The potential to expand antiretroviral therapy by improving health facility efficiency: evidence from Kenya, Uganda, and Zambia.

BACKGROUND: Since 2000, international funding for HIV has supported scaling up antiretroviral therapy (ART) in sub-Saharan Africa. However, such funding has stagnated for years, threatening the sustainability and reach of ART programs amid efforts to achieve universal treatment. Improving health system efficiencies, particularly at the facility level, is an increasingly critical avenue for extending limited resources for ART; nevertheless, the potential impact of increased facility efficiency on ART capacity remains largely unknown. Through the present study, we sought to quantify facility-level technical efficiency across countries, assess potential determinants of efficiency, and predict the potential for additional ART expansion. METHODS: Using nationally-representative facility datasets from Kenya, Uganda and Zambia, and measures adjusting for structural quality, we estimated facility-level technical efficiency using an ensemble approach that combined restricted versions of Data Envelopment Analysis and Stochastic Distance Function. We then conducted a series of bivariate and multivariate regression analyses to evaluate possible determinants of higher or lower technical efficiency. Finally, we predicted the potential for ART expansion across efficiency improvement scenarios, estimating how many additional ART visits could be accommodated if facilities with low efficiency thresholds reached those levels of efficiency. RESULTS: In each country, national averages of efficiency fell below 50 % and facility-level efficiency markedly varied. Among facilities providing ART, average efficiency scores spanned from 50 % (95 % uncertainty interval (UI), 48-62 %) in Uganda to 59 % (95 % UI, 53-67 %) in Zambia. Of the facility determinants analyzed, few were consistently associated with higher or lower technical efficiency scores, suggesting that other factors may be more strongly related to facility-level efficiency. Based on observed facility resources and an efficiency improvement scenario where all facilities providing ART reached 80 % efficiency, we predicted a 33 % potential increase in ART visits in Kenya, 62 % in Uganda, and 33 % in Zambia. Given observed resources in facilities offering ART, we estimated that 459,000 new ART patients could be seen if facilities in these countries reached 80 % efficiency, equating to a 40 % increase in new patients. CONCLUSIONS: Health facilities in Kenya, Uganda, and Zambia could notably expand ART services if the efficiency with which they operate increased. Improving how facility resources are used, and not simply increasing their quantity, has the potential to substantially elevate the impact of global health investments and reduce treatment gaps for people living with HIV.

Performance assessment of the county healthcare systems in Kenya: a mixed-methods analysis.

INTRODUCTION: A well performing public healthcare system is necessary for Kenya to continue progress towards universal health coverage (UHC). Identifying actionable measures to improve the performance of the public healthcare system is critical to progress towards UHC. We aimed to measure and compare the performance of Kenya's public healthcare system at the county level and explore remediable drivers of poor healthcare system performance. METHODS: Using administrative data from fiscal year 2014/2015 through fiscal year 2017/2018, we measured the technical efficiency of 47 county-level public healthcare systems in Kenya using stochastic frontier analysis. We then regressed the technical efficiency measure against a set of explanatory variables to examine drivers of efficiency. Additionally, in selected counties, we analysed surveys and focus group discussions to qualitatively understand factors affecting performance. RESULTS: The median technical efficiency of county public healthcare systems was 84% in fiscal year 2017/2018 (with an IQR of 79% to 90%). Across the four fiscal years of data, 27 out of the 47 Kenyan counties had a declining technical efficiency score. Our regression analysis indicated that impediments to the flow of funding-measured by the budget absorption rate which is the ratio between funds spent and funds released-were significantly related to poor healthcare system performance. Our analysis of interviews and surveys yielded a similar conclusion as nearly 50% of respondents indicated issues stemming from poor budget absorption were significant drivers of poor healthcare system performance. CONCLUSION: Public healthcare systems at the county-level in Kenya general performed well; however, addressing delays in the flow of funding is a concrete step to improve healthcare system performance. As Kenya-and other countries-provides additional funding to meet their UHC goals, establishing a strong and robust public financial management system is critical to ensure that the benefits of UHC are realised.

