The Coronavirus Disease 2019 (COVID-19) pandemic raised the need for rapid approaches for measuring population-based health indicators to understand its impact on service utilization, while avoiding face-to-face interviews. The Rapid Mortality Mobile Phone Surveys (RaMMPS) aimed to test the use of mobile phone surveys (MPS) for measuring maternal health services coverage in Mozambique by comparing estimates to the nationally representative Demographic Health Survey 2022 (DHS 2022). RaMMPS used two strategies: (i) subsampling from the existing national sample registration system, Country-wide Mortality Surveillance for Action (COMSA); (ii) Random Digit Dialing (RDD) of randomly generated phone numbers. We collected data from March to August 2022 for the COMSA MPS sample, and from June to December 2022 for the RDD sample. In both cases, computer-assisted telephone interviews were conducted among women aged 15-49 years. Women were asked questions on utilization of maternal health care (MHC) services for births within the 2 years preceding the survey. We compared adjusted MHC coverage estimates between each RaMMPS sample and the DHS 2022. Despite adjustment to redress the MPS samples, MHC coverage was higher in MPS samples compared to the DHS 2022 sample. The coverage of at least one antenatal care visit was 99% [95% confidence interval (CI): 98-99] in COMSA MPS and 98% (95% CI: 95-99) in RDD samples compared to 81% (95% CI: 79-84) in the DHS 2022 sample. Additionally, coverage of at least four antenatal care visits was 76% (95% CI: 73-80) in COMSA MPS and 85% (95% CI: 81-88) in RDD samples, compared to 48% (95% CI: 45-51) in the DHS 2022. Coverage of health facility delivery was 87% (95% CI: 84-90) in COMSA MPS and 90% (95% CI: 85-93) in RDD samples compared to 40% (95% CI: 38-62) in the DHS 2022. Similarly, skilled birth attendant coverage was 86% (95% CI: 82-89) and 88% (95% CI: 84-92), respectively, in COMSA MPS and RDD samples compared to 48% (95% CI: 46-49) in the DHS 2022. MHC coverage measured through MPS was consistently overestimated compared to nationally representative surveys, even after adjustments.

Background Bangladesh has achieved major reductions in maternal mortality, yet progress has slowed despite high facility delivery coverage. In 2022, 65% of women had facility delivery, suggesting that increased utilisation has not translated into proportional health gains. We estimated effective coverage of facility-based delivery services by linking household and health facility data and examined equity-related differences in coverage. Methods We conducted a cross-sectional analysis using the Bangladesh Demographic and Health Surveys from 2014, 2017-18, and 2022 and Bangladesh Health Facility Surveys in the corresponding years. Facility readiness was assessed using tracer indicators across human resources, guidelines, equipment, diagnostics, and medicines. Ecological linking assigned readiness scores by facility type. Readiness-adjusted coverage was used to construct effective coverage. Inequalities were examined across wealth, education, residence, and division. Findings Although facility-based deliveries increased substantially in Bangladesh, from 37% in 2014 to 65% in 2022, improvements in effective coverage did not keep pace. After adjusting for facility readiness, effective coverage was only 33% in 2022, indicating a substantial gap between service utilization and quality-adjusted coverage. In 2022, lower facility readiness was influenced by limited trained staff, clinical guidelines, logistics, and essential medicines. Additionally, significant socioeconomic disparities were evident, with effective coverage from 22% among the poorest women to 44% among the wealthiest in 2022. Private facilities accounted for 45% of all live births in 2022; however, their readiness was generally lower than that of public facilities. Overall, although facility delivery utilization rose by 28 percentage points between 2014 and 2022, effective coverage increased by only 11 percentage points (from 22% to 33%) within the same years, highlighting persistent coverage gaps in the quality of maternal healthcare services. Conclusion High facility delivery coverage can hide significant quality issues. Improving diagnostics, service readiness, and private-sector quality, combined with equity-focused monitoring, is crucial to turn contact points into effective care.

