Summary: Background: Hypertension in low‐ and middle‐income countries, including Kenya, is of economic importance due to its increasing prevalence and its potential to present an economic burden to households. In this study, we examined the patient costs associated with obtaining care for hypertension in public health care facilities in Kenya.Methods: We conducted a cross‐sectional study among adult respondents above 18 years of age, with at least 6 months of treatment in two counties. A total of 212 patients seeking hypertension care at five public facilities were interviewed, and information on care seeking and the associated costs was obtained. We computed both annual direct and indirect costs borne by these patients.Results: Overall, the mean annual direct cost to patients was US$ 304.8 (95% CI, 235.7‐374.0). Medicines (mean annual cost, US$ 168.9; 95% CI, 132.5‐205.4), transport (mean annual cost, US$ 126.7; 95% CI, 77.6‐175.9), and user charges (mean annual cost, US$ 57.7; 95% CI, 43.7‐71.6) were the highest direct cost categories. Overall mean annual indirect cost was US$ 171.7 (95% CI, 152.8‐190.5). The incidence of catastrophic health care costs was 43.3% (95% CI, 36.8‐50.2) and increased to 59.0% (95% CI, 52.2‐65.4) when transport costs were included.Conclusions: Hypertensive patients incur substantial direct and indirect costs. High rates of catastrophic costs illustrate the urgency of improving financial risk protection for these patients and strengthening primary care to ensure affordability of hypertension care.

Health facility-based data reported through routine health information systems form the primary data source for programmatic monitoring and evaluation in most developing countries. The adoption of District Health Information Software (DHIS2) has contributed to improved availability of routine health facility-based data in many low-income countries. An assessment of malaria indicators data reported by health facilities in Kenya during the first 5 years of implementation of DHIS2, from January 2011 to December 2015, was conducted. Data on 19 malaria indicators reported monthly by health facilities were extracted from the online Kenya DHIS2 database. Completeness of reporting was analysed for each of the 19 malaria indicators and expressed as the percentage of data values actually reported over the expected number; all health facilities were expected to report data for each indicator for all 12 months in a year. Malaria indicators data were analysed for 6235 public and 3143 private health facilities. Between 2011 and 2015, completeness of reporting in the public sector increased significantly for confirmed malaria cases across all age categories (26.5–41.9%, p < 0.0001, in children aged <5 years; 30.6–51.4%, p < 0.0001, in persons aged ≥5 years). Completeness of reporting of new antenatal care (ANC) clients increased from 53.7 to 70.5%, p < 0.0001). Completeness of reporting of intermittent preventive treatment in pregnancy (IPTp) decreased from 64.8 to 53.7%, p < 0.0001 for dose 1 and from 64.6 to 53.4%, p < 0.0001 for dose 2. Data on malaria tests performed and test results were not available in DHIS2 from 2011 to 2014. In 2015, sparse data on microscopy (11.5% for children aged <5 years; 11.8% for persons aged ≥5 years) and malaria rapid diagnostic tests (RDTs) (8.1% for all ages) were reported. In the private sector, completeness of reporting increased significantly for confirmed malaria cases across all age categories (16.7–23.1%, p < 0.0001, in children aged <5 years; 19.4–28.6%, p < 0.0001, in persons aged ≥5 years). Completeness of reporting also improved for new ANC clients (16.2–23.6%, p < 0.0001), and for IPTp doses 1 and 2 (16.6–20.2%, p < 0.0001 and 15.5–20.5%, p < 0.0001, respectively). In 2015, less than 3% of data values for malaria tests performed were reported in DHIS2 from the private sector. There have been sustained improvements in the completeness of data reported for most key malaria indicators since the adoption of DHIS2 in Kenya in 2011. However, major data gaps were identified for the malaria-test indicator and overall low reporting across all indicators from private health facilities. A package of proven DHIS2 implementation interventions and performance-based incentives should be considered to improve private-sector data reporting.

