About the Author(s)

Mwaleso Kishindo symbol
Academic Model Providing Access to Healthcare, Eldoret, Kenya

Jemima Kamano symbol
Academic Model Providing Access to Healthcare, Eldoret, Kenya

School of Medicine, Moi University, Eldoret, Kenya

Division of Non-Communicable Disease, Ministry of Health, Nairobi, Kenya

Ann Mwangi symbol
Department of Mathematics, Physics, and Computing, Moi University, Eldoret, Kenya

Thomas Andale symbol
Academic Model Providing Access to Healthcare, Eldoret, Kenya

Moi Teaching and Referral Hospital, Eldoret, Kenya

Grace W. Mwaura Email symbol
Academic Model Providing Access to Healthcare, Eldoret, Kenya

Moi Teaching and Referral Hospital, Eldoret, Kenya

Obed Limo symbol
Academic Model Providing Access to Healthcare, Eldoret, Kenya

Kenneth Too symbol
Academic Model Providing Access to Healthcare, Eldoret, Kenya

Richard Mugo symbol
Academic Model Providing Access to Healthcare, Eldoret, Kenya

Ephantus Maree symbol
Division of Non-Communicable Disease, Ministry of Health, Nairobi, Kenya

Wilson Aruasa symbol
Moi Teaching and Referral Hospital, Eldoret, Kenya


Kishindo M, Kamano J, Mwangi A, et al. Are outpatient costs for hypertension and diabetes care affordable? Evidence from Western Kenya. Afr J Prm Health Care Fam Med. 2023;15(1), a3889. https://doi.org/10.4102/phcfm.v15i1.3889

Original Research

Are outpatient costs for hypertension and diabetes care affordable? Evidence from Western Kenya

Mwaleso Kishindo, Jemima Kamano, Ann Mwangi, Thomas Andale, Grace W. Mwaura, Obed Limo, Kenneth Too, Richard Mugo, Ephantus Maree, Wilson Aruasa

Received: 24 Oct. 2022; Accepted: 14 Aug. 2023; Published: 29 Sept. 2023

Copyright: © 2023. The Author(s). Licensee: AOSIS.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background: Diabetes and hypertension pose a significant socio-economic burden in developing countries such as Kenya, where financial risk-protection mechanisms remain inadequate. This proves to be a great barrier towards achieving universal health care in such settings unless mechanisms are put in place to ensure greater access and affordability to non-communicable disease (NCD) management services.

Aim: This article aims to examine outpatient management services costs for patients with diabetes and hypertension attending public primary healthcare facilities.

Setting: The study was conducted in Busia and Trans-Nzoia counties in Western Kenya in facilities supported by the PIC4C project, between August 2020 and December 2020.

Methods: This cross-sectional survey included 719 adult participants. Structured interviewer-administered questionnaires were used to collect information on healthcare-seeking behaviour and associated costs. The annual direct and indirect costs borne by patients were computed by disease type and level of healthcare facility visited.

Results: Patients with both diabetes and hypertension incurred higher annual costs (KES 13 149) compared to those with either diabetes (KES 8408) or hypertension (KES 7458). Patients attending dispensaries and other public healthcare facilities incurred less direct costs compared to those who visited private clinics. Furthermore, a higher proportionate catastrophic healthcare expenditure of 41.83% was noted among uninsured patients.

Conclusion: Despite this study being conducted in facilities that had an ongoing NCDs care project that increased access to subsidised medication, we still reported a substantially high cost of managing diabetes and hypertension among patients attending primary healthcare facilities in Western Kenya, with a greater burden among those with comorbidities.

Contribution: Evidenced by the results that there is enormous financial burden borne by patients with chronic diseases such as hypertension and diabetes; we recommend that universal healthcare coverage that offers comprehensive care for NCDs be urgently rolled out alongside strengthening of lower-level public healthcare systems.

Keywords: out-patient costs; non-communicable diseases; catastrophic healthcare expenditure; primary healthcare; comorbidity.


Non-communicable diseases (NCDs) are increasingly imposing a greater disease and economic burden globally.1 For instance, evidence from the 2019 Global Burden of Disease study indicated that nine out of the top 10 drivers of increasing disability-adjusted life years (DALYs) were NCDs.2 With the increasing costs associated with accessing healthcare3 especially in developing countries,4 NCDs would further burden developing countries and derail the move towards universal health coverage (UHC).

