This study examined the determinants and the extent of health poverty and richness in Ekiti State, Nigeria. The study employed survey data; the data were collected through the administration of structured questionnaires administered in six local government areas spread across the three senatorial districts of Ekiti State in 400 respondents using the Taro Yamane method of sampling technique. Descriptive statistics were used to analyze the demographic outcome of the respondents and a binary logit model was used to estimate the determinants of health poverty and richness while the level of expenditure on health of individuals was used to capture the extent in Ekiti state. The findings of the study show that income, which can be induced by quality education is positively and significantly related to health poverty and richness of people in Ekiti state while some other determinants that have to do with people's lifestyle like; drinking and smoking habits are significant but negatively related. The study further revealed that all the explanatory variables are statistically significant at 5%. The study concludes that health status is determined by the level of income and that about 85% of the Ekiti dwellers are health wisely rich. Given these assertions, this study recommends that policy geared towards improving and review of the income of workers which in turn have multiplier effects on both productivity and health status of the populace be considered by policymakers.
Health inequality has been a great concern to all national and international organizations of the world. Developed countries have enjoyed good health facilities but inequality still exists. The difference in access to good healthcare service between developed and developing countries have been a reflection of the level of poverty that exists between them. The levels of poverty in the developed countries are less than that which exists in developing countries [1]. Examining the determinants of health is an important task due to the input it has on public policy. According to Savedoff and Schultz [2], it helps to understand the risks in some habits and the effects these habits have on productivity and economic growth.
Lanre-Abass [3] stresses that Poverty endangers the health and lives of many in developing countries like Nigeria, Togo, Uganda, Liberia and so on which in turn engenders high maternal mortality predominance in developing countries. Data from Nigeria's Five-Year Countdown Strategy for achieving Millennium Development Goals (MDGs) shows that although maternal mortality fell but at a slow pace. In 2003, it was 800 deaths per 100,000 live births and 545 deaths per 100,000 live births in 2008 [4]. The disparities among citizens of Nigeria in terms of access to quality healthcare have been a great concern for health planners. The government of Nigeria has been trying to bring good health facilities to the doorstep of the poor in the country but despite an array of policies and establishment of hospitals and health centers by Federal, State, local governments, and even private institutions, inequality in health among her citizens persists. Quality health services are expected to be available for all citizens of a country not minding the status of the individual but unfortunately, good health has become the right of the rich. Corroborating this, World Health Organization [5] affirms through the Alma Ata Declaration of 1978 that, "health, which is a state of complete physical, mental and social well-being and not merely the absence of diseases or infirmity is a fundamental human right and that attainment of the highest possible level of health is a very important worldwide social goal whose realization requires the action of many other social and economic sectors in addition to the health sector".
WHO [6] confirms that the rich enjoy better healthcare than the poor who are more susceptible to diseases and sickness and this easily leads to the untimely death of the poor, The rich who live above the poverty line, still travel abroad to seek better medical attention especially when they are in the position of authority while the poor may not have the resources to do such even when their ailment is more critical [7].
World Bank [8] estimates that despite a good roadmap provided through the MDGs of the United Nation targeting 2015 for the development of the health sector, Nigeria still lags in the area of accessing basic health by her citizens. The less than 5 mortality rate remains at 108.8 per 100,000 in 2015 from 130.3 of 2010, the maternal mortality remains at 608 per 100,000 in 2015 from 630 in 2010, HIV/AIDS prevalence among adults remains at 3.17%. Blas and Kurup [9], assert that "reducing inequality in health is a goal in itself since achieving the various specific global health and development targets without ensuring equitable distribution across the population is of limited value". Solving the problem of inequality in healthcare has been a challenge to policymakers in health. Also, factors that determine inequality in health should be identified to assist these policy actors in determining the directions to which this threat can be adequately tackled so that the general well-being of both the rich and poor can be worked upon for improvement.
The Literature has shown that several works have been done to explain the relationship between poverty and access to healthcare in Nigeria like Akpomuvie [10], Rivera [11] and Takim [12]. Meanwhile, it was discovered that none of them has domesticated their findings on the determinants of health poverty and richness in Nigeria. Few that worked on poverty and richness were mainly in developed countries, like Wagstaff [13], Nadia, Nuno, Sandrina, and Celeste [14]. Given the above assertion, this research examines the determinants of health poverty and richness in Nigeria concerning Ekiti state. The rest of the paper is organized as follows: Section II presents a short revision of related literature; Section III presents the methodology; Section IV discussed the findings. Finally, Section V presents the conclusions and some policy implications.
Literature Review
Health poverty is a phenomenon in which the health of an individual is poor due to circumstances beyond the control of the individual. This always occurs among the disadvantaged citizens who are the most susceptible to diseases and sicknesses Culyer and Wagstaff [13]. To this end, WHO [15], conclude that poverty is the most dangerous killer and cause of suffering on earth. The health of an individual's mostly influenced by the social condition of their living. Albert and Davia [16], Asserts that Poverty is linked with the educational level of an individual, indicating the higher the educational level of an individual is, the less the level of poverty. In another way round, health richness applicable to those whose health is approximately good and devoid of diseases and sickness. The rich who live above the poverty line enjoyed this. Health richness entails accessing quality health services with ease, even when not available at their disposal, as far as going to other territories to seek such services without any delay or problem.
