have increased.

Stuff we don’t know much about (/yet?) (…/a continuing series?)

“Water is essential for maintaining life on Earth but can also serve as a media for many pathogenic organisms, causing a high disease burden globally. However, how the global distribution of water-associated infectious pathogens/diseases looks like and how such distribution is related to possible social and environmental factors remain largely unknown. In this study, we compiled a database on distribution, biology, and epidemiology of water-associated infectious diseases and collected data on population density, annual accumulated temperature, surface water areas, average annual precipitation, and per capita GDP at the global scale. From the database we extracted reported outbreak events from 1991 to 2008 and developed models to explore the association between the distribution of these outbreaks and social and environmental factors. [...]

Worldwide, water-associated infectious diseases are a major cause of morbidity and mortality [11], [12], [13]. A conservative estimate indicated that 4.0% of global deaths and 5.7% of the global disease burden (in DALYs) were attributable to a small subset of water, sanitation, and hygiene (WSH) related infectious diseases including diarrheal diseases, schistosomiasis, trachoma, ascariasis, trichuriasis, and hookworm infections [11], [14], [15]. Although unknown, the actual disease burden attributable to water-associated pathogens is expected to be much higher. A total of 1415 species of microorganisms have been reported to be pathogenic, among which approximately 348 are water-associated, causing 115 infectious diseases [5].Yet, their distribution and associated factors at the global scale remain largely unexplored. [...]

The population density was shown to be a significant risk factor for reported outbreaks of all categories of water-associated infectious diseases and the probability of outbreak occurrence increased with the population density. The accumulated temperature was a significant risk factor for water-related diseases only. The analysis suggested that occurrence of water-washed diseases had significantly inverse relationship with surface water areas. Such inverse relationship was also observed between the average annual rainfall and water-borne diseases (including water-carried) and water-related diseases.”

From Global Distribution of Outbreaks of Water-Associated Infectious Diseases by Yang, LeJeune et al.

The genetics of type 1 diabetes

There’s this question I’ve been asked many times: “Type 1 diabetes? Is that genetic?”

I was asked it again a couple of weeks ago and it caught me off-guard so I don’t think I was being quite as precise as I’d have liked to be – by having now written this post, I hope that I’ll do better next time (oh yes, there’ll be a next time…). Before going any further I should probably note here that even though I don’t know much about genetics, I estimate that I do know (/significantly?) more than most people who would choose to ask such a question: Having been exposed to stuff like Khan Academy, Razib Khan’s blog, Wikipedia (way too much to link to here), Russell, Dawkins and Majerus for instance means that I know the difference between a recessive allele and a linkage disequilibrium. It also means that I’m very inclined to answer a question like that one by asking another question: “What do you mean by ‘is it genetic?’” Genetics is complex stuff and there are many kinds of genetic disorders. I’ve tended to assume that people who ask do so more because of the implied blame-angle inherent in the question (‘it’s not your own fault you’re sick, right?’) than because of their deep interest in the disease etiology of type 1 diabetes – but I shouldn’t let that affect the way I respond, given that a reasonably clear answer to the question (…I assume they think they are asking) exists today (wikipedia):

“Type 1 diabetes is partly inherited and then triggered by certain infections, with some evidence pointing at Coxsackie B4 virus. There is a genetic element in individual susceptibility to some of these triggers which has been traced to particular HLA genotypes (i.e., the genetic “self” identifiers relied upon by the immune system). However, even in those who have inherited the susceptibility, type 1 diabetes mellitus seems to require an environmental trigger.”

So the simple version is that ‘genetics’ increases disease susceptibility and an infection then triggers the disease process. Here’s the abstract of a new study, Genetics of Type 1 Diabetes, by Steck and Rewers, providing a little more detail:

“BACKGROUND: Type 1 diabetes, a multifactorial disease with a strong genetic component, is caused by the autoimmune destruction of pancreatic β cells. The major susceptibility locus maps to the HLA class II genes at 6p21, although more than 40 non-HLA susceptibility gene markers have been confirmed.

