By  Florence Muriuki and Odette Melvin
High prices of pharmaceutical commodities in low-to-middle-income countries (LMICs) is a great impediment to improved healthcare outcomes in these countries. In Kenya’s private sector, the patient prices for generic medicines range between 3 and 20 times the international reference prices. Patient prices for innovator brand medicines range between 1.8 to 140 times the international reference prices. These high prices of pharmaceutical commodities coupled with 75% of Kenyans paying out of pocket for healthcare, make access and affordability of essential quality medicines difficult. In other countries insurance would pay for the majority of costs. For example in the United States 84% of people have some form of healthcare insurance; and some African countries like Rwanda and Ghana have implemented national health insurance schemes achieving coverage of 91% and 74% respectively. Maisha Meds is working to improve accessibility and affordability of malaria and reproductive health commodities in the private sector by managing a digital platform that offers subsidized commodities at pharmacies and clinics distributed across Kenya and Uganda. Through our reimbursement program, Maisha Meds helps supply subsidised malaria and family planning (FP) products to healthcare facilities and tracks their dispensation to verified patients. In 2021, Maisha Meds supported tech for 875 facilities with 2.6 million patient encounters and directly paid for care on behalf of approximately 30,000 patients in 154 facilities across Kenya.

In our endeavour to scale the reimbursement program cost effectively to reach more patients in low income areas, at Maisha Meds we’re working on a geospatial analysis project aimed at better targeting regions and individuals in need of the program. The targeting project consolidates data on disease burden, relative wealth and access to healthcare by region to create an index that optimally determines which regions to target and potentially what level of subsidies to provide in various regions.  The purpose of this is to make sure that we’re optimising funding to reach patients who most need the subsidies that our technology systems provide.

Mapping malaria by sub-county

In previous mapping of disease burden, counties (the highest administrative level in Kenya) were used to identify malaria and HIV endemic regions, which are mostly in the western and coastal parts of the country. The targeting project is taking this a step further by collating malaria parasite rate and mortality data at sub-county level from the Malaria Atlas Project and the District Health Information Software (DHIS2) malaria positivity rate at ward level (a small administrative level) to  localise targeting at a more granular level. 

Data source: The Malaria Atlas Project

The Maisha Meds Malaria reimbursement program currently focuses on the Malaria Endemic counties represented by dark red on the map on the left. As the program expands, more attention will be paid to the sub-counties in the same areas which have the highest malaria mortality rates and plasmodium falciparum parasite rate.

Assessing need using micro-estimates of wealth

Family planning commodities are often unequally distributed.  For example, use of modern family planning methods remains lower in low income households compared to high income households. Women from higher income households are more likely to access and use long- acting reversible contraceptives compared to their counterparts in low income households. Women from lower income households are likely to resort to using short-term contraceptive methods compared to women from rich households. This is in spite of long-acting contraceptives being available in LMICs as subsidised commodities imported and distributed mainly through government-donors channels. In Kenya, poor women are almost as likely to pay for contraceptives as the wealthiest women. Yet modern data and technology systems can enable the targeting of patients to ensure that funds are being spent where they are most needed, enabling the  precision of subsidised commodities in such a way that they reach more low-income households. Maisha Meds is building an approach to analyse relative wealth and socioeconomic data to determine how to distribute subsidised commodities. Moreover, an index wealth data could be used to vary the level of subsidies such that relatively poor areas get higher subsidies. The map below of Siaya county  shows how estimated wealth can vary widely within one county.

Data source: Chi, Fang, Chatterjee & Blumenstock, Microestimates of wealth for all low- and middle-income countries. Data is weighted by population to average at the ward level. Relative wealth index is modelled from the DHS index, which has a mean of 0 and standard deviation of 1 at the country level.  

This approach can also evaluate the distribution of public health facilities and distance between the nearest public health facility and Maisha Meds facilities. This is to ensure that Maisha Meds subsidies are nearer people who are not benefitting from the already subsidised public sector malaria, HIV and reproductive health services.

Combined mapping of disease burden, relative wealth/socioeconomic status and access to healthcare will result in robust identification of areas that need subsidised health commodities and precise calibration of subsidies based on economic needs. This will ensure that Maisha Meds reimbursement program reaches individuals who need affordable care the most and in a facility nearest to them.


The next steps of this project will be working with the operations team to focus more on the lowest wealth areas, according to estimates we have looked at so far, and understand any implementation challenges. One issue that has come up so far is that socio-economic status changes very rapidly in urban areas. Two kilometer wealth estimates appear to align with perceptions outside urban centres, but make less sense for our team in places like Nairobi and Mombasa. Lower income areas may also have additional challenges, such as having fewer licensed pharmacies and pharmacies that can afford to purchase tablet computers for running the app. In the future if we set subsidies based on area, it will be important to ensure the accuracy of these calculations as well as create a good communications strategy. Working through the operational challenges to align scalable research estimates with on-the-ground conditions will help prove the feasibility of this type of geospatial targeting.


[1]Gichangi, Peter & Alfred, Agwanda & Mary, Thiongo & Waithaka, Michael & Tsui, Amy & Radloff, Scott & Temmerman, Marleen & Zimmerman, Linnea & Ahmed, Saifuddin & Anglewicz, Philip. (2020). Assessing (in) equalities in contraceptives use and family planning demand satisfied with modern contraceptives in Kenya.

[2]Palladium, Diagnostic Assessment of Kenya’s Family Planning market. Pg 26.

[3] Microestimates of wealth for all low- and middle-income countries Guanghua Chi, Han Fang, Sourav Chatterjee, Joshua E. Blumenstock Proceedings of the National Academy of Sciences Jan 2022, 119 (3) e2113658119; DOI: 10.1073/pnas.2113658119