Essential emergency and critical care as a health system response to critical illness and the COVID19 pandemic: what does it cost?

Essential Emergency and Critical Care (EECC) is a novel approach to the care of critically ill patients, focusing on first-tier, effective, low-cost, life-saving care and designed to be feasible even in low-resourced and low-staffed settings. This is distinct from advanced critical care, usually conducted in ICUs with specialised staff, facilities and technologies. This paper estimates the incremental cost of EECC and advanced critical care for the planning of care for critically ill patients in Tanzania and Kenya.The incremental costing took a health systems perspective. A normative approach based on the ingredients defined through the recently published global consensus on EECC was used. The setting was a district hospital in which the patient is provided with the definitive care typically provided at that level for their condition. Quantification of resource use was based on COVID-19 as a tracer condition using clinical expertise. Local prices were used where available, and all costs were converted to USD2020.The costs per patient day of EECC is estimated to be 1 USD, 11 USD and 33 USD in Tanzania and 2 USD, 14 USD and 37 USD in Kenya, for moderate, severe and critical COVID-19 patients respectively. The cost per patient day of advanced critical care is estimated to be 13 USD and 294 USD in Tanzania and USD 17 USD and 345 USD in Kenya for severe and critical COVID-19 patients, respectively.EECC is a novel approach for providing the essential care to all critically ill patients. The low costs and lower tech approach inherent in delivering EECC mean that EECC could be provided to many and suggests that prioritizing EECC over ACC may be a rational approach when resources are limited.

Cost of hospital services in India: a multi-site study to inform provider payment rates and Health Technology Assessment.

The 'Cost of Health Services in India (CHSI)' is the first large scale multi-site facility costing study to incorporate evidence from a national sample of both private and public sectors at different levels of the health system in India. This paper provides an overview of the extent of heterogeneity in costs caused by various supply-side factors.A total of 38 public (11 tertiary care and 27 secondary care) and 16 private hospitals were sampled from 11 states of India. From the sampled facilities, a total of 327 specialties were included, with 48, 79 and 200 specialties covered in tertiary, private and district hospitals respectively. A mixed methodology consisting of both bottom-up and top-down costing was used for data collection. Unit costs per service output were calculated at the cost centre level (outpatient, inpatient, operating theatre, and ICU) and compared across provider type and geographical location.The unadjusted cost per admission was highest for tertiary facilities (₹ 5690, 75 USD) followed by private facilities (₹ 4839, 64 USD) and district hospitals (₹ 3447, 45 USD). Differences in unit costs were found across types of providers, resulting from both variations in capacity utilisation, length of stay and the scale of activity. In addition, significant differences in costs were found associated with geographical location (city classification).The reliance on cost information from single sites or small samples ignores the issue of heterogeneity driven by both demand and supply-side factors. The CHSI cost data set provides a unique insight into cost variability across different types of providers in India. The present analysis shows that both geographical location and the scale of activity are important determinants for deriving the cost of a health service and should be accounted for in healthcare decision making from budgeting to economic evaluation and price-setting.

Cost savings from use of a neonatal sepsis calculator in Australia: A modelled economic analysis.

AIM: To estimate the change in average cost and length of stay (LOS) for the neonatal birth admission resulting from use of the neonatal early-onset sepsis (EOS) calculator compared to guideline-based management, in an Australian perinatal health-care setting. METHODS: A decision-analytic model (decision tree) was constructed to assess admission cost and LOS with EOS calculator use compared to guideline-based management. Probabilities of clinical sepsis-related outcomes were obtained via review of published literature. Costs and average LOS were obtained from Australia's Independent Hospital Pricing Authority. RESULTS: EOS calculator use was associated with a reduction in costs of AUD$25806 and in average LOS of 25.4 days per 1000 babies born. Sensitivity analyses demonstrated greater net benefits could be expected for services where there is a higher baseline rate of antibiotic use. CONCLUSION: This model demonstrates a significant cost reduction for the neonatal birth admission, associated with use of the EOS calculator as compared to existing guidelines. The net benefit may be greater in Australia, where rates of empiric antibiotic use are reportedly high, compared to some European countries and the United States. Future research opportunities include prospective collection of economic data alongside the introduction of the EOS calculator.

