Description
Documetation describes life expectancy of individuals in developing countries. Life expectancy at birth is the statistical term that is commonly used for determining the overall health of the country.
MARKET RESEARCH Life Expectancy Of Individuals In Developing Countries
Table of Contents
Table of Contents Introduction Purpose of Study Method of Study Linear Regression Analysis 2 2 3 3 4 7
Independent factors used in Study.....................................................4 Method: Enter.....................................................................................7 Method: Stepwise ..........................................................................................................10 References 14
2
Introduction
Our project deals with the life expectancy of individuals in developing countries. Life expectancy at birth is the statistical term that is commonly used for determining the overall health of the country. It represents average number of years that a newly born child can be expected to live in a particular country. In general, life expectancy is the expected number of years of life remaining at a given age. If the country’s life expectancy is very low, it may be an indication of a poorly developed health care system, high poverty conditions, major health problems such as AIDS, a high infant death rate, internal wars and conflict that increase the death rate, or a poor health and sanitation infrastructure. The World Health Organization (WHO) reports that, in 2005, 38 percent of the adult population of Swaziland, the African nation with the lowest life expectancy in the world, was inflicted with AIDS or HIV. Poverty also prevents access to safe drinking water and sanitation Factors such as diet, lifestyle, and environmental conditions also influence a country’s life expectancy. However, there are some factors like gender and ethnic origin that are difficult to change but influence a country’s life expectancy. Education is another factor that contributes to a longer life expectancy. Therefore, the main factors that can affect the life expectancy of people in a country are: ? Access to Medical Care ? Diseases (HIV AIDS, Cancer etc) ? Public Health Spending ? Economic circumstances ? Lifestyle (Obesity, Alcohol consumption, Smoking etc) ? Exposure to Pollution/Environmental Conditions ? Improper Diet ? Poverty (Access to safe drinking water and sanitation) ? Education
Purpose of Study
The objective of the study is to determine the factors that affect the life expectancy in a developing country.
3
Method of Study
A sample of 30 developing countries from all continents of the world is taken and regression analysis performed in order to determine the important factors that influence life expectancy in a country. The dependent variable in the analysis is the life expectancy at birth for the country and a total of 12 independent factors (mentioned below) are taken for conducting regression analysis.
Independent factors used in Study
1. Proportion of population using improved drinking-water sources (%) The proportion of the population using an improved drinking water source, total, urban, and rural, is the percentage of the population who use any of the following types of water supply for drinking: piped water into dwelling, plot or yard; public tap/standpipe; borehole/tube well; protected dug well; protected spring; rainwater collection and bottled water (if a secondary available source is also improved). It does not include unprotected well, unprotected spring, water provided by carts with small tanks/drums, tanker truckprovided water and bottled water (if secondary source is not an improved source) or surface water taken directly from rivers, ponds, streams, lakes, dams, or irrigation channels.
2. Proportion of population using improved sanitation facilities (%)
The proportion of the population using an improved sanitation facility, total, urban, rural, is the percentage of the population with access to facilities that hygienically separate human excreta from human contact. Improved facilities include flush/pour flush toilets or latrines connected to a sewer, -septic tank, or -pit, ventilated improved pit latrines, pit latrines with a slab or platform of any material which covers the pit entirely, except for the drop hole and composting toilets/latrines. Unimproved facilities include public or shared facilities of an otherwise acceptable type, flush/pour-flush toilets or latrines which discharge directly into an open sewer or ditch, pit latrines without a slab, bucket latrines, hanging toilets or latrines which directly discharge in water bodies or in the open and the practice of open defecation in the bush, field or bodies or water. 3. Population using solid fuels (%) The percentage of population using solid fuels that include biomass fuels, such as wood, charcoal, crops or other agricultural waste, dung, shrubs and straw, and coal.
4. Adults aged ?15 years who are obese (%)
Percentage of adults classified as obese (BMI ? 30.0 kg/m²) among total adult population (15 years and older).
5. Alcohol consumption among adults (?15 years) (liters of pure alcohol per year)
Liters of pure alcohol per capita, computed as the sum of alcohol production and imports, less alcohol exports, divided by the adult population (aged 15 years or older).
