Socioeconomic Inequity in the Screening and Treatment of Hypertension in Kenya: Evidence From a National Survey

Why is there a gap in self-rated health among people with hypertension in Zambia? A decomposition of determinants and rural‒urban differences

BACKGROUND

Hypertension affects over one billion people globally and is one of the leading causes of premature death. Low- and middle-income countries, especially the sub-Saharan Africa region, bear a disproportionately higher share of hypertension globally. Recent evidence shows a steady shift in the burden of hypertension from more affluent and urban populations towards poorer and rural communities. Our study examined inequalities in self-rated health (SRH) among people with hypertension and whether there is a rural‒urban gap in the health of these patients. We then quantified factors driving the health gap. We also examined how much HIV accounts for differences in self-rated health among hypertension patients due to the relationship between HIV, hypertension and health in sub-Saharan Africa.

METHODS

We utilized the Zambia Household Health Expenditure and Utilization Survey for data on SRH and other demographic and socioeconomic controls. District HIV prevalence information was from the Zambia Population-Based HIV Impact Assessment (ZAMPHIA) survey. We applied the Linear Probability Model to assess the association between self-rated health and independent variables as a preliminary step. We then used the Blinder-Oaxaca decomposition to identify self-rated health inequality between urban and rural patients and determine determinants of the health gap between the two groups.

RESULTS

Advanced age, lower education and low district HIV prevalence were significantly associated with poor health rating among hypertension patients. The decomposition analysis indicated that 45.5% of urban patients and 36.9% of rural patients reported good self-rated health, representing a statistically significant health gap of 8.6%. Most of the identified health gap can be attributed to endowment effects, with education (73.6%), district HIV prevalence (30.8%) and household expenditure (4.8%) being the most important determinants that explain the health gap.

CONCLUSIONS

Urban hypertension patients have better SRH than rural patients in Zambia. Education, district HIV prevalence and household expenditure were the most important determinants of the health gap between rural and urban hypertension patients. Policies aimed at promoting educational interventions, improving access to financial resources and strengthening hypertension health services, especially in rural areas, can significantly improve the health of rural patients, and potentially reduce health inequalities between the two regions.

Examining the responsiveness of the National Health Insurance Fund to people living with hypertension and diabetes in Kenya: a qualitative study

OBJECTIVES

To assess the responsiveness of the National Health Insurance Fund (NHIF) Supa Cover benefit package to the needs of individuals with diabetes and hypertension in Kenya.

DESIGN, SETTING AND PARTICIPANTS

We carried out a qualitative study and collected data using key informant interviews (n=39) and focus group discussions (n=4) in two purposively selected counties in Western Kenya. Study participants were drawn from NHIF officials, county government officials, health facility managers, healthcare workers and individuals with hypertension and diabetes who were enrolled in NHIF. We analysed data using a thematic approach.

RESULTS

Study participants reported that the NHIF Supa Cover benefit package expanded access to services for people living with hypertension and diabetes. However, the NHIF members and healthcare workers had inadequate awareness of the NHIF service entitlements. The NHIF benefit package inadequately covered the range of services needed by people living with hypertension and diabetes and the benefits package did not prioritise preventive and promotive services. Sometimes patients were discriminated against by healthcare providers who preferred cash-paying patients, and some NHIF-empanelled health facilities had inadequate structural inputs essential for quality of care. Study participants felt that the NHIF premium for the general scheme was unaffordable, and NHIF members faced additional out-of-pocket costs because of additional payments for services not available or covered.

CONCLUSION

Whereas NHIF has reduced financial barriers for hypertension and diabetes patients, to enhance its responsiveness to patient needs, NHIF should implement mechanisms to increase benefit package awareness among members and providers. In addition, preventive and promotive services should be included in NHIF's benefits package and mechanisms to monitor and hold contracted providers accountable should be strengthened.

