QUANTIFYING THE ISSUE July 22, 2015 Maria Schymura, PhD 2 1 - PDF document

Evidence ‐ Based Public Health: Supporting the New York State Prevention Agenda MODULE 3: QUANTIFYING THE ISSUE July 22, 2015 Maria Schymura, PhD 2 1

Learning Objectives 1. To measure and characterize disease frequency in defined populations using principles of descriptive epidemiology and surveillance. 2. To find and use disease surveillance data presently available on the Internet. 3 Epidemiology  Study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the control of health problems 4 2

Public Health Surveillance  Ongoing collection and timely analysis, interpretation, and communication of health information for public health action  Public health surveillance systems are important tools for collecting and disseminating descriptive epidemiologic data 5 Public Health Surveillance Collection methods Provide varying levels of confidence in the data Representative Convenience Population-based Samples Samples Vital Statistics National Health Survey at a Interview Survey (NHIS) local mall • Birth and death National Health and Nutrition Reportable diseases Examination Survey (NHANES) Registries Behavioral Risk Factor • Birth defects Surveillance System (BRFSS) • Cancer • Immunizations • Trauma Youth Risk Behavior Survey (YRBS) 6 Level of confidence high low 3

BRFSS  Monitors modifiable risk factors associated with chronic and communicable diseases  All 50 states and DC participate  Sample based on the state’s population, not the population of smaller geographic areas (e.g., counties)  Adults age 18 yrs and older (non- institutionalized)  Random dial telephone survey – Past: landlines – Present and future: landlines (80%) and cell phone (20%) 7 BRFSS  Raking methodology to be introduced (2011 data) – More precise estimates – Need to start new trend analyses  SMART BRFSS (Metropolitan or Micropolitan Statistical Areas) – Metro • Lincoln (Lancaster and Seward) • Omaha–Council Bluffs (Cass, Douglas, Sarpy, Saunders, Washington, plus IA counties) • Sioux City (Dakota, Dixon, plus IA and SD counties) – Micro • Grand Island (Hall, Howard, Merrick) • Hastings (Adams, Clay) • Norfolk (Madison, Pierce, Stanton) • North Platte (Lincoln, Logan, McPherson) • Scottsbluff (Banner, Scotts Bluff)  County-level prevalence estimates – Diabetes, obesity, physical activity (link below) – 11 indicators (2014) http://apps.nccd.cdc.gov/BRFSS-SMART/index.asp http://apps.nccd.cdc.gov/DDT_STRS2/CountyPrevalenceData.aspx?StateId=18&mode=DBT 8 4

Obesity Trends* Among U.S. Adults—1990, 1999, 2008 1 9 9 9 1 9 9 0 2 0 0 8 No data <10% 10%-14% 15%-19% 20%-24% 25%-29% ≥ 30% * Body Mass Index (BMI)  30; or about 30 lbs. overweight for 5’4” person 9 Source: Behavioral Risk Factor Surveillance System Percent of High School Students Considered Obese, United States, 2013 10 Source: Youth Risk Behavior Survey 5

Descriptive and Analytic Epidemiology  Descriptive epidemiology – Frequency and distribution of risk factors in populations – Frequency and distribution of disease in populations – Can provide hypotheses for etiologic research  Analytical epidemiology – Study of factors associated with disease (factors that either increase or decrease risk) 11 Descriptive and Analytic Epidemiology Thematic Example: Obesity and Cancer Relative Risk (RR) Associated with Excess Weight Cancer site and type Summary RR from comprehensive RR Overweight RR Obese meta-analysis and (95% CI) (BMI 25-29) (BMI ≥ 30) per given unit increase in BMI vs BMI <25) vs BMI <25) 1.11 (1.07-1.15) per 1 kg/m 2 increase in BMI Esophagus (adenocarcinoma) 1.55 2.10 1.18 (1.14-1.21) per 5 kg/m 2 increase in BMI Colorectal 1.18 1.36 1.14 (1.07-1.22) per 5 kg/m 2 increase in BMI Pancreas 1.14 1.28 1.42 (1.17-1.72) per 5 kg/m 2 increase in BMI Kidney 1.42 1.84 1.05 (1.03-1.07) per 2 kg/m 2 increase in BMI Post-menopausal breast 1.13 1.25 1.60 (1.52-1.68) per 5 kg/m 2 increase in BMI Endometrial 1.60 2.20 Source: Eheman C, et al Annual Report to the Nation on the Status of Cancer, 1975–2008, Featuring Cancers Associated with Excess Weight and Lack of Sufficient Physical Activity. Cancer 2012; 118:2338-66. 12 6

