Abstract
Background
The aim of this study was to construct a wealth index that could be compared over time in order to understand the trends in wealth in Kenya, and determine predictors of change in wealth index.
Methods
Data was from the Demographic and Health Survey (DHS) program collected in Kenya between 1993 and 2009. Variable categories were collapsed to match and factor analysis was performed on the four year pooled data to generate a harmonized wealth index. Possible predictors of wealth were selected from household variables available for all four years. Household sampling weights and stratification by rural/urban was used.
Results
Overall, wealth increased in Kenya between 1993 and 2008; however, when stratified no significant increase existed in urban areas, and a significant increase in rural areas specifically between 2003 and 2008. The strongest predictor was education, with more than a standard deviation difference for secondary or higher levels of education over those with no education. The association of gender of the head of household, and whether the head of household had a partner differed between rural and urban areas, with household heads who were women and those that had a partner having more wealth in urban areas, but less wealth in rural areas.
Conclusion
Wealth in Kenya increased overtime, specifically in rural regions. Differences were seen in predictors of wealth by urban/rural residence, educational level, and gender of the head-of-household, and should be taken into account when planning interventions to target those in disproportionately low wealth brackets.
Keywords: wealth, rural, urban, Demographic and Health Survey, Kenya
BACKGROUND
The importance of wealth in the health of individuals and populations has been shown across the globe. (1) Poorer nations tend to have inferior health to wealthier nations, and poorer people have worse health outcomes compared to wealthier people; and economic growth has been shown to improve health. (1–3) Health at the population level is an important factor in stimulating economic growth, through accumulation of both human and physical capital. (3–4) For example, between 2000–2011 a quarter of the economic growth in low and middle income countries was attributed to health improvements. (4) Not only adult survival, but also disease prevalence rates and cognitive functioning, have been shown as important factors in maintaining a supply of skilled labor, necessary for economic growth. (5) At the macroeconomic level, health has also been linked to gross domestic product (GDP), such that a 4% raise in GDP per capita is associated with a 1-year increase in life expectancy. (4) At the personal level, income is one of the strongest determinants of health outcomes. (book) Socioeconomic disparities in health due to differential income have been seen in childhood mortality, immunization, treatment of common childhood illnesses, health related behaviors, and health status determinants. (6–7)
Understanding the relationship between health and wealth, and the possible factors that may mediate this relationship, such as education, housing, and sanitation, is in part due to measurement problems surrounding both health and wealth. (3) More recent studies have overcome some difficulty in collecting preferred indicators of economic status, such as income which are less reliable in low and middle income countries, by using a wealth index. (6) This index utilizes easy-to-collect information on assets, housing construction materials, and types of water and sanitation access, to create a continuous scale of relative wealth and generate a measure of a household’s standard of living. (8) One of the strongest utilities of the wealth index is that it creates a standard for comparison across nations, especially with varying reliability and accessibility of income and expenditure data. (8) The wealth index allows for comparison of socioeconomic disparities across health outcomes, and differences in access between poor and wealthy communities.
Although marginal in some areas, current health indicators reveal that Kenya has seen improvement in overall population health. (9–10) Notable of those indicators is the life expectancy increasing from 45.2 years in 1990s to 60 years in 2012. (9) Other indicators such as infant and under-5 mortality rate show similar improvement; from 77 and 115 in the 1990s to 48 and 73 per 1000 live births respectively in 2012. (9–10) Despite these advances, disparities in health along lines of gender, ethnic group, socioeconomic status and geographic access are present and impact healthcare outcomes. (9,11–13) Children that are both economically and geographically vulnerable were found to be the most disadvantaged. (14) Communicable diseases remain high and non-communicable diseases are increasing. (10,15–17) Therefore, addressing barriers to healthcare will likely be a key element in positively impacting health outcomes in the long term. (18)
Utilization of the wealth index has been helpful in identifying differences by socioeconomic status in order to focus intervention efforts. In Kenya, disparities were found among the number of children immunized: where in 2008, 73% of children in Nairobi were immunized as compared to 53% of children living in less urban communities. (19) Among children in Nigeria, children from the wealthiest households are 13 times more likely to receive vaccination than those from the poorest families. (8) African nations including Tanzania show a significantly higher prevalence of HIV among women in the wealthiest households as compared to the poorest. (8)
Understanding such disparities may lead to targeted interventions to improve health outcomes for those in disproportionately low wealth brackets. (15,20) For example, utilization of the wealth index has allowed for identification of various socio-demographic determinants of health in several countries. (8) However, the literature indicates that the wealth index, as a tool, has been underutilized in Kenya, with limited data identified to understand the sociodemographic determinants of health outcomes. Therefore, the aim of this study was to construct a wealth index that can be compared over time in order to understand the trends in wealth in Kenya, and determine predictors of change in wealth index.
METHODS
Study Sample
Data was from the Demographic and Health Survey (DHS) program collected through four surveys conducted in Kenya between 1993 and 2009. (21–24) The DHS program provides nationally representative data on topics such as domestic violence, child health, nutrition, HIV/AIDS, wealth, and women empowerment collected in over 90 countries. (25–26) The household questionnaire is used to record data specific to the household, as well as, specific to the individuals living in the household. Individual specific information includes gender, age, and education of the household head. Household specific characteristics include region type, district, source of drinking water, toilet facilities, cooking fuel, and assets of the household. Surveys are population-based to provide data that is comparable across countries. Each country uses a standard model questionnaire format but questions may be added if of particular interest or deleted if deemed irrelevant. The model questionnaires change over time to improve data collection methods and may lead to differences in questions or response options asked from year to year.
