Over ** percent of Nigerian households reported being able to access necessary maternal as well as pregnancy healthcare when needed. However, over ** percent said they were unable to gain necessary vaccination services. In fact, vaccinations were the medical service harder to access in Nigeria. Nevertheless, over ** percent of adults and ** percent of children were medically taken care of when needed.

In 2023, Singapore dominated the ranking of the world's health and health systems, followed by Japan and South Korea. The health index score is calculated by evaluating various indicators that assess the health of the population, and access to the services required to sustain good health, including health outcomes, health systems, sickness and risk factors, and mortality rates. The health and health system index score of the top ten countries with the best healthcare system in the world ranged between 82 and 86.9, measured on a scale of zero to 100.

Global Health Security Index  Numerous health and health system indexes have been developed to assess various attributes and aspects of a nation's healthcare system. One such measure is the Global Health Security (GHS) index. This index evaluates the ability of 195 nations to identify, assess, and mitigate biological hazards in addition to political and socioeconomic concerns, the quality of their healthcare systems, and their compliance with international finance and standards. In 2021, the United States was ranked at the top of the GHS index, but due to multiple reasons, the U.S. government failed to effectively manage the COVID-19 pandemic. The GHS Index evaluates capability and identifies preparation gaps; nevertheless, it cannot predict a nation's resource allocation in case of a public health emergency.

Universal Health Coverage Index  Another health index that is used globally by the members of the United Nations (UN) is the universal health care (UHC) service coverage index. The UHC index monitors the country's progress related to the sustainable developmental goal (SDG) number three. The UHC service coverage index tracks 14 indicators related to reproductive, maternal, newborn, and child health, infectious diseases, non-communicable diseases, service capacity, and access to care. The main target of universal health coverage is to ensure that no one is denied access to essential medical services due to financial hardships. In 2021, the UHC index scores ranged from as low as 21 to a high score of 91 across 194 countries. 

Background: Nigeria’s healthcare system faces significant challenges in financing and quality, impacting the delivery of services to its growing population. This study investigates healthcare workers’ perceptions of these challenges and their implications for healthcare policy and practice. Methods: A cross-sectional survey was conducted with 600 healthcare professionals from eight states across Nigeria, representing a variety of healthcare occupations. Participants completed a questionnaire that assessed their perceptions of healthcare financing, quality of care, job satisfaction, and motivation using a 5-point Likert scale, closed- and open-ended questions. Descriptive statistics, Chi-squared test, and regression analysis were used to analyze the data. Results: The findings revealed that healthcare workers were generally not satisfied with the current state of healthcare financing and system quality in Nigeria. Poor funding, inadequate infrastructure, insufficient staffing, and limited access to essential resources were identified as major challenges. These challenges contributed to low job satisfaction, demotivation, and a desire to leave the profession. Socioeconomic factors, location State of practice, professional designation (clinical vs nonclinical), clinical designation (profession), and employment type (full-time vs part-time) were found to influence healthcare workers' perceptions (p < 0.05). Conclusion: The findings indicated a need to improve healthcare workers' satisfaction and retention, and quality of care in Nigeria, by increasing healthcare funding, transparent fund management protocols, investing in infrastructure and human resource development, and addressing regional healthcare disparities. By implementing these reforms, Nigeria can enhance the quality and accessibility of healthcare services and improve the health and well-being of its citizens.

Abstract

Despite years of human and financial investment in the Nigerian Health Sector, the country did not achieve the health-related millennium development goals (MDGs) by 2015. According to a 2010 UNDP MDG report, the likelihood that the country will achieve MDG 4 (reducing under-five mortality by two thirds between 1990 and 2015) and MDG 5 (reducing maternal mortality ratio by three quarters between 1990 and 2015) is average at best. Although the under-five mortality rate fell by a fifth in five years, from 201 deaths/1,000 live births in 2003 to 157 deaths/1,000 live births in 2008, and the maternal mortality ratio fell by 32 percent (800 deaths/100,000 live births in 2003 to 545 deaths/100,000 live births in 2008); these figures do not come close to the two-thirds and three quarters level set for the MDGs. The main challenges to achieving these goals have been identified as “declining resources, ensuring universal access to an essential package of care, improving the quality of healthcare services and increasing demand for health services and providing financial access especially to vulnerable groups” (UNDP 2010).

