Cases reported by state health departments to CDC for weekly publication are provisional because of ongoing revision of information and delayed reporting. Case counts in these query tables are presented as they were published in the MMWR. Therefore, numbers listed in later MMWR weeks may reflect changes made to these counts as additional information becomes available. Mortality Tables As part of its national influenza surveillance effort, CDC receives weekly mortality reports from cities and metropolitan areas in the United States within weeks from the date of death.
Weekly Report. Current Volume. Recommendations and Reports. For example, while Legionellosis Fig. S7 in File S1 is emerging in both Europe and the Americas with similar trends or slopes , the case load is twice as high in Europe, and, all else being equal, greater investment to combat this disease in the population where the case load is higher is warranted.
This assumes investment is allocated on the basis of the total number cases rather than the fraction of the population affected, which seems preferable.
Similarly, the number of Lyme cases in Poland is both higher and is increasing at a greater rate than in the Czech Republic Fig. S8 in File S1 , which suggests a greater urgency for additional efforts to combat this disease in Poland.
These examples use trends in two populations for the same disease to make a valid comparison. Comparing different diseases would require a common currency, such as DALYs. This approach also provides a way to examine the initial time points of emergence. For example, Legionellosis showed a sharp rise in case numbers in several regions between and Fig.
S7 in File S1. If changes in case definitions and detectability of cases can be ruled out, underlying factors that led to emergence during this period might be identified.
Alternatively, investigating the cause for a significant rise in cases i. Our approach provides a simple method to define a disease as emerging, re-emerging, receding or non-trending, and to describe the magnitude of the rate of change. It provides a tool to implement the conceptual ideas behind previously proposed definitions that were too vague to be widely adopted.
If this new method is embraced by the scientific and public health community, it should be possible to overcome the subjective definitions of EIDs used previously and regain the utility of designating diseases as emerging or not. Our results highlight two key issues. First, the designation of a disease as emerging implicitly refers to longer-term trends than an individual outbreak or epidemic. Data on a yearly timescale is most likely appropriate for decisions involving funding for research, and non-emergency control measures.
Most previous studies have not defined the time window over which a disease must increase in incidence to be defined as emerging. Our analysis identifies the temporal window timescale for a trend statistically rather than arbitrarily, and characterizes trends across the whole time series such that a disease can emerge and re-emerge multiple times depending on the time period of interest.
Secondly, by focusing on a specific, defined population and by inference, in most cases a geographic region , the analysis provides a more general way of defining an EID than previous studies. Following our approach, diseases can emerge within a population that is a subset of a larger but spatially contiguous population e. This implies that a disease can be classified as an EID in one population at one scale and be stable or receding in another at a different scale [30].
While this is implied in many studies of EIDs, our approach removes contradictions and ambiguities arising from trying to determine whether disease is either emerging, or not, in all regions at any time. One issue which merits discussion is the emergence of diseases associated with spatial spread to new regions. Our framework for detecting trends in temporal data time series does not directly address spatial spreading of diseases, but it can still provide a broad perspective of temporal trends.
For example a disease may invade a new region and fade out Monkeypox in the USA [31] or it may become endemic, but with no significant increase in incidence after its invasion of the new region is complete e.
West Nile virus in North America from — [32]. Thus, while the spatial spread of these two pathogens is consistent with the definition of an EID that includes the spread of a pathogen to a new region, our framework indicates that neither of these diseases has continued to emerge i.
Studies which aim to analyze trends in disease emergence, or the factors that cause them to emerge, require identification of the point in time that a disease first emerges within a population or region [4] , [21] , [22]. Identifying the precise timing of initial emergence events i. For example, evidence suggests HIV infections occurred for several decades before being detected [33].
Our approach identifies emergence as the initial significant rise in incidence or a significant increase in impact and provides a simple method for estimating the time when emergence began — the joinpoint of a segmented regression, or the x-intercept of the initial rise in incidence.
It thus provides a strategy to more accurately analyze global trends in EIDs. In the current analysis, we used annual incidence data to determine whether diseases in specified populations were emerging.
If time series data of the impact of diseases are available, and are quantified in a consistent way e. Our approach can be easily applied to diseases affecting populations of plants, wildlife or livestock. Using a quantitative approach to determine whether a disease is emerging presents some challenges. First, it requires time series data in a common currency to accurately classify a disease as emerging, and to compare the rate or significance of emergence among different diseases.
The ideal would be time series of DALYs for each disease. While this is a challenge for analyzing some historical trends, we believe it is a strength, in that it brings rigor to the analysis.