CONTENT: Although HLA class II alleles account for up to 30%–50% of genetic type 1 diabetes risk, multiple non-MHC loci contribute to disease risk with smaller effects. These include the insulin, PTPN22, CTLA4, IL2RA, IFIH1, and other recently discovered loci. Genomewide association studies performed with high-density single-nucleotide–polymorphism genotyping platforms have provided evidence for a number of novel loci, although fine mapping and characterization of these new regions remain to be performed.

Children born with the high-risk genotype HLADR3/4-DQ8 comprise almost 50% of children who develop antiislet autoimmunity by the age of 5 years. Genetic risk for type 1 diabetes can be further stratified by selection of children with susceptible genotypes at other diabetes genes, by selection of children with a multiple family history of diabetes, and/or by selection of relatives that are HLA identical to the proband.

SUMMARY: Children with the HLA-risk genotypes DR3/4-DQ8 or DR4/DR4 who have a family history of type 1 diabetes have more than a 1 in 5 risk for developing islet autoantibodies during childhood, and children with the same HLA-risk genotype but no family history have approximately a 1 in 20 risk. Determining extreme genetic risk is a prerequisite for the implementation of primary prevention trials, which are now underway for relatives of individuals with type 1 diabetes.”

…

“Children born with the high-risk genotype HLADR3/4-DQ8 comprise almost 50% of children who develop antiislet autoimmunity by the age of 5 years” – in plain English, this means that almost half of all type 1 diabetics who show disease development before the age of 5 have this specific high-risk genotype. Note also how complex this disease is in terms of the genetics – ‘more than 40 non-HLA susceptibility gene markers have been confirmed’. Maybe some of them are just flukes due to p-value hunting, but that’s a lot of genes impacting disease risk.

Steno has some stuff in Danish here if people are interested. According to their numbers, if the mother has type 1 diabetes there’s a 2% risk that her child will have the disease. If the father has the disease the risk is 5%. Lægehåndbogen states that for monozygotic twins, if one twin develops the disease the risk that the other twin will also get it is 50%.

A few papers

From the European Public Choice Society’s meeting this year. There’s a lot more stuff here.

1. Public Investment and Re-election Prospects in Developed Countries.

Last I was home I found out that the idea that managers might decide to deliberately ‘boost their numbers’ in various ways strategically some time before leaving for another job was something my parents had never even considered. I find it obvious that politicians from time to time decide to employ similar strategies by trying to make the important numbers look good up to the election and then take the hit a year or two later, once they’re in office.

2. The end of the news.

3. Econometric Estimates of Deterrence of the Death Penalty: Facts or Ideology? From the concluding remarks:

“Considering all these results, a critical and cautious examination of them leads to the conviction that we cannot draw any strong conclusion: while there is some evidence that a deterrent effect might exist, it is too fragile to be sure about it and the possible quantitative effect usually measured by the number of homicides prevented by each execution is so uncertain that it is difficult to conclude anything that would be relevant for policy purposes.”

4. Benefit Morale and Cross-Country Diversity in Sick Pay Insurance. From the abstract:

“We analyze the impact of benefit morale on sick pay entitlement levels in a political economy framework. Stronger benefit morale reduces the number of recipients. On one hand this reduces the probability of receiving benefits, on the other hand it makes insurance cheaper. Numerical simulations show that the probability effect can dominate the price effect and hence benefit morale might decrease insurance levels.”

The ‘benefit morale’ mentioned is a social norm against benefit fraud, so that you don’t claim benefits if you’re not sick. And yeah, I know I’ve linked to it before but I should probably leave a link to this every time I publish a post like this with multiple studies.

A question

Though I haven’t personally seen (m/?)any of them, I have the impression that the: ‘if you were told you had 24 hours left to live, what would you do in the time you had left?’-theme has been dealt with extensively in movies and pop culture. Now how about this related if still quite different question: “If you had the chance to be told at one point exactly when you were to die, when would you like to get that information?”

Not at all? Ten seconds before? A week before? A year before? Right now?

