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. Disease rates categorized by person, place and time
Person: Who has the disease?
male vs. females, young vs. old, black vs. white
Place: Where is the disease more or less common?
Different scales of geography: regions of earth, countries, states, counties, cities, neighborhoods
Time: Is the disease rate changing over time?
Different scales of time: decades to seasons to days

6. 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

7. Burden of Diabetes in the United States
NHANES:
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 mg/dL

8. 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?”
The prevalence of diagnosed diabetes and selected risk factors by county was estimated using data from CDC's Behavioral Risk Factor Surveillance System (BRFSS) and data from the US Census Bureau’s Population Estimates Program. The BRFSS is an ongoing, monthly, state-based telephone survey of the adult population. The survey provides state-specific information on behavioral risk factors and preventive health practices. Respondents were considered, • To have diabetes if they responded "yes" to the question, "Has a doctor ever told you that you have diabetes?" Women who indicated that they only had diabetes during pregnancy were not considered to have diabetes. • To be obese if their body mass index was 30 or greater. Body mass index (weight [kg]/height [m]2 ) was derived from self-report of height and weight.

9. Prevalence of Self-Reported Diabetes by County: 2004
In recent years the pattern of diabetes prevalence that follows the stroke belt has been termed the diabetes belt. And again you could argue that SC falls within the buckle of the diabetes belt.
The prevalence of diagnosed diabetes and selected risk factors by county was estimated using data from CDC's Behavioral Risk Factor Surveillance System (BRFSS) and data from the US Census Bureau’s Population Estimates Program. The BRFSS is an ongoing, monthly, state-based telephone survey of the adult population. The survey provides state-specific information on behavioral risk factors and preventive health practices. Respondents were considered, • To have diabetes if they responded "yes" to the question, "Has a doctor ever told you that you have diabetes?" Women who indicated that they only had diabetes during pregnancy were not considered to have diabetes. • To be obese if their body mass index was 30 or greater. Body mass index (weight [kg]/height [m]2 ) was derived from self-report of height and weight.
State with the third highest level of diabetes.

10. Prevalence of Self-Reported Diabetes by County: 2007
The prevalence of diagnosed diabetes and selected risk factors by county was estimated using data from CDC's Behavioral Risk Factor Surveillance System (BRFSS) and data from the US Census Bureau’s Population Estimates Program. The BRFSS is an ongoing, monthly, state-based telephone survey of the adult population. The survey provides state-specific information on behavioral risk factors and preventive health practices. Respondents were considered, • To have diabetes if they responded "yes" to the question, "Has a doctor ever told you that you have diabetes?" Women who indicated that they only had diabetes during pregnancy were not considered to have diabetes. • To be obese if their body mass index was 30 or greater. Body mass index (weight [kg]/height [m]2 ) was derived from self-report of height and weight.

11. Prevalence of Self-Reported Diabetes by County: 2012
The prevalence of diagnosed diabetes and selected risk factors by county was estimated using data from CDC's Behavioral Risk Factor Surveillance System (BRFSS) and data from the US Census Bureau’s Population Estimates Program. The BRFSS is an ongoing, monthly, state-based telephone survey of the adult population. The survey provides state-specific information on behavioral risk factors and preventive health practices. Respondents were considered, • To have diabetes if they responded "yes" to the question, "Has a doctor ever told you that you have diabetes?" Women who indicated that they only had diabetes during pregnancy were not considered to have diabetes. • To be obese if their body mass index was 30 or greater. Body mass index (weight [kg]/height [m]2 ) was derived from self-report of height and weight.

12. Age adjusting to the 2000 US Standard Population, the prevalence of diabetes in SC counties ranged from 7.9% in Beaufort County to 15.6% in Allendale County.

13. State Ranking: Age-Adjusted Diagnosed Diabetes
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%
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%
These are based on age-adjusted rates of self-reported diabetes from the BRSFF. So undiagnosed diabetes is not included.

14. If morbidity or mortality from a given disease changes over time, you can infer:
Some cause of the disease must also be changing
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

15. Obesity Prevalence by County: 2004
The prevalence of diagnosed diabetes and selected risk factors by county was estimated using data from CDC's Behavioral Risk Factor Surveillance System (BRFSS) and data from the US Census Bureau’s Population Estimates Program. The BRFSS is an ongoing, monthly, state-based telephone survey of the adult population. The survey provides state-specific information on behavioral risk factors and preventive health practices. Respondents were considered, • To have diabetes if they responded "yes" to the question, "Has a doctor ever told you that you have diabetes?" Women who indicated that they only had diabetes during pregnancy were not considered to have diabetes. • To be obese if their body mass index was 30 or greater. Body mass index (weight [kg]/height [m]2 ) was derived from self-report of height and weight.
State with the third highest level of diabetes.

