HECO: Health Economics

THE DETERMINANTS OF HEALTH(health indicators) AND AN

OVERVIEW OF DEMOGRAPHY

HEALTH• It has been defined by the World Health

Organization (WHO), as:

“the state of mental, physical and social well being, and does not merely connote the absence of illness.”

HEALTH• derived from the word heal (hael) which

means “whole”, signaling that health concerns the whole person and his or her integrity, or well-being

HEALTH• absence of symptoms in an individual may not

necessarily connote a healthy condition (purpose of further medical examination/lab tests.)

• There are varying degrees or states of health (multi-factorial phenomenon)

• Difficult to both qualify and quantify.

ENVIRONMENT

SOCIETAL

Physical Mental

Emotional

SpiritualSexual

Social

DIMENSIONS OF HEALTH

HEALTH ECONOMICS

• Deals with the manipulation of factors that should be able to give people “BETTER HEALTH”

• Since it’s a multi-factorial phenomenon, various aspects to manipulate it.

• Questions like “What determines health?” and “What factors influence health”?

Underlying Socio-Economic, Demographic, and Cultural Factors

Underlying socio-economic, demographic, and cultural factors

Individual

• Age, gender

• Education

• Occupation

• Health benefits, Attitudes

Household

• Income/wealth

• Age-gender composition

• Social network

Community

• Ecological climate

• Markets & prices

• Transportation size, structure, and distribution

• Social structure and organization

Proximate Factors

•Health Care Services

•Environmental Contamination

•Nutrient Dietary intake

•Fertility

•Injury

Health Outcomes

•Mortality

•Morbidity

•Nutritional Status

•Disability

Determinants of Health: Major Effects and Intervention Points

DEMOGRAPHY• the mathematical & statistical study of the

size, composition & spatial distribution of human populations & of changes over time in these aspects through the operation of 5 processes of:

1. Fertility2. Mortality3. Migration4. Marriage5. Social mobility

Uses of DEMOGRAPHY• To determine the number & distribution of a

population in certain area for planning, priority setting & for purposes of fund allocation.

• To determine growth (or decline) & dispersal of population in the past.

• To establish a “casual relationship” between population trends & various aspects of social organization.

• To predict future developments & their possible consequences.

Describing the Population Composition

A. Sex Composition1. Sex Ratio2. Sex Structure

B. Age Composition1. Median Age2. Dependency Ratio

C. Age and Sex Composition1. Population Pyramid

Sex Composition

1. Sex Ratio• Compares the number of males to the number of

femalesSex Ratio = _number of males_ x 100 number of females

Ex: In the 1990 Philippine census, 30, 745,341 males and 30,115,929 females were enumerated.

Sex Ratio = 30, 745,341 x 100 = 102.08 = 102 30,115,929

Interpretation: In 1990, there were 102 males for every 100 females in the Philippines

Sex Composition

1. Sex Structure• Compares the sex ratio across different

categories/levels of another characteristic

Ex: Sex structure across urban-rural classification or across different age groups

Interpretation: There is usually a higher sex ratio in the younger age groups and lower sex ratio at the older age groups.

Sex Composition

1. Sex Structure

Age Composition

1. Median Age• The value which cuts-off the upper 50% and lower

50% of the ages of the population.• Used to gauge whether the population is young or

oldEx: The median age of the Filipinos was 15.8 years in

1970 and 19 years in 1990.

Age Composition

2. Age-Dependency Ratio= Pop 0-14 yo + Pop ≥ 65 yo x 100

Pop 15-64 yo

• The computed value represents the number of dependents that need to be supported by every 100 persons in the economically-active groups.

Age groups Number PercentEx: 0-14 24,004,586 39.5

15-64 34,629,959 57.1 65 & over 2,063,445 3.4

Age Composition

2. Age-Dependency Ratio• = 24,004,586 +2,063,445 x 100 = 75.3

34,629,959

Interpretation: In the 1990, every 100 persons in the economically-productive age groups had to support 75 dependents.

Age & Sex Composition

1. Population Pyramid• A graphical presentation of the age and sex

composition of the population• Also enables one to explain and describe the

demographic trends of the population in the past.