A costing analysis of B-GAP: index-linked HIV testing for children and adolescents in Zimbabwe.

BACKGROUND: By testing children and adolescents of HIV positive caretakers, index-linked HIV testing, a targeted HIV testing strategy, has the ability to identify high risk children and adolescents earlier and more efficiently, compared to blanket testing. We evaluated the incremental cost of integrating index-linked HIV testing via three modalities into HIV services in Zimbabwe. METHODS: A mixture of bottom-up and top-down costing was employed to estimate the provider cost per test and per HIV diagnosis for 2-18 year olds, through standard of care testing, and the incremental cost of index-linked HIV testing via three modalities: facility-based testing, home-based testing by a healthcare worker, and testing at home by the caregiver using an oral mucosal transudate test. In addition to interviews, direct observation and study process data, facility registries were abstracted to extract outcome data and resource use. Costs were converted to 2019 constant US$. RESULTS: The average cost per standard of care test in urban facilities was US$5.91 and US$7.15 at the rural facility. Incremental cost of an index-linked HIV test was driven by the uptake and number of participants tested. The lowest cost approach in the urban setting was home-based testing (US$6.69) and facility-based testing at the rural clinic (US$5.36). Testing by caregivers was almost always the most expensive option (rural US$62.49, urban US$17.49). CONCLUSIONS: This is the first costing analysis of index-linked HIV testing strategies. Unit costs varied across sites and with uptake. When scaling up, alternative testing solutions that increase efficiency such as index-linked HIV testing of the entire household, as opposed to solely targeting children/adolescents, need to be explored.

Cryptococcal Meningitis Screening and Community-based Early Adherence Support in People With Advanced Human Immunodeficiency Virus Infection Starting Antiretroviral Therapy in Tanzania and Zambia: A Cost-effectiveness Analysis.

BACKGROUND: A randomized trial demonstrated that among people living with late-stage human immunodeficiency virus (HIV) infection initiating antiretroviral therapy, screening serum for cryptococcal antigen (CrAg) combined with adherence support reduced all-cause mortality by 28%, compared with standard clinic-based care. Here, we present the cost-effectiveness. METHODS: HIV-infected adults with CD4 count <200 cells/µL were randomized to either CrAg screening plus 4 weekly home visits to provide adherence support or to standard clinic-based care in Dar es Salaam and Lusaka. The primary economic outcome was health service care cost per life-year saved as the incremental cost-effectiveness ratio (ICER), based on 2017 US dollars. We used nonparametric bootstrapping to assess uncertainties and univariate deterministic sensitivity analysis to examine the impact of individual parameters on the ICER. RESULTS: Among the intervention and standard arms, 1001 and 998 participants, respectively, were enrolled. The annual mean cost per participant in the intervention arm was US$339 (95% confidence interval [CI], $331-$347), resulting in an incremental cost of the intervention of US$77 (95% CI, $66-$88). The incremental cost was similar when analysis was restricted to persons with CD4 count <100 cells/µL. The ICER for the intervention vs standard care, per life-year saved, was US$70 (95% CI, $43-$211) for all participants with CD4 count up to 200 cells/µL and US$91 (95% CI, $49-$443) among those with CD4 counts <100 cells /µL. Cost-effectveness was most sensitive to mortality estimates. CONCLUSIONS: Screening for cryptococcal antigen combined with a short period of adherence support, is cost-effective in resource-limited settings.

Correction to: Households forgoing healthcare as a measure of financial risk protection: an application to Liberia.

Following publication of the original article [1], we have been notified that part of the Results part of the Abstract text is mentioned with the mistake.

Assessing the costs and efficiency of HIV testing and treatment services in rural Malawi: implications for future "test and start" strategies.