4
6. Tobacco usage for age>15 (%) Tobacco smoking is the practice where tobacco is burned and the vapors either tasted or inhaled. The term here means percentage of adults involves in tobacco usage/smoking. 7. Children aged <5 years stunted for age (%) It is defined as percentage of stunting (height-for-age less than -2 SD of the WHO Child Growth Standards median) among children aged less than 5 years. 8. Adult literacy rate (%) Adult literacy rate is the percentage of the adult population (> 15 years old) who can both read and write with understanding a short simple statement on everyday life. 9. Children aged <5 years underweight for age (%) It is defined as percentage of underweight (weight-for-age less than -2 standard deviations (SD) of the WHO Child Growth Standards median) among children aged less than 5 years. 10. Deaths due to HIV/AIDS (per 100 000 population) Estimated mortality due to HIV/AIDS is the number of adults and children that have died in a specific year, based on the modeling of HIV surveillance data using standard and appropriate tools. The mortality rates of adults and of children aged less than 15 years are leading indicators of the level of impact of the HIV/AIDS epidemic and of the impact of interventions, particularly the scaling-up of treatment and prevention of mother-to-child transmission in countries with generalized HIV epidemics. 11. Per capita total expenditure on health (PPP int. $) Total health expenditure per capita is the per capita amount of the sum of Public Health Expenditure (PHE) and Private Expenditure on Health (PvtHE). The international dollar is a common currency unit that takes into account differences in the relative purchasing power of various currencies. Figures expressed in international dollars are calculated using purchasing power parities (PPP), which are rates of currency conversion constructed to account for differences in price level between countries. 12. Physicians density (per 10 000 population) The availability and composition of human resources for health is an important indicator of the strength of the health. Physicians' density is the number of physicians per 10 000 population.
5
6
Linear Regression Analysis
Method: Enter
Model Summaryb Change Statistics Model 1 R .973a R Square .946 Adjusted R Square .908 Std. Error of the Estimate 3.018 R Square Change .946 F Change 24.847 df1 12 df2 17 Sig. F Change .000 DurbinWatson 1.524
a. Predictors: (Constant), Physicians density (per 10 000 population), Adults aged ?15 years who are obese (%), Deaths due to HIV/AIDS (per 100 000 population), Children aged <5 years underweight for age (%), Alcohol consumption among adults (?15 years) (liters of pure alcohol per year), Proportion of population using improved drinking-water sources (%), Tobacco usage for age>15 (%), Per capita total expenditure on health (PPP int. $), Proportion of population using improved sanitation facilities (%), Adult literacy rate(%), Children aged <5 years stunted for age (%), Population using solid fuels (%) b. Dependent Variable: Life Expectancy The Model explains 94.6% of the variation for Life expectancy in developing countries. As the Durbin – Watson value is 1.524, so the auto correlation is not there.
