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A Brief Introduction to Epidemiology - IV ( Overview of Vital Statistics & Demographic Methods)

A Brief Introduction to Epidemiology - IV ( Overview of Vital Statistics & Demographic Methods). Betty C. Jung, RN, MPH, CHES . Learning Objectives. To understand the how vital statistics and demographic data are used in Public Health

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A Brief Introduction to Epidemiology - IV ( Overview of Vital Statistics & Demographic Methods)

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  1. A Brief Introduction to Epidemiology - IV ( Overview of Vital Statistics & Demographic Methods) Betty C. Jung, RN, MPH, CHES

  2. Learning Objectives • To understand the how vital statistics and demographic data are used in Public Health • To understand the measures of mortality, fertility, morbidity that are based on vital statistics • To understand the basis for Rate Adjustment

  3. Performance Objectives • Basic understanding of how to use the most commonly available health statistics to quantify disease in Public Health Practice • Basic understanding of the most common vital statistical measures encountered in Practice

  4. Introduction • Demographic data and vital statistics are useful tools in: • Determining a community’s health status • Deciding what’s the best way for providing health services • Planning a public health program • Evaluating a program’s effectiveness

  5. Demographic Data Demographic data include those variables that describe the characteristics of a population (i.e., population size and how it changes over time)

  6. Demographic Variables Population composition include: Age Sex Income Occupation Health services use Geographic location Geographic density

  7. Vital Statistics (Events) Include: Births Deaths Marriages Divorces

  8. Sources of Vital Statistics & Demographic Data in the U.S. • Census • Registration of Vital Events • Morbidity Surveys

  9. Demographic Data:United States Census • The United States conducts a decennial census (every 10 years) since 1790. Each household and resident is enumerated (counted). • Person info: sex,age,race,marital status, place of residence, and relationship to or position as head of household • A systematic sample of households provides: income, housing, number of children born, education, employment status, means of transportation to work, and occupation.

  10. Demographic Data:United States Census Census tables are published for the entire U.S., each state, urbanized areas (Metropolitan Statistical Areas [MSAs]), counties, cities, neighborhoods (census tracts), and city blocks.

  11. Demographic Data:Annual Registration of Vital Events In the U.S., state laws require that all vital events be registered. Birth certificates serve as proof of citizenship, age, birthplace and parentage. Death Certificates - required as burial documents and in settlement of estates and insurance claims.

  12. Demographic Data:US Vital Statistics Data Vital Statistics of the United States - annual - detailed tables of vital events by various demographic characteristics and major geographic subdivision. Data on marriages and divorces are collected and published in a separate volume.

  13. Demographic Data:US National Death Index Prepared by NCHS - a nationwide, computerized index of death records compiled from each state’s vital statistics offices. Allows researchers to decide if persons in their studies have died. Includes death certificate number, state person died in and date of death.

  14. Demographic Data:U.S. Morbidity Surveys • Morbidity data (i.e., prevalence of disease) • Communicable disease reports are shared through CDC’s Morbidity and Mortality Weekly Reports (MMWR) • More serious diseases are well reported (i.e., cholera,plague,yellow fever, rabies, paralytic polio)

  15. Demographic Data:U.S. Sources of Morbidity Data • Reportable diseases • National Health Survey • Hospital records data • Industrial hygiene records • School health records • Medical care subgroups (i.e.,prepaid health insurance plans) • Chronic Disease Registries (i.e., tumor registries) • Insurance industry data

  16. Vital Statistics Rates, Ratios, and Proportions Three rates used in vital statistics: Crude rates - computed for an entire population Specific rates - consider differences among subgroups, computed by age, race, sex or other variables. Adjusted (standardized) rates - to make valid summary comparisons between two or more groups with different age (or other) distributions.

  17. Measures of Mortality Each rate is a measure of the relative frequency of deaths that occurred in a given population over a specific time period (time at risk). Population size is usually defined as the population at midyear (July 1). These measures estimate the population at risk (a+b)/time(t) of one year. If this convention cannot be met, then the calculation should really be considered a “proportion” rather than a rate.

  18. Measure of Mortality: Annual Crude Death Rate • Universally used as generalized indicator of a population’s health. • May not be truly reflective without accounting for age, race, or sex. • Example: State, Yr - population: 5000; deaths: 25 Crude Death Rate = 25/5000 x 1000 5 deaths per 1000 per year

  19. Measure of Mortality: Age-Specific Death Rate • Defined as the number of deaths in a specific age group in a calendar year, divided by the population of the same age group on July 1 of that year, the quotient multiplied by 1000. • Example: Country, Yr - age group: 25-34 yrs; population: 5,000,000; deaths: 200,000 Age-specific death rate= 200,000/5,000,000 x 1000 = 40 deaths per 1000 population per year for age group 25-34

  20. Measure of Mortality: Cause-Specific Death Rate • Defined as the number of deaths assigned to a specific cause in a calendar year, divided by the population on July of that year, the quotient multiplied by 100,000 • Example: Country, Yr - cause: accidents; population: 5,000,000; deaths: 4,000 Cause-specific death rate= 4,000/5,000,000 x 100,000 = 80 accidental deaths per 100,000 population per year