To overcome these challenges and accelerate the progress of the country to achieving the health related MDGs, innovative approaches are needed to effectively manage the Nigeria health system and improve on its efficiency to enhance the health status of the population. The World Bank and the government of Nigeria are in the process of preparing a results-based financing (RBF) project which provides incentives for improving performance at critical levels within the Nigerian health system and aims to address some of these challenges. A key feature of the RBF project in the Nigerian context is the provision of financial incentives to States and Local Government Agencies (LGA) based on results achieved. In addition, select health facilities will also receive performance incentives. This approach will also build institutional capacity for health system management while introducing a culture of performance excellence at the health facility level and higher levels of health systems management. Given the innovative nature of the proposed project interventions, the World Bank and the Government of Nigeria seek to nest a rigorous impact evaluation in the project to provide evidence that can be used to inform decisions on whether to scale up the innovations implemented under the project. The primary goal of the impact evaluation of the RBF project in Nigeria is to determine if providing financial incentives linked directly to performance increases the quantity and quality of maternal and child health (MCH) services. In addition, it is anticipated that the impact evaluation should provide answers that are generalizable to specific regions in Nigeria.

These are the endline data in support of this impact evaluation.

Geographic coverage

Urban and rural areas in the six states of Adamawa, Benue, Nasarawa, Ogun, Ondo, and Taraba.

Analysis unit

Health facility; household

Universe

• Primary and secondary health facilities in treatment states. In control states, a randomly-selected sample of primary and secondary health facilities.

• Households with recent pregnancies (in the last two years) or a currently pregnant woman from the catchment areas of the above facilities.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample frame for the health facility surveys comprised one randomly-chosen facility per ward from all functioning primary and secondary health facilities in each LGA (77 LGAs in total; all but one pre-pilot LGA in treatment state). For indicators that are measured at the level of the health facility, the evaluation is a two-level cluster randomized trial, that is, a study in which units are nested within clusters and the clusters are randomly assigned to the treatment or control condition. In this case, health facilities are nested within LGAs and LGAs are randomly assigned to the treatment or control condition. The referral (secondary) hospital in each LGA was also sampled.

HOUSEHOLDS: The sampling frame consists of households in the 77 LGAs that are part of the evaluation. To ensure an efficient sample, the sampling frame was limited to those households that included at least one woman who has given birth or been pregnant in the last two years. By restricting the sampling frame in such a way, we maximize the proportion of the sample that will have at least one woman who gave birth in the last two years, and the proportion of households that have at least one child under the age of five. While this sampling frame does not give us a fully representative sample of the Nigerian population, it gives a representative sample of the population of interest from this program. Sampling of households was done as follows: First, we listed all enumeration areas in the LGAs that belong to the study, and then randomly drew enumeration areas with probability based on size. Within enumeration areas, the survey firm listed all households within the enumeration area that included at least one woman who has given birth within the last 2 years. Then, 15 households were randomly drawn from that listing.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: • Office editing and coding • During data entry • Structure checking and completeness • Secondary editing • Structural checking of Stata data files

In 2025, South Africa had the highest health care index in Africa with a score of 63.8, followed by Kenya with 62 points. These scores, for both countries, are considered to be reasonably high. The health care index takes into account factors such as the overall quality of the health care system, health care professionals, equipment, staff, doctors, and cost.

In 2012, the Informed Decisions for Action in Maternal and Newborn Health (IDEAS) project, based at the London School of Hygiene and Tropical Medicine and funded by the Bill and Melinda Gates Foundation, collected data to answer the following research question: In Uttar Pradesh in India, Ethiopia, and Gombe state in Nigeria, where innovations to enhance frequency and quality of health care for mothers and newborns are in place, is there evidence to suggest that increases in frequency and quality of health care were linked to increases in the coverage of interventions that save maternal and newborn lives?"

Applying a cluster household survey design in the defined geographies, individual level data were collected in May (Ethiopia), June (Nigeria) and November (India) 2012. Women aged 13-49 years, who had had a live birth in the 12 months prior to survey, were asked a detailed set of questions about behaviours and practices during that pregnancy, birth, and during the first month of newborn life. From these data it is possible to answer questions about frequency and content of care along the continuum from pregnancy to newborn care in three high mortality settings where commitments are currently in place to improve health outcomes.

The data held at the UK Data Archive are an extract from a larger household dataset that recorded information about the knowledge of danger signs, experience of danger signs, access to health care, and costs of accessing care for individual women, and the characteristics of the households they were resident in. Further information on the project and findings for each country may be found on the IDEAS Resources webpages.

