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DESCRIPTIVE EPIDEMIOLOGY

DESCRIPTIVE EPIDEMIOLOGY. CRUDE, SPECIFIC, and ADJUSTED RATES. Descriptive Epidemiology. Includes descriptive statistics that provide information on disease patterns by various characteristics of person, place and time. Uses of descriptive statistics:.

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DESCRIPTIVE EPIDEMIOLOGY

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  1. DESCRIPTIVE EPIDEMIOLOGY CRUDE, SPECIFIC, and ADJUSTED RATES

  2. Descriptive Epidemiology Includes descriptive statistics that provide information on disease patterns by various characteristics of person, place and time.

  3. Uses of descriptive statistics: • Providing clues about disease causation and prevention that are usually investigated further in formal studies. • Assessing the health status of a population (e.g. Healthy People 2020) 3. Allocating resources efficiently and targeting populations for education or preventive programs

  4. A. Descriptive statistics Routinely collected data from many sources: • natality from vital records • reportable diseases from surveillance programs • other diseases from national surveys • Chapter 4 – Sources – online

  5. Burden of Diabetes in the United States • National Health and Nutrition Examination Survey (NHANES) • Population-based nationally representative sample of the United States population • Diabetes • Self-reported doctor diagnosed diabetes • Undiagnosed diabetes • HbA1c ≥ 6.5%; • or fasting plasma glucose ≥ 126 mg/dL

  6. Burden of Diabetes in the United States • NHANES: 2011-2012 • 9.2% self-reported diagnosed diabetes • 3.1% undiagnosed diabetes • 12.3% overall total diabetes prevalence • 25.2% of all diabetes is undiagnosed • 36.5% have pre-diabetes • HbA1c of 5.7% to 6.4% • fasting plasma glucose 100-125 mg/dL • 48.8% of the population has diabetes or prediabetes

  7. Burden of Diabetes in the United States • CDC Behavioral Risk Factor Surveillance System (BRFSS) • BRFSS is an ongoing, state-based telephone survey of the adult population • Diabetes was defined based on responding yes to: • "Has a doctor ever told you that you have diabetes?”

  8. Prevalence of Self-Reported Diabetes by County: 2004

  9. Prevalence of Self-Reported Diabetes by County: 2007

  10. Prevalence of Self-Reported Diabetes by County: 2012

  11. State Ranking: Age-Adjusted Diagnosed Diabetes 2014 • West Virginia, 12.0% • Mississippi, 11.9% • Alabama, 11.8% • Tennessee, 11.7% • Arkansas, 11.5% • Kentucky, 11.3% • Georgia, 11.0% • Oklahoma, 10.9% • Texas, 10.8% • South Carolina, 10.7% 2012 • Mississippi, 11.7% • Louisiana, 11.5% • West Virginal, 11.1% • Alabama, 11.1% • Tennessee, 10.8% • South Carolina, 10.7% • Oklahoma, 10.6% • Texas, 10.6% • Ohio, 10.4% • Arkansas, 10.2%

  12. If morbidity or mortality from a given disease changes over time, you can infer: • Some cause of the disease must also bechanging • Or there is an "artifactual" explanation For example, there are differences in disease definition, diagnosis, or reporting over time. Or there are changes in enumerating the population denominator of the rate

  13. Obesity Prevalence by County: 2004

  14. Obesity Prevalence by County: 2007

  15. Obesity Prevalence by County: 2012

  16. Adult Self-Reported Lifetime Diabetes Prevalence by Race and Gender, S.C. 1995 – 2014 Data Source: SC BRFSS; Generated by the Division of Chronic Disease Epidemiology Published in Heidari et al. (2016) The American Journal of The Medical Sciences; 351(4) .

  17. ED Rates by Race and Sex, for Diabetes as Primary and/or Secondary Diagnoses, S.C. 1996 – 2014 Data Source: SC RFA; Generated by the Division of Chronic Disease Epidemiology Published in Heidari et al. (2016) The American Journal of The Medical Sciences; 351(4) . *Red line indicates a change in the number of secondary diagnoses used to calculate rates. Before 2008, only 9 secondary diagnoses were available and then afterwards, the number of secondary diagnoses increased to 14.

  18. Births to Mothers with Diabetes, S.C. 1990 – 2013 Data Source: SC Vital Records; Generated by the Division of Chronic Disease Epidemiology March 2015

  19. Lòpez-DeFede A, Stewart JE. Diagnosed Diabetes Prevalence and Risk Factor Rankings, by State, 2014 2016: A Ring Map Visualization. Prev Chronic Dis 2019;16:180470. DOI: http://dx.doi.org/10.5888/pcd16.180470Used data from the BRFSS

  20. C. Crude, Specific and Adjusted Rates 1. Crude rates: • A summary measure calculated by dividing the total number of cases in the population by the total number of individuals in that population at a specified time period 2. Category specific rates: • Rates specific to some particular sub-population: age-specific, race-specific, sex-specific 3. Problems comparing crude rates among populations: • Groups differ with respect to underlying characteristics that affect overall rate of disease (especially age, sex, and race)and so you may be making an unfair comparison

  21. Example: Overall Mortality Rates in Alaska and Floridain 2003 Can you think of an explanation for this?

  22. Example: Overall Mortality Rates in Alaska and Florida • The difference in the age structure between the populations of Florida and Alaska make this an unfair comparison

  23. Age-Specific Mortality Rates in Alaska and Floridain 2003 Why not always look at age specific mortality rates?

  24. Age-adjusted rate • Summary rate that accounts for age difference between populations. • Any differences between rates cannot be attributed to age.

