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Health Technology Assessment (HTA), a tool for priority setting, has emerged as a means of ensuring the sustainability of a Universal Health Coverage (UHC) system. However, setting up an effective HTA system poses multiple challenges and knowledge exchange can play a crucial role in helping countries achieve their UHC targets. This article reports the results of the discussion during a preconference session at the 2019 HTAsiaLink Conference, an annual gathering of HTA agencies in Asia, which supports knowledge transfer and exchange among HTA practitioners. As part of this discourse, 3 main HTA challenges were identified based on experiences of selected countries in Asia and Africa, namely Bhutan, Kenya, Thailand, and Zambia: availability of funding, building technical capacity, and achieving buy-in among stakeholders for successful translation of HTA research into UHC policy. The potential solutions identified through this South-South engagement included establishing a legal mandate for HTA, building local technical capacity through partnerships and enhancing strategic communication with stakeholders to increase awareness, among others. South-South Knowledge Exchange can therefore be instrumental in sharing lessons learned from common challenges and offer potential solutions to address capacity building initiatives for HTA in LMICs., Highlights • Health technology assessment is an effective tool for priority setting for countries seeking to achieve Universal Health Coverage. • Three of the main challenges faced by countries in institutionalizing Health Technology Assessment are limited funding, lack of technical capacity, and stakeholder engagement. • South-South knowledge exchange is a useful means of learning from peers to address challenges.

Background There is a need for easily accessible tuberculosis unit cost data, as well as an understanding of the variability of methods used and reporting standards of that data. Objective The aim of this systematic review was to descriptively review papers reporting tuberculosis unit costs from a healthcare provider perspective looking at methodological variation; to assess quality using a study quality rating system and machine learning to investigate the indicators of reporting quality; and to identify the data gaps to inform standardised tuberculosis unit cost collection and consistent principles for reporting going forward. Methods We searched grey and published literature in five sources and eight databases, respectively, using search terms linked to cost, tuberculosis and tuberculosis health services including tuberculosis treatment and prevention. For inclusion, the papers needed to contain empirical unit cost estimates for tuberculosis interventions from low- and middle-income countries, with reference years between 1990 and 2018. A total of 21,691 papers were found and screened in a phased manner. Data were extracted from the eligible papers into a detailed Microsoft Excel tool, extensively cleaned and analysed with R software (R Project, Vienna, Austria) using the user interface of RStudio. A study quality rating was applied to the reviewed papers based on the inclusion or omission of a selection of variables and their relative importance. Following this, machine learning using a recursive partitioning method was utilised to construct a classification tree to assess the reporting quality. Results This systematic review included 103 provider perspective papers with 627 unit costs (costs not presented here) for tuberculosis interventions among a total of 140 variables. The interventions covered were active, passive and intensified case finding; tuberculosis treatment; above-service costs; and tuberculosis prevention. Passive case finding is the detection of tuberculosis cases where individuals self-identify at health facilities; active case finding is detection of cases of those not in health facilities, such as through outreach; and intensified case finding is detection of cases in high-risk populations. There was heterogeneity in some of the reported methods used such cost allocation, amortisation and the use of top-down, bottom-up or mixed approaches to the costing. Uncertainty checking through sensitivity analysis was only reported on by half of the papers (54%), while purposive and convenience sampling was reported by 72% of papers. Machine learning indicated that reporting on ‘Intervention’ (in particular), ‘Urbanicity’ and ‘Site Sampling’, were the most likely indicators of quality of reporting. The largest data gap identified was for tuberculosis vaccination cost data, the Bacillus Calmette–Guérin (BCG) vaccine in particular. There is a gap in available unit costs for 12 of 30 high tuberculosis burden countries, as well as for the interventions of above-service costs, tuberculosis prevention, and active and intensified case finding. Conclusion Variability in the methods and reporting used makes comparison difficult and makes it hard for decision makers to know which unit costs they can trust. The study quality rating system used in this review as well as the classification tree enable focus on specific reporting aspects that should improve variability and increase confidence in unit costs. Researchers should endeavour to be explicit and transparent in how they cost interventions following the principles as laid out in the Global Health Cost Consortium’s Reference Case for Estimating the Costs of Global Health Services and Interventions, which in turn will lead to repeatability, comparability and enhanced learning from others.