Why is there a gap in self-rated health among people with hypertension? A decomposition of determinants and rural-urban differences

Background

Hypertension affects over one billion people globally and is one of the leading causes of premature death. The low- and middle-income countries, especially the sub-Saharan Africa region, bear a disproportionately higher share of hypertension globally. Recent evidence shows a steady shift in the burden of hypertension from the more affluent and urban population towards the poorer and rural communities. Our study examined inequalities in self-rated health among people with hypertension and whether there is a rural-urban gap in the health of these patients. We then quantified factors driving the health gap. We also examined how much HIV accounts for differences in self-rated health among hypertension patients due to the relationship between HIV, hypertension and health in sub-Saharan Africa.

Methods

We utilized the Zambia Household Health Expenditure and Utilization Survey for the data on SRH and other demographic and socioeconomic controls. District HIV prevalence information was from a previous study. The linear probability model provided a preliminary assessment of the association between self-rated health and independent variables. We then used the Blinder-Oaxaca decomposition to identify self-rated health inequality between urban and rural patients and determine determinants of the health gap between the two groups.

Results

Advanced age, lower education and low district HIV prevalence were significantly associated with poor health rating among hypertension patients. The decomposition analysis indicated that 45.5% of urban patients and 36.9% of rural patients reported good self-rated health, representing a statistically significant health gap of 8.6%. Most of the identified health gap can be attributed to endowment effects, with education (62%), district HIV prevalence (26%) and household expenditure (12%) being the most important determinants that explain the health gap.

Conclusions

Urban hypertension patients have better SRH than rural patients in Zambia. Educational interventions, financial protection schemes and strengthening hypertension health services in rural areas can significantly reduce the health gap between the two regions.

Evaluating the effectiveness of the National Health Insurance Fund in providing financial protection to households with hypertension and diabetes patients in Kenya

BACKGROUND

Non-communicable diseases (NCDs) can impose a substantial financial burden to households in the absence of an effective financial risk protection mechanism. The national health insurance fund (NHIF) has included NCD services in its national scheme. We evaluated the effectiveness of NHIF in providing financial risk protection to households with persons living with hypertension and/or diabetes in Kenya.

METHODS

We carried out a prospective cohort study, following 888 households with at least one individual living with hypertension and/or diabetes for 12 months. The exposure arm comprised households that are enrolled in the NHIF national scheme, while the control arm comprised households that were not enrolled in the NHIF. Study participants were drawn from two counties in Kenya. We used the incidence of catastrophic health expenditure (CHE) as the outcome of interest. We used coarsened exact matching and a conditional logistic regression model to analyse the odds of CHE among households enrolled in the NHIF compared with unenrolled households. Socioeconomic inequality in CHE was examined using concentration curves and indices.

RESULTS

We found strong evidence that NHIF-enrolled households spent a lower share (12.4%) of their household budget on healthcare compared with unenrolled households (23.2%) (p = 0.004). While households that were enrolled in NHIF were less likely to incur CHE, we did not find strong evidence that they are better protected from CHE compared with households without NHIF (OR = 0.67; p = 0.47). The concentration index (CI) for CHE showed a pro-poor distribution (CI: -0.190, p < 0.001). Almost half (46.9%) of households reported active NHIF enrolment at baseline but this reduced to 10.9% after one year, indicating an NHIF attrition rate of 76.7%. The depth of NHIF cover (i.e., the share of out-of-pocket healthcare costs paid by NHIF) among households with active NHIF was 29.6%.

CONCLUSION

We did not find strong evidence that the NHIF national scheme is effective in providing financial risk protection to households with individuals living with hypertension and/diabetes in Kenya. This could partly be explained by the low depth of cover of the NHIF national scheme, and the high attrition rate. To enhance NHIF effectiveness, there is a need to revise the NHIF benefit package to include essential hypertension and/diabetes services, review existing provider payment mechanisms to explicitly reimburse these services, and extend the existing insurance subsidy programme to include individuals in the informal labour market.