Through a number of healthcare reforms, Kenya has demonstrated its intention to extend financial risk protection and service coverage for poor and vulnerable groups. These reforms include the provision of free maternity services, user-fee removal in public primary health facilities and a health insurance subsidy programme (HISP) for the poor. However, the available evidence points to inequity and the likelihood that the poor will still be left behind with regards to financial risk protection and service coverage. This study examined the experiences of the poor with health financing reforms that target them. We conducted a qualitative cross-sectional study in two purposively selected counties in Kenya. We collected data through focus group discussions (n = 8) and in-depth interviews (n = 30) with people in the lowest wealth quintile residing in the health and demographic surveillance systems, and HISP beneficiaries. We analyzed the data using a framework approach focusing on four healthcare access dimensions; geographical accessibility, affordability, availability, and acceptability. Health financing reforms reduced financial barriers and improved access to health services for the poor in the study counties. However, various access barriers limited the extent to which they benefited from these reforms. Long distances, lack of public transport, poor condition of the roads and high transport costs especially in rural areas limited access to health facilities. Continued charging of user fees despite their abolition, delayed insurance reimbursements to health facilities that HISP beneficiaries were seeking care from, and informal fees exposed the poor to out of pocket payments. Stock-outs of medicine and other medical supplies, dysfunctional medical equipment, shortage of healthcare workers, and frequent strikes adversely affected the availability of health services. Acceptability of care was further limited by discrimination by healthcare workers and ineffective grievance redress mechanisms which led to a feeling of disempowerment among the poor. Pro-poor health financing reforms improved access to care for the poor to some extent. However, to enhance the effectiveness of pro-poor reforms and to ensure that the poor in Kenya benefit fully from them, there is a need to address barriers to healthcare seeking across all access dimensions.

Summary: Objective: To estimate the direct and indirect costs of diabetes mellitus care at five public health facilities in Kenya.Methods: We conducted a cross‐sectional study in two counties where diabetes patients aged 18 years and above were interviewed. Data on care‐seeking costs were obtained from 163 patients seeking diabetes care at five public facilities using the cost‐of‐illness approach. Medicines and user charges were classified as direct health care costs while expenses on transport, food, and accommodation were classified as direct non–health care costs. Productivity losses due to diabetes were classified as indirect costs. We computed annual direct and indirect costs borne by these patients.Results: More than half (57.7%) of sampled patients had hypertension comorbidity. Overall, the mean annual direct patient cost was KES 53 907 (95% CI, 43 625.4‐64 188.6) (US$ 528.5 [95% CI, 427.7‐629.3]). Medicines accounted for 52.4%, transport 22.6%, user charges 17.5%, and food 7.5% of total direct costs. Overall mean annual indirect cost was KES 23 174 (95% CI, 20 910‐25 438.8) (US$ 227.2 [95% CI, 205‐249.4]). Patients reporting hypertension comorbidity incurred higher costs compared with diabetes‐only patients. The incidence of catastrophic costs was 63.1% (95% CI, 55.7‐70.7) and increased to 75.4% (95% CI, 68.3‐82.1) when transport costs were included.Conclusion: There are substantial direct and indirect costs borne by diabetic patients in seeking care from public facilities in Kenya. High incidence of catastrophic costs suggests diabetes services are unaffordable to majority of diabetic patients and illustrate the urgent need to improve financial risk protection to ensure access to care.

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.

Background: Non-communicable diseases (NCDs) account for 50% of hospitalisations and 55% of inpatient deaths in Kenya. Hypertension is one of the major NCDs in Kenya. Equitable access and utilisation of screening and treatment interventions are critical for reducing the burden of hypertension. This study assessed horizontal equity (equal treatment for equal need) in the screening and treatment for hypertension. It also decomposed socioeconomic inequalities in care use in Kenya.Methods: Cross-sectional data from the 2015 NCDs risk factors STEPwise survey, covering 4,500 adults aged 18–69 years were analysed. Socioeconomic inequality was assessed using concentration curves and concentration indices (CI), and inequity by the horizontal inequity (HI) index. A positive (negative) CI or HI value suggests a pro-rich (pro-poor) inequality or inequity. Socioeconomic inequality in screening and treatment for hypertension was decomposed into contributions of need [age, sex, and body mass index (BMI)] and non-need (wealth status, education, exposure to media, employment, and area of residence) factors using a standard decomposition method.Results: The need for hypertension screening was higher among poorer than wealthier socioeconomic groups (CI = −0.077; p < 0.05). However, wealthier groups needed hypertension treatment more than poorer groups (CI = 0.293; p <0.001). Inequity in the use of hypertension screening (HI = 0.185; p < 0.001) and treatment (HI = 0.095; p < 0.001) were significantly pro-rich. Need factors such as sex and BMI were the largest contributors to inequalities in the use of screening services. By contrast, non-need factors like the area of residence, wealth, and employment status mainly contributed to inequalities in the utilisation of treatment services.Conclusion: Among other things, the use of hypertension screening and treatment services in Kenya should be according to need to realise the Sustainable Development Goals for NCDs. Specifically, efforts to attain equity in healthcare use for hypertension services should be multi-sectoral and focused on crucial inequity drivers such as regional disparities in care use, poverty and educational attainment. Also, concerted awareness campaigns are needed to increase the uptake of screening services for hypertension.