Background How health facilities are financed affects their performance and health system goals. We examined how health facilities in the public sector are financed in Kenya, within the context of a devolved health system. Methods We carried out a cross-sectional study in five purposely selected counties in Kenya, using a mixed methods approach. We collected data using document reviews and in-depth interviews (no = 20). In each county, we interviewed county department of health managers and health facility managers from two and one purposely selected public hospitals and health center respectively. We analyzed qualitive data using thematic analysis and conducted descriptive analysis of quantitative data. Results Planning and budgeting: Planning and budgeting processes by hospitals and health centers were not standardized across counties. Budgets were not transparent and credible, but rather were regarded as “wish lists” since they did not translate to actual resources. Sources of funds: Public hospitals relied on user fees, while health centers relied on donor funds as their main sources of funding. Funding flows: Hospitals in four of the five study counties had no financial autonomy. Health centers in all study counties had financial autonomy. Flow of funds to hospitals and health centers in all study counties was characterized by unpredictability of amounts and timing. Health facility expenditure: Staff salaries accounted for over 80% of health facility expenditure. This crowded out other expenditure and led to frequent stock outs of essential health commodities. Conclusion The national and county government should consider improving health facility financing in Kenya by 1) standardizing budgeting and planning processes, 2) transitioning public facility financing away from a reliance on user fees and donor funding 3) reforming public finance management laws and carry out political engagement to facilitate direct facility financing and financial autonomy of public hospitals, and 4) assess health facility resource needs to guide appropriate levels resource allocation.

ObjectivesCOVID-19 has altered health sector capacity in low-income and middle-income countries (LMICs). Cost data to inform evidence-based priority setting are urgently needed. Consequently, in this paper, we calculate the full economic health sector costs of COVID-19 clinical management in 79 LMICs under different epidemiological scenarios.MethodsWe used country-specific epidemiological projections from a dynamic transmission model to determine number of cases, hospitalisations and deaths over 1 year under four mitigation scenarios. We defined the health sector response for three base LMICs through guidelines and expert opinion. We calculated costs through local resource use and price data and extrapolated costs across 79 LMICs. Lastly, we compared cost estimates against gross domestic product (GDP) and total annual health expenditure in 76 LMICs.ResultsCOVID-19 clinical management costs vary greatly by country, ranging between ConclusionsWe present the first dataset of COVID-19 clinical management costs across LMICs. These costs can be used to inform decision-making on priority setting. Our results show that COVID-19 clinical management costs in LMICs are substantial, even in scenarios of moderate social distancing. Low-income countries are particularly vulnerable and some will struggle to cope with almost any epidemiological scenario. The choices facing LMICs are likely to remain stark and emergency financial support will be needed.

IntroductionVaccines are considered the path out of the COVID-19 pandemic. The government of Kenya is implementing a phased strategy to vaccinate the Kenyan population, initially targeting populations at high risk of severe disease and infection. We estimated the financial and economic unit costs of procuring and delivering the COVID-19 vaccine in Kenya across various vaccination strategies.MethodsWe used an activity-based costing approach to estimate the incremental costs of COVID-19 vaccine delivery, from a health systems perspective. Document reviews and key informant interviews (n=12) were done to inform the activities, assumptions and the resources required. Unit prices were derived from document reviews or from market prices. Both financial and economic vaccine procurement costs per person vaccinated with 2-doses, and the vaccine delivery costs per person vaccinated with 2-doses were estimated and reported in 2021USD.ResultsThe financial costs of vaccine procurement per person vaccinated with 2-doses ranged from $2.89-$13.09 in the 30% and 100% coverage levels respectively, however, the economic cost was $17.34 across all strategies. Financial vaccine delivery costs per person vaccinated with 2-doses, ranged from $4.28-$3.29 in the 30% and 100% coverage strategies: While the economic delivery costs were two to three times higher than the financial costs. The total procurement and delivery costs per person vaccinated with 2-doses ranged from $7.34-$16.47 for the financial costs and $29.7-$24.68 for the economic costs for the 30% and 100% coverage respectively. With the exception of procurement costs, the main cost driver of financial and economic delivery costs was supply chain costs (47-59%) and advocacy, communication and social mobilization (29-35%) respectively.ConclusionThis analysis presents cost estimates that can be used to inform local policy and may further inform parameters used in cost-effectiveness models. The results could potentially be adapted and adjusted to country-specific assumptions to enhance applicability in similar low-and middle-income settings.