Second, surveillance data, like those used in our example analyses, are influenced by changes in reporting, case definitions, diagnostic capabilities, and other aspects that determine the measured case burden. Third, simple measures like case numbers may be a poor measure of disease impact, especially if populations have different resources for treatment e.
As a result, more explicit measures of disease burden should be used whenever possible. Fourth, although we have presented a simple approach based on segmented linear regression, nonlinear approaches may be more appropriate for some data e.
Translating the results of analyses of trends in disease impact into policy requires careful thought. While the elevation and slope of trends in disease impact provide useful information about the potential impact of a disease in the near future, using trends to predict future impact clearly assumes that past trends will continue, and should be interpreted in the context of current control efforts.
For example malaria eradication campaigns have been highly successful in several countries, and analyses of case burdens show strongly receding trends [34]. Diversion of funds away from locations where public health resources are effectively suppressing disease transmission is likely to lead to re-emergence.
Specifically, reducing control measures before eradication campaigns are complete due to low and declining case loads is counter-productive. Clearly, the effort and resources currently invested in a disease in a specific population, and the impact of changes in resources allocated on case burdens is required to properly interpret a trend in disease impact.
This approach allows for the identification of time points associated with changes in case burden that can be used to try to determine the causes of disease emergence. We hope that, with increasingly accurate surveillance data, and given the appropriate context, a quantitative approach like the one suggested here could improve prioritization of resources for infectious disease research, surveillance and control.
Implementing this more rigorous definition of an EID will require buy-in and enforcement from scientists, policy makers, peer reviewers and journal editors. Implementation faces significant challenges because doing so will often demonstrate that little evidence exists to support a claim that a favored disease is in fact emerging. Figure S1, Brucellosis.
Figure S2, Crimean-Congo hemorrhagic fever. Figure S3, Dengue. Figure S4, Hantavirus pulmonary syndrome. Figure S5, Hepatitis B. Figure S6, Hepatitis C. Figure S7, Legionellosis. Figure S8, Lyme disease. They can have various response categories e.
Ideally, code response categories in advance and on the instrument to facilitate data entry and analysis e. Close-ended questions could include cascading questions, which can be an efficient way to get more detailed information as one filters down through a hierarchy of questions e. In compiling questions, consider the flow, needed skip patterns, and order e.
For self-administered surveys, the format needs to be friendly, well-spaced, and easy to follow, with clear instructions and definitions. Content experts should review the draft questionnaire. The epidemiologist should pilot the questionnaire with a few colleagues and members of the study population and edit as necessary.
This will save time in the long run; many epidemiologists have learned the hard way that a survey question was not clear or was asking about more than one concept, or that the menu of answers was missing a key response category. Good sample selection can help improve generalizability of results and ensure sufficient numbers of study participants. Information about determining whom to select is covered in study design discussions in Chapter 7 , but sample size is worth briefly mentioning here.
If the study comprises the entire study population, it is a census ; a subset of the study population is a sample. A sample can be selected through probability sampling or nonprobability sampling e.
Probability sampling is a better choice for statistical tests and statistical inferences. For probability sampling procedures other than a simple random sample e. How large a sample to select depends on resources, study timeline generally the larger the sample, the more expensive and time-consuming , the analyses to be conducted, and the effect size you want to detect. For example, to detect a difference in proportions between two groups using a chi-square test, consider how much of a difference needs to be detected to be meaningful.
Generally, government public health agencies have the authority to access healthcare system data with justification.
The Health Insurance Portability and Accountability Act HIPAA of 31 has specific language allowing for the use of personal health information by government agencies to perform public health activities. Nonetheless, accessing data sources that are not specifically collected and maintained by public health authorities can be challenging. Other scenarios that challenge epidemiologists trying to access external data include concern by healthcare systems that requests for data on hospitalizations, clinic visits, or emergency department visits breach privacy of protected health information; concern by school officials that access to information about children during an outbreak associated with a school activity violates provisions of the Family Educational Rights and Privacy Act 32 ; and concerns by businesses that case-patients in an outbreak associated with a particular food item or establishment might pursue legal action or lawsuits.
Legal counsel can help address these concerns. Having a written data collection section as part of the overall study protocol is essential. As with survey development, borrowing from previous data collection protocols can be helpful.
This protocol can include the following:. Train staff collecting data on the protocol, reviewing instructions carefully and modifying as needed. Involve interviewers in pilot testing the survey instrument and provide feedback. Have a plan for quality checks during questionnaire administration if the survey is not computer-based. Review the first several completed surveys to check completeness of fields, inconsistencies in responses, and how well skip patterns work.
