The data is from a mixed-methods approach to address address three main questions:1 What were the mortality rates in care homes by country? 2. How does the mortality in care homes compare with previous periods? 3. What explains any excess mortality in care homes?List of Tables available in FigshareTable 1. Excess Deaths Study Characteristics Table 2. Care Homes Excess Deaths Study OutcomesTable 3. Quality Assessment: Care Home Excess Deaths studies: Newcastle Ottawa ScaleTable 4. Care Home intervention/exposure studies characteristics Table 5. Care Home intervention/exposure studies outcomesTable 6. Quality Assessment: Care Home Intervention/Exposure studies: Newcastle Ottawa ScaleFigure 1. Flow chartProtocol available at FigshareAppendix 1 National Mortality Data

BackgroundTo compare, through a systematic literature review, the cost-effectiveness ratio of home care compared to hospital care for following up patients with chronic obstructive pulmonary disease (COPD).MethodsThis review was registered in PROSPERO, and the bibliographic search was performed in six primary databases [MedLine (via PubMed), Scopus, LILACS, SciELO, Web of Science, and Embase], two dedicated databases for economic studies (NHS Economic Evaluation Database (NHS EED) and Cost-Effectiveness Analysis (CEA) Registry), and two databases for partially searching the “gray literature” (DansEasy and ProQuest). This review only included studies that compared home and hospital care for patients diagnosed with COPD, regardless of publication year or language. Two reviewers selected the studies, extracted the data, and assessed the risk of bias independently. A JBI tool was used for risk of bias assessment.Results and discussion7,279 studies were found, of which 14 met the eligibility criteria. Only one study adequately met all items of the risk of bias assessment. Thirteen studies found lower costs and higher effectiveness for home care. Home care showed a better cost-effectiveness ratio than hospital care for COPD patients. Regarding effectiveness, there is no possibility of choosing a more effective care for COPD patients, given the incipience of the data presented on eligible studies. However, considering the analyzed data refer only to high-income countries, caution is required when extrapolating this conclusion to low- and low-middle-income countries.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022319488.

IntroductionFor many Kenyans, high-quality primary health care (PHC) services remain unavailable, inaccessible, or unaffordable. To address these challenges, the Government of Kenya has committed to strengthening the country’s PHC system by introducing a comprehensive package of PHC services and promoting the efficient use of existing resources through its primary care network approach. Our study estimated the costs of delivering PHC services in public sector facilities in seven sub-counties, comparing actual costs to normative costs of delivering Kenya’s PHC package and determining the corresponding financial resource gap to achieving universal coverage.MethodsWe collected primary data from a sample of 71 facilities, including dispensaries, health centers, and sub-county hospitals. Data on facility-level recurrent costs were collected retrospectively for 1 year (2018–2019) to estimate economic costs from the public sector perspective. Total actual costs from the sampled facilities were extrapolated using service utilization data from the Kenya Health Information System for the universe of facilities to obtain sub-county and national PHC cost estimates. Normative costs were estimated based on standard treatment protocols and the populations in need of PHC in each sub-county.Results and discussionThe average actual PHC cost per capita ranged from US$ 9.3 in Ganze sub-county to US$ 47.2 in Mukurweini while the normative cost per capita ranged from US$ 31.8 in Ganze to US$ 42.4 in Kibwezi West. With the exception of Mukurweini (where there was no financial resource gap), closing the resource gap would require significant increases in PHC expenditures and/or improvements to increase the efficiency of PHC service delivery such as improved staff distribution, increased demand for services and patient loads per clinical staff, and reduced bypass to higher level facilities. This study offers valuable evidence on sub-national cost variations and resource requirements to guide the implementation of the government’s PHC reforms and resource mobilization efforts.

Abstract

This study examines various dimensions of primary health care delivery in Uganda, using a baseline survey of public and private dispensaries, the most common lower level health facilities in the country.

The survey was designed and implemented by the World Bank in collaboration with the Makerere Institute for Social Research and the Ugandan Ministries of Health and of Finance, Planning and Economic Development. It was carried out in October - December 2000 and covered 155 local health facilities and seven district administrations in ten districts. In addition, 1617 patients exiting health facilities were interviewed. Three types of dispensaries (both with and without maternity units) were included: those run by the government, by private for-profit providers, and by private nonprofit providers, mainly religious.

