TLDR: Operating basic health centers in remote rural Ugandan communities looks more cost-effective than top GiveWell interventions on early stage analysis - with huge uncertainty.
I’m Nick, a medical doctor who is co-founder and director of OneDay Health (ODH). We operate 38 nurse-led health centers in healthcare “black holes,” remote rural areas more than 5 km from government health facilities. About 5 million Ugandans live in these healthcare black holes and only have bad options when they get sick. ODH health centers provide high-quality primary healthcare to these communities at the lowest possible cost. We train our talented nurses to use protocol based guidelines and equip them with over 50 medications to diagnose and treat 30 common medical conditions. In our 5 years of operation, we have so far treated over 150,000 patients – including over 70,000 for malaria.
Since we started up 5 years ago, we’ve raised about $290,000 of which we’ve spent around $220,000 to date. This year we hope to launch another 10-15 OneDay Health centers in Uganda and we're looking to expand to other countries which is super exciting!
If you’re interested in how we select health center sites or more details about our general ops, check our website or send me a message I’d love to share more!
Challenges in Assessing Cost-Effectiveness of OneDay Health
Unfortunately, obtaining high-quality effectiveness data requires data from an RCT or a cohort study that would cost 5-10 times our current annual budget. So we've estimated our impact by estimating the DALYs our health centers avert through treating four common diseases and providing family planning. I originally evaluated this as part of my masters dissertation in 2019 and have updated it to more recent numbers. As we’re assessing our own organisation, the chance of bias here is high.
Summary of Cost-Effectiveness Model
To estimate the impact of our health centers, we estimated the DALYs averted through treating individual patients for 4 conditions: malaria, pneumonia, diarrhoea, and STIs. We started with Ugandan specific data on DALYs lost to each condition. We then adjusted that data to account for the risk of false diagnosis and treatment failure (in which case the treatment would have no effect). We then added impact from family planning. Estimating impact per patient isn’t a new approach. PSI used a similar method to evaluate their impact (with an awesome online calculator), but has now moved to other methods.
Inputs for our approach
For each condition, we multiplied the DALYs averted per treatment by the average number of patients treated with that condition in one health center in one month. When we added this together that each ODH health center averted 13.70 DALYs per month, predominantly through treatment of malaria in all ages, and pneumonia in children under 5.
ODH health centers are inexpensive to open and operate. Each health center currently needs only $137.50 per month in donor subsidies to operate. The remaining $262.50 in expenses are covered by small payments from patients. Many of these patients would have counterfactually received treatment, but would have incurred significantly greater expense to do so (mainly for travel). In addition, about 40% of patient expenses were for treating conditions not included in the cost-effectiveness analysis.
We estimate that In one month, each health center averts 13.70 DALYs and costs $137.50 in donor subsidies. This is roughly equivalent to saving a life for $850, or more conservatively for $1766 including patient expenses. However, there is huge uncertainty in our analysis.
Measuring Impact by Estimating DALYs Averted in Individual Patients
Before doing our assessment, we searched academic literature to find previous estimates of DALYs averted per patent treated in similar contexts. PSI had done the most work in this area.
To estimate the impact of ODH health centers treating individual patients for these four conditions, we used a similar evaluation to PSI - a linear DALYs averted model.
We used the average Global burden of disease DALY burden per patient in Uganda to estimate the DALY benefit of treating individual patients. This average includes everyone who suffered from each disease in Uganda, whether they were treated correctly, poorly or not at all. This accounts somewhat for what might have happened if we hadn’t treated the patient and avoids the counterfactual of assuming that patients would have not been treated without us. That said, OneDay Health treats patients in remote regions of Uganda who have very poor access to healthcare, so our treatment is likely to be more impactful than for treating the average Ugandan. I’m aware that this is probably the weakest link in our modelling efforts.