This study used the household questionnaire data from the Kenya Demographic and Health Survey (KDHS) from 1993, 1998, 2003 and 2008/2009 creating a total sample size of 33,948 households. Population based data collection design involved clustering urban or rural areas from the national master sample frame and systematic sampling of households from an updated list of households. Men and women age 15–49 years who were either usual residents or visitors in the household on the night before the survey were eligible for the household interview. The response rate was 90% for KDHS 1993, 97% for KDHS 1998, 96% for KDHS 2003 and 98% for KDHS 2008/2009. (21–24) Of note, data for the Northeastern district were collected only for the 2003 and 2008/2009 KDHS surveys, and therefore these were used only for the creation of the wealth index but not for the regression analyses (0.9% of the data was discarded).
Construction of the harmonized wealth index
The DHS wealth index is a relative measure of socio economic status (27) and is constructed for each survey using household assets and amenities. (28) The DHS wealth index is survey specific and therefore results are applicable only for a specific country and at a given time. (27,28) To address this issue, the harmonized wealth index (HWI) (29) has been proposed in order to generate an absolute measure of wealth that can be compared over time. The main steps in constructing the HWI are: 1) identify household asset variables measured across all years, 2) harmonize the categorical variables such that equivalent categories are available across all years, 3) perform a data reduction analysis using factor analysis to generate wealth scores for each household.
For Kenya, we identified eleven common asset variables measured by the DHS surveys across four years: assets including electricity, radio, television, refrigerator, bicycle; the source of drinking water; type of toilet facility; main floor material; main roof material; number of people sleeping per room; and whether the household had a domestic worker. Categories from later surveys were collapsed such that they would match earlier surveys with fewer categories. An indicator variable was generated for each category and any missing values were replaced with zero. (27) The number of people sleeping per room was rounded to the nearest integer with the exception of values between 0 and 1, which were rounded to zero, and missing values were imputed with the average value. (29) Following DHS methodological reports on creation of wealth indices, factor analysis was performed on the pooled data, with factor extraction based on principal component analysis and adjusted for household sampling weights. (29) Scores corresponding to the first factor were estimated using the ‘predict’ statement in Stata, with the default regression scoring assumed.
Of note, the wealth index provides a wealth status more specific to the urban area. (30) Questions more relevant to wealth for the rural areas (such as ‘land owned’, ‘number of chattels’) were included only in more recent surveys and therefore could not be used in the construction of the harmonized wealth index. To address this issue, when determining predictors of wealth, analyses were stratified by region (urban vs. rural). (30)
Predictors of wealth
Possible predictors of wealth were selected form household variables that were available for all four years. These included: age of the head of the household (continuous), gender of the head of the household (male, female), whether the head of a household has a wife/husband/partner (yes, no), educational attainment of the head of the household (no education, incomplete primary, complete primary, incomplete secondary, complete secondary, higher), and district (Nairobi, Central, Coast, Eastern, Nyanza, Rift Valley, Western).
Ethics and Consent
Approval was obtained from the DHS program to download and use applicable DHS datasets. The study did not require ethics approval because DHS data is publicly available and contains deindentified data.
Consent to Publish
Not applicable.
Statistical analysis
Analysis was conducted in three steps. First, correlations (for continuous variables) or one-way ANOVAs (for categorical variables) were run between the wealth index and possible predictors. Second, a series of unadjusted regression models were used to assess associations between each predictor and the wealth index. Finally, an adjusted regression model was run between the predictors of interest and the wealth index. The adjusted regression model was constructed in a hierarchical manner with variables entered in blocks: block 1 - demographic variables (age, gender, and partner), block 2 - educational attainment, and block 3 - district.
To correct for over-sampling or under-sampling, we used the household sampling weights (hv005) available in the household record files. The sampling weights provided by DHS are normalized for each year, so for our four-year pooled dataset DHS weights were adjusted by dividing each household weight by four. The adjusted weights were used in the factor analysis for the wealth index as well as in the regression analyses. Regression analyses also accounted for clustering (using primary sampling unit identifier hv021) and stratification (using sample stratum number hv022) in a complex survey design statement (‘svyset’ in Stata). All analyses were performed using Stata Version 13 and a two-tailed alpha of 0.05 was used to assess significance.
RESULTS
Construction of the harmonized wealth index
The harmonized wealth index constructed for the pooled data over the 4 years (1993, 1998, 2003 and 2008/2009) was highly correlated with the original DHS wealth index (0.98, 0.97, 0.93, and 0.94, respectively). Summary statistics of the variables included in the formation of the wealth index and loadings corresponding to the first factor are presented in Table 1. The corresponding scoring coefficients are also presented in Table 1. These represent the regression coefficients used to estimate the wealth index for each individual. The negative loadings in the factor analysis and corresponding negative scoring coefficients suggest that lower wealth was associated with an increased number of people sleeping per room, drinking water from well, river or lake, using a traditional pit or no toilet, and living in a dwelling with natural roof or natural floor. The proportion of total variance explained by the first factor alone was 18%. The mean of the harmonized wealth index for the four-year pooled data was 0.02, with a standard deviation of 1.03 and a range between −1.50 and 3.44.
Table 1.