Abstract copyright UK Data Service and data collection copyright owner. In 2012, the Informed Decisions for Action in Maternal and Newborn Health (IDEAS) project, based at the London School of Hygiene and Tropical Medicine and funded by the Bill and Melinda Gates Foundation, collected data to answer the following research question: In Uttar Pradesh in India, Ethiopia, and Gombe state in Nigeria, where innovations to enhance frequency and quality of health care for mothers and newborns are in place, is there evidence to suggest that increases in frequency and quality of health care were linked to increases in the coverage of interventions that save maternal and newborn lives?" Applying a cluster household survey design in the defined geographies, individual level data were collected in May (Ethiopia), June (Nigeria) and November (India) 2012. Women aged 13-49 years, who had had a live birth in the 12 months prior to survey, were asked a detailed set of questions about behaviours and practices during that pregnancy, birth, and during the first month of newborn life. From these data it is possible to answer questions about frequency and content of care along the continuum from pregnancy to newborn care in three high mortality settings where commitments are currently in place to improve health outcomes. The data held at the UK Data Archive are an extract from a larger household dataset that recorded information about the knowledge of danger signs, experience of danger signs, access to health care, and costs of accessing care for individual women, and the characteristics of the households they were resident in. Further information on the project and findings for each country may be found on the IDEAS Resources webpages. Main Topics: Reports by individual women aged 13-49 about their uptake of health care, and the content of that health care during the pregnancy, intra-partum, and post-natal periods for their most recent live birth that occurred in the 12 months prior to survey date. Multi-stage stratified random sample Face-to-face interview

Despite several political commitments to ensure the availability of and access to post-abortion care services, women in sub-Saharan Africa still struggle to access quality post-abortion care, and with devastating social and economic consequences. Expanding access to post-abortion care while eliminating barriers to utilization could significantly reduce abortions-related morbidity and mortality. We describe the barriers to providing and utilizing post-abortion care across health facilities in Burkina Faso, Kenya, and Nigeria. This paper draws on three data sources: health facility assessment data, patient-exit interview data, and qualitative interviews conducted with healthcare providers and policymakers. All data were based on a cross-sectional survey of a nationally representative sample of health facilities conducted between November 2018 and February 2019. Data on post-abortion care service indicators were collected, including staffing levels and staff training, availability of post-abortion care supplies, equipment and commodities. Patient-exit interviews focused on patients treated for post-abortion complications. In-depth interviews were conducted with healthcare providers within a sample of the study health facilities and national or local decision-makers in sexual and reproductive health. Few primary-level facilities in Burkina Faso (15%), Kenya (46%), and Nigeria (20%) had staff trained on post-abortion care. Only 16.6% of facilities in Kenya had functional operating theaters or MVA rooms, Burkina Faso (20.3%) and Nigeria (50.7%). Primary facilities refer post-abortion care cases to higher-level facilities despite needing to be more adequately equipped to facilitate these referrals. Several challenges that impede the provision of quality and comprehensive post-abortion care across the three countries. The absence of post-abortion care training, equipment, and inadequate referral capacity was among the critical reasons for the lack of services. There is a need to strengthen post-abortion care services across all levels of the health system, but especially at lower-level facilities where most patients seek care first.

Urban slum residents have access to a broad range of facilities of varying quality. The choices they make can significantly influence their health outcomes. Discrete Choice Experiments (DCEs) are a widely-used health economic methodology for understanding how individuals make trade-offs between attributes of goods or services when choosing between them. We carried out a DCE to understand these trade-offs for residents of an urban slum in Ibadan, Nigeria. We conducted 48 in-depth interviews with slum residents to identify key attributes influencing their decision to access health care. We also developed three symptom scenarios worded to be consistent with, but not pathegonian of, malaria, cholera, and depression. This led to the design of a DCE involving eight attributes with 2–4 levels for each. A D-efficient design was created, and data was collected from 557 residents between May 2021 and July 2021. Conditional-logit models were fitted to these data initially. Mixed logit and latent class models were also fitted to explore preference heterogeneity. Conditional logit results suggested a substantial Willingness-to-pay (WTP) for attributes associated with quality. WTP estimates across scenarios 1/2/3 were N5282 / N6080 / N3715 for the government over private ownership, N2599 / N5827 / N2020 for seeing a doctor rather than an informal provider and N2196 / N5421 /N4987 for full drug availability over none. Mixed logit and latent class models indicated considerable preference heterogeneity, with the latter suggesting a substantial minority valuing private over government facilities. Higher income and educational attainment were predictive of membership of this minority. Our study suggests that slum residents value and are willing to pay for high-quality care regarding staff qualifications and drug availability. It further suggests substantial variation in the perception of private providers. Therefore, improved access to government facilities and initiatives to improve the quality of private providers are complementary strategies for improving overall care received.