In addition, debrief interviewers about issues they might have encountered e. Similarly, data entry must have quality checks. When starting data entry, check several records against the completed survey instrument for accuracy and consider double data entry of a sample of surveys to check for errors. Subsequent chapters discuss the details of data analysis. However, it is important to consider conducting some preliminary data analysis even before data collection is complete. Understanding how participants are interpreting and answering questions can enable corrections to the wording before it is too late.
Many an epidemiologist has bemoaned a misinterpreted question, confusing survey formatting, or a missing confounding variable resulting in study questions without meaningful results. The important attributes of a public health surveillance system can and should be applied to data collected in response to an urgent event see Introduction.
In field investigations, tradeoffs exist between these attributes; for example, a more timely collection of data might lead to lower quality data, fewer resources might mean less complete data, and retrospective analysis of preexisting data might be more cost-effective, although prospective data collection from case-patients might enable more targeted questions about specific exposures.
The media can play important and sometimes conflicting roles during an outbreak. The media can be useful in alerting the public to an outbreak and assisting with additional case finding. In addition, with the current calls for government transparency and accountability, field epidemiologists might be reluctant to release information too early, thereby risking additional exposures to the suspected source. Changes in technology also challenge data collection.
Conversely, many new sources of data are opportunities made possible by the expanded use of computer technology by individuals, businesses, and health systems.
It is incumbent upon field epidemiologists to adapt to these changes to be able to investigate and control urgent public health threats. Responding to urgent public health issues expeditiously requires balancing the speed of response with the need for accurate data and information to support the implementation of control measures.
Adapting preexisting protocols and questionnaires will facilitate a timely response and consistency across jurisdictions. In most epidemiologic studies the activities are not done linearly and sequentially; rather, the steps frequently are conducted in parallel and are iterative, with results informing edits or amendments. The analyses and results are only as good as the quality of the data collected remember GIGO! The application period for EIS Class of is now closed.
Skip directly to site content Skip directly to page options Skip directly to A-Z link. Epidemic Intelligence Service. Section Navigation. Facebook Twitter LinkedIn Syndicate. Minus Related Pages. On This Page. Data Collection Activities. Top of Page. Develop a Study Protocol. A field investigation protocol does not have to be long, but it must include the following: Investigation objectives.
Study design e. Study population, case definition, sample size, and selection. Data collection procedures, variables to be collected, procedures to safeguard participants. Data security, privacy, confidentiality, information technology controls. Analysis plan. Logistics, including budget, personnel, and timeline.
Legal considerations, including statutes, rules, and regulations. Major sources of error that need to be considered during data collection include the following: Lack of generalizability because of selection bias, variable participation rates. Information bias, such as measurement error, self-report bias, and interviewer bias. Uncontrolled confounding or bias introduced in the association between exposure and outcome because of third variable.
Small sample size, resulting in inadequate power to detect differences between groups. Identify Possible Data Sources. Mortality Statistics Collecting mortality statistics and classifying the causes of death dates to the s in London, when the Bill of Mortality was periodically published 2.
Notifiable Diseases Reporting In the United States, the legal framework for reporting infectious diseases to public health authorities for investigation and control dates to , when Congress authorized the Public Health Service to collect reports of cholera, smallpox, plague, and yellow fever from consuls overseas to implement quarantine measures to prevent introduction into the United States 6. Laboratory Data Data from laboratories are critical for investigating infectious disease outbreaks.
Ongoing Population Surveys Ongoing population surveys are important for understanding the prevalence of health risk behaviors in the general population.
Environmental Exposure Data Distribution of Vectors Many emerging infectious diseases are zoonotic in origin, so related data are needed. Environmental Contaminants Illness resulting from exposure to environmental contaminants is another area of public health importance requiring surveillance.
Additional Existing Sources of Data Additional existing data sources can help identify cases, determine background rates of human illness, or assess exposures to disease-causing agents e.
Newer Sources of Data Electronic health records EHRs appear to be a promising newer source of data for public health surveillance and for assessing the prevalence of disease or behavioral risk factors in the population seeking healthcare Determine Data Collection Method.
Box 4. Develop the Questionnaire or Survey Instrument. Information and variables to include in a survey instrument are Unique identifier for each record. Date questionnaire is completed. A description of the purpose of the investigation for participants. Participant demographics. Outcome measures. Measures of exposure. Possible confounders and effect modifiers.
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