This research is a Quantitative Service Delivery Survey (QSDS). It collected microlevel data on service provision and analyzed health service delivery from a public expenditure perspective with a view to informing expenditure and budget decision-making, as well as sector policy.

Objectives of the study included: 1) Measuring and explaining the variation in cost-efficiency across health units in Uganda, with a focus on the flow and use of resources at the facility level; 2) Diagnosing problems with facility performance, including the extent of drug leakage, as well as staff performance and availability;
3) Providing information on pricing and user fee policies and assessing the types of service actually provided; 4) Shedding light on the quality of service across the three categories of service provider - government, for-profit, and nonprofit; 5) Examining the patterns of remuneration, pay structure, and oversight and monitoring and their effects on health unit performance; 6) Assessing the private-public partnership, particularly the program of financial aid to nonprofits.

Geographic coverage

The study districts were Mpigi, Mukono, and Masaka in the central region; Mbale, Iganga, and Soroti in the east; Arua and Apac in the north; and Mbarara and Bushenyi in the west.

Analysis unit

• local dispensary with or without maternity unit

Universe

The survey covered government, for-profit and nonprofit private dispensaries with or without maternity units in ten Ugandan districts.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey covered government, for-profit and nonprofit private dispensaries with or without maternity units in ten Ugandan districts.

The sample design was governed by three principles. First, to ensure a degree of homogeneity across sampled facilities, attention was restricted to dispensaries, with and without maternity units (that is, to the health center III level). Second, subject to security constraints, the sample was intended to capture regional differences. Finally, the sample had to include facilities in the main ownership categories: government, private for-profit, and private nonprofit (religious organizations and NGOs). The sample of government and nonprofit facilities was based on the Ministry of Health facility register for 1999. Since no nationwide census of for-profit facilities was available, these facilities were chosen by asking sampled government facilities to identify the closest private dispensary.

Of the 155 health facilities surveyed, 81 were government facilities, 30 were private for-profit facilities, and 44 were nonprofit facilities. An exit poll of clients covered 1,617 individuals.

The final sample consisted of 155 primary health care facilities drawn from ten districts in the central, eastern, northern, and western regions of the country. It included government, private for-profit, and private nonprofit facilities. The nonprofit sector includes facilities owned and operated by religious organizations and NGOs. Approximately one third of the surveyed facilities were dispensaries without maternity units; the rest provided maternity care. The facilities varied considerably in size, from units run by a single individual to facilities with as many as 19 staff members.

Ministry of Health facility register for 1999 was used to design the sampling frame. Ten districts were randomly selected. From the selected districts, a sample of government and private nonprofit facilities and a reserve list of replacement facilities were randomly drawn. Because of the unreliability of the register for private for-profit facilities, it was decided that for-profit facilities would be identified on the basis of information from the government facilities sampled. The administrative records for facilities in the original sample were first reviewed at the district headquarters, where some facilities that did not meet selection criteria and data collection requirements were dropped from the sample. These were replaced by facilities from the reserve list. Overall, 30 facilities were replaced.

The sample was designed in such a way that the proportion of facilities drawn from different regions and ownership categories broadly mirrors that of the universe of facilities. Because no nationwide census of for-profit health facilities is available, it is difficult to assess the extent to which the sample is representative of this category. A census of health care facilities in selected districts, carried out in the context of the Delivery of Improved Services for Health (DISH) project supported by the U.S. Agency for International Development (USAID), suggests that about 63 percent of all facilities operate on a for-profit basis, while government and nonprofit providers run 26 and 11 percent of facilities, respectively. This would suggest an undersampling of private providers in the survey. It is not clear, however, whether the DISH districts are representative of other districts in Uganda in terms of the market for health care.

For the exit poll, 10 interviews per facility were carried out in approximately 85 percent of the facilities. In the remaining facilities the target of 10 interviews was not met, as a result of low activity levels.