Abstract

This survey covering 252 primary health facilities and 30 local governments was carried out in the states of Kogi and Lagos in Nigeria in the latter part of 2002. Nigeria is one of the few countries in the developing world to systematically decentralize the delivery of basic health and education services to locally elected governments. Its health policy has also been guided by the Bamako Initiative to encourage and sustain community participation in primary health care services. The survey data provide systematic evidence on how these institutions of decentralization are functioning at the level local—governments and community based organizations—to deliver primary health service.

The evidence shows that locally elected governments indeed do assume responsibility for services provided in primary health care facilities. However, the service delivery environments between the two states are strikingly different. In largely urban Lagos, public delivery by local governments is influenced by the availability of private facilities and proximity to referral centers in the state. In largely rural Kogi, primary health services are predominantly provided in public facilities, but with extensive community participation in the maintenance of service delivery. The survey identified an issue which is highly relevant for decentralization policies—the non-payment of health staff salaries in Kogi—which is suggestive of problems with local accountability when local governments are heavily dependent on fiscal transfers from higher tiers of government.

Geographic coverage

Data were collected in 30 local governments, 252 health facilities, and from over 700 health workers, in Lagos and Kogi states.

Kind of data

Sample survey data [ssd]

Sampling procedure

A multi-stage sampling process was employed where first 15 local governments were randomly selected from each state; second, 100 facilities from Lagos and 152 facilities from Kogi were selected using a combination of random and purposive sampling from the list of all public primary health care facilities in the 30 selected LGAs that was provided by the state governments; third, the field data collectors were instructed to interview all staff present at the health facility at the time of the visit, if the total number of staff in a facility were less than or equal to 10. In cases where the total number of staff were greater than 10, the field staff were instructed to randomly select 10 staff, but making sure that one staff in each of the major ten categories of primary health care workers was included in the sample.

Health facilities were selected through a combination of random and purposive sampling. First, all facilities were randomly selected from the available list for 30 LGAs. This process resulted in no facility being selected from a few LGAs. Between 1-3 facilities were then randomly selected from these LGAs, and an equal number of facilities were randomly dropped from overrepresented LGAs, defined as those where the proportion of selected facility per LGA is higher than the average proportion of selected facilities for all sampled LGAs.

A list of replacement facilities was also randomly selected in the event of closure or non-functioning of any facility in the original sample. An inordinate amount of facilities were replaced in Kogi (27 in total), some due to inaccessibility given remote locations and hostile terrain, and some due to non-availability of any health staff. The local community volunteered in these cases that the reason there was no staff available was because of non-payment of salaries by the LGA. This characteristic of the functioning of health facilities in Kogi is a striking result that will be discussed in this report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The approach adopted to addressing these issues revolves around extensive and rigorous survey work, at the level of the primary health care facilities and the local governments. Two basic survey instruments of primary data collection were agreed upon, based on collecting information from government officials and public service delivery facilities: 1. Survey of primary health care facilities—including interviews of facility managers and workers, as well as direct collection of data on inputs and outputs from facility records. 2. Survey of local governments (under whose jurisdiction the health facilities reside)—including interviewers of local government treasurers for information on budgeted resources and investment activity, and interviews of primary health care coordinators for roles, responsibilities, and outcomes at the local government level.

Survey instruments at the health facility level

The facility level survey instruments were designed to collect data along the following lines: 1. Basic characteristics of the health facility: who built it; when was it built; what other facilities exist in the neighborhood; access to the facility; hours of service etc. 2. Type of services provided: focusing on ante-natal care; deliveries; outpatient services, with special emphasis on malaria and routine immunization 3. Availability of essential equipment to provide the above services 4. Availability of essential drugs to provide the above services 5. Utilization of the above services, referral practices 6. Tracking and use of epidemiological and public health data 7. Characteristics of health facility staff: professional qualifications; training; salary structure, and whether payments are received in a timely fashion; informal payments received; fringe benefits received; do they have their own private practice; time allocation across different services; residence; place of origin 8. Sources of financing-who finances the building infrastructure and its maintenance; who finances the purchase of basic equipment; who finances the purchase of drugs; what is the user fee policy; revenues from user fees; retention rate of these revenues; financing available from the community 9. Management structure and institutions of accountability: activities of and interaction with the local government and with the community development committees

