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.

Abstract

Nigeria has one of the largest stocks of human resources for health (HRH) in Africa. However, great disparities in health status and access to health care exist among the six geo-political zones, and between rural and urban areas. This assessment measures the size, skills mix, distribution, and growth rate of HRH in the public health sector in Nigeria. The assessment also quantifies the increase in HRH requirements in the public health sector necessary for reaching key PEPFAR targets and the health Millennium Development Goals. The findings are based on a survey conducted in April-May 2006 in 290 public health facilities representing all levels of care (primary, secondary, and tertiary). The study data enabled us to estimate the total number of doctors, nurses, midwives, lab and pharmacy staff, and community health workers currently employed in the public sector. The distribution of health workers by level of care, and HRH availability in rural and urban areas was also quantified.Staff attrition rates, measuring the number of those leaving the public sector as percent of total staff, were determined among all staff categories. The annual growth in HRH in the public sector from new graduates was also measured.

Geographic coverage

National

Analysis unit

Public Health Facilities

Universe

The survey focused on public health facilities representing all levels of care (primary, secondary, and tertiary).

Kind of data

Sample survey data [ssd]

Sampling procedure

Two-Stage Stratified Random Sample A survey was conducted in 290 public health facilities representing all levels of care (primary, secondary, and tertiary). The facilities were selected using two-stage stratified sampling. First, two states were selected from each of the six geo-political zones in Nigeria, with probability of selection of each state proportional to its population size. In addition, the Federal Capital Territory of Abuja (FCT) was added to the two states selected in the North Central zone. The selected states in each zone cover between 32 and 50 percent of the zone's population and in total, the 13 states included in the sample account for 40 percent of Nigeria's population. In the second stage of sampling, a sample of facilities at each level of care was chosen in each selected state. All Federal Medical Centers and teaching hospitals in the sampled states were selected with certainty. All other facilities were selected using systematic random sampling. A higher proportion of hospitals, compared to smaller facilities, were included in the sample in order to increase the number of facilities that have most of the data being collected. Primary care facilities include health centers, health clinics, maternities, and dispensaries. There was non-response from two facilities selected with certainty.

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection instrument In each of the selected facilities, a questionnaire was administered to eligible facility managers and health staff. These were staff in charge of the services included in the survey – for example, information regarding immunizations in a hospital was obtained from the nurse in charge at the hospital’s child health clinic. The questionnaire collected information on: 1. Number of staff employed in 2004, 2005, and at the time of survey (April 2006); 2. Number of incoming and outgoing staff in 2005 by reason for leaving or starting work at the facility; 3. Types of services provided at the facility for HIV/AIDS, TB, malaria, maternal and child health, and family planning; 4. Number of patients seen at the facility in the three months preceding the survey for each of these services; 5. Which types of health staff provide each service; 6. Average time spent per patient-visit for each of the services related to the five focus areas.

Cleaning operations

Data from the survey questionnaires was entered electronically using an EpiInfo database, and all data analysis was performed using Stata v.8 software.

In 2019, Nigerian households spent on average 6.1 percent of their total annual expenditure on health. This share was lower in South South and South West, whereas the expenditure peaked at 10.3 percent in the South Eastern states. Data on health insurance coverage show that only 1.4 percent of people living in rural areas had a health insurance. Overall, insurance coverage in Nigeria is very low. In particular, among individuals without education the coverage was below one percent.

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

Percentage distribution of type of healthcare facility used by slum residents on their most recent visit to health services in three urban slums in Nigeria.

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.

BackgroundNigeria has one of the worst health and development profiles globally. A weak health system, poor infrastructure, and varied socio-cultural factors are cited as inhibitors to optimal health system performance and improved maternal and child health status. eHealth has become a major solution to closing these gaps in health care delivery in low- and middle-income countries (LMICs). This research reports the use of satellite communication (SatCom) technology and the existing 3G mobile network for providing video training (VTR) for health workers and improving the digitization of healthcare data.ObjectiveTo evaluate whether the expected project outcomes that were achieved at the end-line evaluation of 2019 were sustained 12 months after the project ended.MethodsFrom March 2017 to March 2019, digital innovations including VTR and data digitization interventions were delivered in 62 healthcare facilities in Ondo State, southwest Nigeria, most of which lacked access to a 3G mobile network. Data collection for the evaluation combined documents' review with quantitative data extracted from health facility registers, and 24 of the most significant change stories to assess the longevity of the outcomes and impacts of digital innovation in the four domains of healthcare: use of eHealth technology for data management, utilization of health facilities by patients, the standard of care, and staff attitude. Stories of the most significant changes were audio-recorded, transcribed for analysis, and categorized by the above domains to identify the most significant changes 12 months after the project closedown.ResultsFindings showed that four project outcomes which were achieved at end-line evaluation were sustained 12 months after project closedown namely: staff motivation and satisfaction; increased staff confidence to perform healthcare roles; improved standard of healthcare delivery; and increased adoption of eHealth innovations beyond the health sector. Conversely, an outcome that was reversed following the discontinuation of SatCom from health facilities is the availability of accurate and reliable data for decision-making.ConclusionDigital technology can have lasting impacts on health workers, patients, and the health system, through improving data management for decision-making, the standard of maternity service delivery, boosting attendance at health facilities, and utilization of services. Locally driven investment is essential for ensuring the long-term survival of eHealth projects to achieve sustainable development goals (SDGs) in LMICs.