BACKGROUND Screening for genital inflammation can reveal asymptomatic cases of sexually transmitted infections (STIs) and bacterial vaginosis (BV), useful in settings where only syndromic management is available. This study aimed to estimate the incremental cost of screening using a new cytokine biomarker rapid test and determine the budget impact of providing this service in primary health facilities in South Africa. METHODS Costs of adding genital inflammation screening to existing family planning services were estimated for women (15 to 49 years) attending three different family planning clinics in US$2016. The predicted unit cost per patient screened from a provider's perspective were calculated using bottom-up and top-down approaches and were used to analyze the budget impact of scaling-up and providing this service in primary health facilities countrywide. Univariate sensitivity analyses tested the robustness of the findings. RESULTS The incremental cost per woman screened for genital inflammation ranged between US $3.19 and US $4.79. The scaled-up costs ranged between US $7,245,775 and US $22,212,636 countrywide, annually. This was based on the number of women of reproductive age currently seeking contraceptive care at all primary health care facilities, as a proxy for those most susceptible to asymptomatic STIs/BV. The cost estimates were sensitive to changes in personnel costs, utilization rate and population coverage rates. CONCLUSION This screening tool is likely to increase case detection, contributing to better STI/BV management and control, in addition to reducing women's risk of HIV acquisition. The incremental cost estimates could make implementation affordable.

BackgroundCase management of symptomatic COVID-19 patients is a key health system intervention. The Kenyan government embarked to fill capacity gaps in essential and advanced critical care needed for the management of severe and critical COVID-19. However, given scarce resources, gaps in both essential and advanced critical care persist. This study assessed the cost-effectiveness of investments in essential and advanced critical care to inform the prioritization of investment decisions.MethodsWe employed a decision tree model to assess the incremental cost-effectiveness of investment in essential care (EC) and investment in both essential and advanced critical care (EC+ACC) compared to current health care provision capacity (status quo) for COVID-19 patients in Kenya. We used a health system perspective, and an inpatient care episode time horizon. Cost data was obtained from primary empirical analysis while outcomes data was obtained from epidemiological model estimates. We used univariate and probabilistic sensitivity analysis (PSA) to assess the robustness of the results.ResultsThe status quo option is more costly and less effective compared to investment in essential care and is thus dominated by the later. The incremental cost effectiveness ratio (ICER) of Investment in essential and advanced critical care (EC+ACC) was US $1,378.21 per DALY averted and hence not a cost-effective strategy when compared to Kenya’s cost-effectiveness threshold (USD 908).ConclusionWhen the criterion of cost-effectiveness is considered, and within the context of resource scarcity, Kenya will achieve better value for money if it prioritizes investments in essential care before investments in advanced critical care. This information on cost-effectiveness will however need to be considered as part of a multi-criteria decision-making framework that uses a range of criteria that reflect societal values of the Kenyan society.Key questionsWhat is already known?The COVID-19 pandemic is responsible for substantial health effects in low- and middle-income countriesThe case management of COVID-19 is one of the key control interventions deployed by country health systems.Similar to other low- and middle-income countries, Kenya had substantial gaps in both essential and advanced critical care at the beginning of the pandemic.What are the new findings?Provision of essential care and advanced critical care for COVID-19 at the current health system capacity (status quo) was costly and the least effective strategy.Investment in both essential care and advanced critical care for COVID-19 is not cost-effective in Kenya when compared to investment in essential care.What do the new findings imply?Prioritizing investments in filling capacity gaps in essential care before investing in filling capacity gaps in advanced critical care for COVID-19 is more cost-effective in KenyaThese findings are intended to inform the sequencing of investments in case management rather than the selection of either strategy, within a context of substantial resource constraint, and capacity gaps in both essential and advanced critical care or COVID-19Kenya will need to consider these findings on cost-effectiveness within a multi-criteria decision-making framework that use a range of criteria that reflect societal values.

