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Socio-economic factors & longevity in a cohort of Kerala State, India
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Risks were estimated for gender, education level, and monthly income in a multivariate analysis, adjusted for age, gender, education level, income, household assets, house type, randomization group, and number of household residents. For each year of follow up, frequency tables were constructed including the number of participants present at the beginning of that year and the number of deaths occurring during the year, for each single year of age.
Then the frequency tables were combined in a single table, by single year of age; it included the total number of persons followed up [or person-years P-Yrs ] and the total number of deaths by single year of age. Frequency tables were constructed using Excel software. Probabilities were estimated for each category of the demographic and socio-economic variables of interest: sex, level of education, occupation, household assets, number of permanent household residents, type of house, and total household income per month. Probabilities q x were entered in single-year single-decrement life tables in order to estimate life expectancy.
At the age of 80, a value of 1 was given i. Life expectancy according to the socio-economic factors was estimated for all participants, then in men and women separately, for categories including more than 80 death cases. A total of 11, deaths were registered during the follow-up period. The main causes of death, recorded for 88 per cent of death cases, were chronic and non-communicable diseases. The Table shows death rates and mortality risks according to gender, education level and monthly income.
About 20 per cent were not literate while more than 12 per cent of the study population received a college education. More than half of the subjects had a low household income, and only 8 per cent had an income of more than Rupees per month. Women had lower mortality rates and risks than men. Mortality rates and adjusted risks decreased with increased level of education, and monthly income in a significant dose-response relationship. Characteristics of the participants, mortality rates per Person-Years and mortality risks according to main determinants.
At age 40, the study participants were expected to live on average an additional Women were expected to live three years longer than men Longevity increased with increased education level and increased income level 2 years difference between the low- and the high-income groups. Similarly, according to the household assets, the longevity gap between the deprived and privileged households was 1.
Remaining years of life also differed according to the housing conditions, such as construction quality of the house 1. Those unemployed had a shorter life expectancy, followed by the blue-collar workers and the business people. Remaining years to live at age 40 according to demographic and socio-economic factors. In both genders, a high socio-economic level was synonymous with a longer life, although the difference between the lower and the higher groups was larger in men.
Unemployed men had the shortest remaining life to live, while white collar workers had the longest. Women in the white-collar occupation category lived 6 months longer than housewives. An unequal socio-economic distribution in populations is the result of historical, cultural, political, economic and governance processes. These factors ultimately influence the resources available to individuals and populations and shape the nature of investments in public infrastructure related to education, health services, social welfare, household food availability, housing, employment opportunities, transportation, and environmental controls, among others.
Socio-economic differentials have generally been associated with significant differences in the general health of populations. These differences are accompanied by many differences in quality of life, both at the individual and the community level that may adversely affect health and survival of populations. The effect of socio-economic inequality on health and longevity reflects a combination of negative exposures and lack of resources by individuals.
Although successive administrations in Kerala in the past several decades are well-known for egalitarian policies in terms of healthcare and social reforms 4 , and greater redistributive actions, we studied socio-economic differentials in health among a peri-urban population near the capital city of Thiruvananthapuram. Those belonging to the socially disadvantaged categories experienced a higher mortality rate. This is exemplified by the longer life expectancy for the better-off group, with high income. The unfavourable longevity experienced among the unemployed may be a reflection of a poor physical or mental health, often linked to increased mortality rates However, this association could not be verified.
Social inequalities in longevity have been extensively reported in affluent societies; evidence from low-resource countries has also recently been described 28 — To our knowledge, these are the first estimates of life expectancy in India according to the socio-economic status, based on the results of a large prospective study. Our findings are consistent with observations from developed and developing countries.
An ecological study of factors associated with mortality rates among middle-aged Indian adults, based on national data, showed higher mortality rates among the more deprived Progressive healthcare policies in Kerala have provided an easy access to healthcare services to everyone, with a health dispensary within 2 km and a hospital within 5 km of every home 4.
However, a survey showed that, with the rapid expansion of the private medical sector in Kerala, wealthy individuals are more likely to seek for expensive medical care than for free care Nevertheless, it is difficult to say whether this phenomenon can explain the observed health disparities. In the present study, causes of death were similarly distributed across the different socio-economic indicators, the first cause of death being cardiovascular diseases, and the second being either cancer or respiratory diseases. Death registration in low-resource countries is well known for its incompleteness, and Kerala State is no exception.
However, we tried to overcome this problem by actively collecting death information from mosques, churches, and repeated house visits. These visits have significantly amplified the proportion of information on death, as compared to the information from the government registries. Moreover, the cause of death also neared completeness by using verbal autopsy, when medical information was not available.
The study population included rural and semi-urban communities, with a socio-economic distribution similar to the overall s0 tate, allowing generalization of the findings for Kerala State. Although a complete life table from birth could not be constructed, the present truncated estimates are based on actual age-specific death rates thanks to the large study sample and the long follow-up duration.
The main causes of high mortality, such as poor sanitation, wide-spread hunger, famine and malnutrition, inadequate access to healthcare and poor education, are not generally applicable to most populations living in Kerala State. Chronic diseases such as cardiovascular diseases and cancers lead to greater morbidity, one of the characteristics of the State 5.
Increasing life expectancy and the ageing of the population go along with increasing prevalence of non-communicable diseases It is important to recognize and respond to emerging challenges in health and welfare in view of the longer longevity, ageing populations and high frequency of chronic diseases, if the already achieved health benefits and progress are to be sustained. Despite the overall progress in health and longevity in a low-resource region like the State of Kerala, India, socio-economic disparities in health are prominently observed at the population level The authors are grateful to the study participants and their families, the assistance of the staff of the Panchayath offices, of mortality registries, and of the Trivandrum population-based cancer registry.
National Center for Biotechnology Information , U. Indian J Med Res. Author information Article notes Copyright and License information Disclaimer. Received Nov This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3. This article has been cited by other articles in PMC. Methods A cohort of 1,67, participants aged 34 years and above in Thiruvananthapuram district, having completed a lifestyle questionnaire at baseline in , was followed up for mortality and cause of death until Results At 40 years, men and women were expected to live another 34 and 37 years, respectively.
Keywords: Developing countries, India, life expectancy, socio-economic factors. Results A total of 11, deaths were registered during the follow-up period. Table Characteristics of the participants, mortality rates per Person-Years and mortality risks according to main determinants. Open in a separate window. Discussion An unequal socio-economic distribution in populations is the result of historical, cultural, political, economic and governance processes. References 1. Rogers RG, Wofford S. Life expectancy in less developed countries: socioeconomic development or public health?
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