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Availability of equipment and medications for non-communicable diseases and injuries at public first-referral level hospitals: a cross-sectional analysis of service provision assessments in eight low-income countries

Context and objectives Non-communicable diseases and injuries (NCDIs) comprise a large share of mortality and morbidity in low-income countries (LICs), many of which occur earlier in life and with greater severity than in higher income settings. Our objective was to assess availability of essential equipment and medications required for a broad range of acute and chronic NCDI conditions. Design Secondary analysis of existing cross-sectional survey data. Setting We used data from Service Provision Assessment surveys in Bangladesh, the Democratic Republic of the Congo, Ethiopia, Haiti, Malawi, Nepal, Senegal and Tanzania, focusing on public first-referral level hospitals in each country. Outcome measures We defined sets of equipment and medications required for diagnosis and management of four acute and nine chronic NCDI conditions and determined availability of these items at the health facilities. Results Overall, 797 hospitals were included. Medication and equipment availability was highest for acute epilepsy (country estimates ranging from 40% to 95%) and stage 1–2 hypertension (28%–83%). Availability was low for type 1 diabetes (1%–70%), type 2 diabetes (3%–57%), asthma (0%–7%) and acute presentations of diabetes (0%–26%) and asthma (0%–4%). Few hospitals had equipment or medications for heart failure (0%–32%), rheumatic heart disease (0%–23%), hypertensive emergencies (0%–64%) or acute minor surgical conditions (0%–5%). Data for chronic pain were limited to only two countries. Availability of essential medications and equipment was lower than previous facility-reported service availability. Conclusions Our findings demonstrate low availability of essential equipment and medications for diverse NCDIs at first-referral level hospitals in eight LICs. There is a need for decentralisation and integration of NCDI services in existing care platforms and improved assessment and monitoring to fully achieve universal health coverage.

District hospitals and district designated hospitals (561,000) Not described *Calculation not possible with facility types in report table-some of the 524 hospitals include tertiary/provincial hospitals **Calculation not possible with facility types in report table, but total number in dataset should be close to total since hospital sample was census ***Calculation not possible with Senegal survey combined from two years, but should be close to facility census combining the surveys For 95% confidence intervals for estimates from countries without censuses of facilities, we used finite population corrections. In Bangladesh, we knew that there were 425 total facilities of interest, of which 140 were surveyed, so we corrected using 425 as the total population. In the DRC, tables in the SPA report gave total numbers of general referral hospitals but not the specific type of interest to us. The dataset allowed us to exclude tertiary/provincial hospitals, but we were not able to obtain the exact number of public first-referral hospitals. In the sample, there were 283 hospitals we included in the analysis. We know that 491 out of 524 hospitals (including private) were selected for the sample, so 33 were not included. Conservatively, no more than 316 (283+33) facilities could be in the population of hospitals of interest. We used this number for the finite population correction as a conservative cap (confidence intervals are therefore conservatively large.