BACKGROUND: Reaching the 90-90-90 targets requires efficient resource use to deliver HIV testing and treatment services. We investigated the costs and efficiency of HIV services in relation to HIV testing yield in rural Karonga District, Malawi. METHODS: Costs of HIV services were measured over 12 months to September 2017 in five health facilities, drawing on recognised health costing principles. Financial and economic costs were collected in Malawi Kwacha and United States Dollars (US$). Costs were calculated using a provider perspective to estimate average annual costs (2017 US$) per HIV testing episode, per HIV-positive case diagnosed, and per patient-year on antiretroviral therapy (ART), by facility. Costs were assessed in relation to scale of operation and facility-level annual HIV positivity rate. A one-way sensitivity analysis was undertaken to understand how staffing levels and the HIV positivity rate affected HIV testing costs. RESULTS: HIV testing episodes per day and per full-time equivalent HIV health worker averaged 3.3 (range 2.0 to 5.7). The HIV positivity rate averaged 2.4% (range 1.9 to 3.7%). The average cost per testing episode was US$2.85 (range US$1.95 to US$8.55), and the average cost per HIV diagnosis was US$116.35 (range US$77.42 to US$234.11), with the highest costs found in facilities with the lowest daily number of tests and lowest HIV yield respectively. The mean facility-level cost per patient-year on ART was approximately US$100 (range US$90.67 to US$115.42). ART drugs were the largest cost component averaging 71% (range 55 to 76%). The cost per patient-year of viral load tests averaged US$4.50 (range US$0.52 to US$7.00) with cost variation reflecting differences in the tests to ART patient ratio across facilities. CONCLUSION: Greater efficiencies in HIV service delivery are possible in Karonga through increasing daily testing episodes among existing health workers or allocating health workers to tasks in addition to testing. Costs per diagnosis will increase as yields decline, and therefore, encouraging targeted testing strategies that increase yield will be more efficient. Given the contribution of drug costs to per patient-year treatment costs, it is critical to preserve the life-span of first-line ART regimens, underlining the need for continuing adherence support and regular viral load monitoring.

Households forgoing healthcare as a measure of financial risk protection: an application to Liberia.

INTRODUCTION: Access to Liberia's health system is reliant on out-of-pocket (OOP) health expenditures which may prevent people from seeking care or result in catastrophic health expenditure (CHE). CHE and impoverishment due to OOP, which are used by the World Bank and World Health Organization as the sole measures of financial risk protection, are limited: they do not consider households who, following a health shock, do not incur expenditure because they cannot access the healthcare services they need (i.e., households forgoing healthcare (HFH) services). This paper attempts to overcome this limitation and improve financial risk protection by measuring HFH incidence and comparing it with CHE standard measures using household survey data from Liberia. METHODS: Data from the Liberia Household Income and Expenditure Survey 2014 were analysed. An OOP health expenditure is catastrophic when it exceeds a total or non-food household expenditure threshold. A CHE incidence curve, representing CHE incidence at different thresholds, was developed. To overcome CHE limitations, an HFH incidence measure was developed based on CHE, OOP and health shocks data: households incurring health shocks and having negligible OOP were considered to have forgone healthcare. HFH incidence was compared with standard CHE measures. RESULTS: CHE incidence and intensity levels depend on the threshold used. Using a 30% non-food expenditure threshold, CHE incidence is 2.1% (95% CI: 1.7-2.5%) and CHE intensity is 37.4% (95% CI: 22.7-52.0%). CHE incidence is approximately in line with other countries, while CHE intensity is higher than in other countries. CHE pushed 1.6% of households below the food poverty line in 2014. c approximately 4 times higher than CHE (8.0, 95% CI, 7.2-8.9%). CONCLUSION: Lack of financial risk protection is a significant problem in Liberia and it may be underestimated by CHE: this study confirms that HFH incidence can complement CHE measures in providing a complete picture of financial risk protection and demonstrates a simple method that includes measures of healthcare forgone as part of standard CHE analyses. This paper provides a new methodology to measure HFH incidence and highlights the need to consider healthcare forgone in analyses of financial risk protection, as well as the need for further development of these measures.

The Development of the Guide to Economic Analysis and Research (GEAR) Online Resource for Low- and Middle-Income Countries' Health Economics Practitioners: A Commentary.