Survey instrument at the local government level

The local government survey instruments were designed to collect data along the following lines: 1. Basic characteristics: when was the local government created, population, proportion urban and rural, presence of an urban center, presence of NGOs and international donors 2. Number of primary health care facilities by type (types 1 and 2) and ownership (public-local government, state, and federal government; private-for-profit; private-not-for-profit) 3. Supervisory responsibilities over the general functioning of the primary health care centers 4. Health staff: number of staff by type of professional training and civil service cadre; salary; 5. Monitoring the performance of health staff: how is staff performance monitored and by whom; are staff rewarded for good performance or sanctioned for poor performance, and how; instances when local government has received complaints; what disciplinary action was taken 6. Budget and financing: data on actual LGA revenues and expenditure from available budget documents; 7. Management structures: functioning of the Primary Health Care Management Committee (PHCMC), the Primary Health Care Technical Committee (PHCTC), and the community based organizations-the Village Development Committee (VDC) and the District Development Committee (DDC) 8. Health services outputs at the local government level: records of immunization, and environmental health activities

The focus of the study is thus public service delivery outcomes as measured at the level of frontline delivery agencies—the public primary health care facilities. We also originally planned to include interviews of patients present at the health facilities, to get the user’s perspective on public service delivery, but found that difficult to follow-through given local capacity constraints in implementing a survey of this kind.

The survey instruments were developed through an iterative process of discussions between the World Bank team, NPHCDA, and local consultants at the University of Ibadan, over the months of March-May 2002. During May 2002, four questionnaires were finalized through repeated field-testing—1) Health Facility Questionnaire: to be administered to the health facility manager, and to collect recorded data on inputs and outputs at the facility level; 2) Staff Questionnaire: to be administered to individual health workers; 3) Local Government Treasurer Questionnaire: to collect local government budgetary information; and 4) Primary Health Care Coordinator Questionnaire: to collect information on local government activities and policies in primary health care service service delivery.

Cleaning operations

Random Data Checking Procedure

Following the dual data entry of all records by Nigerian consultants and the merging and cleaning of the data files(as outlined below) by World Bank staff, the hard copies of the questionnaires were randomly checked against the entries in the data files (*) for errors by World Bank staff. Five LGAs were selected at random in both the Kogi and Lagos states. In each of these ten LGAs, the hard copy of the PHC Coordinator Questionnaire, the hard copy of the LGA Treasurer Questionnaire, and up to five hard copies of both the Staff Questionnaires and the Health Facility Questionnaires were randomly selected and checked against the entries in the data files. While in several instances parts of the alphanumeric entries were abbreviated or omitted, no substantive differences between the hard copies of the

Abstract

The Maternal and Child Health (MCH) project of the Subsidy Reinvestment and Empowerment Programme (SURE-P), was set up by the Federal Government of Nigeria to reduce maternal and newborn morbidity and mortality inthe country. MCH initiative is a follow-up program to Midwives Service Scheme, implemented by the Nigeria National Primary Health Care Development Agency, that provides demand and supply side incentives, community monitoring, and increased human resources to improve the rates and quality of antenatal care and skilled birth attendance in Nigeria.

On the supply-side, SURE-P aims to recruit, train and deploy 5,400 midwives and 14,100 community health extension workers, as well as to upgrade essential infrastructures and guarantee the adequate provision of supplies and equipment to primary health centres between the end of 2012 and 2015. In addition SURE-P will hire and train a total of 38,700 village health workers, who are expected to establish the connection between the primary healthcare centres (PHC) and pregnant women in each village.

On the demand-side, SURE-P introduces a CCT, whereby all pregnant women will be given a total cash payout of 5,000 Naira (about USD 32), conditional on attending antenatal care, skilled birth attendance and postnatal care. Also, an information campaign aims to target all women of reproductive age to encourage them to register with their nearest PHC.

The rigorous impact evaluation is being implemented to determine the causal impact of this programme. The IE comprises a quasi-experimental impact evaluation whose aim is to evaluate the SURE-P package, and four experimental evaluations which will evaluate the impact that distinct components have within the SURE-P package, such as: - the effect of alternative incentives regimes to midwives on their retention rates - the effect of conditional cash transfers on utilization of MCH services - the effect of community monitoring of essential commodities on incidence of stock-out of supplies at the PHC

The baseline data collection was carried out in September-November 2013. The first follow-up survey will be implemented in November 2014 - January 2015, after SURE-P Phase I implementation. The final data collection is planned one year later, from November 2015 to January 2016, after SURE-P Phase II implementation.