DALYs averted treating patients for malaria, Pneumonia and STIs
Our model accounts for the estimated false-diagnosis and treatment-failure rates to estimate DALYs averted per patient treated. We estimated these rates from relevant peer-reviewed literature. Based on the model for treatment of malaria in children under 5, each treatment averts on average .179 DALYs per patient treated:
We used similar models for these other treatments (See Appendix 1 for diagrams)
Malaria treatment over 5 - 0.131 DALYs averted per treatment
Pneumonia treatment under 5 - 0.375 DALYs averted per treatment
Pneumonia treatment over 5 - 0.036 DALYs averted per treatment
Diarrhoea treatment under 5 - 0.021 DALYs averted per treatment
Although the benefit of treatng diarrhoea is low, this is consistent with PSI’s assessment and also my experience that diarrhoea is no longer a leading cause of morbidity and mortality in rural Uganda. We assumed that treatment of diarrhoea in over-5s averted 0 DALYs.
DALYs averted per patient for treating STIs were also unexpectedly small (0.00038 DALYs per patient (only 3.33 disability-adjusted life hours!). We aren't sure why the Global Burden of Disease weighed STIs so minimally. We believe our model significantly underestimates the benefits of STI treatment, especially since STIs are associated with miscarriage, stillbirth and even neonatal death.
DALYs averted for Family Planning
For family planning, We cheated and sponged off Lafiya Nigeria’s calculations (thank you and they are amazing!). We discounted by an arbitrary 30%, guessing that the benefits of FP in the rural Nigerian area they work are probably higher than in remote rural Uganda. Lafiya Nigeria delivered 2400 injections, averting an estimated 503 DALYs, meaning they averted an estimated 0.21 DALYs per patient treated. Discounting by 30% that yields a figure of 0.146 DALYs per treatment for our assessment. This calculation does not incorporate all potential benefits of family planning such as positive environmental impacts. While some ODH nurses insert long-term implants for family planning, they insert too few at this stage for the result to be significant.
Calculating DALYs Averted Each Month by Each ODH Health Center
For each condition, we determined the average number of patients treated every month at each health center using data collected throughout 2022 in our 38 ODH Health Centers. We multiplied the average monthly patient volume in each category by the DALY figures described above to calculate the average DALYs averted per ODH health center per month. We estimate that each ODH health center averted 13.70 DALYs through treatment of the four conditions and family planning. The bulk of the DALYs were averted through malaria treatment (10.30 DALYs) and treatment of pneumonia in under-5s (2.625 DALYs). Specific calculations are in the chart below:
|Condition Treated or Intervention
|Under-5s treated monthly
|DALYs averted per under-5 treated
|Over-5s treated monthly
|DALYs averted per over-5 treated
|Total DALYs averted
Our current model does not capture benefits that accrue to patients treated for any other condition. About 40% of all patients are treated for a range of other conditions like skin infections, urinary tract infections, and high blood pressure. One example of a potentially high impact intervention is provision of antibiotic injections for severely ill patients while referring them to more definitive care.
Finally, our model does not account for the psychosocial benefits of having quality, reliable healthcare close to home, as residents no longer have to worry about lack of accessible care when they or their children become ill.
Donors or grants fund startup costs of $3000 per health center, as well as 25% of operating costs ($87.50 per month). Launch costs include furniture, initial medication supply, tests, a solar unit, and initial rental. Ongoing operating costs are around $350 a month. At present, patients pay 75% of ongoing costs. Three years ago, our health centers were closer to 100% locally funded but a combination of COVID, inflation and general poor economic conditions here in Uganda has reduced average monthly patient volume and our revenues.
Our model conservatively estimates that each health center will operate for an average of 5 years, and prorates the startup costs over a 5-year period. Thus, we allocate $50 of the startup costs to each month. ODH health centers are designed to be a permanent fixture in the community, but we will close a health center if the patient volume is too low and it appears that the community doesn’t place a high value on the health center. So far we have closed 8 of 46 health centers opened (15%). This means total cost of operation is $400 monthly
In calculating the benefits of each health center, we did not include any benefits from the 40% of patient visits that address other medical conditions. Rather than accounting for these visits on the benefit side, we have deducted $105 (i.e., 40% of patient expenditures) from the patient-cost figure. This adjustment ensures we incorporate only patient costs that are related to treatment for the specified medical conditions. Based on patents’ willingness to pay for these visits, we assume that patients receive at least $105 of value from them. So with this assumption we adjust monthly cost is $400- $105 = $295
A major reason patients visit OneDay Health centers is because care at an ODH facility costs less than transport to distant facilities. The average cost to a patient of visiting an ODH health center is $1.50, while the average round trip transport cost to the nearest government health facility is $3.00. In our patient survey many patients expressed gratefulness that ODH health centers saved them money. OneDay Health may therefore cost saving for patients or at least cost neutral.