Public health authorities around the world are increasingly using economic evaluation to set priorities and inform decision making in health policy, especially in the development of health benefit packages. Nevertheless, researchers in low- and middle-income countries (LMICs) encounter many barriers when conducting economic evaluations. In 2015, the Health Intervention and Technology Assessment Program identified key technical and context-specific challenges faced in conducting and using health economic evaluations in LMICs. On the basis of these research findings, the Guide to Economic Analysis and Research (GEAR) online resource (www.gear4health.com) was developed as a reliable aid to researchers in LMICs that would help overcome those challenges. Funded by the Thailand Research Fund and the Bill and Melinda Gates Foundation, GEAR is a free online resource that provides a visual aid tool for planning economic evaluation studies (GEAR mind maps), a repository of national and international economic evaluation guidelines (GEAR guideline comparison), and an active link to a network of volunteer international experts (GEAR: Ask an expert). GEAR will evolve over time to provide relevant, reliable, and up-to-date information through inputs from its users (e.g., periodic survey on methodological challenges) and experts (e.g., in responding to users' questions). The objective of this commentary was to give a brief description of the development and key features of this unique collective information hub aimed at facilitating high-quality research and empowering health care decision makers and stakeholders to use economic evaluation evidence.

Cryptococcal meningitis screening and community-based early adherence support in people with advanced HIV infection starting antiretroviral therapy in Tanzania and Zambia: an open-label, randomised controlled trial.

BACKGROUND: Mortality in people in Africa with HIV infection starting antiretroviral therapy (ART) is high, particularly in those with advanced disease. We assessed the effect of a short period of community support to supplement clinic-based services combined with serum cryptococcal antigen screening. METHODS: We did an open-label, randomised controlled trial in six urban clinics in Dar es Salaam, Tanzania, and Lusaka, Zambia. From February, 2012, we enrolled eligible individuals with HIV infection (age ≥18 years, CD4 count of <200 cells per µL, ART naive) and randomly assigned them to either the standard clinic-based care supplemented with community support or standard clinic-based care alone, stratified by country and clinic, in permuted block sizes of ten. Clinic plus community support consisted of screening for serum cryptococcal antigen combined with antifungal therapy for patients testing antigen positive, weekly home visits for the first 4 weeks on ART by lay workers to provide support, and in Tanzania alone, re-screening for tuberculosis at 6-8 weeks after ART initiation. The primary endpoint was all-cause mortality at 12 months, analysed by intention to treat. This trial is registered with the International Standard Randomised Controlled Trial Number registry, number ISCRTN 20410413. FINDINGS: Between Feb 9, 2012, and Sept 30, 2013, 1001 patients were randomly assigned to clinic plus community support and 998 to standard care. 89 (9%) of 1001 participants in the clinic plus community support group did not receive their assigned intervention, and 11 (1%) of 998 participants in the standard care group received a home visit or a cryptococcal antigen screen rather than only standard care. At 12 months, 25 (2%) of 1001 participants in the clinic plus community support group and 24 (2%) of 998 participants in the standard care group had been lost to follow-up, and were censored at their last visit for the primary analysis. At 12 months, 134 (13%) of 1001 participants in the clinic plus community support group had died compared with 180 (18%) of 998 in the standard care group. Mortality was 28% (95% CI 10-43) lower in the clinic plus community support group than in standard care group (p=0·004). INTERPRETATION: Screening and pre-emptive treatment for cryptococcal infection combined with a short initial period of adherence support after initiation of ART could substantially reduce mortality in HIV programmes in Africa. FUNDING: European and Developing Countries Clinical Trials Partnership.

Does integration of HIV and SRH services achieve economies of scale and scope in practice? A cost function analysis of the Integra Initiative.