In Zimbabwe, the neonatal mortality rate (NMR) is higher than the regional average, and the country is not on track to reach the Sustainable Development Goal of reducing the NMR by 2030. While other child mortality indicators have improved, NMR has increased. Using machine learning, we aimed to identify the key predictors of neonatal mortality in Zimbabwe. Pooled secondary data analysis of three rounds of the Zimbabwe Demographic Health Survey (ZDHS) from 2005 to 2015 was done. The study population was all the live births born to women aged 15-49 years within the 5 years prior to each round of the survey (n = 16,941). Multiple supervised binary classification machine learning models were built to predict neonatal death based on socio-economic, mother's demographic, prenatal, delivery, and neonatal characteristics available in ZDHS. Sensitivity and area under the receiver operating curve (AUC ROC) were used to select the best model for the prediction of neonatal mortality. The best model was used to identify relatively important variables, and logistic regression was used to assess the magnitude and direction of effect. The eXtreme Gradient Boosting Model outperformed other models with a sensitivity of 0.74. Early breastfeeding initiation, birth weight, household size, and newborn post-natal care (PNC) were identified as the top predictors of neonatal mortality. Logistic regression revealed that lower birth weight (aOR [95%CI]: 0.9997 [0.9995 - 0.9999]) was positively associated with odds of neonatal mortality, while household size (aOR [95%CI]: 0.84 [0.80 - 0.89]), early breastfeeding initiation, aOR (0.28 [95%CI]: [0.21 - 0.37]) and newborn postnatal care, (aOR [95%CI]: 0.08 [0.06 - 0.11]) were negatively associated with the odds of neonatal mortality. This study demonstrates the potential of machine learning in identifying key predictors of neonatal mortality in Zimbabwe. To accelerate the reduction in neonatal mortality, interventions should focus on preventing and specially managing low birth weight babies to improve survival. Furthermore, health facilities and community-level support for early initiation of breastfeeding and PNC checks should be promoted for all eligible newborns.

Background:The COVID-19 pandemic disrupted country health systems and necessitated urgent actions to offset its effects on service provision, especially for vulnerable populations such as mothers and children. We aimed to analyse the experiences of healthcare workers in Burkina Faso and Mozambique, and the perceived effects of COVID-19 on reproductive, maternal, newborn, and child health (RMNCH) service provision and utilisation. Methods:We conducted key informant interviews with healthcare workers involved in direct patient care and managerial positions in two provinces in Burkina Faso (Kadiogo and Boulkiemdé) (n = 33) and three provinces in Mozambique (Maputo City, Maputo Province, and Nampula) (n = 66). We audio-recorded, transcribed, and coded the interviews using a deductive and inductive coding approach. We analysed perceptions of RMNCH service disruptions and compared the results between the two countries. We used an inductive analysis method. Results:The health systems in Burkina Faso and Mozambique reacted quickly and in a similar way to contain the COVID-19 pandemic. However, the adoption of COVID-19 activities and the implementation of rotational staff schedules may have slowed down the provision of services. Some services, such as antenatal care and child nutritional services, were limited. In both countries, respondents reported that unforeseen patient costs, such as for face masks, shortages in child medications, and fear from patients of getting COVID-19 virus at health facilities appeared to have hindered service utilisation. Conclusions:The COVID-19 pandemic did not appear to have ceased the availability of or cause substantial disruptions to RMNCH services at health facilities in either country, but our findings showed that key informants perceived the pandemic did influence the reorganisation of health services, and the provision and utilisation of RMNCH services.