Therefore if we assume that visiting a OneDay Health center is at least cost neutral for patients, we can account for only the donor contribution to operating the facility. The total donor/grantor cost per health center per month is $137.50 ($87.50 + $50),
Overall Cost Effectiveness
We then divide the total monthly operational cost by total DALYs averted, to find our cost per DALY averted.
Final Cost-Benefit calculations
So our more conservative estimated cost per DALY averted including patient costs
1. 295 / 13.7 = $21.5 per DALY averted
2. Assuming an 82 year lifespan, equivalent to $1766 per life saved.
Assuming accessing our health centers is cost neutral for patients, accounting for Donor funds only
- 137.5 / 13.7 = $10.0 per DALY averted
- Equivalent to $820 per life saved.
The level of uncertainty here is high as we made many assumptions and don't have direct RCT or other longitudinal evidence from communities served by ODH health centers.
In an attempt to quantify uncertainty, I performed a probabilistic Monte Carlo simulation on a previous version of this model to explore what happened when our inputs were varied. In order to perform the simulation, distributions were assigned to important data inputs, based on the nature of available data. These included costs, GBD incidence, DALY data, and the positive predictive values of diagnosis. During my masters thesis, I had help from a health economist and embarrassingly I can’t remember how I did the analysis. I haven’t run the simulation again with these latest figures but when I did, 1000 simulations produced a range of impacts between half and double the original point estimate. I think this analysis massively underestimates the uncertainty, but include it to both acknowledge the enormous uncertainty that does exist and to show that at least I tried ;). I’m keen to learn about better ways to quantify uncertainty here.
This analysis carries a number of limitations, which are likely to bias the results in various ways.
Limitations and effects more likely to cause overestimate of effectiveness
- Most interventions show reduced cost-effectiveness as the intervention is more fully studied - this is an early stage analysis.
- This analysis is based on 5 year old global burden of disease data, with the burden of malaria and pneumonia likely to have reduced since then
- This analysis was performed by ODH’s co-founder and director (that’s me folks), increasing the risk of bias
Limitations and effects that could contribute to inaccuracy in either direction
- Absence of high-quality evidence on the effectiveness of the ODH intervention, such as from an RCT or cohort study
- Potential inaccuracy of the DALY data used
- Potentially inaccurate assumptions about counterfactual patient outcomes
Limitations and effects more likely to cause underestimate of effectiveness
- Calculated benefit from STI treatment is unreasonably low, and does not account for effect on miscarriage, stillbirth, and neonatal death
- Analysis excludes 40% of patients treated for other conditions
- Potential saving benefits to the community are not properly accounted for here
- Assumption that patient fees will only support 75% of operating expenses may be pessimistic, given current challenging macroeconomic climate in Uganda
Could ODH be a highly cost-effective intervention?
This analysis provides some evidence that ODH may be a high-impact charity. Each of our health centers treat on average 1800 patients a year and avert an estimated 166.4 DALYs. This is equivalent to saving about 2.25 lives (range = 1.07 to 4.50) for a donor/grantor cost of $1,650. Although these figures are highly uncertain, our ODH remote health center intervention may remain competitive on cost-effectiveness even if these estimates are several times too optimistic.
Thanks so much for taking the time to read this, I’m super keen for feedback either in the comments or direct messaging - on how to improve this analysis, and any other thoughts on ODH in general.
Huge thanks to @Jason for huge help in providing insights and editing, @Klau Chmielowska from Lafiya Nigeria for allowing us to sponge of their data and to @Lizka for encouraging me to go ahead with it!
Appendix: Details of DALY Analysis for Other Conditions
Malaria treatment over 5
Pneumonia treatment under 5
Pneumonia treatment over 5
Diarrhoea treatment under 5
One could perform an RCT similar to the Ugandan Living Goods study, randomising remote communities to either receiving an ODH health center or not. In addition to the cost, it would be difficult for the study to capture a full range of health outcomes.
Under GiveWell’s moral weights as of 2020, the value of averting one neonatal death from syphilis is 84 times as large as the value of doubling consumption for one person for one year. The value of averting a stillbirth (one month before birth) is 33.4 times as large.