Harmonized Wealth Index Factor Analysis
| Variable | Overall (N=33,948) | 1993 (n=7,950) | 1998 (n=8,380) | 2003 (n=8,561) | 2008/09 (n=9,057) | Factor Loadings | Scoring coefficients | |
|---|---|---|---|---|---|---|---|---|
|
| ||||||||
| 1 | Domestic Worker | .017 | 0.010 | 0.013 | 0.029 | 0.014 | 0.272 | 0.055 |
| 2 | Sleep per room | 2.788 | 2.837 | 2.795 | 2.636 | 2.723 | −0.289 | −0.061 |
| 3 | Has electricity | .171 | 0.099 | 0.124 | 0.214 | 0.254 | 0.771 | 0.151 |
| 4 | Has radio | .647 | 0.514 | 0.618 | 0.744 | 0.712 | 0.402 | 0.081 |
| 5 | Has television | .172 | 0.055 | 0.115 | 0.240 | 0.297 | 0.678 | 0.135 |
| 6 | Has refrigerator | .052 | 0.026 | 0.033 | 0.087 | 0.091 | 0.550 | 0.115 |
| 7 | Has bicycle | .256 | 0.220 | 0.249 | 0.276 | 0.287 | 0.010 | 0.004 |
| 8 | Source of drinking water | |||||||
| piped_house | .228 | 0.192 | 0.209 | 0.243 | 0.262 | 0.670 | 0.128 | |
| piped_public | .124 | 0.145 | 0.124 | 0.122 | 0.106 | 0.032 | 0.002 | |
| well | .211 | 0.203 | 0.215 | 0.181 | 0.243 | −0.148 | −0.033 | |
| river | .198 | 0.332 | 0.042 | 0.315 | 0.115 | −0.313 | −0.054 | |
| lake | .183 | 0.073 | 0.374 | 0.057 | 0.223 | −0.290 | −0.057 | |
| rain | .019 | 0.021 | 0.013 | 0.023 | 0.019 | 0.043 | 0.006 | |
| other_water | .035 | 0.029 | 0.021 | 0.057 | 0.032 | 0.048 | 0.013 | |
| 9 | Type of toilet facility | |||||||
| own_flush | .073 | 0.046 | 0.057 | 0.083 | 0.102 | 0.615 | 0.126 | |
| public_flush | .050 | 0.040 | 0.035 | 0.061 | 0.064 | 0.353 | 0.062 | |
| traditional_pit | .615 | 0.685 | 0.689 | 0.600 | 0.500 | −0.315 | −0.057 | |
| ventilated_pit | .096 | 0.067 | 0.070 | 0.079 | 0.161 | 0.171 | 0.027 | |
| no_facility | .158 | 0.152 | 0.142 | 0.169 | 0.166 | −0.370 | −0.074 | |
| other_toilet | .005 | 0.005 | 0.002 | 0.006 | 0.007 | 0.012 | 0.004 | |
| 10 | Main floor material | |||||||
| Natural | .615 | 0.696 | 0.650 | 0.579 | 0.545 | −0.807 | −0.154 | |
| Rudimentary | .006 | 0.004 | 0.011 | 0.006 | 0.004 | 0.050 | 0.013 | |
| Finished | .375 | 0.296 | 0.335 | 0.409 | 0.449 | 0.800 | 0.152 | |
| Other_floor | .001 | 0.0005 | 0.0002 | 0.004 | 0.0006 | 0.056 | 0.012 | |
| 11 | Main roof material | |||||||
| Natural | .268 | 0.353 | 0.291 | 0.224 | 0.213 | −0.539 | −0.104 | |
| Rudimentary | .661 | 0.607 | 0.673 | 0.669 | 0.690 | 0.259 | 0.041 | |
| Finished | .061 | 0.028 | 0.026 | 0.090 | 0.095 | 0.520 | 0.111 | |
| Other_roof | .007 | 0.004 | 0.007 | 0.015 | 0.002 | −0.010 | −0.001 | |
Sample demographics
The sample demographic characteristics stratified by region (urban/rural) are presented in Table 2. There were 8,565 households in the urban region and 24,280 households in the rural region in the 4 year pooled dataset. For the urban region, the head of the household was 37.8 years old on average, usually a male (75%), and more likely to have completed primary (21.3%) or secondary education (27.7%). Most households in the urban area were sampled from Nairobi, Coast, or Rift Valley (72.7% overall). For the rural region, the head of household was slightly older (46.2 years) and was more likely to have no education (26.8%), incomplete primary (28.2%) or complete primary education (23.6%). Similar to the urban areas, the head of the household was usually a male (65%). Most rural households were sampled from Rift Valley, Eastern Region or Nyanza (63.2% overall). Across years, there were statistically significant differences in the levels of education of the head of the household and levels of wealth index that suggested an increasing trend over time, for both rural and urban areas.
Table 2.