Nadia, et al. [14] examined the measurements and determinants of health poverty and richness of Portuguese using information from the office of National Health survey and converted them to EuroQol to measure inequality, using the ordered Probit model to evaluate the determinants of health inequalities in Portugal. It was found that there is a remarkable level of health inequality. Their study revealed that gender, age, education, region of residence, and eating habit are among the most critical determinants of health status.
David, Leoni, and Reinhad examined the wider determinants of health inequalities using decomposition analysis. They used representation data from the German socioeconomic panel by applying field regression based on decomposition techniques to decompose variations in health into its sources. Controlling for income, education, occupation, and wealth, they assess the relative importance of the explanatory factors over and above their effects on the variation of health channeled through the commonly applied measures of socioeconomic status. The analysis suggested that three main factors persistently contribute to variance in health care are; capability, cultural-behavioral variables, and to a lower extent, the materialistic approach. Juan and Manuel, examined the determinants of the health status of people in developing countries using Colombia as a case study. Their empirical evidence showed that there is a strong connection between individual socioeconomic, institutional variables with their health status. Lawson, et al. [17], in their work determinants of health-seeking behaviour in Uganda, found that there is a significant relationship between socioeconomic variables like (income, education, and user fee) and the health status of Ugandans. Audura, John, Stephany, and Barbara, find that income and wealth are jointly significant correlates individual health. This implies that wealth plays a strong role in determining the health status of every individual at the age bracket 25-54 which constitutes the labour force.
Study Area
The survey was carried out in Ekiti State using six local government areas from the three senatorial districts of the state, namely Ikole, Ilejemeje, Ekiti West, Irepodun Ifelodun, Emure, and Ekiti South West. The headquarters of each of the six local government areas namely; Ikole, Iye, Aramoko, Igede, Emure, and Ilawe were used as the proxy for relative urban from the three senatorial districts while some other villages from the local governments; Ayebode, Iludun, Ido Ile, Eyio, Eporo, and Ogotun represent relative rural. This is due to the higher availability of accessible health facilities and a higher population at the local government headquarters and less in the rural areas.
Research Design and Population
The research design used in this study is a cross-sectional research design method where data were collected randomly in the selected unit of population. The study adopts this because it allows identified groups in the survey to be purposely selected and provides useful data for simple static description and interpretation [18]. According to the National Population Commission projection for local governments as of 2016, the population of Ekiti West was 244,900, Ikole was 232,300, Emure was 128,500, Irepodun/Ifelodun was 179,100, Ilejemeje was 59,300 while Ekiti Southwest was 225,100. The total population of the six areas is 1,069,200. NBS.
This study used a structured questionnaire to collect information about the dwellers in the areas of study. To construct the questionnaires, a review of previous studies was used to identify variables that are relevant to the objective of this study. The questionnaire was divided into four sections, namely biological factors, socioeconomic factors, Behavioural factors, and Health status. A total of 400 questionnaires were administered across the six communities selected for this study but a total of 398 questionnaires were returned. After cle208aning, only 396 were valid which represents 99% of the sample size.
Estimation Techniques
Complementing the descriptive analysis on health poverty and richness in this section, we investigate the factors of the individual health state (HS) econometrically and statistically. Since health state is classified into discrete categories that have an ordinal nature (1 = rich heath and 0 = poor health)), the Binary Logit model framework is used. This model is based on a latent measure of health a continuous and unobserved variable which can be defined as a linear function of the observed explanatory variables and a random error term which is logistically distributed with zero mean and variance () greater than unity Greene.
Technically, the Binary Logit regression model can be represented thus; is the probability that weekly expenditure on health is below average while 1- Hsi is the probability that weekly expenditure on health is above average. is simply the odd ratio of rich health to poor health.
Findings and Discussion
This section is organized to represents the findings based on the data gathered through the structured questionnaires and are analyzed based on the methodology of the study. Frequency and percentage are used to analyze the demographic characteristics of the respondents while the empirical analysis is conducted with a Binary Logit regression model.
Descriptive Analysis
Table 1 shows the demographic representation of the respondents. The age distribution of the respondents shows that the respondent within the age bracket 18-35 constitutes the largest proportion by accounting for 63.13% of the total sample followed by the respondent within the age bracket 36-50 which are 20.2% of the total sample. The least is the respondents above the age of 65; this set of respondents can be economically categorized as the aging bracket, and they account for 5.3% of the total sample size. This is good for this work in that population of age 18 - 50 are active and engage more in lifestyles that are dangerous to health. From the table, it gathered that the gender distribution of the respondents reveal that female constitute the largest proportions of 51.52% of the total sample while male constitute the least proportion by accounting for 48.48% of the total sample. Generally, it is believed that the population of the female is more than that of males. From the marital status of the respondents, the result revealed that the married constitutes the largest proportion by accounting for 33.08% of the total sample followed by the single respondents that accounted for 23.48% of the total respondents and 18.69% were apportioned to the divorces. The count of widow/widower is relatively small and this accounted for 10.10% of the total respondent. The number of the respondents that are married but living separately constituted 14.65% of the total sample and this magnitude may be suggesting the possibilities of couples that are working in different local governments or may be due to some other reasons. This is in tandem with the work of Hughes and Waite that marriage is positively associated with health and well-being, marital dissolution or long-term separation may lead to health decline.