7
Coefficientsa Unstandardized Coefficients Model 1 (Constant) Proportion of population using improved drinking-water sources (%) Proportion of population using improved sanitation facilities (%) Population using solid fuels (%) Adults aged ?15 years who are obese (%) Alcohol consumption among adults (?15 years) (litres of pure alcohol per year) Tobacco usage for age>15 (%) Children aged <5 years stunted for age (%) Adult literacy rate(%) B 32.332 .386 Std. Error 8.635 .104 .545 Standardized Coefficients Beta T 3.744 3.723 Sig. .002 .002
-.028
.060
-.070
-.464
.648
.123 .160 -.503
.057 .079 .249
.370 .148 -.172
2.146 2.021 -2.017
.047 .059 .060
.073 -.340
.091 .107
.078 -.480
.804 -3.180
.433 .005
.080
.094
.124
.857
.403
8
The significant factors in the analysis are: 1) The proportion of population who can avail safe drinking water resources(X1) 2) Deaths due to HIV/AIDS(X2) 3) Children below the age of 5 showing stunted growth(X3) 4) Proportion of population using solid fuels(X4) 5) Adult population above the age of 15 who are obese(X5) The Regression Equation is: Y = 32.332 + 0.386(X1) – 0.022(X2) – 0.340(X3) + 0.123(X4) +0.160(X5) Where Y = Life Expectancy in yrs in developing countries
9
Method: Stepwise
Model Summary Change Statistics Model 1 2 3 4 5 R .758a .922b .947
c
R Square .575 .850 .896 .915 .931
Adjusted R Square .559 .839 .884 .901 .916
Std. Error of the Estimate 6.605 3.992 3.390 3.132 2.883
R Square Change .575 .276 .046 .019 .016
F Change 37.815 49.664 11.432 5.467 5.498
df1 1 1 1 1 1
df2 28 27 26 25 24
Sig. F Change .000 .000 .002 .028 .028
DurbinWatson
.956d .965e
1.667
a. Predictors: (Constant), Proportion of population using improved drinking-water sources (%) b. Predictors: (Constant), Proportion of population using improved drinking-water sources (%), Deaths due to HIV/AIDS (per 100 000 population) c. Predictors: (Constant), Proportion of population using improved drinking-water sources (%), Deaths due to HIV/AIDS (per 100 000 population), Children aged <5 years stunted for age (%) d. Predictors: (Constant), Proportion of population using improved drinking-water sources (%), Deaths due to HIV/AIDS (per 100 000 population), Children aged <5 years stunted for age (%), Population using solid fuels (%) e. Predictors: (Constant), Proportion of population using improved drinking-water sources (%), Deaths due to HIV/AIDS (per 100 000 population), Children aged <5 years stunted for age (%), Population using solid fuels (%), Adults aged ?15 years who are obese (%) f. Dependent Variable: Life Expectancy The significant factors affecting the life expectancy rate in developing nations (Y) are 1) The proportion of population who can avail safe drinking water resources(X1)
10
2) Deaths due to HIV/AIDS(X2) 3) Children below the age of 5 showing stunted growth(X3) 4) Proportion of population using solid fuels(X4) 5) Adult population above the age of 15 who are obese(X5) Findings 1) R square is 93.1 for model 5 i.e. by choosing the abovementioned factors we can explain 93.1% of the changes in the life expectancy rates in developing nations which is a little less than 94.6% which could be explained by choosing the “enter” method. 2) Standard error is 2.883 which has gone down from 3.018 (enter method) 3) As the Durbin – Watson value is 1.667, so the auto correlation is not there.
11
Coefficientsa Unstandardized Coefficients Model 1 (Constant) Proportion of population using improved drinking-water sources (%) 2 (Constant) Proportion of population using improved drinking-water sources (%) Deaths due to HIV/AIDS (per 100 000 population) 3 (Constant) Proportion of population using improved drinking-water sources (%) Deaths due to HIV/AIDS B 20.068 .536 Std. Error 7.866 .087 .758 Standardized Coefficients Beta t 2.551 6.149 Sig. .016 .000
25.283 .499
4.811 .053 .706
5.255 9.426
.000 .000
-.023
.003
-.528
-7.047
.000
42.423 .357
6.511 .062 .504
6.516 5.789
.000 .000
-.021
.003
-.477
-7.310
.000
12
Among the factors chosen we can see that all factors have significance levels less than 0.05; hence all these 5 factors affect the life expectancy rate in developing nations. The regression equation for the same would be: Y=29.279 + 0.473(X1) - 0.023(X2) - 0.342(X3) + 0.142(X4) + 0.172(X5) However since obesity among adults 15 and above does not affect the life expectancy in developing nations a lot. In case of developed nations it would have more relevance. Hence we may choose to drop it.
13
References
? World Health Organization – Global Health Observatory (http://apps.who.int/ghodata/#)
? United Nations Website
(http://www.un.org/ru/development/progareas/statistics/overview.shtml)
? International Comparisons: Life Expectancy by Country (Sept 24, 2009) derived
from Retire Abroad: A Magazine for Overseas Living and Retirement ? Article on Life expectancy (Sept 2009) derived from the Conference Board of Canada Website
14
doc_773358383.doc
Documetation describes life expectancy of individuals in developing countries. Life expectancy at birth is the statistical term that is commonly used for determining the overall health of the country.