OBJECTIVE: Policy-makers have long argued about the potential efficiency gains and cost savings from integrating HIV and sexual reproductive health (SRH) services, particularly in resource-constrained settings with generalised HIV epidemics. However, until now, little empirical evidence exists on whether the hypothesised efficiency gains associated with such integration can be achieved in practice. METHODS: We estimated a quadratic cost function using data obtained from 40 health facilities, over a 2-year-period, in Kenya and Swaziland. The quadratic specification enables us to determine the existence of economies of scale and scope. FINDINGS: The empirical results reveal that at the current output levels, only HIV counselling and testing services are characterised by service-specific economies of scale. However, no overall economies of scale exist as all outputs are increased. The results also indicate cost complementarities between cervical cancer screening and HIV care; post-natal care and HIV care and family planning and sexually transmitted infection treatment combinations only. CONCLUSIONS: The results from this analysis reveal that contrary to expectation, efficiency gains from the integration of HIV and SRH services, if any, are likely to be modest. Efficiency gains are likely to be most achievable in settings that are currently delivering HIV and SRH services at a low scale with high levels of fixed costs. The presence of cost complementarities for only three service combinations implies that careful consideration of setting-specific clinical practices and the extent to which they can be combined should be made when deciding which services to integrate. TRIAL REGISTRATION NUMBER: NCT01694862.

Methodological Issues to Consider When Collecting Data to Estimate Poverty Impact in Economic Evaluations in Low-income and Middle-income Countries.

Out-of-pocket spending is increasingly recognized as an important barrier to accessing health care, particularly in low-income and middle-income countries (LMICs) where a large portion of health expenditure comes from out-of-pocket payments. Emerging universal healthcare policies prioritize reduction of poverty impact such as catastrophic and impoverishing healthcare expenditure. Poverty impact is therefore increasingly evaluated alongside and within economic evaluations to estimate the impact of specific health interventions on poverty. However, data collection for these metrics can be challenging in intervention-based contexts in LMICs because of study design and practical limitations. Using a set of case studies, this letter identifies methodological challenges in collecting patient cost data in LMIC contexts. These components are presented in a framework to encourage researchers to consider the implications of differing approaches in data collection and to report their approach in a standardized and transparent way.

Understanding the extent of economic evidence usage for informing policy decisions in the context of India's national health insurance scheme: Ayushman Bharat Pradhan Mantri Jan Aarogya Yojana (PM-JAY).

INTRODUCTION: Ayushman Bharat Pradhan Mantri Jan Aarogya Yojana (PM-JAY) is one of the world's largest tax-funded insurance schemes. The present study was conducted to understand the decision-making process around the evolution (and revision) of health benefit packages (HBPs) and reimbursement rates within PM-JAY, with a specific focus on assessing the extent of use of economic evidence and role of various stakeholders in shaping these policy decisions. METHODS: A mixed-methods study was adopted involving in-depth interviews with seven key stakeholders involved in HBP design and reimbursement rates decisions, and a survey of 80 government staff and other relevant stakeholders engaged in the implementation of PM-JAY. The data gathered were thematically analysed, and a coding framework was developed to explore specific themes. Additionally, publicly available documents were reviewed to ensure a comprehensive understanding of the decision-making processes. RESULTS: Findings reveal a progressive transition towards evidence-based practices for policy decisions within PM-JAY. The initial version of HBP relied heavily on key criteria like disease burden, utilisation rates, and out-of-pocket expenditures, along with clinical opinion in shaping decisions around the inclusion of services in the HBP and setting reimbursement rates. Revised HBPs were informed based on evidence from a national-level costing study and broader stakeholder consultations. The use of health economic evidence increased with each additional revision with consideration of health technology assessment (HTA) evidence for some packages and reimbursement rates based on empirical cost evidence in the most recent update. The establishment of the Health Financing and Technology Assessment unit further signifies the use of evidence-based policymaking within PM-JAY. However, challenges persist, notably with regard to staff capacity and understanding of HTA principles, necessitating ongoing education and training initiatives. CONCLUSION: While substantial progress has been made in transitioning towards evidence-based practices within PM-JAY, sustained efforts and political commitment are required for the ongoing systematisation of processes.

Healthcare cost accounting in the Indian hospital sector.