  21. Measure of Mortality: Proportional Mortality Ratio • Defined as the number of deaths assigned to a specific cause in a calendar year, divided by the total number of deaths in that year, the quotient multiplied by 100 • Example: Country, Yr - total deaths from all causes: 1,500,000; deaths from cancer: 675,000 Proportional mortality ratio= 675,000/1,500,000 x 100 = 45% of total deaths per year from cancer

  22. Measure of Mortality: Infant Mortality Rate • Defined as the number of deaths of persons age 0-1 in a calendar year, divided by the number of live births in that year, quotient multiplied by 1000 • Example: State, Yr - live births: 325,000; infant deaths: 1,750 Infant mortality= 1,750/325,000 x 1000 = 5.4 infant deaths per 1000 live births per year

  23. Measure of Mortality: Maternal Mortality Ratio* • Defined as the number of deaths assigned to puerperal causes (i.e., childbearing) in a calendar year divided by the number of live births in that year, the quotient multiplied by 100,000. • Example: Country, Yr - deaths due to puerperal causes: 275; live births: 1,750,000. Maternal mortality ratio: 275/1,750,000 x 100,000 = 15.71 maternal deaths per 100,000 live births per year

  24. Measure of Mortality: Neonatal Mortality Proportion • Defined as the number of deaths of neonates (infants <28 days of age) in a calendar year, divided by number of live births in that year, the quotient multiplied by 1000 • Example: State, Yr - deaths at <28 days=2,750; live births: 325,000 Neonatal mortality proportion= 2,750/325,000 x 1000 = 8.5 neonatal deaths per 1000 live births

  25. Measure of Mortality: Fetal Death Ratio • Defined as the number of fetal deaths in a calendar years, divided by the number of live deaths in that year, the quotient multiplied by 1000. • Example: State, Yr - fetal deaths: 2,450; live births: 525,000 Fetal death ratio = 2,450/525,000 x 1000 = 4.7 fetal deaths per 1000 live births

  26. Measure of Mortality: Perinatal Mortality Proportion • Defined as the number of fetal plus neonatal deaths, divided by the number of live births plus fetal deaths, the quotient multiplied by 1000 • Example: State, Yr - fetal deaths: 3,250; neonatal deaths: 5,750; live births: 475,000 Perinatal mortality proportion= 3,250+5,750/475,000+ 3,250 x 1000 = 18.8 perinatal deaths per 1000 fetal deaths plus live births

  27. Measure of Fertility: Crude Birthrate • Defined as the number of live births in a calendar year, divided by the population at July 1 of that year, the quotient multiplied by 1000 • Example: State, Yr - live births: 250,000; population: 30,000,000 Crude birthrate= 250,000/30,000,000 x 1000 = 8.3 live births per 1000 population per year

  28. Measure of Fertility: General Fertility Rate • Defined as the number of live births in a calendar year, divided by the number of women ages 15-44 at midyear, quotient multiplied by 1000 • Example: Country, Yr - live births: 7,500,000; number of women ages 15-44: 35,000,000 General fertility rate= 7,500,000/35,000,000 x 1000 = 214.3 live births per 1000 women ages 15-44 per year

  29. Measure of Morbidity: Incidence Rate • Defined as the number of newly reported cases of a given disease in a calendar year, divided by the population on July 1 of that year, the quotient multiplied by either 1000, 100,000, or 1,000,000 (whatever’s convenient). • Example: State, Yr - new cases of AIDS reported: 5,250; population: 35,000,000 Incidence rate: 5,250/35,000,000 x 100,000 = 15 new AIDS cases per 100,000

  30. Measure of Morbidity: Prevalence Proportion • Defined as the number of existing cases of a given disease at a given time, divided by the population at that time, the quotient multiplied by 1000, 100,000, or 1,000,000 (whatever’s convenient) • Example: Country, Yr - number of men alive with AIDS: 3,750; population: 15,000,000 men Prevalence proportion= 3,750/15,000,000 x 100,000 = 25 AIDS cases per 100,000 men

  31. Measure of Morbidity: Case-Fatality Proportion • Defined as the number of deaths assigned to a given cause in a certain period, divided by number of cases of the disease reported during the same period, the quotient multiplied by 100. • Example: Country, Yr - report number of male AIDS cases: 45,000; deaths from the disease: 37,000. Case-fatality proportion: 37,000/45,000 x 100 = 82.2% mortality among reported cases of AIDS

  32. Adjustment of Rates (or, Rate Adjustment) • Adjusting, or standardizing, rates is used to make valid comparisons between populations that may differ in some significant way (i.e., age distribution). • Standardized rates have no meaning in isolation, since adjusted rates are artificial. • Depending on type of data - there are two methods to adjust rates - direct (preferred) and indirect. • The numerical values of the adjusted rates depend on the choice of the standard population.

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