Econstudentlog

Infectious Disease Surveillance (II)

Some more observation from the book below.

“There are three types of influenza viruses — A, B, and C — of which only types A and B cause widespread outbreaks in humans. Influenza A viruses are classified into subtypes based on antigenic differences between their two surface glycoproteins, hemagglutinin and neuraminidase. Seventeen hemagglutinin subtypes (H1–H17) and nine neuraminidase subtypes (N1–N9) have been identifed. […] The internationally accepted naming convention for influenza viruses contains the following elements: the type (e.g., A, B, C), geographical origin (e.g., Perth, Victoria), strain number (e.g., 361), year of isolation (e.g., 2011), for influenza A the hemagglutinin and neuraminidase antigen description (e.g., H1N1), and for nonhuman origin viruses the host of origin (e.g., swine) [4].”

“Only two antiviral drug classes are licensed for chemoprophylaxis and treatment of influenza—the adamantanes (amantadine and rimantadine) and the neuraminidase inhibitors (oseltamivir and zanamivir). […] Antiviral resistant strains arise through selection pressure in individual patients during treatment [which can lead to treatment failure]. […] they usually do not transmit further (because of impaired virus fitness) and have limited public health implications. On the other hand, primarily resistant viruses have emerged in the past decade and in some cases have completely replaced the susceptible strains. […] Surveillance of severe influenza illness is challenging because most cases remain undiagnosed. […] In addition, most of the influenza burden on the healthcare system is because of complications such as secondary bacterial infections and exacerbations of pre-existing chronic diseases, and often influenza is not suspected as an underlying cause. Even if suspected, the virus could have been already cleared from the respiratory secretions when the testing is performed, making diagnostic confirmation impossible. […] Only a small proportion of all deaths caused by influenza are classified as influenza-related on death certificates. […] mortality surveillance based only on death certificates is not useful for the rapid assessment of an influenza epidemic or pandemic severity. Detection of excess mortality in real time can be done by establishing specific monitoring systems that overcome these delays [such as sentinel surveillance systems, US].”

“Influenza vaccination programs are extremely complex and costly. More than half a billion doses of influenza vaccines are produced annually in two separate vaccine production cycles, one for the Northern Hemisphere and one for the Southern Hemisphere [54]. Because the influenza virus evolves constantly and vaccines are reformulated yearly, both vaccine effectiveness and safety need to be monitored routinely. Vaccination campaigns are also organized annually and require continuous public health efforts to maintain an acceptable level of vaccination coverage in the targeted population. […] huge efforts are made and resources spent to produce and distribute influenza vaccines annually. Despite these efforts, vaccination coverage among those at risk in many parts of the world remains low.”

“The Active Bacterial Core surveillance (ABCs) network and its predecessor have been examples of using surveillance as information for action for over 20 years. ABCs has been used to measure disease burden, to provide data for vaccine composition and recommended-use policies, and to monitor the impact of interventions. […] sites represent wide geographic diversity and approximately reflect the race and urban-to-rural mix of the U.S. population [37]. Currently, the population under surveillance is 19–42 million and varies by pathogen and project. […] ABCs has continuously evolved to address challenging questions posed by the six pathogens (H. influenzae; GAS [Group A Streptococcus], GBS [Group B Streptococcus], S.  pneumoniae, N. meningitidis, and MRSA) and other emerging infections. […] For the six core pathogens, the objectives are (1) to determine the incidence and epidemiologic characteristics of invasive disease in geographically diverse populations in the United States through active, laboratory, and population-based surveillance; (2) to determine molecular epidemiologic patterns and microbiologic characteristics of isolates collected as part of routine surveillance in order to track antimicrobial resistance; (3) to detect the emergence of new strains with new resistance patterns and/or virulence and contribute to development and evaluation of new vaccines; and (4) to provide an infrastructure for surveillance of other emerging pathogens and for conducting studies aimed at identifying risk factors for disease and evaluating prevention policies.”

“Food may become contaminated by over 250 bacterial, viral, and parasitic pathogens. Many of these agents cause diarrhea and vomiting, but there is no single clinical syndrome common to all foodborne diseases. Most of these agents can also be transmitted by nonfoodborne routes, including contact with animals or contaminated water. Therefore, for a given illness, it is often unclear whether the source of infection is foodborne or not. […] Surveillance systems for foodborne diseases provide extremely important information for prevention and control.”

“Since 1995, the Centers for Disease Control and Prevention (CDC) has routinely used an automated statistical outbreak detection algorithm that compares current reports of each Salmonella serotype with the preceding 5-year mean number of cases for the same geographic area and week of the year to look for unusual clusters of infection [5]. The sensitivity of Salmonella serotyping to detect outbreaks is greatest for rare serotypes, because a small increase is more noticeable against a rare background. The utility of serotyping has led to its widespread adoption in surveillance for food pathogens in many countries around the world [6]. […] Today, a new generation of subtyping methods […] is increasing the specificity of laboratory-based surveillance and its power to detect outbreaks […] Molecular subtyping allows comparison of the molecular “fingerprint” of bacterial strains. In the United States, the CDC coordinates a network called PulseNet that captures data from standardized molecular subtyping by PFGE [pulsed field gel electrophoresis]. By comparing new submissions and past data, public health officials can rapidly identify geographically dispersed clusters of disease that would otherwise not be apparent and evaluate them as possible foodborne-disease outbreaks [8]. The ability to identify geographically dispersed outbreaks has become increasingly important as more foods are mass-produced and widely distributed. […] Similar networks have been developed in Canada, Europe, the Asia Pacifc region, Latin America and the Caribbean region, the Middle Eastern region and, most recently, the African region”.