Potential for additional government spending on HIV/AIDS in 137 low-income and middle-income countries: an economic modelling study.

BACKGROUND: Between 2012 and 2016, development assistance for HIV/AIDS decreased by 20·0%; domestic financing is therefore critical to sustaining the response to HIV/AIDS. To understand whether domestic resources could fill the financing gaps created by declines in development assistance, we aimed to track spending on HIV/AIDS and estimated the potential for governments to devote additional domestic funds to HIV/AIDS. METHODS: We extracted 8589 datapoints reporting spending on HIV/AIDS. We used spatiotemporal Gaussian process regression to estimate a complete time series of spending by domestic sources (government, prepaid private, and out-of-pocket) and spending category (prevention, and care and treatment) from 2000 to 2016 for 137 low-income and middle-income countries (LMICs). Development assistance data for HIV/AIDS were from Financing Global Health 2018, and HIV/AIDS prevalence, incidence, and mortality were from the Global Burden of Disease study 2017. We used stochastic frontier analysis to estimate potential additional government spending on HIV/AIDS, which was conditional on the current government health budget and other finance, economic, and contextual factors associated with HIV/AIDS spending. All spending estimates were reported in 2018 US$. FINDINGS: Between 2000 and 2016, total spending on HIV/AIDS in LMICs increased from $4·0 billion (95% uncertainty interval 2·9-6·0) to $19·9 billion (15·8-26·3), spending on HIV/AIDS prevention increased from $596 million (258 million to 1·3 billion) to $3·0 billion (1·5-5·8), and spending on HIV/AIDS care and treatment increased from $1·1 billion (458·1 million to 2·2 billion) to $7·2 billion (4·3-11·8). Over this time period, the share of resources sourced from development assistance increased from 33·2% (21·3-45·0) to 46·0% (34·2-57·0). Care and treatment spending per year on antiretroviral therapy varied across countries, with an IQR of $284-2915. An additional $12·1 billion (8·4-17·5) globally could be mobilised by governments of LMICs to finance the response to HIV/AIDS. Most of these potential resources are concentrated in ten middle-income countries (Argentina, China, Colombia, India, Indonesia, Mexico, Nigeria, Russia, South Africa, and Vietnam). INTERPRETATION: Some governments could mobilise more domestic resources to fight HIV/AIDS, which could free up additional development assistance for many countries without this ability, including many low-income, high-prevalence countries. However, a large gap exists between available financing and the funding needed to achieve global HIV/AIDS goals, and sustained and coordinated effort across international and domestic development partners is required to end AIDS by 2030. FUNDING: The Bill & Melinda Gates Foundation.

Funding and services needed to achieve universal health coverage: applications of global, regional, and national estimates of utilisation of outpatient visits and inpatient admissions from 1990 to 2016, and unit costs from 1995 to 2016.