To gather baseline data four different groups of respondents were interviewed using different questionnaires. These respondents were: - Managers in all 500 SURE-P health facilities across the country - Midwives recruited by SURE-P - Women who gave birth three months preceding the survey, in sampled households in each SURE-P facility catchment area - Ward Development Committee (WDC) chairpersons or representatives in all SURE-P facilities.

Overall, 476 facility manager questionnaires, 1,291 midwife questionnaires, 2,378 household qustionnaires and 477 WDC questionnaires were administered.

The baseline data is documented here.

Geographic coverage

National

Analysis unit

• primary health care facilities
• midwives
• pregnant women

Kind of data

Sample survey data [ssd]

Sampling procedure

• Primary Health Care facilities: The survey targeted all 500 SURE-P MCH Phase 1 PHC facilities.

• Midwives: The survey targeted all midwives currently working for the SURE-P MCH (up to four per Primary Health Care facility). The list of all SURE-P midwives with their identification numbers was provided to the survey firm. Some midwives, whose names were not on the list, were found and interviewed during the survey.

• Ward Development Committees: The survey targeted all 500 ward development committees operating in areas with SURE-P MCH Phase 1 Primary Health Care facilities.

• Households sampling

The interviewers first visited the SURE-P facilities and asked for the names of the communities within its catchment area. The names of communities were written in a piece of paper, crumpled and placed in a bag. The papers were randomly drawn and two communities selected.

All structures in communities with 50 or less structures were listed. Communities with 50 to 100 structures were split into Enumeration Areas (EAs) of approximately 25 structures, out of those two EAs were randomly selected and fully listed. Communities with more than 100 structures were also split into EAs and three EAs randomly selected.

The listing was conducted using the World Bank designed listing form. All listed households with eligible women were entered into a generated sampling form. Households with the smallest numbers in the "sampling order" where chosen for sample.

A sketch of the community maps was also obtained.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used to collect data for SURE-P MCH IE baseline survey.

1) Primary Health Care Facilities Questionnaire includes the following sections: (i) general information; (ii) facility characteristics; (iii) administration and management; (iv) human resources; (v) organizational citizenship and behaviors; (vi) Maslach Burnout Inventory (MBI); (vii) patient records; (viii) community outreach; (ix) health services; (x) user fees; (xi) national protocols; (xii) equipment; (xiii) drug storage and availability;

2) Midwives Questionnaire includes the following sections: (i) general information; (ii) post-secondary education; (iii) exposure to rural settings; (iv) job attributes preferences; (v) Maslach Burnout Inventory (MBI); (vi) work conditions; (vii) family; (viii) altruism game; (ix) other sources of income; (x) household assets, land, and animals; (xi) non-experimental measure of intrinsic motivation; (xii) time preferences game; (xiii) community relations and support; (xiv) prosocial scales; (xv) midwifery courses preferences; (xvi) antenatal care (ANC); (xvii) opinions about work and family; (xviii) contact information; (ixi) risk preferences game; (xix) post-contract expectations*; (xix) social norms game.

The study tests the effectiveness of three different incentives regimes for midwives (monetary only, non-monetary only and monetary plus non-monetary) versus a control group. The midwives baseline survey was used to deliver the relevant contract to each midwife, with midwives in the control group receiving a generic letter. The post-contract expectations section of the midwives questionnaire asked a basic set of questions on midwives' expectations related to various aspects of their work immediately following receipt of their contract letter.

3) Households Questionnaire includes the following sections: (i) contact information; (ii) household roster; (iii) education; (iv) transfers and other income; (v) adverse events; (vi) household health services utilization and payment; (vii) community organizations; (viii) male adult expectations; (ix) reproductive health; (x) antenatal care service utilization; (xi) labor and delivery; (xii) Edinburg Postnatal Depression Scale; (xiii) postpartum care and breastfeeding; (xiv) female adult expectations; (xv) maternal knowledge; (xvi) delivery problems; (xvii) exposure to media and mobile phones; (xviii) village leader and ward development committee interaction; (xix) dwelling characteristics and household amenities; (xx) household assets; (xxi) food and non-food consumption.

4) Ward Development Committees Questionnaire includes the following sections: (i) general information; (ii) access to basic services and community characteristics; (iii) social capital and community empowerment; (iv) external shocks; (v) direct observation.

Cleaning operations

Data cleaning was carried out in phases at the end of the field exercise. Data cleaning commenced with the correction of wrongly captured midwives identification (ID) numbers in the midwives and post-contract surveys. Corrected midwives IDs were further matched using STATA to identify missing midwives IDs. At the end of this exercise, a number of missing IDs were discovered and addressed by conducting fresh interviews. Plateau and Taraba states recorded the highest cases of midwives with missing post-contract survey forms.