16. Obesity Prevalence by County: 2007
The prevalence of diagnosed diabetes and selected risk factors by county was estimated using data from CDC's Behavioral Risk Factor Surveillance System (BRFSS) and data from the US Census Bureau’s Population Estimates Program. The BRFSS is an ongoing, monthly, state-based telephone survey of the adult population. The survey provides state-specific information on behavioral risk factors and preventive health practices. Respondents were considered, • To have diabetes if they responded "yes" to the question, "Has a doctor ever told you that you have diabetes?" Women who indicated that they only had diabetes during pregnancy were not considered to have diabetes. • To be obese if their body mass index was 30 or greater. Body mass index (weight [kg]/height [m]2 ) was derived from self-report of height and weight.
State with the third highest level of diabetes.

17. Obesity Prevalence by County: 2012
The prevalence of diagnosed diabetes and selected risk factors by county was estimated using data from CDC's Behavioral Risk Factor Surveillance System (BRFSS) and data from the US Census Bureau’s Population Estimates Program. The BRFSS is an ongoing, monthly, state-based telephone survey of the adult population. The survey provides state-specific information on behavioral risk factors and preventive health practices. Respondents were considered, • To have diabetes if they responded "yes" to the question, "Has a doctor ever told you that you have diabetes?" Women who indicated that they only had diabetes during pregnancy were not considered to have diabetes. • To be obese if their body mass index was 30 or greater. Body mass index (weight [kg]/height [m]2 ) was derived from self-report of height and weight.
State with the third highest level of diabetes.

18. Age-adjusted prevalence of obesity in SC counties ranged from 23
Age-adjusted prevalence of obesity in SC counties ranged from 23.1% in Beaufort County to 44.1% in Lee County
In 2012 their were only two counties in SC with an obesity prevalence lower than 25%, Beaufort and Charleston county.
A number of counties along the I95 corridor had prevalence over 40%.

19. 2004-2012 Diabetes and Obesity County
Motion Chart by County (Three-Year Age-Adjusted Prevalence % Adults)
The prevalence of diagnosed diabetes and selected risk factors by county was estimated using data from CDC's Behavioral Risk Factor Surveillance System (BRFSS) and data from the US Census Bureau’s Population Estimates Program. The BRFSS is an ongoing, monthly, state-based telephone survey of the adult population. The survey provides state-specific information on behavioral risk factors and preventive health practices. Respondents were considered, • To have diabetes if they responded "yes" to the question, "Has a doctor ever told you that you have diabetes?" Women who indicated that they only had diabetes during pregnancy were not considered to have diabetes. • To be obese if their body mass index was 30 or greater. Body mass index (weight [kg]/height [m]2 ) was derived from self-report of height and weight.

20. Adult Self-Reported Lifetime Diabetes Prevalence by Race and Gender, S
Adult Self-Reported Lifetime Diabetes Prevalence by Race and Gender, S.C – 2014
In SC the prevalence of diabetes has increased from around 5% in the mid 90’s to 12% two decades later.
Projections based on this data indicate that the prevalence of diabetes in SC could reach 21% by 2035.
In the mid 90’s in white males and females the prevalence is at or below 5%, but by two decades later it is over 10% in both while males and females.
In African American males and females the prevalence starts out between 5 and 10% in men and women and by two decades later it is well over 15% in black women and close to 15% in black men.
Cohort AAPC
SC Total 2.9
BF 3.2
BM 3.8
WF 1.8
WM 3.9
Prevalence Mortality
Total BF BM WF WM
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) .

21. ED Rates by Race and Sex, for Diabetes as Primary and/or Secondary Diagnoses, S.C. 1996 – 2014
The age-adjusted rates of diabetes related ED visits have increased substantially from 1996 to 2014 across all race-ethnic groups with rates being highest in black women, followed by black men, white women and white men. Overall there was more than a four-fold increase in the number of ED visits in patients with diabetes which held across all race-ethnic groups.
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.

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

23. 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

24. Example: Overall Mortality Rates in Alaska and Florida in 2003
# of Deaths
168,657
3,180
Total Population
17,019,068
648,818
Crude Mortality Rate (per 100,000)
990.99
490.12

25. 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
You could get around the problem by comparing age-specific mortality rates between the two states but this is cumbersome.

26. Age-Specific Mortality Rates in Alaska and Florida in 2003
Death rates per 100,000
Age groups
Florida
Alaska
<5
179.26
182.82
5-19
40.28
60.50
20-44
167.06
165.09
45-64
698.25
543.92
65+
4,399.65
4,241.58

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

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

29. Calculating crude rates
Florida death rates per 100,000
(% of population in age category)
Age groups
<5
(6.2%)
5-19
(19.4%)
20-44
(33.4%)
45-64
(23.9%)
65+
4, (17.0%)
Total
(100%)

30. 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) = / 100,000
Note: Even if the two population 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.

31. 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! Usually the U.S. population in a census year is used.

32. for Florida and Alaska Death rates per 100,000 2003 US population
  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

33. 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) = / 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) = / 100,000
Note that the weights are the same for Florida and Alaska

34. 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.

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

36. ? for Mount Pleasant and Charleston Death rates per 10,000
  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
100
20-44 30
300
45-64
350
>65 40
200
Total ? ?

37. 25.3 for Mount Pleasant and Charleston Death rates per 10,000
  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
100
20-44 30
300
45-64
350
>65 40
200
Total
25.3
1000 27

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

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

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