Association between socio-economic status and non-communicable disease risk in young adults from Kenya, South Africa, and the United Kingdom

There is a pressing need for global health preventions to curb the escalating burden of non-communicable diseases (NCDs). Utilising multi-country study designs can improve our understanding of how socio-economic context shapes the aetiology of NCDs, and this has great potential to advance global health interventions. We examined the association between socio-economic status (SES) and NCD risk, and the potential confounding effects of smoking and alcohol intake in young adults (18-35-year-olds) from Kenya, South Africa (SA), and the United Kingdom (UK). Our study was a cross-sectional online survey that included 3000 respondents (n = 1000 per country, 50% women) conducted in April 2022. We utilised information on twelve NCDs to classify respondents as having "no condition", "one condition", and "multimorbidity" (having two or more conditions). A total household asset score was calculated and used as a proxy of SES, and subsequently categorised into quintiles (Q1-Q5; lowest-highest). Ordered logistic regression was used to test the associations between NCD risk and exposure variables. In the UK sample, we found that those in the second lowest SES quintile (Q2) had lower odds of developing NCDs than their lowest SES counterparts (Q1). In contrast, South African and Kenyan youth with a SES score between middle and highest quintiles (Q3-Q5) were more likely to develop NCDs than the lowest SES quintile group. In all countries, smoking and/or alcohol intake were associated with higher odds of developing NCDs, and showed some confounding effects on the SES-NCD relationships. Specifically, in Kenya, the risk of developing NCD was more than two times higher in those in the middle (Q3) SES group (OR 2.493; 95% CI 1.519-4.091; p < 0.001) compared to their lowest (Q1) SES counterparts. After adjusting for smoking and alcohol, the ORs of middle (Q3) SES group changed from 2.493 to 2.241 (1.360-3.721; p = 0.002). Overall, we found that the strength and direction of SES-NCD associations differed within and between countries. This study highlights how different SES contexts shape the risk of NCDs among young adults residing in countries at different levels of economic development.

Decomposing socioeconomic inequality in blood pressure and blood glucose testing: evidence from four districts in Kerala, India

Background

Non-Communicable Diseases (NCDs) constitute a significant danger to the nation’s public health system, both in terms of morbidity and mortality, as well as the financial burden they inflict. Kerala is undergoing an epidemiologic transition, which has significantly impacted the state’s morbidity and mortality figures. For decades, the state has been putting in place myriad programs to reduce the burden of NCDs across population groups. Socioeconomic inequalities in NCD testing have been documented in India, although they are understudied in Kerala. The study aimed to estimate and characterize districtwise socioeconomic inequality in Blood Pressure (BP) and Blood Glucose (BG) testing.

Methods

A cross-sectional household survey was conducted between July–October 2019 in Kasaragod, Alappuzha, Kollam and Thiruvananthapuram districts of Kerala, India. A total of 6383 participants aged 30 years and above were interviewed using multistage random sampling. Descriptive statistics were derived district-wise. We computed ratios, differences, equiplots, and Erreygers concentration indices for each district to measure socioeconomic inequality in BP and BG testing. Erreygers decomposition techniques were used to estimate the relative contribution of covariates to socioeconomic inequality.

Results

There was a significant concentration of BP and BG testing favouring wealthier quintiles in Alappuzha, Kollam, and Thiruvananthapuram districts. The inequality in BP and BG testing was highest in Thiruvananthapuram (0.087 and 0.110), followed by Kollam (0.077 and 0.090), Alappuzha (0.083 and 0.073) and Kasaragod (0.026 and 0.056). Decomposition analysis revealed that wealth quintile and education contributed substantially to socioeconomic inequality in BP and BG testing in all four districts. It was also found that family history of NCDs significantly contributed to observed socioeconomic inequality in BP testing (29, 11, 16, and 27% in Kasaragod, Alappuzha, Kollam, and Thiruvananthapuram, respectively). Similarly, in BG testing, family history of NCDs substantially contributed to observed socioeconomic inequality, explaining 16–17% in Kasaragod, Alappuzha, Kollam, and Thiruvananthapuram respectively of the total inequality.

Conclusion

While the magnitude of socioeconomic inequality in NCD risk factor testing did not appear to be very high in four Kerala districts, although levels were statistically significant in three of them. Greater exploration is needed on how education and caste contribute to these inequalities and their relationship to NCD risk factors such as family history. From such analyses, we may be able to identify entry points to mitigate inequalities in testing access, as well as burden.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12939-022-01737-x.