Sample demographic characteristics stratified by region
| Variable | Urban | Rural | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
| ||||||||||||
| Overall (N=7,932) |
1993 (n=1,527) |
1998 (n=1,988) |
2003 (n=2,104) |
2008 (n=2,314) |
p- val |
Overall (N=25,630) |
1993 (n=6,423) |
1998 (n=6,392) |
2003 (n=6,270) |
2008 (n=6,545) |
p- val |
|
|
|
|
|||||||||||
| % or Mean ± standard deviation | % or Mean ± standard deviation | |||||||||||
|
| ||||||||||||
| Gender1 | ||||||||||||
| Male | 75.0 | 78.5 | 76.8 | 74.5 | 71.6 | 0.08 | 65.2 | 64.7 | 65.6 | 66.1 | 64.5 | 0.57 |
| Female | 25.0 | 21.5 | 23.2 | 25.5 | 28.4 | 34.8 | 35.3 | 34.4 | 33.9 | 35.5 | ||
|
| ||||||||||||
| Partner1 | ||||||||||||
| No | 52.8 | 55.7 | 54.3 | 51.5 | 50.9 | 0.22 | 44.5 | 44.3 | 44.6 | 42.7 | 46.2 | 0.09 |
| Yes | 47.2 | 44.3 | 45.7 | 48.5 | 49.1 | 55.5 | 55.7 | 55.4 | 57.3 | 53.8 | ||
|
| ||||||||||||
| Education1 | ||||||||||||
| No educ. | 8.2 | 9.8 | 7.9 | 7.1 | 7.0 | 0.00 | 26.8 | 35.7 | 20.1 | 24.0 | 19.7 | 0.00 |
| Some | 12.1 | 14.3 | 13.6 | 12.2 | 9.5 | 28.2 | 27.7 | 27.5 | 29.9 | 27.9 | ||
| prim. | 21.3 | 22.3 | 19.7 | 24.3 | 19.4 | 23.6 | 20.8 | 22.2 | 23.6 | 27.7 | ||
| Primary | 15.9 | 43.6 | 10.7 | 10.2 | 7.9 | 8.7 | 14.4 | 6.7 | 6.7 | 7.0 | ||
| Some | 27.7 | 6.4 | 38.1 | 28.0 | 32.8 | 9.2 | 0.9 | 13.3 | 10.1 | 12.6 | ||
| sec. | 14.9 | 3.8 | 10.1 | 18.1 | 23.4 | 3.4 | 0.5 | 2.2 | 5.7 | 5.3 | ||
| Secondary Higher | ||||||||||||
|
| ||||||||||||
| District2 | ||||||||||||
| Nairobi3 | 39.2 | 42.2 | 43.1 | 39.1 | 34.1 | 0.97 | 0 | 0 | 0 | 0 | 0 | 0.63 |
| Central | 7.3 | 8.4 | 5.2 | 8.8 | 6.9 | 16.7 | 18.0 | 17.0 | 18.6 | 14.01 | ||
| Coast | 16.0 | 15.7 | 13.4 | 14.7 | 19.6 | 5.6 | 6.8 | 5.3 | 5.9 | 4.5 | ||
| Eastern | 6.8 | 6.2 | 8.5 | 5.3 | 7.1 | 19.0 | 19.5 | 17.7 | 19.2 | 20.5 | ||
| Nyanza | 7.6 | 6.3 | 10.2 | 6.8 | 7.0 | 19.2 | 17.9 | 22.5 | 18.3 | 19.1 | ||
| Rift | 17.5 | 16.5 | 14.5 | 19.4 | 18.9 | 25.0 | 23.3 | 24.1 | 24.3 | 29.3 | ||
| Valley Western | 4.8 | 4.8 | 5.2 | 4.3 | 4.9 | 13.4 | 14.5 | 13.4 | 14.0 | 12.6 | ||
|
| ||||||||||||
| Age1 | 37.8±12.44 | 36.7±11.81 | 37.6±12.14 | 37.4±12.26 | 38.5 +13.04 | 0.46 | 46.2±16.16 | 46.7±16.13 | 46.2±15.76 | 45.5±16.26 | 46.3±16.26 | 0.01 |
|
| ||||||||||||
| Wealth Index | 1.13±1.09 | 0.87±1.03 | 0.94±1.03 | 1.12±1.11 | 1.38±1.08 | 0.00 | −0.34±0.68 | −0.47±0.61 | −0.38±0.66 | −0.28±0.71 | −0.21±0.73 | 0.00 |
Variable is specific to the head of household
Northeastern region was included in wealth index construction but excluded from sample demographics and regression analyses since it did not appear in all datasets
No rural population in NAIROBI
educ=education, prim=primary, sec=secondary, N/n is the number of weighted cases.
Trends in wealth index
Figure 1 shows plots of mean wealth index across the four years, based on the unadjusted and final adjusted models, overall and stratified by region. Overall, wealth index increased over time with adjusted means showing no increase until after 1998. For the urban area, the unadjusted means suggest an increase in wealth over time. However, the adjusted means suggest a non-linear relationship between wealth and time, with a decrease in wealth from 1993 to 1998, a slow increase from 1998 to 2003, and a stepper increase from 2003 to 2008/2009. For the rural area, both adjusted and unadjusted means suggest a linear increasing trend in wealth over time, with a similar delay in the increase in wealth as seen in the overall model.
Figure 1.
Trends in wealth index over four years of pooled data in Kenya
Predictors of wealth
Table 3 presents results of the unadjusted and adjusted regression analyses. In the unadjusted models for the urban region, in 2008/2009 the average wealth was significantly higher comparative to 1993 (β=0.46, p<0.01). Increasing age of head of household was significantly associated with increased wealth (β=0.01, p<0.01). Households where a woman was the head of household were associated with less wealth (β= −0.08, p<0.05), whereas head of households with a partner was associated with more wealth (β=0.24, p<0.001). Increasing education was associated with greater wealth when compared with no education for those who completed primary (β=0.19, p<0.01), had incomplete secondary (β=0.60, p<0.001), completed secondary (β=0.93, p<0.001) and had higher education (β=1.66, p<0.001). Nairobi had the highest levels of wealth comparative to all other regions.
Table 3.