Of course this question is somewhat related to the fact that more information sometimes makes us worse off rather than better off. Knowing is not always better than not knowing. This is a well known fact in i.e. health economics. However the devil is in the details as well and uncertainty sometimes deceives us, makes us think things that aren’t true (and perhaps makes some states of uncertainty preferable to others to us). I’ll illustrate this in a model below. It’s a bit technical, but not too much and you needn’t know any fancy math to understand what’s going on. It’s pretty basic but a lot of people get stuff like this wrong. The “|” thing I’m using below should be read as ‘conditional on‘, that ought to be (if anything) the only thing you haven’t seen before. So let’s set up the following model:

Say you have a genetic test that will determine with 99,9% certainty whether you have terrible disease X. More precisely, assume 1 out of 1000 tests gives a false positive (let you think you have the disease even though you don’t) and that no false negatives ever happen (everybody with the disease will be caught by the test, you will never get a negative test result if you have the disease). X is incurable and deadly; think of it as a ticking time bomb version of the worst disease you can imagine (you don’t know you have it before it gets very bad). Say the background incidence of X is 0,001% (1 out of 100.000 people get it). This is low enough that ‘ordinary people’ would never worry about having the disease (it’s not genetic). Say a public screening protocol is implemented using the test mentioned above. The screening protocol is implemented solely with the purpose of giving people more information about their health status, as no cure exists. Now what would happen?

Let’s say a guy gets a positive test result. What’s the probability that he has the disease? Well, we know that the test is 99,9% accurate, so it should be pretty high, right? Wrong. People familiar with Bayes’ Rule probably know what I’m getting at.

There are six relevant probabilities here:

P(X) = 0,00001 (1/100.000; this is the probability that a random test taker has the disease)
P(not X) = 1 – 1/100.000 = 0,99999 (the -ll- does not have the disease)
P(negative test | X) = 0 (probability that a test taker has the disease but tests negative)
P(positive test | X) = 1 (-ll- and tests positive)
P(positive test | not X) = 0,001 (probability that the test is positive even though you don’t have the disease)
P(negative test | not X) = 0,999 (probability that the test is negative and you don’t have the disease).

Now we calculate P( X | positive test), i.e. the probability that you get a positive result and you actually have the disease. This is equal to

[ P(positive test | X) * P(X) ] / [ P(positive test | X)*P(X) + P(positive test | not X)*P(not X)] =

[ 1*0,00001 ] / [ 1*0,00001 + 0,001*0,99999 ] = 0,009901. Multiply by 100 and you realise that this amounts to less than 1%. The probability that you get a positive result but aren’t sick is of course 1 minus that number, so more than 99% of all people who are tested positive aren’t sick even though the test we’re talking about is 99,9% accurate.

Some information just can’t be unlearned, and people usually are very bad at interpreting probabilities and dealing with numbers like these. Even a lot of doctors get this stuff wrong and might never even have heard about Bayes Rule (or have forgotten all about it even if they have). Note that if people who are actually sick would prefer to know in advance, even a blunt screening process like the one above makes the sick people much better off; they have a far more accurate assessment of their probability of developing the disease than they did before the screening, as their estimate of having X changes from 1/100.000 to ~1/100. The other side of the coin is that some people who’re not sick will think they’re much more likely to be than they really are – perhaps some of them would have preferred never to have been screened.

Note also in the context of i.e. genetic testing that adding additional information to an insurance market can sometimes make that insurance market break down, because the uncertainty that made insurance a sensible move is no longer there. Insurance is about risk diversification and if you take away the risk and replace it with certainties, well there’s not much left. If a life insurance firm knows that you with probability p will die within the next 5 years, there’ll often exist some potential insurance contract making both the firm and you better off. But what’s the price (/premium) of such an insurance contract if p is suddenly no longer uncertain, but rather equal to 1 or 0? What if it’s not about the probability of dying but rather the probability of getting a horrible disease, say, 40 years down the line? Same thing. If uncertainty is replaced by certainty it often also might have some distributional consequences to the parties involved (insurance always involves some element of (statistical) cost sharing across individuals).

Going back to the beginning of the post, I find the “when would you prefer to get that information?”-question a much more interesting question than the “how would you react when you’d already gotten it?” It is not at all, in my mind, an easier question to answer.