Setting reimbursement rates in national insurance schemes requires robust cost data. Collecting provider generated cost accounting information is a potential mechanism for improving the cost evidence. To inform strategies for obtaining cost data to set reimbursement rates, this analysis aims to describe the role of cost accounting in public and private health sectors in India and describe the importance, perceived barriers, and facilitators to improving cost accounting systems. In-depth interviews (IDI) were conducted with 11 key informants. The interview tool guide was informed by a review of published and grey literature and government websites. The interviews were recorded for both audio and video and transcribed. A thematic coding framework was developed for the analysis. Multiple discussions were held to add, delete, classify, or merge the themes. The themes identified were: the status of cost accounting in the Indian hospital sector, legal and regulatory requirements for cost reporting, challenges to implementing cost accounting, and recommendations for improving cost reporting by healthcare providers. The findings indicate that the sector lacks maturity in cost accounting due to a lack of understanding of its benefits, limited capacity, and weak enforcement of cost reporting regulations. Providers recognize the value of cost analysis for investment decisions but have mixed opinions on the willingness to gather and report cost information, citing resource constraints and a lack of trust in payers. Additionally, heterogeneity among providers will require tailored approaches in developing cost accounting reporting frameworks and regulations. Healthcare cost accounting systems in India are rudimentary with a few exceptions, raising questions about how to source these data sustainably. Strengthening cost accounting systems will be contingent upon developing standardized formats that generate sufficient information for policymaking, are acceptable to private providers, and can be integrated with the existing data management systems.

National hospital costing systems matter for universal healthcare: the India PM-JAY experience.

India envisions achieving universal health coverage to provide its people with access to affordable quality health services. A breakthrough effort in this direction has been the launch of the world's largest health assurance scheme Ayushman Bharat Pradhan Mantri Jan Arogya Yojana, the implementation of which resides with the National Health Authority. Appropriate provider payment systems and reimbursement rates are an important element for the success of PM-JAY, which in turn relies on robust cost evidence to support pricing decisions. Since the launch of PM-JAY, the health benefits package and provider payment rates have undergone a series of revisions. At the outset, there was a relative lack of cost data. Later revisions relied on health facility costing studies, and now there is an initiative to establish a national hospital costing system relying on provider-generated data. Lessons from PM-JAY experience show that the success of such cost systems to ensure regular and routine generation of evidence is contingent on integrating with existing billing or patient information systems or management information systems, which digitise similar information on resource consumption without any additional data entry effort. Therefore, there is a need to focus on building sustainable mechanisms for setting up systems for generating accurate cost data rather than relying on resource-intensive studies for cost data collection.

Modelling Methods of Economic Evaluations of HIV Testing Strategies in Sub-Saharan Africa: A Systematic Review.

BACKGROUND AND OBJECTIVE: Economic evaluations, a decision-support tool for policy makers, will be crucial in planning and tailoring HIV prevention and treatment strategies especially in the wake of stalled and decreasing funding for the global HIV response. As HIV testing and treatment coverage increase, case identification becomes increasingly difficult and costly. Determining which subset of the population these strategies should be targeted to becomes of vital importance as well. Generating quality economic evidence begins with the validity of the modelling approach and the model structure employed. This study synthesises and critiques the reporting around modelling methodology of economic models in the evaluation of HIV testing strategies in sub-Saharan Africa. METHODS: The following databases were searched from January 2000 to September 2020: MEDLINE, Embase, Scopus, EconLit and Global Health. Any model-based economic evaluation of a unique HIV testing strategy conducted in sub-Saharan Africa presenting a cost-effectiveness measure published from 2013 onwards was eligible. Data were extracted around three components: general study characteristics; economic evaluation design; and quality of model reporting using a novel tool developed for the purposes of this study. RESULTS: A total of 21 studies were included; 10 cost-effectiveness analyses, 11 cost-utility analyses. All but one study was conducted in Eastern and Southern Africa. Modelling approaches for HIV testing strategies can be broadly characterised as static aggregate models (3/21), static individual models (6/21), dynamic aggregate models (5/21) and dynamic individual models (7/21). Adequate reporting around data handling was the highest of the three categories assessed (74%), and model validation, the lowest (45%). Limitations to model structure, justification of chosen time horizon and cycle length, and description of external model validation process were all adequately reported in less than 40% of studies. The predominant limitation of this review relates to the potential implications of the narrow inclusion criteria. CONCLUSIONS: This review is the first to synthesise economic evaluations of HIV testing strategies in sub-Saharan Africa. The majority of models exhibited dynamic, stochastic and individual properties. Model reporting against the 13 criteria in our novel tool was mixed. Future model-based economic evaluations of HIV testing strategies would benefit from transparency around the choice of modelling approach, model structure, data handling procedures and model validation techniques.