The educational qualification of the respondents shows that several people with primary education accounted for 23.7% of the total sample while 23.96% are for secondary education and the highest number with 27.3% are for tertiary education. The most worrisome aspect of these statistics is that the number of illiterate people is still high and the table shows that they constitute 25% of the total population.
The average monthly income distribution of the respondents was also shown in the table and presented as follows. The respondents that earn the least among the sample are those who earn less than ₦10,000 per month. The respondents that fall within the range ₦10,000-₦30,000 accounts for 22.72% of the total sample which is the highest and this may be suggesting that most of the Ekiti people hardly/could not pass the poverty test. The respondents that earn between ₦50,001 and ₦70,000 constitute the second-largest component by accounting for 22.48% of the sample size and this magnitude may be due to the relatively high number of educated people in Ekiti state which makes them earn averagely.
Finally, the Table 1 presented the occupational status of the dwellers. It can be seen that the largest proportion of the respondents are those engaging in one business or the other activities which accounted for 20.45% of the total sample and this is quite revealing that the number of dynamic entrepreneurs is rising over the years due to the positive technological advancement which is propagated down to Ekiti State. The artisan constitutes the second largest proportion (18.69%) followed by the retirees which accounted for 17.93%, this cannot be unconnected with the high percentage of civil servants that got relieved due to the length of service or attained the age of retirement. Next according to the table are the civil servants and the farmers which constitute the same proportion (14.65%); this may not be true as the study is based on the available sample. There are some uncategorized occupations like taxi drivers and motorcyclists of which the statistics revealed that the respondents that fall within this group accounted for about 13.6% of the total sample.
Table 1: Presents The Estimated Logit Regression Coefficients
Variable | Odds ratio | Std. Error | Z-Statistic | Prob. |
AGE | 1.2170 | 0.107960 | 2.55004 | 0.0310** |
EDU | 1.0435 | 0.168726 | 2.065167 | 0.0458** |
INCOME | 1.2031 | 0.114062 | 2.040121 | 0.0474** |
SMOKING HABIT | 0.8658 | 0.030790 | -2.283914 | 0.0273** |
DRINK HABIT | 0.9508 | 0.031872 | -2.683914 | 0.0102** |
Dependent variable: Health Richness/Poverty, Source: Author's computation
(**) (***) denotes null hypothesis at 5% and 1% respectively. Odds ratios greater than unity imply positive coefficients while odd ratios less than unity imply negative coefficients.
From the Logit regression model above, it is apparent that Age, Education, Income, Smoking, and Drinking (strong alcohol) are statistically significant in determining health status i.e. these variables are highly permissible to determine health richness or health poverty.
The model reveals that if Age increases by one year, the odds ratio in favour of good health with 1.2170. This implies that rising age determines health richness; this result corroborates by the descriptive statistics showing that the majority of the respondents fall between age 18-50 which implies youthful age, active, and rich in health. Although, this proposition is liable to be untrue until one reaches a certain age where health will start to deteriorate, and hence there may need to account for the threshold effect [15].
The model further reveals that if the duration spent on Education increases by one academic period, the odds ratio in favour of good health will be about 1.0435. This implies that better and improved education has a long way to go in the determination of health richness. The model again reveals that if the Income level increases by one unit, the odds ratio in favour of good health will be about 1.2031. This implies that the role played by income in the determination of health richness and poverty cannot be overemphasized. People with a high level of income will have access to highly hygienic foods and environments which in essence improve the quality of health and hence life expectancy. The model reveals that a unit of cigarettes smoked increases by one. The odds ratio in favour of good health will be about 0.8658. This implies that smoking is highly dangerous to health. This is highly in support of the popular slogan by most producers of these cigarettes inscribed on the packet that "smokers are liable to die young". The model reveals that if the unit of Alcohol intake increases by one, the odd ratio in favour of good health will be about 0.9508. This implies that the intake of alcohol is highly dangerous to health. This result is consonant with Kwhal, which affirms that eating a balanced diet, abstinence from alcohol drinking, and smoking as reduces the mortality rate.
The study concludes that the health status of every individual is majorly determined by the level of their education, income, and age, and these serve as a determinant of health richness and poverty in Ekiti State. Furthermore, as an individual grows in age, knowledge, and matures towards understanding what, how, and when to carry out required hygienic activities that will enhance quality health that will assist the individual at old age. Finally, the study observed that alcohol intake and smoking are inimical to quality health. Based on the major findings of this study, the study recommends that policies aimed at improving the education status of Ekiti dwellers should be taken into consideration by embarking on free or subsidized education at all level for its citizens; workers' salary should be reviewed upward to commensurate with international labour organization specifications, and ensuring prompt payment of wages/salaries at all levels to improve the income of the citizens.
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