MARKET RESEARCH Life Expectancy Of Individuals In Developing Countries
Table of Contents
Table of Contents Introduction Purpose of Study Method of Study Linear Regression Analysis 2 2 3 3 4 7
Independent factors used in Study.....................................................4 Method: Enter.....................................................................................7 Method: Stepwise ..........................................................................................................10 References 14
2
Introduction
Our project deals with the life expectancy of individuals in developing countries. Life expectancy at birth is the statistical term that is commonly used for determining the overall health of the country. It represents average number of years that a newly born child can be expected to live in a particular country. In general, life expectancy is the expected number of years of life remaining at a given age. If the country’s life expectancy is very low, it may be an indication of a poorly developed health care system, high poverty conditions, major health problems such as AIDS, a high infant death rate, internal wars and conflict that increase the death rate, or a poor health and sanitation infrastructure. The World Health Organization (WHO) reports that, in 2005, 38 percent of the adult population of Swaziland, the African nation with the lowest life expectancy in the world, was inflicted with AIDS or HIV. Poverty also prevents access to safe drinking water and sanitation Factors such as diet, lifestyle, and environmental conditions also influence a country’s life expectancy. However, there are some factors like gender and ethnic origin that are difficult to change but influence a country’s life expectancy. Education is another factor that contributes to a longer life expectancy. Therefore, the main factors that can affect the life expectancy of people in a country are: ? Access to Medical Care ? Diseases (HIV AIDS, Cancer etc) ? Public Health Spending ? Economic circumstances ? Lifestyle (Obesity, Alcohol consumption, Smoking etc) ? Exposure to Pollution/Environmental Conditions ? Improper Diet ? Poverty (Access to safe drinking water and sanitation) ? Education
Purpose of Study
The objective of the study is to determine the factors that affect the life expectancy in a developing country.
3
Method of Study
A sample of 30 developing countries from all continents of the world is taken and regression analysis performed in order to determine the important factors that influence life expectancy in a country. The dependent variable in the analysis is the life expectancy at birth for the country and a total of 12 independent factors (mentioned below) are taken for conducting regression analysis.
Independent factors used in Study
1. Proportion of population using improved drinking-water sources (%) The proportion of the population using an improved drinking water source, total, urban, and rural, is the percentage of the population who use any of the following types of water supply for drinking: piped water into dwelling, plot or yard; public tap/standpipe; borehole/tube well; protected dug well; protected spring; rainwater collection and bottled water (if a secondary available source is also improved). It does not include unprotected well, unprotected spring, water provided by carts with small tanks/drums, tanker truckprovided water and bottled water (if secondary source is not an improved source) or surface water taken directly from rivers, ponds, streams, lakes, dams, or irrigation channels.
2. Proportion of population using improved sanitation facilities (%)
The proportion of the population using an improved sanitation facility, total, urban, rural, is the percentage of the population with access to facilities that hygienically separate human excreta from human contact. Improved facilities include flush/pour flush toilets or latrines connected to a sewer, -septic tank, or -pit, ventilated improved pit latrines, pit latrines with a slab or platform of any material which covers the pit entirely, except for the drop hole and composting toilets/latrines. Unimproved facilities include public or shared facilities of an otherwise acceptable type, flush/pour-flush toilets or latrines which discharge directly into an open sewer or ditch, pit latrines without a slab, bucket latrines, hanging toilets or latrines which directly discharge in water bodies or in the open and the practice of open defecation in the bush, field or bodies or water. 3. Population using solid fuels (%) The percentage of population using solid fuels that include biomass fuels, such as wood, charcoal, crops or other agricultural waste, dung, shrubs and straw, and coal.
4. Adults aged ?15 years who are obese (%)
Percentage of adults classified as obese (BMI ? 30.0 kg/m²) among total adult population (15 years and older).
5. Alcohol consumption among adults (?15 years) (liters of pure alcohol per year)
Liters of pure alcohol per capita, computed as the sum of alcohol production and imports, less alcohol exports, divided by the adult population (aged 15 years or older).