Descriptive and Analytic Epidemiology  Descriptive epidemiology – Frequency and distribution of risk factors in populations – Frequency and distribution of disease in populations – Can provide hypotheses for etiologic research  Analytical epidemiology – Study of factors associated with disease (factors that either increase or decrease risk) 13 Descriptive Epidemiology Terminology and uses  Prevalence vs. incidence  Incidence vs. mortality  Role of intermediate indicators  Small number issues  Types of rates  Estimate error and confidence intervals 14 7

Descriptive Epidemiology Terminology and uses  Prevalence vs. incidence  Incidence vs. mortality  Role of intermediate indicators  Small number issues  Types of rates  Estimate error and confidence intervals 15 Prevalence vs. Incidence  Prevalence is the number of existing cases of disease in the population during a defined period  Incidence is the number of new cases of disease that develop in the population during a defined period 16 8

Prevalence vs. Incidence Question Are we measuring prevalence or incidence?  The number of persons living with HIV in your community as of December 31, 2012  The number of persons diagnosed with breast cancer in your community during 2012 17 Descriptive Epidemiology Terminology and uses  Prevalence vs. incidence  Incidence vs. mortality  Role of intermediate indicators  Small number issues  Types of rates  Estimate error and confidence intervals 18 9

Incidence vs. Mortality Question Which data are better for estimating disease rates? incidence or mortality data 19 Incidence vs. Mortality Mortality rates are used to estimate disease frequency when  Incidence data are not available;  Case-fatality rates are high; or  Goal is to reduce mortality among screened or targeted populations 20 10

Descriptive Epidemiology Terminology and uses  Prevalence vs. incidence  Incidence vs. mortality  Role of intermediate indicators  Small number issues  Types of rates  Estimate error and confidence intervals 21 Role of Intermediate Outcomes Intermediate outcomes may be used  When it is not feasible to wait years to see the effects of a new public health program, or  There is sufficient type I evidence supporting the relationship between modifiable risk factors and disease reduction 22 11

Role of Intermediate Outcomes Long-term outcomes Intermediate outcomes  cardiovascular disease  obesity, physical activity  lung cancer  cigarette smoking  breast cancer mortality  mammography screening  arthritis  ? 23 Descriptive Epidemiology Terminology and uses  Prevalence vs. incidence  Incidence vs. mortality  Role of intermediate indicators  Small number issues  Types of rates  Estimate error and confidence intervals 24 12

Small Number Issues  Rates are often available for national and state-wide populations  Not always available for smaller geographic areas or demographically defined populations – Rates are not considered stable if fewer than 20 cases in the numerator 25 Small Number Issues Role of standard error 100 90 relative standard error* *RSE = 1 / cases 80 70 60 50 40 30 20 10 0 10 20 30 40 50 60 70 80 90 100 numerator size 26 13

Small Number Issues Possible solutions, combine…  Years  Groups – e.g., “other races”  Geographic areas – Public health department regions – Congressional districts – Program regions – School districts 27 Descriptive Epidemiology Terminology and uses  Prevalence vs. incidence  Incidence vs. mortality  Role of intermediate indicators  Small number issues  Types of rates  Estimate error and confidence intervals 28 14

Types of Rates  Crude, or unadjusted  Standardized, or adjusted  Category-specific, or stratified 29 Types of Rates  Crude, or unadjusted  Standardized, or adjusted  Category-specific, or stratified 30 15

Crude (or unadjusted) Rates  Estimate the actual disease frequency for a population  Can be used to provide data for allocation of health resources and public health planning  Can be misleading if compared over time or across populations 31 Crude (or unadjusted) Rates Defining your population  Define disease  Define population at risk  Select time frame 32 16

Crude (or unadjusted) Rates Defining your population Breast Cancer  Define disease Standard inclusion and exclusion • criteria (e.g., invasive, specific ICD- O-3 codes)  Define population at risk New York Females  Select time frame 2010 33 Crude (or unadjusted) Rates Defining your population Where do you find this data? Define disease Breast Cancer Standard inclusion and exclusion • criteria (e.g., invasive, specific ICD-O-3 codes) Define population at risk New York Females Select time frame 2010 34 17