BACKGROUND: To inform plans to achieve universal health coverage (UHC), we estimated utilisation and unit cost of outpatient visits and inpatient admissions, did a decomposition analysis of utilisation, and estimated additional services and funds needed to meet a UHC standard for utilisation. METHODS: We collated 1175 country-years of outpatient data on utilisation from 130 countries and 2068 country-years of inpatient data from 128 countries. We did meta-regression analyses of annual visits and admissions per capita by sex, age, location, and year with DisMod-MR, a Bayesian meta-regression tool. We decomposed changes in total number of services from 1990 to 2016. We used data from 795 National Health Accounts to estimate shares of outpatient and inpatient services in total health expenditure by location and year and estimated unit costs as expenditure divided by utilisation. We identified standards of utilisation per disability-adjusted life-year and estimated additional services and funds needed. FINDINGS: In 2016, the global age-standardised outpatient utilisation rate was 5·42 visits (95% uncertainty interval [UI] 4·88-5·99) per capita and the inpatient utilisation rate was 0·10 admissions (0·09-0·11) per capita. Globally, 39·35 billion (95% UI 35·38-43·58) visits and 0·71 billion (0·65-0·77) admissions were provided in 2016. Of the 58·65% increase in visits since 1990, population growth accounted for 42·95%, population ageing for 8·09%, and higher utilisation rates for 7·63%; results for the 67·96% increase in admissions were 44·33% from population growth, 9·99% from population ageing, and 13·55% from increases in utilisation rates. 2016 unit cost estimates (in 2017 international dollars [I$]) ranged from I$2 to I$478 for visits and from I$87 to I$22 543 for admissions. The annual cost of 8·20 billion (6·24-9·95) additional visits and 0·28 billion (0·25-0·30) admissions in low-income and lower-middle income countries in 2016 was I$503·12 billion (404·35-605·98) or US$158·10 billion (126·58-189·67). INTERPRETATION: UHC plans can be based on utilisation and unit costs of current health systems and guided by standards of utilisation of outpatient visits and inpatient admissions that achieve the highest coverage of personal health services at the lowest cost. FUNDING: Bill & Melinda Gates Foundation.

Efficiency of Health Care Production in Low-Resource Settings: A Monte-Carlo Simulation to Compare the Performance of Data Envelopment Analysis, Stochastic Distance Functions, and an Ensemble Model.

Low-resource countries can greatly benefit from even small increases in efficiency of health service provision, supporting a strong case to measure and pursue efficiency improvement in low- and middle-income countries (LMICs). However, the knowledge base concerning efficiency measurement remains scarce for these contexts. This study shows that current estimation approaches may not be well suited to measure technical efficiency in LMICs and offers an alternative approach for efficiency measurement in these settings. We developed a simulation environment which reproduces the characteristics of health service production in LMICs, and evaluated the performance of Data Envelopment Analysis (DEA) and Stochastic Distance Function (SDF) for assessing efficiency. We found that an ensemble approach (ENS) combining efficiency estimates from a restricted version of DEA (rDEA) and restricted SDF (rSDF) is the preferable method across a range of scenarios. This is the first study to analyze efficiency measurement in a simulation setting for LMICs. Our findings aim to heighten the validity and reliability of efficiency analyses in LMICs, and thus inform policy dialogues about improving the efficiency of health service production in these settings.

Serial testing for latent tuberculosis using QuantiFERON-TB Gold In-Tube: A Markov model.

Healthcare workers (HCWs) in low-incidence settings are often serially tested for latent TB infection (LTBI) with the QuantiFERON-TB Gold In-Tube (QFT) assay, which exhibits frequent conversions and reversions. The clinical impact of such variability on serial testing remains unknown. We used a microsimulation Markov model that accounts for major sources of variability to project diagnostic outcomes in a simulated North American HCW cohort. Serial testing using a single QFT with the recommended conversion cutoff (IFN-g > 0.35 IU/mL) resulted in 24.6% (95% uncertainty range, UR: 23.8-25.5) of the entire population testing false-positive over ten years. Raising the cutoff to >1.0 IU/mL or confirming initial positive results with a (presumed independent) second test reduced this false-positive percentage to 2.3% (95%UR: 2.0-2.6%) or 4.1% (95%UR: 3.7-4.5%), but also reduced the proportion of true incident infections detected within the first year of infection from 76.5% (95%UR: 66.3-84.6%) to 54.8% (95%UR: 44.6-64.5%) or 61.5% (95%UR: 51.6-70.9%), respectively. Serial QFT testing of HCWs in North America may result in tremendous over-diagnosis and over-treatment of LTBI, with nearly thirty false-positives for every true infection diagnosed. Using higher cutoffs for conversion or confirmatory tests (for initial positives) can mitigate these effects, but will also diagnose fewer true infections.