4
6. Tobacco usage for age>15 (%) Tobacco smoking is the practice where tobacco is burned and the vapors either tasted or inhaled. The term here means percentage of adults involves in tobacco usage/smoking. 7. Children aged <5 years stunted for age (%) It is defined as percentage of stunting (height-for-age less than -2 SD of the WHO Child Growth Standards median) among children aged less than 5 years. 8. Adult literacy rate (%) Adult literacy rate is the percentage of the adult population (> 15 years old) who can both read and write with understanding a short simple statement on everyday life. 9. Children aged <5 years underweight for age (%) It is defined as percentage of underweight (weight-for-age less than -2 standard deviations (SD) of the WHO Child Growth Standards median) among children aged less than 5 years. 10. Deaths due to HIV/AIDS (per 100 000 population) Estimated mortality due to HIV/AIDS is the number of adults and children that have died in a specific year, based on the modeling of HIV surveillance data using standard and appropriate tools. The mortality rates of adults and of children aged less than 15 years are leading indicators of the level of impact of the HIV/AIDS epidemic and of the impact of interventions, particularly the scaling-up of treatment and prevention of mother-to-child transmission in countries with generalized HIV epidemics. 11. Per capita total expenditure on health (PPP int. $) Total health expenditure per capita is the per capita amount of the sum of Public Health Expenditure (PHE) and Private Expenditure on Health (PvtHE). The international dollar is a common currency unit that takes into account differences in the relative purchasing power of various currencies. Figures expressed in international dollars are calculated using purchasing power parities (PPP), which are rates of currency conversion constructed to account for differences in price level between countries. 12. Physicians density (per 10 000 population) The availability and composition of human resources for health is an important indicator of the strength of the health. Physicians' density is the number of physicians per 10 000 population.
5
6
Linear Regression Analysis
Method: Enter
Model Summaryb Change Statistics Model 1 R .973a R Square .946 Adjusted R Square .908 Std. Error of the Estimate 3.018 R Square Change .946 F Change 24.847 df1 12 df2 17 Sig. F Change .000 DurbinWatson 1.524
a. Predictors: (Constant), Physicians density (per 10 000 population), Adults aged ?15 years who are obese (%), Deaths due to HIV/AIDS (per 100 000 population), Children aged <5 years underweight for age (%), Alcohol consumption among adults (?15 years) (liters of pure alcohol per year), Proportion of population using improved drinking-water sources (%), Tobacco usage for age>15 (%), Per capita total expenditure on health (PPP int. $), Proportion of population using improved sanitation facilities (%), Adult literacy rate(%), Children aged <5 years stunted for age (%), Population using solid fuels (%) b. Dependent Variable: Life Expectancy The Model explains 94.6% of the variation for Life expectancy in developing countries. As the Durbin – Watson value is 1.524, so the auto correlation is not there.