Universal health coverage is a primary target of the Sustainable Development Goal 3, which compels countries to streamline their health systems to ensure that everyone has access to good quality healthcare services that they need without experiencing any financial hardship as a result.5,6,7 Central to UHC is ensuring that all citizens are protected from financial ruin resulting from accessing healthcare.8 However, financial risk protection mechanisms remain hardly adopted in most developing countries. For example, only four countries in sub-Saharan Africa have health insurance coverage greater than 20%.9 Consequently, financing of healthcare has largely depended on out-of-pocket payments (OOP) and donors. For instance, 39.6% and 23.4% of Kenya’s total health expenditure in 2016 was composed OOP and donor funding, respectively.10

The higher dependence on OOP as a financing mechanism results in a higher incidence of catastrophic health expenditure (CHE) (defined as OOP health payments exceeding a certain threshold of household’s capacity to pay or non-subsistence spending)11,12 and impoverishments,13 especially among patients with NCD. Evidence from a study in Kenya indicated that nearly three in five patients with hypertension experienced CHE as a result of direct healthcare costs.14 In another study among patients with diabetes in Kenya, the incidence of CHE was 75.4%.15

Despite Kenya’s commitment to achieve UHC by 202216 and the prioritisation to deliver NCD services through primary health care (PHC) facilities (level 1–3) – dispensaries and health centres, treatment for NCDs has traditionally been available only in level 4–6 facilities – secondary (county) and tertiary (national) referral hospitals. As a result, very little effort has been put towards community prevention activities, early detection and treatment or improved continuity of care for NCDs; yet these elements are fundamental for successfully addressing the threat of NCDs. It remains unclear how much it costs patients with comorbidities to receive services from these facilities. Although some patient cost of illness studies exists in Kenya, they have focused on single diseases such as diabetes15 and hypertension14 separately, yet comorbidities such as hypertension and diabetes often exist. Furthermore, these studies have generally looked at aggregate costs of the diseases at secondary and tertiary facilities.

Against this backdrop, this study aims to examine the cost of outpatient services that patients with two NCDs, diabetes and hypertension, incur to access services at PHC facilities in two Western Kenya counties. Diabetes and hypertension are important modifiable risk factors for cardiovascular disease (CVD), which accounts for over 29% of global deaths.17 They also have shared pathways of complications and clinical approaches.18,19 This presents an opportunity for a unified model of care at the PHC level to alleviate a considerable cause of mortality and disability.20 The focus is on outpatient visits as they form the largest share of interaction between patients with NCDs and the health care system and because there is an emphasis on providing these services at PHC facilities that are often closer to patients than secondary facilities. This study aims to generate evidence essential for adequately understanding the cost of delivering these services at PHC facilities. It is our hope that these data will facilitate policy making efforts aimed at increasing access to care of these two conditions at the PHC level in line with the WHO Package of Essential Non-communicable Disease Interventions (WHO PEN) for Primary Care in low-resource settings.21

Research methods and design

Study design

This was a cross-sectional survey.


The study was nested within a pilot implementation program that sought to integrate promotive, curative care for diabetes, hypertension, breast and cervical cancer at the PHC level (Primary Health Integrated Care for Chronic Conditions, PIC4C) within the Academic Model providing Access to Healthcare (AMPATH) in western Kenya.22,23,24,25 The PIC4C program was piloted in Busia and Trans-Nzoia counties for their unique peculiarity of high disease burden,26 and a strong commitment offered by the two county governments towards strengthening health systems management of NCDs via increased county budget allocation27 The two counties are part of AMPATH catchment area having some health facilities offering chronic disease management program through specialised clinics for diabetes and hypertension management, 2 days a week.

Study population

We included patients with diabetes, hypertension and both hypertension and diabetes who had actively sought care within the last 6 months in the selected health facilities. All consenting adults who were 18 years and above were included in this study.

Sample size estimation

The targeted sample size was 768 (384 per county) based on a conservative assumption of population disease proportion of 50%, a confidence interval (CI) of 95% and a precision of 0.05.28 The formula used to estimate the required sample was:29

Sampling procedure

A stratified purposive sampling strategy was used to select a total of 22 health facilities in the two counties based on the levels, workload and spatial distribution: eight sub-county hospitals, four health centres, eight dispensaries and two private clinics.29

Patients were recruited on clinic days within the selected health facilities by research assistants through simple random sampling of the eligible patients. The interviews were conducted while the patients were awaiting their consultations. No inducement or incentive was offered for participation.

Data collection

Data were collected using a structured questionnaire in English or Kiswahili language where applicable using mobile tablets. Respondents were asked to recall and report on their health service utilisation and costs incurred for the different cost types while seeking outpatient care in the selected facilities (Table 1).