Sampling deviation

In the first stage in the sampling process, eight districts (out of 45) had to be dropped from the sample frame due to security concerns. These districts were Bundibugyo, Gulu, Kabarole, Kasese, Kibaale, Kitgum, Kotido, and Moroto.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available:

• District Health Team Questionnaire;
• District Facility Data Sheets;
• Uganda Health Facility Survey Questionnaire;
• Facility Data Sheets;
• Facility Patient Exit Poll Questionnaire.

The survey collected data at three levels: district administration, health facility, and client. In this way it was possible to capture central elements of the relationships between the provider organization, the frontline facility, and the user. In addition, comparison of data from different levels (triangulation) permitted cross-validation of information.

At the district level, a District Health Team Questionnaire was administered to the district director of health services (DDHS), who was interviewed on the role of the DDHS office in health service delivery. Specifically, the questionnaire collected data on health infrastructure, staff training, support and supervision arrangements, and sources of financing.

The District Facility Data Sheet was used at the district level to collect more detailed information on the sampled health units for fiscal 1999-2000, including data on staffing and the related salary structures, vaccine supplies and immunization activity, and basic and supplementary supplies of drugs to the facilities. In addition, patient data, including monthly returns from facilities on total numbers of outpatients, inpatients, immunizations, and deliveries, were reviewed for the period April-June 2000.

At the facility level, the Uganda Health Facility Survey Questionnaire collected a broad range of information related to the facility and its activities. The questionnaire, which was administered to the in-charge, covered characteristics of the facility (location, type, level, ownership, catchment area, organization, and services); inputs (staff, drugs, vaccines, medical and nonmedical consumables, and capital inputs); outputs (facility utilization and referrals); financing (user charges, cost of services by category, expenditures, and financial and in-kind support); and institutional support (supervision, reporting, performance assessment, and procurement). Each health facility questionnaire was supplemented by a Facility Data Sheet (FDS). The FDS was designed to obtain data from the health unit records on staffing and the related salary structure; daily patient records for fiscal 1999-2000; the type of patients using the facility; vaccinations offered; and drug supply and use at the facility.

Finally, at the facility level, an exit poll was used to interview about 10 patients per facility on the cost of treatment, drugs received, perceived quality of services, and reasons for using that unit instead of alternative sources of health care.

Cleaning operations

Detailed information about data editing procedures is available in "Data Cleaning Guide for PETS/QSDS Surveys" in external resources.

STATA cleaning do-files and the data quality reports on the datasets can also be found in external resources.

BackgroundPrimary healthcare (PHC) systems attain improved health outcomes and fairness and are affordable. However, the proportion of PHC spending to Total Current Health Expenditure in Kenya reduced from 63.4% in 2016/17 to 53.9% in 2020/21 while external funding reduced from 28.3% (Ksh 69.4 billion) to 23.9% (Ksh 68.2 billion) over the same period. This reduction in PHC spending negatively affects PHC performance and the overall health system goals.MethodsWe conducted a cost-benefit analysis and computed costs against the economic benefits of a PHC scale-up. Activity-Based Costing (ABC) on the provider perspective was employed to estimate the incremental costs. The OneHealth Tool was used to estimate the health impact of operationalizing PHC over five years. Finally, we quantified Return on Investment (ROI) by estimating monetized DALYs based on a constant value per statistical life year (VSLY) derived from a VSL estimate.ResultsThe total projected cost of PHC interventions in the Kenya was Ksh 1.65 trillion (USD 15,581.91 billion). Human resource was the main cost driver accounting for 75% of the total cost. PHC investments avert 64,430,316 Disability Adjusted Life-Years (DALYs) and generate cost savings of Ksh. 21.5 trillion (USD 204.4 Billion) over five years. Shifting services from high-level facilities to PHC facilities generates Ksh 198.2 billion (USD 1.9 billion) and yields a benefit-cost ratio of 16:1 in 5 years. Thus, every $1 invested in PHC interventions saves up to $16 in spending on conditions like stunting, NCDs, anaemia, TB, Malaria, and maternal and child health morbidity.ConclusionsEvidence of the economic benefits of continued prioritization of funding for PHC can strengthen the advocacy argument for increased domestic and external financing of PHC in Kenya. A well-resourced and functional PHC system translates to substantial health benefits with positive economic benefits. Therefore, governments and stakeholders should increase investments in PHC to accelerate economic growth.