Accurate estimation of cause-specific mortality fractions (CSMFs), the percentage of deaths attributable to each cause in a population, is essential for global health monitoring. Challenge arises because computer-coded verbal autopsy (CCVA) algorithms, commonly used to estimate CSMFs, frequently misclassify the cause of death (COD). This misclassification is further complicated by structured patterns and substantial variation across countries. To address this, we introduce the R package 'vacalibration'. It implements a modular Bayesian framework to correct for the misclassification, thereby yielding more accurate CSMF estimates from verbal autopsy (VA) questionnaire data. The package utilizes uncertainty-quantified CCVA misclassification matrix estimates derived from data collected in the CHAMPS project and available on the 'CCVA-Misclassification-Matrices' GitHub repository. Currently, these matrices cover three CCVA algorithms (EAVA, InSilicoVA, and InterVA) and two age groups (neonates aged 0-27 days, and children aged 1-59 months) across countries (specific estimates for Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and South Africa, and a combined estimate for all other countries), enabling global calibration. The 'vacalibration' package also supports ensemble calibration when multiple algorithms are available. Implemented using the 'RStan', the package offers rapid computation, uncertainty quantification, and seamless compatibility with openVA, a leading COD analysis software ecosystem. We demonstrate the package's flexibility with two real-world applications in COMSA-Mozambique and CA CODE. The package and its foundational methodology applies more broadly and can calibrate any discrete classifier or their ensemble.

Background Routine health information system (RHIS) data in sub-Saharan Africa are a critical but underused source for maternal, newborn, and child health (MNCH) monitoring. Rapid digitization through DHIS2 creates an opportunity to standardize data extraction, data-quality assessment, adjustment, and analysis. In this study, we demonstrate how the Countdown to 2030 for Women’s, Children’s and Adolescents’ Health (CD2030) collaboration has fostered cross-country partnerships across 32 sub-Saharan Africa and developed a harmonized data extraction and analysis platform to enhance the systematic use of RHIS data. Methods CD2030 developed an integrated data suite: (1) a standardized DHIS2 extractor that retrieves and maps MNCH indicators into a harmonized template and (2) an analytical application that assesses data quality at national, regional, and district levels, including adjustments for reporting completeness and outliers. The suite was implemented mainly as an R Shiny application and deployed in 2025 with country teams from Ministries of Health and public health institutions in 32 sub-Saharan African countries. Results Despite variation in DHIS2 metadata and indicator definitions, all 32 country teams used the application; 30 extracted 2020–2024 data successfully, and all applied analytical modules. Median overall data-quality scores declined from 83.7% (2020) to 79.3% (2024), with low 2024 scores in Chad, Central African Republic, South Sudan, Guinea-Bissau, Ghana, Senegal, and Somalia. Reporting completeness remained high throughout (median ≥ 95%), though Senegal and South Sudan were lower in 2024. Inconsistencies persisted between ANC-1 visit counts and first-dose DTP counts when compared with ratios expected from household survey estimates. Extreme outliers were rare (mainly Guinea and Somalia) and adjustments had negligible impact on estimates. Facility-derived denominators generally outperformed population projections: Penta-1–derived denominators were most often selected for immunization coverage, while institutional delivery typically relied on ANC-1–derived denominators. For Penta-3, 16/22 countries were within ± 5 percentage points of survey estimates (median absolute difference [MAD] 3; IQR 1–5). For institutional delivery, 15/21 were within ± 5 points (MAD 4; IQR 2–7). Conclusions CD2030’s harmonized DHIS2 extraction and analytics enable more standardized use of RHIS data for MNCH monitoring. Combining routine data with household surveys provides complementary, more frequent, and more localized insights to support health system performance monitoring.