BackgroundUser fees have been reported to limit access to services and increase inequities. As a result, Kenya introduced a free maternity policy in all public facilities in 2013. Subsequently in 2017, the policy was revised to the Linda Mama programme to expand access to private sector, expand the benefit package and change its management.MethodsAn interrupted time-series analysis on facility deliveries, antenatal care (ANC) and postnatal care (PNC) visits data between 2012 and 2019 was used to determine the effect of the two free maternity policies. These data were from 5419 public and 305 private and faith-based facilities across all counties, with data sourced from the health information system. A segmented negative binomial regression with seasonality accounted for, was used to determine the level (immediate) effect and trend (month-on-month) effect of the policies.ResultsThe 2013 free-maternity policy led to a 19.6% and 28.9% level increase in normal deliveries and caesarean sections, respectively, in public facilities. There was also a 1.4% trend decrease in caesarean sections in public facilities. A level decrease followed by a trend increase in PNC visits was reported in public facilities. For private and faith-based facilities, there was a level decrease in caesarean sections and ANC visits followed by a trend increase in caeserean sections following the 2013 policy.Furthermore, the 2017 Linda Mama programme showed a level decrease then a trend increase in PNC visits and a 1.1% trend decrease in caesarean sections in public facilities. In private and faith-based facilities, there was a reported level decrease in normal deliveries and caesarean sections and a trend increase in caesarean sections.ConclusionThe free maternity policies show mixed effects in increasing access to maternal health services. Emphasis on other accessibility barriers and service delivery challenges alongside user fee removal policies should be addressed to realise maximum benefits in maternal health utilisation.

Background In 2013, Kenya introduced a free maternity policy in all public healthcare facilities. In 2016, the Ministry of Health shifted responsibility for the program, now called Linda Mama, to the National Hospital Insurance Fund (NHIF) and expanded access beyond public sector. This study aimed to examine the implementation of the Linda Mama program. Methods We conducted a mixed-methods cross-sectional study at the national level and in 20 purposively sampled facilities across five counties in Kenya. We collected data using in-depth interviews (n = 104), administered patient-exit questionnaires (n = 108), and carried out document reviews. Qualitative data were analysed using a framework approach while quantitative data were analysed descriptively. Results Linda Mama was designed and resulted in improved accountability and expand benefits. In practice however, beneficiaries did not access some services that were part of the revised benefit package. Second, out of pocket payments were still being incurred by beneficiaries. Health facilities in most counties had lost financial autonomy and had no access to reimbursements from NHIF for services provided; but those with financial autonomy were able to boost facility revenue and enhance service delivery. Further, fund disbursements from NHIF were characterised by delays and unpredictability. Implementation experiences reveal that there was inadequate communication, claim processing challenges and reimbursement rates were deemed insufficient. Conclusions Our findings show that there are challenges associated with the implementation of the Linda Mama program and highlights the need for process evaluations for programs to track implementation, ensure continuous learning, and provide opportunities for course correcting programs' implementation.