Early diagnosis of SARS-CoV-2 is fundamental to reduce the risk of community transmission and mortality, as well as public sector expenditures. Three years after the onset of the SARS-CoV-2 pandemic, there are still gaps on what is known regarding costs and cost drivers for the major diagnostic testing strategies in low- middle-income countries (LMICs). This study aimed to estimate the cost of SARS-CoV-2 diagnosis of symptomatic suspected patients by reverse transcription polymerase chain reaction (RT-PCR) and antigen rapid diagnostic tests (Ag-RDT) in Mozambique. We conducted a retrospective cost analysis from the provider’s perspective using a bottom-up, micro-costing approach, and compared the direct costs of two nasopharyngeal Ag-RDTs (Panbio and Standard Q) against the costs of three nasal Ag-RDTs (Panbio, COVIOS and LumiraDx), and RT-PCR. The study was undertaken from November 2020 to December 2021 in the country’s capital city Maputo, in four healthcare facilities at primary, secondary and tertiary levels of care, and at one reference laboratory. All the resources necessary for RT-PCR and Ag-RDT tests were identified, quantified, valued, and the unit costs per test and per facility were estimated. Our findings show that the mean unit cost of SARS-CoV-2 diagnosis by nasopharyngeal Ag-RDTs was MZN 728.00 (USD 11.90, at 2020 exchange rates) for Panbio and MZN 728.00 (USD 11.90) for Standard Q. For diagnosis by nasal Ag-RDTs, Panbio was MZN 547.00 (USD 8.90), COVIOS was MZN 768.00 (USD 12.50), and LumiraDx was MZN 798.00 (USD 13.00). Medical supplies expenditures represented the main driver of the final cost (>50%), followed by personnel and overhead costs (mean 15% for each). The mean unit cost regardless of the type of Ag-RDT was MZN 714.00 (USD 11.60). Diagnosis by RT-PCR cost MZN 2,414 (USD 39.00) per test. Our sensitivity analysis suggests that focussing on reducing medical supplies costs would be the most cost-saving strategy for governments in LMICs, particularly as international prices decrease. The cost of SARS-CoV-2 diagnosis using Ag-RDTs was three times lower than RT-PCR testing. Governments in LMICs can include cost-efficient Ag-RDTs in their screening strategies, or RT-PCR if international costs of such supplies decrease further in the future. Additional analyses are recommended as the costs of testing can be influenced by the sample referral system.

Contextual factors and sample sizes for cost calculations.

The lack of health cover in low-income countries is a real barrier to emergency care. The objective of our study was to evaluate the immediate management of pediatric emergencies by deferred recovery of the costs of care at Douala Laquintinie Hospital. A prospective cross-sectional study was conducted from 1st February to 30 June 2020 on patients admitted for life-threatening emergencies to the pediatric emergency department. Deferred recovery of healthcare costs was triggered by the issuance of a “green voucher, an internal reimbursement voucher issued by the doctor for expenses incurred upon patient admission in a life-threatening emergency and reimbursable within 72 hours after initial emergent management was received. Of the 786 patients admitted to the pediatric emergency department, 502 (63.8%) patients presented with a life-threatening emergency at a median age of 1 year [IQR: 0-5]. According to the indigence criteria, 40.4% of the patients were indigent and nearly 40% of the families’ patients declared having a monthly income < 50,000 franc of the French Colonies of Africa (FCFA) or 85 USD. The majority of patients with life-threatening 456 (90.8%) had benefited from the “green voucher” and 71.5% from care within 15 minutes of admission. The average household health expenditure during hospitalization was 143.9 ± 52.3 USD (53.5–393.9). A total of 76.1% of patients benefited from deferred care cost recovery, including 43.6% from moratorium payment facilities. The mortality rate was 9.8%. The deferred healthcare cost recovery system has proven effective in lowering avoidable child mortality in life-threatening emergencies, despite the heavy burden of healthcare costs for the underprivileged.