TABLE 1: Description of cost category estimates.
Data analysis

Costs for each cost component described in Table 1 were summed up and annuitised following their respective recall period. For instance, medicine costs were annuitised by multiplying the summed-up cost by 12 as the recall period was monthly. Further, costs were broadly categorised into either direct or indirect costs. Direct costs comprised outright OOP costs patient paid to access care such as the medicines, consultation and investigation costs (cumulatively here referred to as direct medical costs) and the transport cost (hereafter referred as direct non-medical cost) incurred to and from the health facility. On the other hand, indirect costs are related to the productivity losses associated with the patient’s foregone income because of seeking care and the associated caregiver costs (in cases where caregivers were engaged). Indirect costs were estimated by calculating the total number of hours a patient spent while seeking care. Productivity losses for the unemployed were estimated using the median income.30 A workday was defined to have 8 h while a month was assumed to have 22 working days. Caregiver costs were annuitised by adding up the costs paid by the patient to engage the caregivers.

Wealth quintiles were calculated using the principal component analysis.31 Income was estimated by asking patients about their income categories or brackets and then assigning the mean in each of the categories. The median income was assigned to patients who did not know or report their incomes. To examine the incidence of CHE, the total annual direct costs incurred by the patient was compared against the annual household income. Catastrophic health expenditure was defined as utilisation of more than 10% of total annual household income on direct medical costs.11,32,33 In this study, we restricted CHE analysis to patients who were uninsured by the national insurance scheme (NHIF). Those with private insurance were included as there may be a chance for co-payment in public facilities.

Two approaches were employed to examine the inequalities in the incidence of CHE across the different disease categories. Firstly, concentration curves11 for the CHE were constructed by specific disease condition and overall. A concentration curve is a plot of the cumulative percentage of a variable of interest (CHE) (y-axis) against the cumulative proportion of the population, ranked by wealth status, from the poorest to the richest (x-axis). The concentration curve is a 45-degree line (line of equality) when every individual, irrespective of their socio-economic status, receives the same value of the variable of interest, but a curve that lies below (above) the line of equality indicates that the variable of interest is pro-rich (pro-poor).11 Secondly, the Wagstaff concentration index was generated, which is mathematically defined as twice the area between the concentration curve and the line of equality. A zero value indicates equality, whereas a negative (positive) value indicates that the variable of interest is more concentrated among the poor (rich).11

Each questionnaire was automatically uploaded to a REDCap database where a data manager inspected to identify any issues or missing data and eliminated data errors. Data completeness was ascertained by examining data consistency and plausible ranges. Data were analysed using STATA 16.1.34 Frequency counts and percentages were generated to present the distribution of patient characteristics across selected demographic and socio-economic factors. Costs were calculated and presented as means and medians with their 95% CI and interquartile range (IQR). The costs were converted from Kenya Equivalent Shilling (KES) into 2020 United States Dollars ($) using the following exchange rate: $ 1.00 = KES 108.315.35

Ethical considerations

The study received ethics approval from the Moi Teaching and Referral Hospital or Moi University School of Medicine Institutional Research and Ethics Committee (IREC) (approval number 0002090). Furthermore, permission to conduct the study was also sought from National Commission for Science Technology and Innovation (NACOSTI) (approval number NACOSTI/P/18/74238/24329) and County health management teams in Busia and Trans-Nzoia Counties. In addition, health facility managers in each facility provided permission to conduct the study. Data privacy was ensured by password protecting the computer.


Descriptive analysis

A total of 719 patients were interviewed across the two counties. Of the 719 patients, 11.68% had only diabetes, 65.92% hypertension and 22.39% had both diabetes and hypertension. More than half of the patients were female (75.80%), were informally employed (54.16%), had no form of health insurance (65.23%) and had hypertension (65.92%). Additionally, most patients were between 41 and 60 years (44.37%) and above 60 years of age (47.57%). The mean age was 51.35 years for the diabetes patients, 56.79 years for hypertension and 61.71 years for those with both conditions. Slightly more than a third (33.10%) were subscribed to the National Hospital Insurance Fund (NHIF) – the statutory health insurer (Table 2). There were a total of 1274 outpatient visits with a mean of 1.77 and a median of two visits over a 4-month period.

TABLE 2: Patients’ characteristics.
Patient costs for seeking outpatient care

Overall, the mean annual direct costs (KES 7881 [95% CI: 6950–8812]) for patients with diabetes only were higher than the mean annual indirect costs (KES 683 [95% CI: 425–941]). Under direct costs, medication costs were the highest followed by transport costs (Table 3). In addition, the costs of hiring a paid caregiver were higher (mean annual cost of KES 4600 [95% CI: −4836 to 14 036]) relative to patient productivity losses for seeking care (mean annual cost of KES 508 [95% CI: 399–617]).

TABLE 3: Mean diabetes and hypertension annual outpatient costs in Busia and Trans-Nzoia Counties in 2020.