Refining the provider payment system of India's government-funded health insurance programme: an econometric analysis.

OBJECTIVES: Reimbursement rates in national health insurance schemes are frequently weighted to account for differences in the costs of service provision. To determine weights for a differential case-based payment system under India's publicly financed national health insurance scheme, the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (PM-JAY), by exploring and quantifying the influence of supply-side factors on the costs of inpatient admissions and surgical procedures. DESIGN: Exploratory analysis using regression-based cost function on data from a multisite health facility costing study-the Cost of Health Services in India (CHSI) Study. SETTING: The CHSI Study sample included 11 public sector tertiary care hospitals, 27 public sector district hospitals providing secondary care and 16 private hospitals, from 11 Indian states. PARTICIPANTS: 521 sites from 57 healthcare facilities in 11 states of India. INTERVENTIONS: Medical and surgical packages of PM-JAY. PRIMARY AND SECONDARY OUTCOME MEASURES: The cost per bed-day and cost per surgical procedure were regressed against a range of factors to be considered as weights including hospital location, presence of a teaching function and ownership. In addition, capacity utilisation, number of beds, specialist mix, state gross domestic product, State Health Index ranking and volume of patients across the sample were included as variables in the models. Given the skewed data, cost variables were log-transformed for some models. RESULTS: The estimated mean costs per inpatient bed-day and per procedure were 2307 and 10 686 Indian rupees, respectively. Teaching status, annual hospitalisation, bed size, location of hospital and average length of hospitalisation significantly determine the inpatient bed-day cost, while location of hospital and teaching status determine the procedure costs. Cost per bed-day of teaching hospitals was 38-143.4% higher than in non-teaching hospitals. Similarly, cost per bed-day was 1.3-89.7% higher in tier 1 cities, and 19.5-77.3% higher in tier 2 cities relative to tier 3 cities, respectively. Finally, cost per surgical procedure was higher by 10.6-144.6% in teaching hospitals than non-teaching hospitals; 12.9-171.7% higher in tier 1 cities; and 33.4-140.9% higher in tier 2 cities compared with tier 3 cities, respectively. CONCLUSION: Our study findings support and validate the recently introduced differential provider payment system under the PM-JAY. While our results are indicative of heterogeneity in hospital costs, other considerations of how these weights will affect coverage, quality, cost containment, as well as create incentives and disincentives for provider and consumer behaviour, and integrate with existing price mark-ups for other factors, should be considered to determine the future revisions in the differential pricing scheme.

Cost Effectiveness of Strategies for Caring for Critically Ill Patients with COVID-19 in Tanzania.

BACKGROUND: The resources for critical care are limited in many settings, exacerbating the significant morbidity and mortality associated with critical illness. Budget constraints can lead to choices between investing in advanced critical care (e.g. mechanical ventilators in intensive care units) or more basic critical care such as Essential Emergency and Critical Care (EECC; e.g. vital signs monitoring, oxygen therapy, and intravenous fluids). METHODS: We investigated the cost effectiveness of providing EECC and advanced critical care in Tanzania in comparison with providing 'no critical care' or 'district hospital-level critical care' using coronavirus disease 2019 (COVID-19) as a tracer condition. We developed an open-source Markov model ( https://github.com/EECCnetwork/POETIC_CEA ) to estimate costs and disability-adjusted life-years (DALYs) averted, using a provider perspective, a 28-day time horizon, patient outcomes obtained from an elicitation method involving a seven-member expert group, a normative costing study, and published literature. We performed a univariate and probabilistic sensitivity analysis to assess the robustness of our results. , RESULTS: EECC is cost effective 94% and 99% of the time when compared with no critical care (incremental cost-effectiveness ratio [ICER] $37 [-$9 to $790] per DALY averted) and district hospital-level critical care (ICER $14 [-$200 to $263] per DALY averted), respectively, relative to the lowest identified estimate of the willingness-to-pay threshold for Tanzania ($101 per DALY averted). Advanced critical care is cost effective 27% and 40% of the time, when compared with the no critical care or district hospital-level critical care scenarios, respectively. CONCLUSION: For settings where there is limited or no critical care delivery, implementation of EECC could be a highly cost-effective investment. It could reduce mortality and morbidity for critically ill COVID-19 patients, and its cost effectiveness falls within the range considered 'highly cost effective'. Further research is needed to explore the potential of EECC to generate even greater benefits and value for money when patients with diagnoses other than COVID-19 are accounted for.