Unadjusted and adjusted hierarchical models for the association between wealth index and predictors
| Urban | Rural | |||||||
|---|---|---|---|---|---|---|---|---|
|
| ||||||||
| Variable | Unadjusted Univariate Analyses | Model 1 | Model 2 | Model 3 | Unadjusted Univariate Analyses | Model 1 | Model 2 | Model 3 |
|
| ||||||||
| Year | ||||||||
| 1993 | (Ref) | (Ref) | (Ref) | (Ref) | (Ref) | (Ref) | (Ref) | (Ref) |
| 1998 | 0.10 | 0.08 | −0.13 | −0.12 | 0.09** | 0.08** | −0.003 | 0.01 |
| 2003 | 0.13 | 0.14 | −0.12 | −0.09 | 0.19*** | 0.19*** | 0.08* | 0.08** |
| 2008 | 0.46** | 0.44** | 0.07 | 0.13 | 0.32*** | 0.31*** | 0.16*** | 0.18*** |
|
| ||||||||
| Age | 0.01** | 0.01** | 0.01*** | 0.01*** | −0.002** | −0.001** | 0.01*** | 0.01* |
|
| ||||||||
| Gender | ||||||||
| Male | (Ref) | (Ref) | (Ref) | (Ref) | (Ref) | (Ref) | (Ref) | (Ref) |
| Female | −0.08* | 0.03 | 0.16*** | 0.21*** | −0.07*** | −0.27*** | −0.13* | −0.11 * |
|
| ||||||||
| Partner | ||||||||
| No | (Ref) | (Ref) | (Ref) | (Ref) | (Ref) | (Ref) | (Ref) | (Ref) |
| Yes | 0.24*** | 0.23*** | 0.15*** | 0.19*** | −0.05*** | −0.25*** | −0.27* | −0.24* |
|
| ||||||||
| Education | ||||||||
| No education | (Ref) | -------------------- | (Ref) | (Ref) | (Ref) | ---------- | (Ref) | (Ref) |
| Some primary | 0.04 | 0.23*** | 0.22** | 0.12*** | 0.21*** | 0.20*** | ||
| Primary | 0.19** | 0.41*** | 0.38*** | 0.32*** | 0.44*** | 0.39*** | ||
| Some secondary | 0.60*** | 0.78*** | 0.75*** | 0.46*** | 0.60*** | 0.57*** | ||
| Secondary | 0.93*** | 1.14*** | 1.08*** | 0.75*** | 0.87*** | 0.82*** | ||
| Higher | 1.66*** | 1.81*** | 1.70*** | 1.38*** | 1.45*** | 1.41*** | ||
|
| ||||||||
| District | ||||||||
| Nairobi | (Ref) | ------------------- | ------------------- | (Ref) | -- | -- | ||
| Central | −0.61*** | −0.49*** | (Ref) | ---------- | ---------- | (Ref) | ||
| Coast | −0.68*** | −0.47*** | −0.57*** | −0.41*** | ||||
| Eastern | −0.53** | −0.36*** | −0.32*** | −0.25*** | ||||
| Nyanza | −0.67*** | −0.66*** | −0.56*** | −0.48*** | ||||
| Rift Valley | −0.57*** | −0.50*** | −0.41*** | −0.33*** | ||||
| Western | −0.70*** | −0.64*** | −0.54*** | −0.48*** | ||||
p<0.05,
p<0.01,
p<0.001, ref = reference group
Unadjusted analyses between wealth index and each covariate of interest Adjusted analyses entered in blocks based on theoretical relationships – block 1: demographics, block 2: socioeconomic, block 3: region
In unadjusted models for the rural region, there was a significant increase in the wealth index for all 3 years (1998, 2003, 2008/2009) comparative to the reference year 1993 (β=0.09, β=0.19, and β=0.32, respectively). Increasing age of head of household was significantly associated with decreased wealth (β= −0.002, p<0.01). Household where a woman was the head of household (β= −0.07, p<0.001) and those with a partner (β= −0.05, p<0.001) were associated with less wealth. Similar to the urban regions increasing education was associated with greater wealth when compared to no education for those with some primary (β=0.32, p<0.001), complete primary (β=0.32, p<0.001), incomplete secondary (β=0.46, p<0.001), complete secondary (β=0.75, p<0.001) and higher education (β=1.38, p<0.001). The Central region had the highest levels of wealth comparative to all other regions.
In the final model (model 3) for the urban region, after adjusting for demographics, education and district, there was no statistical difference in wealth across the four years. Moreover, results suggest that there was a decrease in wealth in 1998 and 2003 prior to an increase in 2008, though none of these coefficients were significant. Increased wealth was associated with a head of household that was older (β=0.01, p<0.001), female (β=0.21, p<0.001) or had a partner (β=0.19, p<0.001), and those with increasing levels of education (incomplete primary (β=0.22, p<0.01), complete primary (β=0.38, p<0.001), incomplete secondary (β=0.46, p<0.001), complete secondary (β=0.75, p<0.001) or higher education (β=1.38, p<0.001)). Nairobi had the highest levels of wealth.
For the rural region, the final adjusted model (model 3) suggests that the wealth index increased over time, and was significantly higher for 2003 (β=0.08, p<0.01) and 2008/2009 (β=0.18, p<0.001) when compared to 1993. Similar to the urban region, increased wealth was associated with a head of household that was older (β=0.01, p<0.05), and those with increasing levels of education (incomplete primary (β=0.20, p<0.001), complete primary (β=0.39, p<0.001), incomplete secondary (β=0.57, p<0.001), complete secondary (β=0.82, p<0.001) and higher education (β=1.41, p<0.001)). Unlike for the urban region, decreased wealth was associated with a head of the household that was a female (β= −0.11, p<0.05) or had a partner (β= −0.24, p<0.05). The Central region had the highest levels of wealth.