The mean annual outpatient cost for patients with hypertension only per patient was KES 624.00 (95% CI: 534–713) excluding medication. Relative to diabetes costs, the mean annual costs per patient were lower for hypertension. For example, whereas the mean annual medication costs for diabetes was KES 6130.00 (95% CI: 5321–6940), the mean annual costs for hypertension medication were KES 5344.00 (95% CI: 4770–5917). A similar pattern was seen for other cost categories such as direct non-medical costs and productivity losses (Table 3). This suggests that, in general, the mean annual diabetes costs per patient were higher compared to hypertension.

Diabetes and hypertension comorbidity

Notably, patients who had both diabetes and hypertension (22.39%) incurred higher direct medical costs compared to patients who had only diabetes or hypertension. For instance, whereas the mean annual costs for outpatient costs per diabetic and hypertensive patient were KES 851.00 (95% CI: 639–1063) and KES 624.00 (95% CI: 536–712), respectively; the mean annual cost for outpatient care for comorbid patients was KES 1169.00 (95% CI: 831–1507) (Table 3). Overall, medication costs were the highest cost driver for patients who had both diabetes and hypertension.

Costs by health facility type

At the facility level, both medical and non-medical direct costs for accessing care were highest for private clinics KES 14 235.00 ($131.00) and lowest for dispensaries KES 6390.00 ($59.00) (Table 4).

TABLE 4: Mean annual direct cost for accessing care stratified by facility type and disease.
Source of prescribed medicines

Patients could obtain drugs from the county government chemist, private chemists nearby or from a community revolving fund pharmacy (RFP) established in the facilities as a complementary drug supply system because of frequent stock outs of drugs in the main chemist and exorbitant cost in the private sector. The RFPs involved a tripartite agreement with a Memorandum of Understanding between the project that provided seed stocks of drugs, the facility that provided a pharmaceutical technologist and the community that conducted advocacy. A hub and spoke model was adopted with mini RFPs at lower-level facilities that would be expected to refill drugs from the main RFP when stocks dropped.36 Majority of patients (66%) obtained their prescribed medication from the RFP. Further, 21% of patients obtained medication from a county government chemist while only 6% got their medication from either a dispensary or health centre (Figure 1).

FIGURE 1: Sources of prescribed medicines.

Catastrophic health expenditure

About two in five of the uninsured patients with NCDs incurred CHE annually as a result of the direct costs (direct medical and direct non-medical costs) in outpatient facilities (Table 5). Notably, the incidence of CHE was higher among patients with diabetes and hypertension comorbidity (44.19% [95% CI: 33.90 – 55.00]) compared to diabetes only (35.00% [95% CI: 23.77 – 48.18]) and patients with hypertension only (42.46% [95% CI: 37.17 – 47.93]).

TABLE 5: Incidence of Catastrophic Health Expenditure by disease and overall.
Inequalities in the distribution of catastrophic health expenditure

Overall, the incidence of CHE was more concentrated in the higher wealth quintile (CIX = 0.112 [95% CI: −0.008 to 0.231], p-value = 0.069) (Table 5). The highest inequalities were among patients with diabetes and hypertension comorbidity (has the largest absolute concentration index) and its related concentration curve was furthest from the line of equality (Figure 2). Besides, the distribution of the incidence of CHE also varied by gender with women having greater inequalities than men (Figure 3).

FIGURE 2: Concentration curves for catastrophic health expenditure by non-communicable disease type and overall.

FIGURE 3: Concentration curves for catastrophic health expenditure by gender.


The rising burden of NCDs in developing countries and its resultant economic burden has increasingly attracted the attention of key global health actors and policymakers. Specifically, ensuring NCD healthcare services are affordable is critical in addressing the NCDs burden. This study set out to examine the affordability of outpatient costs two NCD conditions across different levels of health facilities. In general, we found that patients with diabetes and hypertension comorbidity incur higher OOP costs particularly in higher level health facilities and private clinics, but all categories of patients experiencing significant CHE. A study by Subramanian et al.37 established that screening, diagnosis and treatment costs for NCDs are significantly higher in private facilities relative to public facilities, and that these costs were unaffordable to Kenyans. This may be explained by the subsidies in the public sector through government payments. Despite the cost differences, quality studies do not support the claim that the private sector has better outcomes than the public sector, although it is usually more client orientated and performing better in drug availability.38,39