“Food consumption and practices have changed during the past 20 years in the United States, resulting in a shift from readily detectable, point-source outbreaks (e.g., attendance at a wedding dinner), to widespread outbreaks that occur over many communities with only a few illnesses in each community. One of the changes has been establishment of large food-producing facilities that disseminate products throughout the country. If a food product is contaminated with a low level of pathogen, contaminated food products are distributed across many states; and only a few illnesses may occur in each community. This type of outbreak is often difficult to detect. PulseNet has been critical for the detection of widely dispersed outbreaks in the United States [17]. […] The growth of the PulseNet database […] and the use of increasingly sophisticated epidemiological approaches have led to a dramatic increase in the number of multistate outbreaks detected and investigated.”

“Each year, approximately 35 million people are hospitalized in the United States, accounting for 170 million inpatient days [1,2]. There are no recent estimates of the numbers of healthcare-associated infections (HAI). However, two decades ago, HAI were estimated to affect more than 2 million hospital patients annually […] The mortality attributed to these HAI was estimated at about 100,000 deaths annually. […] Almost 85% of HAI in the United States are associated with bacterial pathogens, and 33% are thought to be preventable [4]. […] The primary purpose of surveillance [in the context of HAI] is to alert clinicians, epidemiologists, and laboratories of the need for targeted prevention activities required to reduce HAI rates. HAI surveillance data help to establish baseline rates that may be used to determine the potential need to change public health policy, to act and intervene in clinical settings, and to assess the effectiveness of microbiology methods, appropriateness of tests, and allocation of resources. […] As less than 10% of HAI in the United States occur as recognized epidemics [18], HAI surveillance should not be embarked on merely for the detection of outbreaks.”

“There are two types of rate comparisons — intrahospital and interhospital. The primary goals of intrahospital comparison are to identify areas within the hospital where HAI are more likely to occur and to measure the efficacy of interventional efforts. […] Without external comparisons, hospital infection control departments may [however] not know if the endemic rates in their respective facilities are relatively high or where to focus the limited fnancial and human resources of the infection control program. […] The CDC has been the central aggregating institution for active HAI surveillance in the United States since the 1960s.”

“Low sensitivity (i.e., missed infections) in a surveillance system is usually more common than low specificity (i.e., patients reported to have infections who did not actually have infections).”

“Among the numerous analyses of CDC hospital data carried out over the years, characteristics consistently found to be associated with higher HAI rates include affiliation with a medical school (i.e., teaching vs. nonteaching), size of the hospital and ICU categorized by the number of beds (large hospitals and larger ICUs generally had higher infection rates), type of control or ownership of the hospital (municipal, nonprofit, investor owned), and region of the country [43,44]. […] Various analyses of SENIC and NNIS/NHSN data have shown that differences in patient risk factors are largely responsible for interhospital differences in HAI rates. After controlling for patients’ risk factors, average lengths of stay, and measures of the completeness of diagnostic workups for infection (e.g., culturing rates), the differences in the average HAI rates of the various hospital groups virtually disappeared. […] For all of these reasons, an overall HAI rate, per se, gives little insight into whether the facility’s infection control efforts are effective.”

“Although a hospital’s surveillance system might aggregate accurate data and generate appropriate risk-adjusted HAI rates for both internal and external comparison, comparison may be misleading for several reasons. First, the rates may not adjust for patients’ unmeasured intrinsic risks for infection, which vary from hospital to hospital. […] Second, if surveillance techniques are not uniform among hospitals or are used inconsistently over time, variations will occur in sensitivity and specificity for HAI case finding. Third, the sample size […] must be sufficient. This issue is of concern for hospitals with fewer than 200 beds, which represent about 10% of hospital admissions in the United States. In most CDC analyses, rates from hospitals with very small denominators tend to be excluded [37,46,49]. […] Although many healthcare facilities around the country aggregate HAI surveillance data for baseline establishment and interhospital comparison, the comparison of HAI rates is complex, and the value of the aggregated data must be balanced against the burden of their collection. […] If a hospital does not devote sufficient resources to data collection, the data will be of limited value, because they will be replete with inaccuracies. No national database has successfully dealt with all the problems in collecting HAI data and each varies in its ability to address these problems. […] While comparative data can be useful as a tool for the prevention of HAI, in some instances no data might be better than bad data.”

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August 10, 2017 - Posted by | Books, Data, Epidemiology, Infectious disease, Medicine, Statistics

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