As of 2018, about ** percent of people surveyed in Nigeria did not have any health insurance. People with a health insurance mainly had an employer-based coverage. Specifically, ***** percent of men and *** percent of women were provided with an employed based health insurance. Privately purchased insurances were notably uncommon. In total, only about ***** percent of individuals had a health insurance.

📝 Dataset Overview: This enhanced dataset captures the real-world operational and clinical performance data from a major hospital in Nigeria — Lagos University Teaching Hospital (LUTH). It includes detailed information on admissions, patient care, medical services, billing, and staff activities, ideal for healthcare analytics, hospital management dashboards, and machine learning projects.

🔍 Dataset Features (Suggested Columns): Column Name Description Patient_ID Unique anonymized patient ID Admission_Date Date of admission Discharge_Date Date of discharge Gender Patient’s gender Age Patient’s age Department Medical department involved Diagnosis Primary diagnosis Doctor Attending physician (anonymized) Treatment_Provided Type of treatment/procedure Lab_Tests Count of lab tests conducted Medications_Administered Total medications given Surgery_Cost (₦) If applicable, cost of surgery Bill_Amount (₦) Total bill charged to patient Ward Hospital ward assigned Length_of_Stay (days) Duration of hospitalization

🎯 Use Cases: Build hospital operations dashboards in Power BI

Analyze billing and cost patterns across departments

Predict length of stay or discharge outcomes

Explore departmental workload and performance

Use as a base for AI in hospital management systems

🏥 Clinical & Operational Value: This dataset empowers analysts and healthcare professionals to:

Track patient outcomes and billing efficiency

Reduce operational bottlenecks

Improve patient care with data-driven recommendations

Benchmark departmental performance

Train predictive models for resource allocation

👤 Created By: Fatolu Peter (Emperor Analytics) Dedicated to transforming public healthcare using analytics and real-world data across Nigerian hospitals. This is Project 14 in my growing health-tech analytics journey.

✅ LinkedIn Post: 🚑 New Kaggle Dataset: LUTH Hospital Enhanced Clinical & Operations Data 📊 Real hospital data on admissions, billing, treatments, and care metrics 🔗 Access the dataset now on Kaggle

This dataset gives you: ✅ Real hospital operations data ✅ Billing and medication insights ✅ Doctor and ward-level activity ✅ A perfect base for building Power BI dashboards or training ML models

Whether you're a data scientist, health analyst, or Power BI pro — this is real-world data to make real impact. Let’s build something powerful together. 💡

HealthcareAnalytics #LUTH #HospitalData #KaggleDataset #PowerBI #FatoluPeter #EmperorAnalytics #DataForGood #Project14 #PublicHealth #NigeriaHealthData

The number of users in the 'Digital Treatment & Care' segment of the digital health market in Nigeria was modeled to stand at ************* users in 2024. Following a continuous upward trend, the number of users has risen by ************ users since 2017. Between 2024 and 2029, the number of users will rise by ************ users, continuing its consistent upward trajectory.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Digital Treatment & Care.

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.

Unadjusted odds ratios, with 95% confidence intervals of formal healthcare service use for environmental and population characteristics in urban slums in Nigeria.

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 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

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 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. 