DISCUSSION
By creating a harmonized wealth index measure, this study shows that overall wealth increased in Kenya between 1993 and 2008; however, when stratified by rural vs. urban regions, the wealth index showed no significant increase in urban areas, and a significant increase in rural areas specifically between 2003 and 2008. As may be expected, the strongest predictor of wealth was education, with more than a standard deviation difference over those with no education for those with secondary or higher levels of education in both rural and urban areas. Wealth consistently increased with increasing levels of education, however the change in wealth index for secondary and higher levels of education was higher for those in urban areas than those in rural areas. The association of gender of the head of household, and whether the head of household had a partner also differed between rural and urban areas with household heads who were women and those that had a partner having more wealth in urban areas, but less wealth in rural areas. Increased age was associated with higher wealth, but the degree of association was low. Finally, region was a significant factor in wealth with Nairobi (for urban households) and the Central region (for rural households) having significantly higher wealth. The Nyanza and Western regions had the lowest wealth for both rural and urban households.
These results have implications for addressing the health disparities resulting from socioeconomic differences in low- and middle-income countries such as Kenya. Given the immense literature supporting the relationship between health and wealth (1–6), addressing economic opportunities when seeking to improve health in communities is one way to promote health. Good health promotes the capacity to obtain education, as well as the capacity to work, dramatically impacting income at the household level. (3) Structural interventions promote health by altering the structural contexts that impact the health of the population. (31) Upgrading healthcare institutions, integrating disease specific treatment into other essential services, community mobilization, or rooting interventions in laws and policies can all have multi-level influence and be highly sustainable. (3, 31) Results of this study can help guide development of structural interventions in Kenya, and other low- and middle- income countries facing similar predictors of wealth by focusing efforts on areas that may have the most impact on the codependent relationship between increased health and increased wealth. For example, results showed that changes in wealth and predictors of higher wealth differed by urban and rural residence. Higher wealth existed in Nairobi and the rural region closes to it (Central region), while wealth was lowest in the rural regions furthest from the capital (Nyanza and Western regions). This suggests a need to target rural communities far from city centers differently as the structural determinants of wealth may differ. For example, an intervention focused on improving health through improved food security in Rwanda focused on rural districts. (32) Initiatives such as capital investment in agricultural inputs, assistance in small livestock projects, and provision of nutrition education, were all targeted for the rural communities. (32)
Results of this study also show the utility of a harmonized wealth index on guiding health policy recommendations, especially in the rural area. Geographic inequities and the impact on health have been noted globally, with more than 70% of those living in extreme poverty located in rural areas. (IFAD, WHO, 33,34) Use of a wealth index to understand differences between rural and urban areas can offer insight on factors that are determinants of gaps and inform interventions to address them. For example, this study found an incremental benefit of education to the wealth of a household differed by rural/urban residence. While the difference between no education and either some primary or primary was similar, the amount of benefit shown by an increase in wealth difference for higher levels of education was rather significant (difference of 0.3 standard deviations). Higher education predicted 1.70 standard deviations increase in wealth in urban areas, and 1.40 in rural areas compared to those with no education. Additionally, a secondary education predicted a full standard deviation higher difference in wealth in urban areas. While increased education is linked to an increase in wealth across residence type, these results may suggest a need to tailor interventions for the type of education most appropriate for economic opportunities available. For instance, providing secondary education along with technical skills appropriate for the area may be important in rural areas. One way to improve the health of rural communities while increasing education may be promotion of training in health professions. Comprehensive community programs that aim to increase community engagement with health, while at the same time building the capacity to meet the healthcare needs to the community could result in increased education, increased wealth, and improved health outcomes. Investment in healthcare can have a multiplier effect, but policies to generate employment opportunities that do not take barriers such as access to education, gender equality, and access to preventative health services into account can lead to larger disparities. (4)
Finally, these results illustrate the importance of considering multiple factors influencing differences in wealth when implementing health policy. For example, it is important to note the gender differences in wealth changes by rural/urban residence. Wealth for female head-of-households was higher than males in urban areas, but lower in rural areas. This may indicate a difference in the opportunities available to women in urban areas, and suggests interventions are needed specifically to increase opportunities for economic advancement for women in rural areas. Micro-credit programs are one example of an opportunity that aims to improve women’s economic status and increase autonomy by removing economic dependency. (31) A four-year project in Nairobi to reduce adolescent’ vulnerability to poor reproductive health outcomes through improving livelihood options using microfinance found that after the program participants had significantly higher incomes, more assets, and lower risk reproductive practices. (35) The wealth index can be used to compare across populations target groups that may benefit most from creation of a micro-credit or micro-financing opportunity. Since the index can be compared across countries, regions, and communities, non-governmental organizations can use it to inform spending and locating new programs.
While this study used a population based sample and a large sample size there were some limitations. First, to harmonize the wealth index newer questions used to calculate the index had to be removed. These newer questions were generally focused on better measuring wealth in rural areas, so would not be expected to change the difference seen in wealth overtime, but may influence comparisons between rural and urban areas. Second, the data was collected as four cross-sectional panels, and therefore, cannot be used as evidence of causation between variables. A number of factors over time may have influenced the predictors of wealth and how they changed. Finally, additional factors that were not measured as part of the DHS survey likely influence and predict wealth, and should not be ignored when data on additional factors exists.