7
Coefficientsa Unstandardized Coefficients Model 1 (Constant) Proportion of population using improved drinking-water sources (%) Proportion of population using improved sanitation facilities (%) Population using solid fuels (%) Adults aged ?15 years who are obese (%) Alcohol consumption among adults (?15 years) (litres of pure alcohol per year) Tobacco usage for age>15 (%) Children aged <5 years stunted for age (%) Adult literacy rate(%) B 32.332 .386 Std. Error 8.635 .104 .545 Standardized Coefficients Beta T 3.744 3.723 Sig. .002 .002
-.028
.060
-.070
-.464
.648
.123 .160 -.503
.057 .079 .249
.370 .148 -.172
2.146 2.021 -2.017
.047 .059 .060
.073 -.340
.091 .107
.078 -.480
.804 -3.180
.433 .005
.080
.094
.124
.857
.403
8
The significant factors in the analysis are: 1) The proportion of population who can avail safe drinking water resources(X1) 2) Deaths due to HIV/AIDS(X2) 3) Children below the age of 5 showing stunted growth(X3) 4) Proportion of population using solid fuels(X4) 5) Adult population above the age of 15 who are obese(X5) The Regression Equation is: Y = 32.332 + 0.386(X1) – 0.022(X2) – 0.340(X3) + 0.123(X4) +0.160(X5) Where Y = Life Expectancy in yrs in developing countries
9
Method: Stepwise
Model Summary Change Statistics Model 1 2 3 4 5 R .758a .922b .947
c
R Square .575 .850 .896 .915 .931
Adjusted R Square .559 .839 .884 .901 .916
Std. Error of the Estimate 6.605 3.992 3.390 3.132 2.883
R Square Change .575 .276 .046 .019 .016
F Change 37.815 49.664 11.432 5.467 5.498
df1 1 1 1 1 1
df2 28 27 26 25 24
Sig. F Change .000 .000 .002 .028 .028
DurbinWatson
.956d .965e
1.667
a. Predictors: (Constant), Proportion of population using improved drinking-water sources (%) b. Predictors: (Constant), Proportion of population using improved drinking-water sources (%), Deaths due to HIV/AIDS (per 100 000 population) c. Predictors: (Constant), Proportion of population using improved drinking-water sources (%), Deaths due to HIV/AIDS (per 100 000 population), Children aged <5 years stunted for age (%) d. Predictors: (Constant), Proportion of population using improved drinking-water sources (%), Deaths due to HIV/AIDS (per 100 000 population), Children aged <5 years stunted for age (%), Population using solid fuels (%) e. Predictors: (Constant), Proportion of population using improved drinking-water sources (%), Deaths due to HIV/AIDS (per 100 000 population), Children aged <5 years stunted for age (%), Population using solid fuels (%), Adults aged ?15 years who are obese (%) f. Dependent Variable: Life Expectancy The significant factors affecting the life expectancy rate in developing nations (Y) are 1) The proportion of population who can avail safe drinking water resources(X1)
10
2) Deaths due to HIV/AIDS(X2) 3) Children below the age of 5 showing stunted growth(X3) 4) Proportion of population using solid fuels(X4) 5) Adult population above the age of 15 who are obese(X5) Findings 1) R square is 93.1 for model 5 i.e. by choosing the abovementioned factors we can explain 93.1% of the changes in the life expectancy rates in developing nations which is a little less than 94.6% which could be explained by choosing the “enter” method. 2) Standard error is 2.883 which has gone down from 3.018 (enter method) 3) As the Durbin – Watson value is 1.667, so the auto correlation is not there.
11
Coefficientsa Unstandardized Coefficients Model 1 (Constant) Proportion of population using improved drinking-water sources (%) 2 (Constant) Proportion of population using improved drinking-water sources (%) Deaths due to HIV/AIDS (per 100 000 population) 3 (Constant) Proportion of population using improved drinking-water sources (%) Deaths due to HIV/AIDS B 20.068 .536 Std. Error 7.866 .087 .758 Standardized Coefficients Beta t 2.551 6.149 Sig. .016 .000
25.283 .499
4.811 .053 .706
5.255 9.426
.000 .000
-.023
.003
-.528
-7.047
.000
42.423 .357
6.511 .062 .504
6.516 5.789
.000 .000
-.021
.003
-.477
-7.310
.000
12
Among the factors chosen we can see that all factors have significance levels less than 0.05; hence all these 5 factors affect the life expectancy rate in developing nations. The regression equation for the same would be: Y=29.279 + 0.473(X1) - 0.023(X2) - 0.342(X3) + 0.142(X4) + 0.172(X5) However since obesity among adults 15 and above does not affect the life expectancy in developing nations a lot. In case of developed nations it would have more relevance. Hence we may choose to drop it.
13
References
? World Health Organization – Global Health Observatory (http://apps.who.int/ghodata/#)
? United Nations Website
(http://www.un.org/ru/development/progareas/statistics/overview.shtml)
? International Comparisons: Life Expectancy by Country (Sept 24, 2009) derived
from Retire Abroad: A Magazine for Overseas Living and Retirement ? Article on Life expectancy (Sept 2009) derived from the Conference Board of Canada Website
14
doc_773358383.doc