BackgroundNigeria has one of the worst health and development profiles globally. A weak health system, poor infrastructure, and varied socio-cultural factors are cited as inhibitors to optimal health system performance and improved maternal and child health status. eHealth has become a major solution to closing these gaps in health care delivery in low- and middle-income countries (LMICs). This research reports the use of satellite communication (SatCom) technology and the existing 3G mobile network for providing video training (VTR) for health workers and improving the digitization of healthcare data.ObjectiveTo evaluate whether the expected project outcomes that were achieved at the end-line evaluation of 2019 were sustained 12 months after the project ended.MethodsFrom March 2017 to March 2019, digital innovations including VTR and data digitization interventions were delivered in 62 healthcare facilities in Ondo State, southwest Nigeria, most of which lacked access to a 3G mobile network. Data collection for the evaluation combined documents' review with quantitative data extracted from health facility registers, and 24 of the most significant change stories to assess the longevity of the outcomes and impacts of digital innovation in the four domains of healthcare: use of eHealth technology for data management, utilization of health facilities by patients, the standard of care, and staff attitude. Stories of the most significant changes were audio-recorded, transcribed for analysis, and categorized by the above domains to identify the most significant changes 12 months after the project closedown.ResultsFindings showed that four project outcomes which were achieved at end-line evaluation were sustained 12 months after project closedown namely: staff motivation and satisfaction; increased staff confidence to perform healthcare roles; improved standard of healthcare delivery; and increased adoption of eHealth innovations beyond the health sector. Conversely, an outcome that was reversed following the discontinuation of SatCom from health facilities is the availability of accurate and reliable data for decision-making.ConclusionDigital technology can have lasting impacts on health workers, patients, and the health system, through improving data management for decision-making, the standard of maternity service delivery, boosting attendance at health facilities, and utilization of services. Locally driven investment is essential for ensuring the long-term survival of eHealth projects to achieve sustainable development goals (SDGs) in LMICs.

Apnea of prematurity (AOP) is a common complication among preterm infants (<37 weeks gestation), globally. However, access to caffeine citrate (CC) that is a proven safe and effective treatment in high income countries is largely unavailable in low-and-middle income countries, where most preterm infants are born. Therefore, the overall aim of this study was to describe the demand, policies, and supply factors affecting the availability and clinical use of CC in LMICs. A mixed methods approach was used to collect data from diverse settings in LMICs including Ethiopia, Kenya, Nigeria, South Africa, and India. Qualitative semi-structured interviews and focus group discussions were conducted with different health care providers, policymakers, and stakeholders from industry. Additional data was collected using standard questionnaires. A thematic framework approach was used to analyze the qualitative data and descriptive statistics were used to summarize the quantitative data. The findings indicate that there is variation in in-country policies on the use of CC in the prevention and treatment of AOP and its availability across the LMICs. As a result, the knowledge and experience of using CC also varied with clinicians on Ethiopia having no experience of using it while those in India have greater knowledge and experience of using it. The in turn influenced the demand and our findings show that only 29% of eligible preterm infants are receiving CC in these countries. There is an urgent need to address the multilevel barriers to accessing CC for management of AOP in Africa. These include cost, lack of national policies and therefore lack of demand stemming from its clinical equivalency with aminophylline. Practical ways to reduce the cost of CC in LMICs could potentially increase its availability and use. Methods Study design, setting, population, sampling We conducted a landscape evaluation involving stakeholders in Africa (Ethiopia, Kenya, Nigeria, South Africa) and South Asia (India – five states of Delhi; Bihar, Uttar Pradesh, Telangana and Madhya Pradesh) on CC availability and use from 1 July 2022 to 31 December 2022. We used a mixed methods study design to understand the complexity of CC availability and use across these LMICs. We selected a geographically and culturally diverse countries with high annual preterm births (~200,000). The selection of stakeholders within each focus country was by convenience and/or purposive sampling. We selected health facilities providing care for preterm infants and were able to provide the data required to achieve the study’s objectives. Proximity and ease of data collection was also factored into selection by research teams. Data collection Qualitative The research teams conducted key informant interviews and focus group discussions (FGD’s) with stakeholders in newborn health. The interviews with healthcare providers sought to explore their experience of using CC as a treatment for AOP. Interviews with WHO and Ministry of Health officials sought to understand current global and national health policies and CC’s inclusion in the essential drug list for using CC to treat AOP. Interviews with major drug suppliers and distributors of CC aimed to determine the current local market pricing of CC and its alternatives within and between countries. Also, to evaluate the factors determining the end-customer price of CC. The available average end-customer price per country was used to determine the daily cost of managing AOP for aminophylline and CC. We compared the average daily cost between aminophylline and cc for both public and private hospitals in each country. The dosing regimen for CC was a loading dose of 20 mg/kg/dose and a daily maintenance dose of between 5 to 10 mg/kg/day. The dosing regimen for aminophylline was a loading dose of 6 mg/kg administered intravenously (IV), followed by a maintenance dose of 2.5 mg/kg/dose/IV administered every 8 hours. Interviews and FGD’s were done in person or virtually over video or audio teleconferencing based on the preferences of the participants. All interviews were conducted in English. teams were situated in each country of focus and had previous training and experience conducting qualitative interviews and FGDs and in qualitative data analysis. The interviews and FGDs were semi structured using guide with a set of open-ended questions, in a set order and allowing for in-depth insights into the subject area. These guides were pilot tested across the 3 countries prior to data collection. Quantitative Additional interviews were conducted using standard questionnaires and had been piloted and refined in these settings prior to being used for data collection.The research team surveyed 107 providers: 20 from Ethiopia, 18 from India, 23 from Kenya, 28 from Nigeria, and 18 from South Africa. Providers were from 45 private or public health facilities across the five study countries. Of these, 12 (27%) were primary or secondary public, 7 (16%) were primary or secondary private, 25 (56%) were tertiary public, and 1 (2%) tertiary private Demand forecast for caffeine citrate. A demand forecast was conducted to determine the amount of CC needed per country. Using data from demographic health survey data from each country, we estimated the proportion of infants who would be eligible for CC treatment. Given AOP risk can be as high as 80% in preterm infants with birthweight ≤1500g (very low birth weight (VLBW)), we estimated that all VLBW infants met eligibility criteria for treatment with CC. We limited this forecast to public facilities where limited government funding constrains drug availability. We applied country-specific policies and assumptions to determine the percentage of VLBW infants who received or had a missed opportunity for CC treatment. These assumptions included, availability of CC, VLBW infants born in secondary facilities will be transferred to a tertiary center capable of providing AOP treat; some transfers will be unsuccessful and even when successful, AOP treatment will be unavailable. Data management and analysis All interviews were transcribed verbatim by an experienced transcriber. Authors reviewed the interview transcripts for errors. A coding framework was generated, and an emergent thematic analysis approach was used to analyze the data, to identify patterns and themes. Descriptive statistics were used to summarize the quantitative data.