Health, Retirement and Mortality (1?)

Some bits from the first chapter of the Phd Thesis by Malene Lamb.

“75% of the males in the sample work full-time, whereas only 54% of the women are employed full-time.”

“Finally, we have information about contributions to both labor market pension schemes and private pension schemes. [...] The variables show how much the individual has contributed to the different schemes each year making it a good indicator for how much the individual has put aside to supplement the public retirement schemes. [...] Around 95% of the individuals in our sample have made no contributions within a given year to a labor market pension schemes independently of which of the two types we consider. [...] For private schemes the picture looks slightly better since ‘only’ 70% have no private capital pension and 75% have no private annuity pension.”

“If the spouse is working full-time it lowers the probability for the individual to enter early retirement. However, this effect only holds for women indicating that women actively participating in the labor market have a lower retirement hazard if their husband works full time. [...] A longer spell of illness of the spouse significantly increases the retirement hazard indicating that people may leave the labor market earlier in order to take care of their spouse. Looking at the two sub-samples this effect is only significant for the men. [...] In this context it is important to note that free medical care is available to everyone thus not forcing one member of the household to continue working in order to maintain a health insurance.”

“We have [...] shown that the husbands’ characteristics do affect the retirement behaviour of their wives differently than the wives’ characteristics affect their husbands. Within all the variable groups (labor market, education, age, occupation, sector, financial indicator, and pension) included in the model we find that spousal differences exists. However we never find opposite significant spousal effects, it is always the case that it is only significant for one of them.

Women’s retirement hazard is affected positively by the husband’s experience, if the husband has a short education compared to basic education, the husband’s high contributions to a labor market capital pension scheme and finally medium or high contributions to a private annuity pension scheme. On the other hand women’s retirement hazard is affected negatively (thus retire later) if the husband works full time, is self-employed or works in construction compared to the public sector. Men’s retirement hazard is affected positively by the wife’s age, labor market pension payments or if she receives sickness benefits. Men retire later if the wife has been unemployed, is high educated, not working as a high-salaried worker, is working in the construction, trade or transport sector compared to the public sector. Overall, we find more significant effects for men indicating that they are more influenced by their spouse in the early retirement decision compared to women. This corresponds to the results found in Gustman and Steinmeier (2000) and Coile (2004).”

…

Most of this I didn’t know. The sample is based on all Danish workers who were active in the labor market at the age of 50 in 1985 (99.498 individuals) – so many of the results probably don’t hold for the whole population (/entire workforce, all Danes/…). For instance private savings and the level of education are both variables likely to be somewhat higher in the younger cohorts. It seems that a majority in the cohort they looked at didn’t consider it to be necessary to save any money for retirement at all – I was simply flabbergasted when I read those numbers. Though it is worth remembering the role real estate plays in the savings equation (/and of course also the impact of the public pension scheme); in a way it makes more sense for a Dane to implicitly put the savings into the house than it does for an American, as the median Dane will never get into a situation where he or she suddenly needs to raise a lot of money fast to pay for a medical procedure – the liquidity part is much less important.

Drug resistance, ctd.

You can watch Megan McArdle’s brief presentation on the subject at the Economic Bloggers Forum 2011 here. If I could embed it, I would. Here’s a slightly related article on carbapenem resistance.

On a somewhat different if still related note, Stakbogladen (a university bookstore in Aarhus) had some sort of ‘spring sale’ last week. I’m currently reading Scientifica, one of the books I bought there (a somewhat disappointing read so far and I should have been smart enough not to buy that book, anyway…) but I also bought a book called Disease Prevention (Sygdomsforebyggelse) which I’ve yet to start reading. I’ll probably blog a bit about that book once I get to it – it looks quite interesting. It was very cheap compared to the pre-sale price. Incidentally, I’ve been browsing Marlene Lamb’s Phd-thesis – ‘Health, Retirement and Mortality’ – which is currently standing on my bookshelf, and I’ll probably give it a blog post or two as well at some point unless I’m told not to.