INTRODUCTION:Computer-coded verbal autopsy (CCVA) algorithms are routinely used to determine individual cause of death (COD) and derive population-level estimates of cause-specific mortality fractions (CSMFs). But frequent COD misclassification leads to biased CSMF estimates. The VA-calibration framework reduces the bias by estimating misclassification rates; but it overlooks systematic patterns and cross-country variation, reducing the accuracy of CSMF estimates. METHODS:Using CHAMPS (Child Health and Mortality Prevention Surveillance) data and the framework in Pramanik et al (2025), we estimate misclassification rates of three widely used CCVA algorithms (Expert Algorithm VA, InSilicoVA and InterVA), two age groups (neonates aged 0-27 days and children aged 1-59 months), and eight countries (Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, South Africa and 'other'). We then demonstrate their utility and use the Mozambique-specific rates to calibrate VA-only data from the Countrywide Mortality Surveillance for Action (COMSA) project in Mozambique. RESULTS:We report three key findings. First, the country-specific model better fits CHAMPS misclassification rates than the homogeneous model, reducing average absolute loss by 34%-38% for neonates and 13%-24% for children. Second, CCVA algorithms show consistent misclassification patterns, systematically overestimating or underestimating certain causes. Third, calibrating COMSA data increases neonatal CSMF for sepsis/meningitis/infection and decreases it for intrapartum-related events and prematurity; among children, CSMF increases for malaria and decreases for pneumonia. CONCLUSIONS:We present an inventory of VA misclassification rate estimates across two age groups, three CCVA algorithms and eight countries. These publicly available estimates enable the calibration of VA-only data from any country without needing access to CHAMPS data. More generally, these analyses reveal systematic algorithmic biases and highlight opportunities to refine future CCVA algorithms. As reliance on computer-coded and AI-driven approaches to COD determination grows, our integrated VA-calibration workflow, grounded in robust statistical frameworks and open-source software (misclassification matrix modeling, VA-calibration R package on GitHub and CRAN), offers a critical step towards improving the accuracy of mortality surveillance.

Objective:Vaccine hesitancy has become one of the biggest challenges in combating the COVID-19 pandemic globally. This paper aims to determine the factors associated with COVID-19 vaccination and hesitancy among women of reproductive age in Mozambique. Methods:A cross-sectional mobile phone survey was conducted among women ages 15-49 to test the use of mobile phone interviews to collect data on household deaths and other topics related to pregnancy, delivery care, women's empowerment, and COVID-19 vaccination. We calculated COVID-19 vaccination coverage rates, defined as women who received at least one dose of the COVID-19 vaccine, and described reasons for not taking the vaccine. Multivariate logistic regression was used to assess factors associated with COVID-19 vaccine uptake in the study population. All estimates were adjusted using post-adjustment weighting based on the raking approach to redress the sample to be nationally representative. Results:The mobile phone survey response rate was estimated at 39.1% (n = 13,235). The adjusted COVID-19 vaccination rate was estimated at 77.2% [95%CI: 74.9-79.5] among women aged 15-49. Among the unvaccinated women, about 11.9% were hesitant to take the COVID-19 vaccine if offered. The primary reasons reported for not taking the vaccine were the dislike of needles (17.1%), COVID-19 vaccine safety concerns (12.0%), COVID-19 vaccine effectiveness concerns (10.2%), and medical reasons (5.6%). We found a positive and significant association between COVID-19 vaccine acceptance and age group, education, marital status, province and place of residence, and women who used maternal health facility services. However, women empowerment factors were not significantly associated with increased COVID-19 vaccine acceptance. Conclusion:Our findings showed high rates of COVID-19 vaccination among women aged 15-49 with social, economic, and residential inequalities. To increase COVID-19 vaccine coverage among women in Mozambique, more effort should be put into vaccinating younger women, uneducated women, and those delivering outside a health facility. More attention should also be given to factors related to vaccine acceptance and hesitancy in Mozambique.

Minimally invasive tissue sampling (MITS) has been used as an alternative to complete autopsy to track causes of death (CoDs) in South Asia and sub-Saharan Africa as part of the Child Health and Mortality Prevention Surveillance program. However, community acceptance, rapid identification of deaths, and adequate functional laboratory infrastructures (e.g., pathology, conventional microbiology, and molecular microbiology) are critical for successful implementation. We describe the experience of implementing MITS in an urban district with socioeconomic and cultural diversity in Zambézia Province, central Mozambique. For successful implementation of mortality surveillance using MITS, high-level advocacy involving the Provincial Government and all stakeholders as well as engagement and sensitization of all segments of the communities, including traditional healers, community leaders, and mass media, were critical for the acceptability of the procedure. Additionally, social and behavior studies were conducted to assess perceptions, sociocultural factors, acceptability, and feasibility of the MITS procedure. These studies helped adapt the MITS protocol to the local context to minimize the risk of misunderstanding the mortality surveillance using MITS procedures. There was significant investment in capacity building, including financial support for laboratory equipment acquisition and maintenance, reagents, and consumables required for microbiological screening protocols of MITS and to support the needs for diagnostics of patients with severe disease seeking care. Experiences from Quelimane and other sites and data generated in the Countrywide Mortality Surveillance for Action to support evidence-based decision-making processes on health policy were critical for the community to understand the benefit of determining young children CoD to guide future interventions.

