Post on 02-Jan-2016
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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