Poverty and service availability
We defined the prevalence of extreme poverty using a modified version of the Multidimensional Poverty Index. [10] We defined extreme poverty using 5 of 8 deprivations (Appendix Table 3). Using household survey data from recent surveys (Appendix Table 4), we found the proportion of the population in districts or regions that met this definition of extreme poverty.
To assess whether there might be an association between poverty prevalence in the subnational areas in which hospitals are located and the availability of medications and equipment at these hospitals, we conducted country-specific regressions using two different specification strategies.
In our construction of the sets of medication and equipment, there was information loss because the absence of one item made the whole set unavailable. To include this information in our assessment of the possible association between poverty and availability of medications and equipment, we included each of the individual items that are part of the various disease-specific sets (main text Table 1), deduplicating the items contained in multiple sets. We calculated the percent of the total items available at each facility. This proportion, transformed to logit space, was the outcome in both of our regression specifications.
In the first regression specification, we regressed the logit-transformed proportion of total items available in hospitals (h) on the proportion of population living in poverty (% Pop Poverty) in the subnational area (region or district) where the hospital is located. We conducted a separate regression for each country because we anticipated different associations were possible in different countries based on differences in health systems across countries.
(% ) ,ℎ = 1, + 2, * % ,ℎ + ,ℎ The second specification allowed for nonlinear associations between the two variables. We created bins of facilities, grouped using cutoffs in the poverty prevalence variable. To illustrate, in a country where hospitals were binned into four groups based on poverty prevalence, the groups would be included as "dummy" variables (I) as follows, where I2, I3, and I4 represent the second two groups, while the first group is the reference group.
(% ) ,ℎ = 1, + 2, * 2, ,ℎ + 3, * 3, ,ℎ + 4, * 4, ,ℎ + ,ℎ We did two sets of regressions using groups created in two different ways-based on the quartiles of poverty prevalence and based on dividing the poverty prevalence range of the subnational units in the country into four categories evenly spaced on the scale of poverty prevalence. To create the first category, we ranked the hospitals by poverty prevalence in the corresponding subnational unit and calculated the cumulative proportion of hospitals. We split groups based on the cumulative proportions closest to 25%, 50%, and 75%. To create the second category, we subtracted the poverty prevalence in the lowest-poverty area from the poverty prevalence in the highest-poverty area. We then divided this range into four even parts and assigned hospitals to the four groups using the poverty prevalence in the subnational unit corresponding to the hospital. Sometimes, one of the parts of the range did not contain any of the public first-referral level hospitals. For instance, in Ethiopia, the ranges were approximately 2. Scatter plots of the percent of items available and the percent of the population in poverty are found in Appendix Figure 1. Points on a vertical line indicate facilities located within the same subnational unit. While  The second set of results, using regressions with hospitals grouped based on ranges of poverty prevalence, gave results that were similar in some ways but differed in others. The quartile regression found lower availability in the highest-poverty quartile in Bangladesh (p < 0.05), while the regression based on the ranges found that the hospitals in the highest poverty range showed lower availability under weaker criteria for significance (p < 0.1). In the Democratic Republic of the Congo, the selection of bins made a large difference in interpretation. Using quintiles showed lower availability in hospitals in the poverty quintiles above the first, while using ranges found higher availability in the third range compared to the first. The plot showed a downward trend in availability with poverty in the units with poverty over 50%, but the subnational unit with very low poverty created an odd distribution. Similarly, in Ethiopia, the range was made large by few hospitals in low-poverty areas, but the specification using quartiles found higher availability in higherpoverty quartiles (p < 0.01). In Haiti, both methods, but particularly the quartile method, suggested higher availability in hospitals in higher-poverty areas. Both methods showed higher availability in the third group in Senegal. No association was found in Malawi, Nepal, or Tanzania.
While both the continuous and binned specifications suggested lower availability in higher-poverty subnational units in Bangladesh and the Democratic Republic of the Congo, the story appears more complex. The trend in Congo appears to be mostly among the subnational units in higher-poverty areas rather than compared to the hospitals in the low-poverty area, evidenced by Appendix Figure 1 and the differing results from the bins using the range method rather than the quartile method. Additionally, the binned methods suggested availability was higher in high-poverty areas in Haiti and Ethiopia, though somewhat inconsistently between the two binning methods.
From this analysis, we can conclude that there is some evidence for differences in availability of medications and equipment across public first-referral level hospitals that may correlate with poverty, though not in a consistent manner across countries or across methods of evaluation. There appear to be both positive and negative correlations in different locations. More detailed data, for example, relating to poverty in specific facility catchment areas, would be necessary to make nuanced claims about inequities in availability of medications and equipment. There is variation in availability within subnational units (points oriented vertically in Appendix Figure 1), which means that facility catchment area factors, for example, could correlate with availability. Further, to understand the drivers of any patterns, more complex administrative data, for instance on supply chains and procurement, would be necessary.

Poverty and hospital density per population
Another factor contributing to inequitable access to care for individuals and families living in poverty is geographic access to health facilities. The density of health facilities tends to be higher in urban areas for many reasons, including availability of human resources and proximity to larger populations. We tested whether the density of hospitals per population in subnational areas was associated with the prevalence of extreme poverty, both for public first-referral level hospitals and hospitals overall. We excluded Bangladesh and the Democratic Republic of the Congo from this analysis. The survey in Bangladesh did not contain a BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) BMJ Open doi: 10.1136/bmjopen-2020-038842 :e038842. census of first-referral level hospitals, and the survey in the Democratic Republic of the Congo was not as close to a complete census as the surveys in the other countries. We totaled the number of first-referral level and overall hospitals per subnational area, and calculated the density of hospitals per population. We hypothesized that the density of public first-referral level hospitals per population in a subnational area would not be associated with subnational poverty because governments tend to distribute these hospitals to have relatively consistent catchment areas and to cover the whole population. We hypothesized that the density of hospitals overall in a subnational area would be lower in subnational areas with higher poverty because hospitals tend to be more concentrated in large urban areas, which tend to have lower poverty prevalence than rural areas. We regressed the hospital density per capita in each subnational area (s) on the proportion of the population in extreme poverty, pooling countries (c) and including a set of country indicators (I) to account for country differences in overall density of facilities.
We also tested a specification including the log of the subnational population in the subnational area as a predictor, as one might expect high-population areas to have a greater density of hospitals because of concentration of resources in large urban centers.
We found no association between the density of public first-referral level hospitals per population and poverty prevalence. Meanwhile, the density of hospitals per population overall was lower in higher-poverty areas (p < 0.01). The log of the population was a significant predictor, but its inclusion did not substantially change the results. These findings are consistent with our hypothesis that the density of public first-referral level hospitals is relatively consistent with respect to poverty prevalence but that hospitals overall are more common in higher-population, lower-poverty areas. These findings reinforce that availability of services at public first-referral level hospitals is important for equity, as these facilities are more evenly distributed with respect to poverty prevalence. However, these findings do not incorporate information about other aspects of accessibility including travel time, distance, or fees, for example.