  25. Calculating crude rates • Crude death rate in Florida can be calculated in two ways: • Total deaths / total population = 168,657 / 17,019,068 = .0099099 = 990.99 / 100,000

  26. Calculating crude rates

  27. Calculating crude rates Or crude rate can be considered the weighted average of age-specific rates, with weights equal to the proportion of the population in each category. Thus, the crude death rate in Florida can be calculated as weighted average: (percent of population in that age group) x (age-specific rate) (.062) (179.26/100,000)+(.194)(40.28/100,000)+ (.334) (167.06/100,000)+(.239)(698.25/100,000)+ (.170) (4,399.65/100,000) = 990.99 / 100,000 Note: Even if the two populations have identical age-specific rates, the crude rate will vary if the age distribution of the populations differ -- that is, if there are different proportions of people in each age category.

  28. Now let’s calculate age-adjusted rates • We want a summary number for both Alaska and Florida that allows for comparison with differences in age accounted for. These numbers are "adjusted" for age – they are called age-adjusted or age-standardized rates. • They answer the question: what would the death rate be in each state if the population in each state had identical age distributions? • What age distribution? Any that you want! Often the U.S. population in a census year is used.

  29. Data needed to construct age adjusted rates for Florida and Alaska Death rates per 100,000 2003 US population (% of total) Age groups Florida Alaska <5 179.26 182.82 19,778,166 (6.8%) 5-19 40.28 60.50 61,447,723 (21.1%) 20-44 167.06 165.09 105,031,453 (36.1%) 45-64 698.25 543.92 68,640,274 (23.6%) >65 4,399.65 4,241,58 35,952,389 (12.4%) Total 490.12 290,850,005 (100%) 990.99

  30. Calculating age-adjusted rates • Age standardized rate: weighted average of age specific rates where the weights are the distribution of age in the standard population. This is called direct standardization. Age adjusted rate in Florida (.068) (179.26/100,000)+ (.211) (40.48/100,000) +(.361) (167.06/100,000)+(.236) (698.25/100,000) +(.124) (4,399.65/100,000) = 789.61 / 100,000 Age adjusted rate in Alaska (.068) (182.82/100,000)+ (.211) (60.50/100,000) +(.361) (165.09/100,000)+(.236) (554.92/100,000) +(.124) (4,241.58/100,000) = 737.45 / 100,000 Note that the same weights are used for Florida and Alaska.

  31. Calculating age-adjusted rates • These are hypothetical death rates that would have occurred in each state if each state had the age distribution of the entire U.S. population in 2003. • The remaining difference between the two adjusted rates is not due to age. • Adjusted rates are good only for comparison -- alone they are meaningless.

  32. Comparison of crude and age-adjusted rates • Crude rate in Florida: 990.99/100,000 • Crude rate in Alaska: 490.12/100,000 • Age-adjusted rate in Florida: 789.61/100,000 • Age-adjusted rate in Alaska: 737.45/100,000 • Was the crude comparison confounded by age? What is your conclusion about the difference in mortality rates?

  33. Data needed to construct age adjusted rates for Mount Pleasant and Charleston Death rates per 10,000 Standard Population - number Age groups MP Charleston <5 20 35 50 5-19 15 15 100 20-44 20 30 300 45-64 30 20 350 >65 40 30 200 Age Adjusted ? ?

  34. Data needed to construct age adjusted rates for Mount Pleasant and Charleston Death rates per 10,000 Standard Population - number Age groups MP Charleston <5 20 35 50 (5%) 5-19 15 15 100 (10%) 20-44 20 30 300 (30%) 45-64 30 20 350 (35%) >65 40 30 200 (20%) Age-Adjusted 25.25 1000 27 Can you calculate the crude death rates in each city from the information provided?

  35. N Engl J Med. 2016 Aug 31. Zika Virus and the Guillain-Barré Syndrome - Case Series from Seven Countries. Dos Santos T et al http://www.nasa.gov/centers/marshall/news/news/releases/2016/nasa-helps-forecast-zika-risk.html

  36. N Engl J Med. 2016 Aug 31. Zika Virus and the Guillain-Barré Syndrome - Case Series from Seven Countries. Dos Santos T et al

  37. N Engl J Med. 2016 Aug 31. Zika Virus and the Guillain-Barré Syndrome - Case Series from Seven Countries. Dos Santos T et al

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