IntroductionCase management for COVID-19 patients is one of key interventions in country responses to the pandemic. Countries need information on the costs of case management to inform resource mobilization, planning and budgeting, purchasing arrangements, and assessments of the cost-effectiveness of interventions. We estimated unit costs for COVID-19 case management for patients with asymptomatic, mild to moderate, severe, and critical COVID-19 disease in Kenya.MethodsWe estimated per patient per day unit costs of COVID-19 case management for patients that are asymptomatic and those that have mild to moderate, severe, and critical symptoms. For asymptomatic and mild to moderate patients, we estimated unit costs for home-based care and institutional (hospitals and isolation centers). We used an ingredients approach, adopted a health system perspective and patient episode of care as our time horizon. We obtained data on inputs and their quantities from COVID-19 case management guidelines, home based care guidelines, and human resource guidelines, and augmented this with data provided by three public covid-19 treatment hospitals in Kenya. We obtained input prices for services from a recent costing survey of 20 hospitals in Kenya and for pharmaceuticals, non-pharmaceuticals, devices and equipment from market price databases for Kenya.ResultsPer day per patient unit cost for asymptomatic patients and patients with mild to moderate COVID-19 disease under home based care are KES 1,993.01 (USD 18.89) and 1995.17 (USD 18.991) respectively. When these patients are managed in an isolation center of hospital, the same unit costs for asymptomatic patients and patients with mild to moderate disease are 7,415.28 (USD 70.29) and 7,417.44 (USD 70.31) respectively. Per day unit costs for patients with severe COVID-19 disease managed in general hospital wards and those with critical COVID-19 disease admitted in intensive care units are 12,570.75 (USD 119.16) and 59,369.42 (USD 562.79).ConclusionCOVID-19 case management costs are substantial. Unit costs for asymptomatic and mild to moderate COVID-19 patients in home-based care is 4-fold lower compared institutional care of the same patients. Kenya will not only need to mobilize substantial resources to finance COVID-19 case management but also explore additional service delivery adaptations that will reduce unit costs.

Background There is a need for easily accessible tuberculosis unit cost data, as well as an understanding of the variability of methods used and reporting standards of that data. Objective The aim of this systematic review was to descriptively review papers reporting tuberculosis unit costs from a healthcare provider perspective looking at methodological variation; to assess quality using a study quality rating system and machine learning to investigate the indicators of reporting quality; and to identify the data gaps to inform standardised tuberculosis unit cost collection and consistent principles for reporting going forward. Methods We searched grey and published literature in five sources and eight databases, respectively, using search terms linked to cost, tuberculosis and tuberculosis health services including tuberculosis treatment and prevention. For inclusion, the papers needed to contain empirical unit cost estimates for tuberculosis interventions from low- and middle-income countries, with reference years between 1990 and 2018. A total of 21,691 papers were found and screened in a phased manner. Data were extracted from the eligible papers into a detailed Microsoft Excel tool, extensively cleaned and analysed with R software (R Project, Vienna, Austria) using the user interface of RStudio. A study quality rating was applied to the reviewed papers based on the inclusion or omission of a selection of variables and their relative importance. Following this, machine learning using a recursive partitioning method was utilised to construct a classification tree to assess the reporting quality. Results This systematic review included 103 provider perspective papers with 627 unit costs (costs not presented here) for tuberculosis interventions among a total of 140 variables. The interventions covered were active, passive and intensified case finding; tuberculosis treatment; above-service costs; and tuberculosis prevention. Passive case finding is the detection of tuberculosis cases where individuals self-identify at health facilities; active case finding is detection of cases of those not in health facilities, such as through outreach; and intensified case finding is detection of cases in high-risk populations. There was heterogeneity in some of the reported methods used such cost allocation, amortisation and the use of top-down, bottom-up or mixed approaches to the costing. Uncertainty checking through sensitivity analysis was only reported on by half of the papers (54%), while purposive and convenience sampling was reported by 72% of papers. Machine learning indicated that reporting on ‘Intervention’ (in particular), ‘Urbanicity’ and ‘Site Sampling’, were the most likely indicators of quality of reporting. The largest data gap identified was for tuberculosis vaccination cost data, the Bacillus Calmette–Guérin (BCG) vaccine in particular. There is a gap in available unit costs for 12 of 30 high tuberculosis burden countries, as well as for the interventions of above-service costs, tuberculosis prevention, and active and intensified case finding. Conclusion Variability in the methods and reporting used makes comparison difficult and makes it hard for decision makers to know which unit costs they can trust. The study quality rating system used in this review as well as the classification tree enable focus on specific reporting aspects that should improve variability and increase confidence in unit costs. Researchers should endeavour to be explicit and transparent in how they cost interventions following the principles as laid out in the Global Health Cost Consortium’s Reference Case for Estimating the Costs of Global Health Services and Interventions, which in turn will lead to repeatability, comparability and enhanced learning from others. Electronic supplementary material The online version of this article (10.1007/s40273-020-00910-w) contains supplementary material, which is available to authorized users.