The direct medical costs across the conditions examined in this study were significantly higher compared to the direct non-medical costs. For example, the total mean annual direct medical cost was KES 7199.00 while the total mean annual direct non-medical cost was KES 1116.00. Of note, prescribed medicines costs were the key cost driver under direct medical costs. While this finding reflects what other cost of illness studies for NCDs have established from other African countries,40,41,42 this status of affairs is nonetheless worrisome for three reasons. Firstly, it has been established that unaffordability of medicines is the key limitation in the continuity of care for NCDs as patients tend to forego the needed treatment until they have access to funds, resulting in serious medical complications. Secondly, given that more than 20% of the patients in this study obtained their prescribed medication from a government health facility (where the prices of the medicines are subsidised), it is evident that more needs to be done by policy makers to increase budget allocation for healthcare and county managers to ensure lower cost of medication hence increase access and affordability of NCD medicines as medication costs still significantly contribute to financial hardship. For example, the total mean annual medication cost per patient in the overall sample was KES 6528.00 compared to the total mean annual outpatient cost per patient, which was KES 772.00. Further, given that a majority of the patients (66%) sourced their medicines from the RFP, it highlights the inaccessibility of medicines in government facilities because of costs or stock-outs and reinforces the utility of RFP in improving access to NCD medicines especially in rural areas.36 Finally, the high medication costs observed in this study is a concern given that previous studies have established the existence of a wealth gradient in the access to medicines for NCDs in developing countries. A study conducted in eight counties in Kenya, for example, found that poorest households, relative to wealthier households are more likely to have limited access to medication and had to pay higher costs overall to obtain medication for hypertension, diabetes and asthma.43 Moreover, in as much as the finding of this study suggested that most patients obtained their prescribed medication from a government health facility and the RFP, another study conducted in Kenya showed that NCDs medicines were more readily available in the private sector compared to public health facilities.44

Transport costs were also another cost driver in this study, highlighting the need to have NCD services closer to the patients. Indeed, transport costs were higher than outpatient costs for NCDs like diabetes and hypertension. Whereas the mean annual outpatient cost was KES 772.00, the mean annual transport cost per patient was KES 1116.00. Previous studies have established that transport cost increases the proportion of NCD patients incurring CHE in Kenya, and transport-related expenses are therefore a likely barrier in seeking healthcare for NCDs.14,15,45 Another study that evaluated whether health financing reforms that target the poor benefit them identified transport cost as one of the key barriers hindering the poor benefiting from these reforms.46 It is worthy to note that although the indirect costs either because of seeking healthcare or hiring caregivers because of the NCDs are lower than the direct costs in this study, they also contributed to the economic burden experienced by households. Elsewhere, a South African study has shown that productivity losses because of long waiting times at health facilities are likely to reduce the demand for NCD healthcare services.47

While hypertension and diabetes outpatient services are presently majorly offered in public secondary care facilities and private clinics, our findings suggest that these services as currently distributed are not affordable to most patients, especially those with comorbidity. Overall, 41.83% of the uninsured patients used more than 10% of their income in meeting healthcare costs. Elsewhere, it has been shown that Kenyan households with a member who has a NCD are twice as likely to incur CHE compared to households without a member with a NCD or chronic ailment.45 A study comparing CHE by disease area in six countries showed that the share of CHE from NCDs rises with the share of DALYs, defined as years of life lost to disability and death. Non-communicable disease CHE was also more likely to arise from many visits rather than a one-time event, when compared to communicable diseases.48 Given the high levels of poverty in Kenya and the low prepayment mechanisms in place to cushion patients from financial hardship because of healthcare payments (80% of Kenyans are not subscribed to any health insurance scheme),49 county and national governments need to put strategies to improve the population’s financial risk protection in accessing NCD services. For example, given that most people reside in rural areas in Kenya, dispensaries and health centres should be the focus in increasing service delivery for NCDs as we have shown here that both the direct and indirect costs of care at these primary level facilities are lower than the secondary level of care.

It is noteworthy that this study was undertaken in facilities that had substantial investment in the care cascade for hypertension and diabetes through the PIC4C project. As a result, the cost of care may be lower than that in many other facilities as drugs were made available through the community revolving pharmacies at a lower cost than in private yet in most public settings stock outs are frequent with most patients accessing NCD medication in private. As a result, it is possible that the cost of care is even higher in the rest of the counties.

This study had several limitations. Firstly, as the respondents were recruited in facility chronic care clinics, patients not on care and those that could not afford these care services may be underrepresented. Secondly, because of the recall periods, results may be affected by recall bias. Thirdly, the reliance on an official minimum wage to calculate productivity losses for those without a stated income could have overestimated indirect costs. Lastly, as some respondents did not have income estimates, the CHE estimates should be interpreted with caution. Nonetheless, the data presented are potentially useful inputs in economic evaluation models that require patient costs.


Patients with NCDs still experience substantial costs when accessing healthcare services in Kenya. Evidence from the two Western Kenya counties indicates that NCD comorbid patients incur substantial OOP costs in seeking healthcare, with the poor households spending a higher proportion of their income in receiving health services for NCDs. Measures to ensure affordability of outpatient NCD services should be urgently implemented while concerting prevention and screening efforts to reduce NCDs burden in the country. Provision of NCD services at PHC facilities could ensure achievement of both. Estimates from our study can be used to inform the inclusion of NCD services in the rollout of the UHC scheme in Kenya.