CONCLUSION
In conclusion, this study shows that overall wealth increased in Kenya between 1993 and 2008; however, when stratified by rural vs. urban regions, the wealth index showed no significant increase in urban areas, and a significant increase in rural areas specifically between 2003 and 2008. Differences were seen in predictors of wealth by urban/rural residence, educational level, and gender of the head-of-household, and these should be taken into account when planning structural interventions, guiding health policy changes, and targeting priority populations that can impact wealth and health in low- and middle-income countries such as Kenya.
Acknowledgments
FUNDING: This study was supported by Grant K24DK093699 from The National Institute of Diabetes and Digestive and Kidney Disease (PI: Leonard Egede).
This article represents the views of the authors and not those of NIH.
LIST OF ABBREVIATIONS
- ANOVA
Analysis of Variance
- DHS
Demographic Health Survey
- HWI
Harmonized Wealth Index
- KDHS
Kenya Demographic Health Survey
Footnotes
DECLARATIONS
ETHICS APPROVAL AND CONSENT TO PARTICIPATE: Not applicable as ethics approval was not needed to analyze data collected by the Demographic Health Survey (DHS).
CONSENT FOR PUBLICATION: Not applicable
AVAILABILITY OF DATA AND MATERIALS: Primary data is collected as part of the Demographic Health Survey (DHS) and the files used for this analysis are publicly available from the DHS.
COMPETING INTERESTS: The authors report no potential conflicts of interest relevant to this article.
AUTHOR’S CONTRIBUTIONS: LEE obtained funding for the study. RJW and LEE acquired the data. DV, RJW, CT and LEE designed the study, analyzed and interpreted the data, drafted the article and critically revised the manuscript for important intellectual content. All authors approved the final manuscript.
AUTHORS INFORMATION: Not applicable
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Contributor Information
Leonard E. Egede, Department of Medicine, Division of General Internal Medicine, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226 USA. Center for Patient Care and Outcomes Research (PCOR), Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226 USA.
Delia Voronca, Emmes, Vaccine and Infectious Diseases, 401 North Washington Street, Suite 700, Rockville, Maryland USA 20850-0401.
Rebekah J. Walker, Department of Medicine, Division of General Internal Medicine, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226 USA. Center for Patient Care and Outcomes Research (PCOR), Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226 USA.
Craig Thomas, United States Navy, Naval Hospital Pensacola, 6000 West Hwy 98, Pensacola, FL 32512.
References
- 1.Wagstaff A. Poverty and health sector inequalities. Bulletin of the World Health Organization. 2002;80:97–105. [PMC free article] [PubMed] [Google Scholar]
- 2.Ensor, et al. The impact of economic recession on maternal and infant mortality: lessons from history. BMC Public Health. 2010;10:727. doi: 10.1186/1471-2458-10-727. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Spence M, Lewis M. Health and Growth: Commission on Growth and Development. World Bank; 2009. [Google Scholar]
- 4.WHO. Working for health and growth: investing in the health workforce. Report of the High-Level Commission on Health Employment and Economic Growth. 2016
- 5.Bhargava A, Jamison DT, Lau L, Murray CJL. Modeling the effects of health on economic growth. GPE Discussion Paper Series: No. 33. doi: 10.1016/s0167-6296(01)00073-x. [DOI] [PubMed] [Google Scholar]
- 6.Gwatkin DR, Rutstein S, Johnson K, Suliman E, Wagstaff A, et al. Socioeconomic differences in health, nutrition, and population within developing countries: an overview. Washington, DC: The World Bank; 2007. [PubMed] [Google Scholar]
- 7.Houweling TA, Kunst AE. Socio-economic inequalities in childhood mortality in low- and middle-income countries: a review of the international evidence. British Medical Bulletin. 2010;93:7–26. doi: 10.1093/bmb/ldp048. [DOI] [PubMed] [Google Scholar]
- 8.Kenya National Bureau of Statistics (KNBS) and ICF Macro. Kenya Demographic and Health Survey 2008–09. Calverton, Maryland: KNBS and ICF Macro; 2010. [Google Scholar]
- 9.Kenya, Country Cooperation Strategy at a Glance. World Health Organization; May, 2014. WHO/CCU/14.03/Kenya. [Google Scholar]
- 10.National Coordinating Agency for Population and Development (NCAPD) [Kenya], Ministry of Medical Services (MOMS) [Kenya], Ministry of Public Health and Sanitation (MOPHS) [Kenya], Kenya National Bureau of Statistics (KNBS) [Kenya], ICF Macro. Kenya Service Provision Assessment Survey 2010. Nairobi, Kenya: National Coordinating Agency for Population and Development, Ministry of Medical Services, Ministry of Public Health and Sanitation, Kenya National Bureau of Statistics, and ICF Macro; 2011. [Google Scholar]
- 11.Moisi JC, Gatatakaa H, Noor AM, William TN, Bauni E, Tsofa B, Levine OS, Scott JA. Geographic Access to care is not a determinant of Child mortality in a rural Kenyan setting with high health facility density. BMC Public Health. 2010 Mar 12;10:142. doi: 10.1181/1471-2485-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Brockerhoff M, Hewett P. Inequality of child mortality among ethnic groups in sub-Saharan Africa. Bulletin of the World Health Organization. 2000;78(1):30–41. [PMC free article] [PubMed] [Google Scholar]