Health systems in low- and middle-income countries (LMICs) like Bangladesh face persistent challenges in delivering timely and equitable care, often exacerbated by poor planning and inefficient resource allocation. Forecasting service utilization using routine health data can support more responsive and data-driven health system planning, yet such approaches remain under utilized in Bangladesh. By analyzing service utilization trends and projecting future service volume at national and regional levels, we aim to improve region-specific health planning. This can promote more efficient and equitable service provision. We analyzed monthly routine health service data reported into the District Health Information Software 2 (DHIS2) platform between January 2021 and March 2025 in Bangladesh. We examined key indicators across maternal, newborn, child and hospital-based services. Bayesian log-linear Poisson regression models, adjusted for seasonality and autocorrelation, were applied to forecast service utilization for the final nine months of 2025 and all of 2026. Relative changes in 2025 and 2026 were calculated using 2024 as the reference year. The analysis revealed rising trends across most service areas relative to 2024 levels. Kangaroo Mother Care (KMC) has the highest projected expansion, with coverage forecast to rise by over 75% by 2026. Over the same time period, outpatient visits and pneumonia treatment are also expected to increase by about 30%. More moderate increases are seen in low birth weight (LBW) deliveries, cesarean sections, and normal deliveries. Notable regional disparities persist, with Dhaka and Chittagong showing the highest service utilization, while Barishal and Sylhet consistently report the lowest levels. Bangladesh's health system must prepare for increasing service utilization across all service categories. Forecasting using DHIS2 data supports for proactive planning and equitable resource allocation. Strategic investments in infrastructure, workforce, and data-driven planning are essential for building a resilient health system.

Background:Experience of care is typically measured through client exit surveys administered in the facility. Evidence suggests that such measures suffer from courtesy reporting bias whereby respondents do not accurately report on their experiences while in the facility. We explored the presence of courtesy bias by comparing women's reported experience of person-centred maternity care (PCMC) from facility-based client exit surveys to mobile phone-based surveys out of the facility in Nairobi and Lusaka's urban informal settlements. Methods:We randomly and independently sampled women in the facilities for either a facility-based survey (n = 233 in Lusaka and n = 112 in Nairobi) or a mobile phone-based survey (n = 203 in Lusaka and n = 300 in Nairobi) within one to two weeks of facility discharge. The questionnaire included a validated PCMC scale. After adjusting for differences in women's characteristics across groups, we compared PCMC scores between facility and phone-based samples. We ran multilevel linear regression models to assess PCMC by survey modality in each city. Results:In both cities, over 70.0% of women were aged 20-34 years and were married, at least two thirds had secondary education, and over 95.0% were unaccompanied during labour/delivery. The overall PCMC score was 69.3% among women surveyed on the phone compared to 70.2% among those surveyed in the facility in Nairobi. In Lusaka, it was 57.5% on the phone compared to 56.8% in-facility. We found no statistically significant differences in PCMC scores between survey modalities in both cities, after adjusting for differences in women's characteristics. Conclusions:We did not detect significant courtesy reporting bias in PCMC in facility-based client exit surveys in the context of urban informal settlements in Nairobi and Lusaka. Experience of PCMC can be measured through in-facility client exit surveys or mobile phone surveys. However, it is critical to address challenges related to a mobile phone-based approach.