The authors would like to acknowledge Phelix Were and Lilian Lusibo for their critical role in data collection. Additionally, they recognise Gerald Lwande and Prof. Violet Naanyu for their role in writing, reviewing and editing the manuscript. They also acknowledge the County health-management teams of Busia and Trans Nzoia counties for their support and cooperation.

Competing interests

The authors declare that they have no financial or personal relationship(s) that may have inappropriately influenced them in writing this article.

Authors’ contributions

J.K., O.L., T.A., A.M., M.K. and W.A. were involved in the conceptualisation. J.K., A.M., M.K. and T.A. were involved in methods, formal analysis and investigation. Data software, validation, visualisation, curation and interpretation were conducted by M.K., A.M., R.M. and K.T. The article was drafted by M.K., J.K. and G.W.M. J.K. provided critical revision of the article O.L., J.K., E.M. and W.A. were involved in supervision, resources and fund acquisition. All authors were involved in writing reviewing, editing and approved the final version of the manuscript to be published.

Funding information

This study received financial support from Access Accelerated through World Bank under the terms of grant number World Bank TFA5636.

Data availability

Raw data were generated at AMPATH. Derived data supporting the findings of this study are available from the corresponding author (G.W.M.) on request.


The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.


  1. Bloom D, Cafiero E, Jané-Llopis E. The global economic burden of non-communicable diseases [homepage on the Internet]. 2011 [cited 2022 Oct 19]. Available from: www.weforum.org/EconomicsOfNCD
  2. Vos T, Lim SS, Abbafati C, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–1222. https://doi.org/10.1016/S0140-6736(20)30925-9
  3. Garson AJ, Levin SA. Ten 10-year trends for the future of healthcare: Implications for academic health centers. Ochsner J. 2001;3(1):10–15.
  4. Bloom D, Llopis E, Stein A, Feigl A. From burden to ‘Best Buys’: Reducing the economic impact of non-communicable disease in low- and middle-income countries [homepage on the Internet]. 2011 [cited 2022 Oct 19]. Available from: https://EconPapers.repec.org/RePEc:gdm:wpaper:7511
  5. Research for universal health coverage [homepage on the Internet]. World health report. 2013 [cited 2022 Oct 19]. Available from: https://www.who.int/publications/i/item/9789240690837
  6. Evans DB, Hsu J, Boerma T. Universal health coverage and universal access. Bull World Health Organ. 2013;91(8):546–546A. https://doi.org/10.2471/BLT.13.125450
  7. World Health Organization (WHO). World health statistics 2016: Monitoring health for the SDGs [homepage on the Internet]. Sustainable development goals. World Health Organization; 2016 [cited 2022 Jan 11]. Available from: https://apps.who.int/iris/handle/10665/206498
  8. Saksena P, Hsu J, Evans DB. Financial risk protection and universal health coverage: Evidence and measurement challenges. PLoS Med. 2014;11(9):e1001701. https://doi.org/10.1371/JOURNAL.PMED.1001701
  9. Barasa E, Kazungu J, Nguhiu P, Ravishankar N. Examining the level and inequality in health insurance coverage in 36 sub-Saharan African countries. BMJ Glob Health. 2021;6(4):e004712. https://doi.org/10.1136/BMJGH-2020-004712
  10. Ministry of Health. Kenya National health accounts FY 2015/16: 2019 update [homepage on the Internet]. 2019 [cited n.d.]. Available from: http://www.healthpolicyplus.com/ns/pubs/16339-16616_KenyaNHAmainreport.pdf
  11. O’Donnell O, Van Doorslaer E, Wagstaff A, Lindelow M. Analyzing health equity using household survey data : A guide to techniques and their implementation. Washington, DC: World Bank; 2008.
  12. Xu K, Evans DB, Kawabata K, Zeramdini R, Klavus J, Murray CJL. Household catastrophic health expenditure: A multicountry analysis. Lancet. 2003;362(9378):111–117. https://doi.org/10.1016/S0140-6736(03)13861-5
  13. Salari P, Di Giorgio L, Ilinca S, Chuma J. The catastrophic and impoverishing effects of out-of-pocket healthcare payments in Kenya, 2018. BMJ Glob Health. 2019;4(6):e001809. https://doi.org/10.1136/BMJGH-2019-001809
  14. Oyando R, Njoroge M, Nguhiu P, et al. Patient costs of hypertension care in public health care facilities in Kenya. Int J Health Plann Manage. 2019;34(2):e1166. https://doi.org/10.1002/HPM.2752