Age & Sex Composition1. Population Pyramid

Age & Sex Composition1. Population Pyramid

Age & Sex Composition

How to construct a Population Pyramid?1. Compute the percentage of the population falling in each

age-sex group using the total population, that is, males and females combined) as the denominator.

2. Each group is represented by a horizontal bar. The first bar representing the youngest age group is drawn at the base of the pyramid.

3. The bars for males are traditionally presented on the left side of the central vertical axis while bars for females are presented on the right side.

4. The length of each bar corresponds to the percent (%) of the population falling in the specific age and sex group being plotted.

Age & Sex Composition1. Population Pyramid

HEALTH INDICATORS• Quantitative measures• Describe & summarize various aspects of

health status of the population• Usually expressed as ratios, proportions or

rates

HEALTH INDICATORSUSES• Determine factors that may contribute to

causation & control of diseases• Identify public health problems & needs• Indicate priorities for resource allocation

(health economics)• Monitor health program implementation• Evaluate health programs

Crude Birth Rate (CBR)

• Measures how fast people are added to the population

• Crude rate because the denominator is not the population at risk

Crude Birth Rate (CBR)CBR = no. of registered livebirths in year x 1000

mid-year population

CBR = 2,036,944 x 1000 = 28.4/1000 71,723,373

Interpretation:CBR ≥ 45 l.b./1000 pop/yr high fertility rateCBR ≤ 20 l.b./1000 pop/yr low fertility rate

Crude Death Rate (CDR)• Measures rate at which mortality occurs in a

given population

CDR = total deaths in one year x 1000 total midyear population

CDR = _437,513_ x 1000 = 6.1/1000 pop 71,723,373

(US CDR = 9.2/1000)

General Fertility Rate (GFR)

GFR = # registered l.b. in a year x 1000 midyear population women 15-44 yo

GFR = _1,658,568_ x 1000 1,563,836

Interpretation:GFR = 200 l.b./1000 pop/yr high fertility rateGFR = 60 l.b./1000 pop/yr low fertility rate

MORTALITY INDICATORS• CDR = total deaths in one year x 1000

total midyear population

• Cause-of-death rate = # deaths in a specific cause x 1000 midyear population-- for determining the leading causes of mortality

MORTALITY INDICATORSINFANT MORTALITY RATE• A sensitive index of the health conditions of the

general population!!!= total deaths < 1 yo x 1000

# of l.b.Poor populations

60-150 deaths per 1000 births per year

Severe conditions≥ 200 deaths per 1000 births per year

MORTALITY INDICATORSNEONATAL MORTALITY RATE (< 28 days old)POST-NEONATAL MORTALITY RATE (28 days old to <1 yo)PERINATAL MORTALITY RATE (28 weeks gestation to 7 days)

MATERNAL MORTALITY RATEMMR = # pregnancy-related deaths in year x 1000

# of l.b. in the same year

MORTALITY INDICATORSSWAROOP’S INDEX

a special kind of proportionate mortality ratioa sensitive indicator of the standards of healthcareDeveloped countries have higher compared to developing

= # deaths ≥ 50 yo in a year x 100 total # of deaths

MORTALITY INDICATORSCASE FATALITY RATE

how much of the afflicted die from the diseasea higher CFR means more fatal disease the “killing power” of a disease the probability of dying of a certain disease

CFR = # deaths due to a disease x 100 # of cases of the disease

MORBIDITY INDICATORSPREVALENCE PROPORTION (ratio)

Measures the frequency of existing disease (cases)Measure the burden of the disease to the communityAssess the public health impact of a diseaseProjection of medical care needsProportion with the disease at a point in time“point in time”: calendar time, birth, employment,

retirement

PR = # cases at a point in time x 1000 # of persons examined

MORBIDITY INDICATORSPREVALENCE PROPORTION (ratio)Example 1:

In a large industrial concern employing 10,000 people on January 1, 2005, 50 people have diabetes. An additional 100 cases of diabetes were diagnosed between January 1, 2005 and January 1, 2006. During the year, no employees moved out of the company due to retrenchment or retirement; neither were new employees hired. The prevalence of diabetes as January 1, 2005 is:

PR = _50_ x 1000 = 5 cases/1000 employees

10,000

MORBIDITY INDICATORSINCIDENCE

Measures the occurrence of new cases, episodes, eventsFor identifying etiologic factors2 types of incidence measures

• Cumulative incidence or incidence proportion• Incidence density

Indicator of trendEvaluate program effectivenessAssociated to RISK = the probability that a person will

develop within a specified period of time

MORBIDITY INDICATORSINCIDENCE1. Cumulative incidence or incidence proportion

Proportion of “disease” free individuals who contract the “disease within a specified period of time

The average risk of developing the “disease”

CI = # cases that developed during the period x 1000 # of persons followed up (DISEASE-FREE/AT RISK!)

MORBIDITY INDICATORSINCIDENCE1. Cumulative incidence or incidence proportionStill using Example 1:

In a large industrial concern employing 10,000 people on January 1, 2005, 50 people have diabetes. An additional 100 cases of diabetes were diagnosed between January 1, 2005 and January 1, 2006. During the year, no employees moved out of the company due to retrenchment or retirement; neither were new employees hired. The prevalence of diabetes as January 1, 2005 is:

CI= _100_ x 100 = 1% per year 9,950

MORBIDITY INDICATORSINCIDENCE2. Incidence Density (ID)

Rate at which new cases occurDenominator can either be ave. pop x followup period or

the midyear pop

ID = # cases that developed during the period x F ave. pop x duration of followup

ID = # cases that developed during the period x F mid year pop

MORBIDITY INDICATORSINCIDENCE2. Incidence Density (ID)

Example: new cases of Leprosy = 5,265 (1 yr followup) mid year pop = 71,960, 594

ID = # cases that developed during the period x F ave. pop x duration of followup

ID = # cases that developed during the period x Fmid year pop

ID = 7.3/100,000 popInterpretation: On the average, 7 out of 100,000 pop will develop

leprosy during a one year period

Underlying Socio-Economic, Demographic, and Cultural Factors

Underlying socio-economic, demographic, and cultural factors

Individual

• Age, gender

• Education

• Occupation

• Health benefits, Attitudes

Household

• Income/wealth

• Age-gender composition

• Social network

Community

• Ecological climate

• Markets & prices

• Transportation size, structure, and distribution

• Social structure and organization

Proximate Factors

•Health Care Services

•Environmental Contamination

•Nutrient Dietary intake

•Fertility

•Injury

Health Outcomes

•Mortality

•Morbidity

•Nutritional Status

•Disability

Determinants of Health: Major Effects and Intervention Points

Proximate factors and health programs developed

Proximate factors Health Programs Developed

Health Care Service Utilization

OPLAN Bakuna with Jollibee, OPLAN Alis Disease

Environmental Contamination

Anti-pollution Campaign, Worker Health and Safety Program

Nutrient Dietary Intake Barangay Day Care Centers, Vitamin A Campaign

Fertility “Responsible Parenthood” Information Drive

Injury “Don’t Drink and Drive”

Health outcome before the health program

Health outcome after the health program

Compare

Health Program

Example

A: Individual LevelOccupation as a >>> Exposure to mining >>> increased

coal miner contaminants incidence of

worker’s lung disease

(Occupying as (Environmental (Health Status/ underlying

contamination outcome)

determinant) as proximate

determinant)

Example

B. Household LevelLow household >>> Poor nutritional >>>> Poor nutritional

Income intake status (low weight, height)

(Income/Wealth as (Nutritional intake as (Health status/ underlying proximate Outcome) determinant) determinant)

Example

C. Community LevelPoor transportation >>> Poor health care >>> More deaths in transportation

service utilization the area network

(Transportation (Healthcare service (Health Status/ as underlying

utilization as a Outcome) determinant) proximate factor

Improvement in Health status

Contributes to better economy

Improvement in worker productivity

More resources allocated for health

?

!

Relationship Of health and Economic