nutritional epidemiology n.
Skip this Video
Loading SlideShow in 5 Seconds..
Download Presentation


1739 Vues Download Presentation
Télécharger la présentation


- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. NUTRITIONAL EPIDEMIOLOGY The study of eating behavior and how it influences the etiology, occurrence, prevention, and treatment of disease.

  2. Epidemiology • Uses biostatistics, statistical processes and methods applied to the analysis of biological phenomena, to describe and quantitate health events and the factors that place individuals at risk for health events.

  3. Epidemiology • It began as an approach to control communicable disease. • The methods and principles have been applied successfully to improve our understanding of chronic disease. • It is the basic science for public and community health. • Baseline data are used as the foundation for program planning.

  4. Nutritional Epidemiology • Focuses on eating behavior and how that behavior influences health status, disease morbidity, and mortality. • These approaches are in contrast to clinical medicine, which focuses on diagnosing and treating “sick” people.

  5. The Territory of Epidemiology-Principles and Application • Epidemiologic methods are tools to explore disease occurrence, treatment, prevention, and cure. • A traditional model explores the causes of an infectious disease, using three components: an agent (microbe, chemical, behavior,…), a host (host factors include age, ethnicity, sex, SES, eating patterns,…), and an environment conducive to linking the first two components (geology, climate, housing conditions,…).

  6. Levels of Epidemiologic Research

  7. Types of Studies • Either controlled (experimental) or uncontrolled (observational). • Controlled studies mean that an artificial or planned situation exists.

  8. Uncontrolled Studies • Cross-Sectional Studies: • Snapshot look at the population at one point in time. • Represent a sample from a population. • Cannot explore or test cause and effect. • Can assist with formulating public health education and intervention programs. • Examples: National Health and Nutrition Examination Survey (NHANES)in the US, Lebanon Maternal and Child Health Survey (PAP Child). • Provide data to calculate prevalence rates.

  9. Cross-Sectional Studies • Provide quick “snapshot” look. • Have low cost (can use telephone, mail). • Define prevalence. • Determine association between variables (independent and dependent). • Generate hypotheses.

  10. Cross-Sectional Studies • Cannot establish cause and effect. • Use “live” individuals (problems of data collection). • Are insensitive to rare characteristics. • Requires a sample or subsample of a population. • Describe current, not past or future events.

  11. Uncontrolled Studies • Case-Control Studies • Retrospective approach compares cases and controls for the presence of antecedent variables. • Require that cases and controls come from similar populations. • Cases must have the health outcome (disease), they must be similar in all other aspects. • The confounding effect of a variable can be avoided by selecting cases and controls of the same age, ethnicity, and gender (matching) • Commonly used statistic is relative risk or odds ratio

  12. Case-Control Studies • Cases are easily available in clinical settings. • They are relatively quick and inexpensive. • Results support, but do not prove, causal hypotheses. • Secondary analyses are possible (records available). • Sample size is small. • Study of rare diseases is possible.

  13. Case-Control Studies • Data are from memory, increasing bias. • Records may be inadequate. • Criteria for diagnoses may vary among clinicians. • Cases are selective survivors. • Cases may not be representative because coming from treatment. Antecedent data reflect only living cases.

  14. Uncontrolled Studies • Cohort Studies • Prospective studies that can project risk estimates for future events. • Cohort is followed over time. • Results can be provided to decision makers. • Confirm causation. • Relative risk can be calculated

  15. Cohort Studies • Increase potential for confirmation of cause and effect. • Quantify extent of effect due to risk factor. • Provide baseline rates for new cases in a community. • Estimate prevalence. • Allow for hypothesis testing. • Participant cooperation is high.

  16. Cohort Studies • Reduce information bias. • Minimize selection factors, since all members of the cohort are disease-free. • Follow original cohort even if individuals relocate.

  17. Cohort Studies • Require a long observation period. • Loss to follow-up occurs, which creates selection bias. • Expensive • Ecological changes can affect and/or create associations and outcomes. • Rare diseases cannot be studied.

  18. Controlled (Experimental) Studies • Investigator’s control over assignment of individuals to groups (random assignment). • Randomization ensures comparability of individuals and groups to all known and unknown factors except the one studied.

  19. Controlled (Experimental) Studies • Experiment evaluates what occurs after exposure to a risk factor. • An intervention group receives an intervention, and a control group does not. • Comparison is carried out between the two groups.