- 13.Noor AM, Omumbo JA, Amin AA, Zurovac D, Snow RW. Wealth, mother’s education and physical access as determinants of retail sector net use in rural Kenya. Malar J. 2006;5(5):5. doi: 10.1186/1475-2875-5-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Noor AM, Zurovac D, Hay SI, Ochola SA, Snow RW. Defining equity in physical access to clinical services using geographical information systems as part of malaria planning and monitoring in Kenya. Tropical medicine & international health : TM & IH. 2003;8(10):917–926. doi: 10.1046/j.1365-3156.2003.01112.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Feikin DR, Olack B, Bigogo GM, Audi A, Cosmas L, et al. The Burden of Common Infectious Disease Syndromes at the Clinic and Household Level from Population-Based Surveillance in Rural and Urban Kenya. PLoS ONE. 2011;6(1):e16085. doi: 10.1371/journal.pone.0016085. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Gaziano TA. Cardiovascular disease in the developing world and its cost-effective management. Circulation. 2005;112:3547–53. doi: 10.1161/CIRCULATIONAHA.105.591792. [DOI] [PubMed] [Google Scholar]
- 17.Lozano R, Naghavi M, Foreman K, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380:2095–128. doi: 10.1016/S0140-6736(12)61728-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Chuma J, Okungu V, Molyneux C. Barriers to prompt and effective malaria treatment among the poorest population in Kenya. Malaria Journal. 2010;9:144. doi: 10.1186/1475-2875-9-144. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Egondi T, Oyolola M, Mutua MK, Elung’ata P. Determinants of immunization inequality among urban poor children: evidence from Nairobi’s informal settlements. International Journal for Equity in Health. 2015;14:24. doi: 10.1186/s12939-015-0154-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Van Malderen C, Ogali I, Khasakhala A, et al. Decomposing Kenyan socio-economic inequalities in skilled birth attendance and measles immunization. International Journal for Equity in Health. 2013;12:3. doi: 10.1186/1475-9276-12-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.DHS Final Reports; Kenya DHS, 1993. National Council for Population and Development, Central Bureau of Statistics, Office of the Vice President and Ministry of Planning and National Development, Nairobi, Kenya and Macro International Inc; Calverton, Maryland, USA: May, 1994. PID FR54. [Google Scholar]
- 22.DHS Final Reports; Kenya DHS, 1998. National Council for Population and Development, Central Bureau of Statistics, Office of the Vice President and Ministry of Planning and National Development, Nairobi, Kenya and Macro International Inc; Calverton, Maryland, USA: Apr, 1999. PID FR102. [Google Scholar]
- 23.DHS Final Reports; Kenya DHS, 2003. Central Bureau of Statistics, Ministry of Health, Kenya Medical Research Institute, National Council for Population and Development, Centers for Disease Control and Prevention, Nairobi, Kenya and ORC Macro; Calverton, Maryland, USA: Jul, 2004. PID FR151. [Google Scholar]
- 24.DHS Final Reports; Kenya DHS, 2008–09. Kenya National Bureau of Statistics, Nairobi, Kenya and MEASURE DHS, ICF Macro; Calverton, Maryland, USA: Jun, 2010. PID FR229. [Google Scholar]
- 25.DHS overview. http://dhsprogram.com/What-We-Do/Survey-Types/DHS.cfm.
- 26.DHS working papers: Chalasani, Satvika. The Changing Relationship between Household Wealth and Child Survival in India. Calverton, Maryland, USA: ICF Macro; 2010. DHS Working Papers No. 69. [Google Scholar]
- 27.DHS data collection. http://dhsprogram.com/data/data-collection.cfm.
- 28.DHS wealth index construction. http://dhsprogram.com/topics/wealth-index/Wealth-Index-Construction.cfm.
- 29.DHS Methodological Reports; Staveteig, Sarah and Lindsay Mallick. Intertemporal Comparisons of Wealth with DHS Data: A Harmonized Asset Index Approach. Rockville, Maryland, USA: ICF International; 2014. DHS Methodological Reports No. 15. [Google Scholar]
- 30.DHS working papers: Rutstein, Shea O. The DHS wealth index: Approaches for rural and urban areas. Calverton, Maryland, USA: Macro International; 2008. DHS Working Papers No. 60. Available at http://dhsprogram.com/pubs/pdf/WP60/WP60.pdf. [Google Scholar]
- 31.Blankenship KM, Friedman SR, Dworkin S, Mantell JE. Structural interventions: Concepts, Challenges and Opportunities for Research. Journal of Urban Health. 2006;83(1):59–72. doi: 10.1007/s11524-005-9007-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Nsabuwera V, Hedt-Gauthier B, Khogali M, Edginton M, Hinderaker SG, Nisingizwe MP, Tihabyona JD, Sikubwabo B, Sembagare S, Habinshuti A, Drobac P. Making progress towards food security: evidence form an intervention in three rural districts of Rwanda. Public Halth Nutr. 2015;6:1–9. doi: 10.1017/S1368980015002207. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.IFAD. Rural poverty report 2011 – new realities, new challenges: new opportunities for tomorrow’s generation. Rome International Fund for Agricultural Development; 2010. [Google Scholar]
- 34.Rural poverty and health systems in the WHO European Region. Copenhagen: WHO Regional Office for Europe; 2010. [Google Scholar]
- 35.Eruklar AS, Chong E. Evaluation of a savings and micro-credit program for vulnerable young women in Nairobi. The Population Council; 2005. Available at http://www.populationcouncil.us/pdfs/TRY_Evaluation.pdf. [Google Scholar]