Since this technique is not precise, widen the probable period of exposure by, say, 20% to 50% on either side of these dates, and then ask about exposures during this widened period in an attempt to identify the source. They may be able to answer some critical questions: What were their exposures before becoming ill? The descriptive epidemiology may provide useful clues that can be turned into hypotheses. Section 1: Introduction Section 2: Purpose and Characteristics of Public Health Surveillance Section 3: Identifying Health Problems for Surveillance Section 4: Identifying or Collecting Data for Surveillance Section 5: Analyzing and Interpreting Data Section 6: Disseminating Data and Interpretations Section 7: Evaluating and Improving Surveillance Several counties are shaded to indicate the number of cases in that location. If the tests confirm that all of those case-patients were infected with the same organism, for example norovirus, the other persons with compatible clinical illness are all presumed to be part of the same outbreak and to be infected with the same organism. The illness was given the name eosinophilia-myalgia syndrome. AFP-SURVEILLANCE Nationwide AFP (acute flaccid paralysis) surveillance is the gold standard for detecting cases of poliomyelitis. Although this confidence interval is quite wide and includes a wide range of values compatible with the data in the study, it does not include the null hypothesis value of 1.0. The 5 Whys and/or 8 Disciplines of Problem-Solving (8D) approaches are good root-cause analysis techniques that can help them work together on crafting a good problem statement, identifying the root cause/s, and brainstorming on appropriate solutions to address non-conformance. Solna, Sweden: Smittskyddsinstitutet [updated 2004 Sep 27; cited 2006 Sep 22]. Acceptance. For other diseases and conditions, the expected number may be based on locally available data such as hospital discharge records, mortality statistics, or cancer or birth defect registries. It is an axiom of field epidemiology that if you cannot generate good hypotheses (for example, by talking to some case-patients or local staff and examining the descriptive epidemiology and outliers), then proceeding to analytic epidemiology, such as a case-control study, is likely to be a waste of time.
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