Hospital care for critical illness in low-resource settings: lessons learned during the COVID-19 pandemic.

Care for the critically ill patients is often considered synonymous with a hospital having an intensive care unit. However, a focus on Essential Emergency and Critical Care (EECC) may obviate the need for much intensive care. Severe COVID-19 presented a specific critical care challenge while also being an exemplar of critical illness in general. Our multidisciplinary team conducted research in Kenya and Tanzania on hospitals' ability to provide EECC as the COVID-19 pandemic unfolded. Important basic inputs were often lacking, especially sufficient numbers of skilled health workers. However, we learnt that higher scores on resource readiness scales were often misleading, as resources were often insufficient or not functional in all the clinical areas they are needed. By following patient journeys, through interviews and group discussions, we revealed gaps in timeliness, continuity and delivery of care. Generic challenges in transitions between departments were identified in the receipt of critically ill patients, the ability to sustain monitoring and treatment and preparation for any subsequent transition. While the global response to COVID-19 focused initially on providing technologies and training, first ventilators and later oxygen, organisational and procedural challenges seemed largely ignored. Yet, they may even be exacerbated by new technologies. Efforts to improve care for the critically ill patients, which is a complex process, must include a whole system and whole facility view spanning all areas of patients' care and their transitions and not be focused on a single location providing 'critical care'. We propose a five-part strategy to support the system changes needed.

Role of healthcare cost accounting in pricing and reimbursement in low-income and middle-income countries: a scoping review.

OBJECTIVES: Progress towards universal health coverage (UHC) requires evidence-based policy including good quality cost data systems. Establishing these systems can be complex, resource-intensive and take time. This study synthesises evidence on the experiences of low-income and middle-income countries (LMICs) in the institutionalisation of cost data systems to derive lessons for the technical process of price-setting in the context of UHC. DESIGN: A scoping review and narrative synthesis of publicly available information. DATA SOURCES: PubMed, MEDLINE, EconLit, the Web of Science and grey literature searched from January 2000 to April 2021. ELIGIBILITY CRITERIA: English-language papers published since 2000 that identified and/or described development of and/or methods used to estimate or inform national tariffs for hospital reimbursement in LMICs. Papers were screened by two independent reviewers. DATA EXTRACTION AND SYNTHESIS: Extraction was performed by one reviewer and checked by the second reviewer on: the method and outputs of cost data collection; commentary on the use of cost data; description of the technical process of tariff setting; and strengths and challenges of the approach. Evidence was summarised using narrative review. RESULTS: Thirty of 484 papers identified were eligible. Fourteen papers reported on primary cost data collection; 18 papers explained how cost evidence informs tariff-setting. Experience was focused in Asia (n=22) with countries at different stages of developing cost systems. Experiences on cost accounting tend to showcase country costing experiences, methods and implementation. There is little documentation how data have been incorporated into decision making and price setting. Where cost information or cost systems have been used, there is improved transparency in decision making alongside increased efficiency. CONCLUSIONS: There are widely used and accepted methods for generating cost information. Countries need to build sustainable cost systems appropriate to their settings and budgets and adopt transparent processes and methodologies for translating costs into prices.