Abstract

The SURE-P MCH midline (second round) data collection was administered in 500 primary healthcare facilities participating in the first phase of the program and in their surrounding catchment areas, and 500 more primary healthcare facilities participating in the second phase of the program and in their surrounding catchment areas.

The midline survey had four modules: - midwife; - primary healthcare facility; - ward development committee; - household/individual.

Geographic coverage

All 36 states and the Federal Capital Territory

Analysis unit

• Midwife
• Primary healthcare facility
• Ward Development Committee
• Women that have given birth in the three months preceding the survey

Universe

Five hundred primary healthcare facilities participating in the first phase of the program, and 500 more primary healthcare facilities participating in the second phase of the program; 1,285 midwives enrolled the first phase (during 2013/14) in an experiment to test the effectiveness of different forms of incentives (monetary; non-monetary; combined) on midwife attrition and 2,180 midwives enrolled in the second phase; ward development committees in the catchment areas of all primary healthcare facilities included in the study; women that had given birth in the three months preceding the survey in the catchment areas of all primary healthcare facilities included in the study.

Kind of data

Sample survey data [ssd]

Sampling procedure

Primary health care facility: the survey targeted 500 primary healthcare facilities participating in the first phase of the SURE-P MCH program and additional 500 primary healthcare facilities in Phase 2.

Midwife: 1,285 midwives from the 500 primary healthcare facilities enrolled the first phase (during 2013/14) and additional 2180 midwives from the 500 primary healthcare facilities enrolled in the second phase from the additional 500 SURE-P MH.

Ward development committee: the survey targeted all 500 ward development committees operating in areas with a SURE-P MCH primary healthcare facility in Phase 1 and additional 500 ward development committees operating in areas with a SURE-P MCH primary healthcare facility in Phase 2.

Household/individual: within the catchment area of each SURE-P MCH Phase 1 and Phase 2 primary healthcare facility, the survey was administered to five randomly selected households containing a woman that had given birth in the past three months for a total of 5000 households/women.

Mode of data collection

Face-to-face [f2f]

Response rate

• Midwife: phase 1 response rate: 100%; Phase 2 response rate: not defined
• Primary health care facility target: 1,000; achievement: 912; response rate: 91%
• Ward development committee target: 1,000; achievement: 928; response rate: 93%
• Household target: 5,000; achievement: 4,950; response rate: 99%

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%