  15. Oyando R, Njoroge M, Nguhiu P, et al. Patient costs of diabetes mellitus care in public health care facilities in Kenya. Int J Health Plann Manage. 2020;35(1):290. https://doi.org/10.1002/HPM.2905
  16. Ministry of Health. Kenya health sector strategic plan 2018–2023: Transforming health systems achieving universal health coverage by [cited n.d.] 2022. [homepage on the Internet]. 2018 [cited n.d.]. Available from: http://guidelines.health.go.ke:8000/media/Kenya_Health_Sector_Strategic_Plan_July_2018-_June_2023.pdf.
  17. Roth GA, Abate D, Abate KH, et al. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1736. https://doi.org/10.1016/S0140-6736(18)32203-7
  18. Flood D, Edwards EW, Giovannini D, et al. Integrating hypertension and diabetes management in primary health care settings: HEARTS as a tool. Rev Panam Salud Pública. 2022;46:e150. https://doi.org/10.26633/RPSP.2022.150
  19. Petrie JR, Guzik TJ, Touyz RM. Diabetes, hypertension, and cardiovascular disease: Clinical insights and vascular mechanisms. Can J Cardiol. 2018;34(5):575–584. https://doi.org/10.1016/J.CJCA.2017.12.005
  20. Narain JP. Integrating services for noncommunicable diseases prevention and control: Use of primary health care approach. Indian J Community Med. 2011;36(Suppl1):S67–S71. https://doi.org/10.4103/0970-0218.94712
  21. World Health Organization. WHO Package of essential Noncommunicable (PEN) diseases interventions for Primary health care in low-resource settings. Geneva: World Health Organization; 2010, p. 1–65.
  22. Andale T, Kirui N, Naanyu V, et al. Primary health care integration for four chronic diseases in Western Kenya: The PIC4C baseline study protocol. J Kenya Assoc Phys. 2020;3(2):90–100. Available: https://kapkenya.org/wp-content/uploads/2022/10/JOKAP-Sept-2020-1.pdf
  23. Nolte E, Kamano JH, Naanyu V, et al. Scaling up the primary health integrated care project for chronic conditions in Kenya: Study protocol for an implementation research project. BMJ Open. 2022;12(3):e056261. https://doi.org/10.1136/BMJOPEN-2021-056261
  24. Naanyu V, Koros H, Maritim B, et al. A protocol on using the RE-AIM framework in the process evaluation of the primary health integrated care project for four chronic conditions in Kenya. Front Public Health. 2022;9:781377. https://doi.org/10.3389/fpubh.2021.781377
  25. Einterz RM, Kimaiyo S, Mengech HN, et al. Responding to the HIV pandemic: The power of an academic medical partnership. Acad Med. 2007;82(8):812–818. https://doi.org/10.1097/ACM.0b013e3180cc29f1
  26. Kenya National Bureau of Statistics. W. Ministry of Health. Kenya STEPwise survey for non communicable diseases risk factors 2015 report [homepage on the Internet]. 2015 [cited n.d.], p. 91–97. Available from: http://www.nutritionhealth.or.ke/wp-content/uploads/Downloads/Kenya%20STEPwise%20Survey%20for%20Non-Communicable%20Diseases%20Risk%20Factors%20Report%202015.pdf
  27. Kenya National Bureau of Statistics. 2019 Kenya population and housing census volume I: Population by county and sub-county – Kenya National Bureau of Statistics. 2019 [cited 2022 Jan 11]. Available from: https://www.knbs.or.ke/?wpdmpro=2019-kenya-population-and-housing-census-volume-i-population-by-county-and-sub-county
  28. Singh AS, Masuku MB. Sampling techniques & determination of sample size in applied statistics research: An overview. Int J Econ Commerce Manag [serial online]. [cited n.d.]. Available: http://ijecm.co.uk/
  29. Wayne DW, Chad CL. Biostatistics: A foundation for analysis in the health sciences [homepage on the Internet]. 11th ed. Wiley; 2018 [cited 2022 Jan 18]. Available from: https://www.wiley.com/en-us/Biostatistics%3A+A+Foundation+for+Analysis+in+the+Health+Sciences%2C+11th+Edition-p-9781119282372
  30. Hakkaart-van Roijen L, Essink-Bot MLE. The health and labour questionnaire (Manual). Rotterdam: Erasmus University Rotterdam; 2000.
  31. Firestone R, Fry K, Chakraborty N. Measuring equity with nationally representative wealth quintiles. Washington, DC: POPULATION SERVICES INTERNATIONAL (PSI); 2014.
  32. Liu H, Zhu H, Wang J, et al. Catastrophic health expenditure incidence and its equity in China: A study on the initial implementation of the medical insurance integration system. BMC Public Health. 2019;19(1):1–12. https://doi.org/10.1186/S12889-019-8121-2/TABLES/4