Household data was cleaned by identifying duplicate IDs within the facilities and by correcting household IDs which were not correctly recorded. The household listing and sampling order forms served as reference books for confirmation of the IDs where concerns were raised. Facility and WDC files were cleaned by identifying duplicated facility IDs within the states. Identified IDs were cleaned by calling the person in charge of the facilities and WDC chairs to clarify which facilities they fall under.

Response rate

• Primary Health Care Facilities Questionnaire Target: 500; interviewed: 476; response rate: 95%

• Midwives Questionnaire Target: 1,215; interviewed: 1,285; response rate: 106%

• Ward Development Committees Questionnaire Target: 500; interviewed: 473; response rate: 95%

• Household Questionnaire Target: 2,500; interviewed: 2,384; response rate: 95%

BackgroundThe recommendation of universal diagnostic testing before malaria treatment aimed to address the problem of over-treatment with artemisinin-based combination therapy and the heightened risk of selection pressure and drug resistance and the use of malaria rapid diagnostic test (MRDT) was a key strategy, particularly among primary healthcare (PHC) workers whose access to and use of other forms of diagnostic testing were virtually absent. However, the use of MRDT can only remedy over-treatment when health workers respond appropriately to negative MRDT results by not prescribing anti-malarial drugs. This study assessed the use of MRDT and anti-malarial drug prescription practices, and the predictors, among PHC workers in Ebonyi state, Nigeria.MethodsWe conducted an analytical cross-sectional questionnaire survey, among consenting PHC workers involved in the diagnosis and treatment of malaria, from January 15, 2020 to February 5, 2020. Data was collected via structured self-administered questionnaire and analysed using descriptive statistics and bivariate and multivariate generalized estimating equations.ResultsOf the 490 participants surveyed: 81.4% usually/routinely used MRDT for malaria diagnosis and 18.6% usually used only clinical symptoms; 78.0% used MRDT for malaria diagnosis for all/most of their patients suspected of having malaria in the preceding month while 22.0% used MRDT for none/few/some; 74.9% had good anti-malarial drug prescription practice; and 68.0% reported appropriate response to negative MRDT results (never/rarely prescribed anti-malarial drugs for the patients) while 32.0% reported inappropriate response (sometimes/often/always prescribed anti-malarial drugs). The identified predictor(s): of the use of MRDT was working in health facilities supported by the United States’ President’s Malaria Initiative (PMI-supported health facilities); of good anti-malarial drug prescription practice were having good opinion about MRDT, having good knowledge about malaria diagnosis and MRDT, being a health attendant, working in PMI-supported health facilities, and increase in age; and of appropriate response to negative MRDT results was having good opinion about MRDT.ConclusionsThe evidence indicate the need for, and highlight factors to be considered by, further policy actions and interventions for optimal use of MRDT and anti-malarial drug prescription practices among the PHC workers in Ebonyi state, Nigeria, and similar settings.

The total consumer spending on healthcare in Ghana was forecast to continuously increase between 2024 and 2029 by in total 459.6 million U.S. dollars (+38.63 percent). After the seventh consecutive increasing year, the healthcare-related spending is estimated to reach 1.6 billion U.S. dollars and therefore a new peak in 2029. Consumer spending, in this case healthcare-related spending, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group 06. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.Find more key insights for the total consumer spending on healthcare in countries like Ivory Coast and Nigeria.

Despite several political commitments to ensure the availability of and access to post-abortion care services, women in sub-Saharan Africa still struggle to access quality post-abortion care, and with devastating social and economic consequences. Expanding access to post-abortion care while eliminating barriers to utilization could significantly reduce abortions-related morbidity and mortality. We describe the barriers to providing and utilizing post-abortion care across health facilities in Burkina Faso, Kenya, and Nigeria. This paper draws on three data sources: health facility assessment data, patient-exit interview data, and qualitative interviews conducted with healthcare providers and policymakers. All data were based on a cross-sectional survey of a nationally representative sample of health facilities conducted between November 2018 and February 2019. Data on post-abortion care service indicators were collected, including staffing levels and staff training, availability of post-abortion care supplies, equipment and commodities. Patient-exit interviews focused on patients treated for post-abortion complications. In-depth interviews were conducted with healthcare providers within a sample of the study health facilities and national or local decision-makers in sexual and reproductive health. Few primary-level facilities in Burkina Faso (15%), Kenya (46%), and Nigeria (20%) had staff trained on post-abortion care. Only 16.6% of facilities in Kenya had functional operating theaters or MVA rooms, Burkina Faso (20.3%) and Nigeria (50.7%). Primary facilities refer post-abortion care cases to higher-level facilities despite needing to be more adequately equipped to facilitate these referrals. Several challenges that impede the provision of quality and comprehensive post-abortion care across the three countries. The absence of post-abortion care training, equipment, and inadequate referral capacity was among the critical reasons for the lack of services. There is a need to strengthen post-abortion care services across all levels of the health system, but especially at lower-level facilities where most patients seek care first.