  33. Wagstaff A, Flores G, Hsu J, et al. Progress on catastrophic health spending in 133 countries: A retrospective observational study. Lancet Glob Health. 2018;6(2):e169–e179. https://doi.org/10.1016/S2214-109X(17)30429-1
  34. StataCorp. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC; 2019.
  35. OANDA. Kenyan shilling to US dollar average exchange rate for 2020 [homepage on the Internet]. [cited 2022 Jan 27] Available from: https://www1.oanda.com/currency/converter/
  36. Manji I, Manyara SM, Jakait B, et al. The Revolving Fund Pharmacy Model: Backing up the Ministry of Health supply chain in western Kenya. Int J Pharm Pract. 2016;24(5):358–366. https://doi.org/10.1111/IJPP.12254
  37. Subramanian S, Gakunga R, Kibachio J, et al. Cost and affordability of non-communicable disease screening, diagnosis and treatment in Kenya: Patient payments in the private and public sectors. PLoS One. 2018;13(1):e0190113. https://doi.org/10.1371/JOURNAL.PONE.0190113
  38. Basu S, Andrews J, Kishore S, Panjabi R, Stuckler D. Comparative performance of private and public healthcare systems in low- and middle-income countries: A systematic review. PLoS Med. 2012;9(6):e1001244. https://doi.org/10.1371/JOURNAL.PMED.1001244
  39. Berendes S, Heywood P, Oliver S, Garner P. Quality of private and public ambulatory health care in low and middle income countries: Systematic review of comparative studies. PLoS Med. 2011;8(4):e1000433. https://doi.org/10.1371/JOURNAL.PMED.1000433
  40. Mutyambizi C, Pavlova M, Chola L, Hongoro C, Groot W. Cost of diabetes mellitus in Africa: A systematic review of existing literature. Global Health. 2018;14(1):3. https://doi.org/10.1186/s12992-017-0318-5
  41. Mutyambizi C, Pavlova M, Hongoro C, Booysen F, Groot W. Incidence, socio-economic inequalities and determinants of catastrophic health expenditure and impoverishment for diabetes care in South Africa: A study at two public hospitals in Tshwane. Int J Equity Health. 2019;18(1):1–15. https://doi.org/10.1186/S12939-019-0977-3/TABLES/5
  42. Kotei Amon S, Kweku Sekyi Aikins M. Economic burden of type 2 diabetes mellitus complications among patients in the eastern region of Ghana: A descriptive cross-sectional cost-of-illness study. Diabetes Manag [serial online]. 2017 [cited 2022 Jan 19];7(5):367–376. Available from: https://www.openaccessjournals.com/articles/economic-burden-of-type-2-diabetes-mellitus-complications-among-patients-in-the-eastern-region-of-ghana-a-descriptive-crosssection-12164.html
  43. Rockers PC, Laing RO, Wirtz VJ. Equity in access to non-communicable disease medicines: A cross-sectional study in Kenya. BMJ Glob Health. 2018;3(3):e000828. https://doi.org/10.1136/BMJGH-2018-000828
  44. Ashigbie PG, Rockers PC, Laing RO, et al. Availability and prices of medicines for non-communicable diseases at health facilities and retail drug outlets in Kenya: A cross-sectional survey in eight counties. BMJ Open. 2020;10(5):e035132. https://doi.org/10.1136/BMJOPEN-2019-035132
  45. Barasa EW, Maina T, Ravishankar N. Assessing the impoverishing effects, and factors associated with the incidence of catastrophic health care payments in Kenya. Int J Equity Health. 2017;16(1):1–14. https://doi.org/10.1186/S12939-017-0526-X/TABLES/6
  46. Kabia E, Mbau R, Oyando R, et al. ‘We are called the et cetera’: Experiences of the poor with health financing reforms that target them in Kenya. Int J Equity Health. 2019;18(1):1–14. https://doi.org/10.1186/S12939-019-1006-2/TABLES/2
  47. Rayner B, Blockman M, Baines D, Trinder Y. A survey of hypertensive practices at two community health centres in Cape Town. S Afr Med J. 2007;97(4):280. https://doi.org/10.7196/SAMJ.789
  48. Haakenstad A, Coates M, Bukhman G, McConnell M, Verguet S. Comparative health systems analysis of differences in the catastrophic health expenditure associated with non-communicable vs communicable diseases among adults in six countries. Health Policy Plan. 2022;37(9):1107–1115. https://doi.org/10.1093/HEAPOL/CZAC053
  49. Kazungu JS, Barasa EW. Examining levels, distribution and correlates of health insurance coverage in Kenya. Trop Med Int Health. 2017;22(9):1175. https://doi.org/10.1111/TMI.12912

Crossref Citations

No related citations found.