The current healthcare spending per capita in Ghana was forecast to continuously increase between 2024 and 2029 by in total 20.5 U.S. dollars (+22.15 percent). After the fourth consecutive increasing year, the spending is estimated to reach 113.05 U.S. dollars and therefore a new peak in 2029. Depicted here is the average per capita spending, in a given country or region, with regards to healthcare. The spending refers to the average current spending of both governments and consumers per inhabitant.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the current healthcare spending per capita in countries like Ivory Coast and Nigeria.

The number of physicians in Ghana was forecast to continuously increase between 2024 and 2029 by in total 2.1 thousand physicians (+26.92 percent). After the tenth consecutive increasing year, the number of physicians is estimated to reach 9.94 thousand physicians and therefore a new peak in 2029. Depicted here is the estimated number of physicians in the geographical unit at hand. Thereby physicians include medical specialists as well as general practitioners.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of physicians in countries like Ivory Coast and Nigeria.

Abstract

This data was produced for an impact evaluation (IE) which estimates the impact of a community-based anti-malaria intervention (training volunteers to provide malaria-related care to members of their extended families) and a private sector one (training and subsidized high-quality drugs to local medicine vendors known as patent medicine vendors) on the following outcomes: bednet use, access to malaria diagnosis and treatment, and incidence of malaria.

Geographic coverage

The survey was conducted across Anambra State and Gombe State. In Anambra, 280 wards were covered; in Gombe, 106 wards were covered.

Analysis unit

Household/individual; Community volunteer; Patent medicine vendor; Primary health facility; Community

Universe

Households, Community-Directed Distributors, Patent Medicine Vendors, Public Healthcare Facility, Public Healthcare Facility Workers, Community Leaders

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design for the impact evaluation is a cluster-randomized experimental design that gives equal chance to both the treatment and control groups for being selected. In order to accommodate the implementation context and generate a valid counterfactual, the initial randomization was done as follows:

1) Primary healthcare facilities (PHFs), the CDI implementation unit, were randomized into treatment and control groups. 2) Political wards, the PMV implementation unit, were randomized into treatment and control groups.

These yield four study arms, each comprising an average of 106 PHFs in 106 wards of Gombe State: - Treatment 1: CDI intervention only - Treatment 2: PMV intervention only - Treatment 3: CDI and PMV interventions - Control (existing public-sector regime only)

The impact evaluation study was carried out in all the four intervention arms for the CDI and PMV components and the sampling procedure consisted of two stages: 1) The first sampling stage: the selection of the first clusters, the primary sampling unit (PSU) which are the catchment areas for the public primary health care facilities within the four treatment groups above are identified. Each of the PSU was divided into Standard Enumerators' Areas (SEAs). 2) The second sampling stage: Following community sensitization, the survey team created a list and mapped of all households within the SEA. This list was used as the basis for the selection of households to be surveyed by the simple random sampling technique.

Mode of data collection

Computer Assisted Personal Interview [capi]

The number of hospitals in Ghana was forecast to continuously increase between 2024 and 2029 by in total one hopsital (+0.27 percent). The number of hospitals is estimated to amount to 371 hospitals in 2029. Depicted is the number of hospitals in the country or region at hand. As the OECD states, the rules according to which an institution can be registered as a hospital vary across countries.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of hospitals in countries like Nigeria and Ivory Coast.

Public health care providers, estimated under-five population, and number of under-fives per provider in the study areas, by provider type and country (all data for 2018).

The current health expenditure as a share of the GDP in Ghana was forecast to continuously increase between 2024 and 2029 by in total 0.1 percentage points. The share is estimated to amount to 4.29 percent in 2029. According to Worldbank health spending includes expenditures with regards to healthcare services and goods. It is depicted here in relation to the total gross domestic product (GDP) of the country or region at hand.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the current health expenditure as a share of the GDP in countries like Senegal and Nigeria.