Background Substantial gaps exist between pregnant women's contact with health facilities and the quality of care they receive (effective coverage) in low- and middle-income countries (LMICs). An effective coverage cascade is a useful analytical approach to uncover gaps due to poor facility service readiness and quality of care. We estimated readiness-adjusted antenatal care (ANC) coverage and built an effective coverage cascade in countries with available data. Methods We used data from latest household and health facility surveys in eight countries accounting for 28 925 women and 8621 facilities. Service readiness was assessed based on the availability of core items needed to provide quality ANC. We linked the household surveys with health facility data by subnational region and facility type to estimate readiness-adjusted ANC coverage for at least one, four, and eight or more ANC contacts and ANC content. We built a four-step ANC effective coverage cascade and calculated loss of coverage in terms of ANC readiness coverage gaps and missed opportunities. Results The majority of women sought ANC services in lower-level facilities, except in Bangladesh, Nepal and Senegal. While at least one antenatal care contact (ANC1+) service coverage was high, ranging from 89.2% (95% confidence interval (CI) = 87.2-90.9) in Haiti to 98.1% (95% CI = 97.5-98.6) in Malawi, readiness-adjusted ANC1+ coverage was lower, ranging from 64% (95% CI = 62.4-65.5) in Haiti to 76.2% (95% CI = 75.1-77.2) in Nepal. We obtained readiness gaps as high as 33.7 percentage points in Malawi and missed opportunities of 21 percentage points in Tanzania. Poor diagnostic capacity and insufficient trained human resources drove the low ANC facility readiness. We found large inequalities in readiness-adjusted ANC1+ by socioeconomic status favouring wealthier and urban resident women. Conclusions The effective coverage cascade for ANC services helped uncover large readiness gaps, missed opportunities, and socioeconomic inequalities. Improvements in facilities' diagnostic capacity and availability of trained human resources will enhance their ability to provide high quality health services and ensure health gains.

Background:High-quality postnatal care (PNC) is essential for newborn survival. However, newborn PNC coverage indicators do not reflect the quality of care received. We estimated effective coverage of newborn PNC by incorporating content of care and calculated the contact-content gap in 32 low-and middle-income countries (LMICs). Methods:Using household survey data from 32 LMICs, we defined effective coverage of newborn PNC as the proportion of mothers or babies who received five essential signal functions as part of their newborn's PNC check by a medically trained provider within the first two days of birth. We calculated the contact-content gap as the absolute difference between service coverage of newborn PNC and effective coverage of newborn PNC, in percentage points (pp). We described inequalities in effective coverage of newborn PNC by mother's age, parity, wealth, education, residence, antenatal care seeking, delivery facility type, and managing authority. Results:The median effective coverage of newborn PNC was 27.5% across countries, ranging from 2.1% (95% confidence interval (CI) = 1.6, 2.7) in Burundi to 78.3% (95% CI = 74.3, 81.8) in Armenia. We identified large PNC contact-content gaps, up to 52.6 pp in The Gambia. Content of care was generally poor, with only 37.1% of mothers across countries receiving counselling on newborn danger signs. We found important demographic, socioeconomic, and health systems inequalities in newborn PNC effective coverage, disproportionately favouring newborns whose mother was older (35-49 years), primiparous, lived in urban areas, belonged to the wealthiest households, had at least secondary education, and delivered in a hospital or the private sector in most countries. Conclusions:In most countries, a substantial number of newborns who are checked during PNC do not receive all basic services. Effective coverage measures offer a more comprehensive estimate for assessing service gaps, driving action, and ensuring health gains. Addressing the missed opportunities of inadequate care content and focussing on equity will be critical to improve survival of all mothers and newborns.

OBJECTIVES:The use of mobile phone surveys in low- and middle-income countries is increasing as a low-cost and rapid alternative to in-person interviews. However, ensuring they are representative of women and, when women are included reducing potential response bias and harm are important considerations. To improve women's participation in phone surveys, we conducted a qualitative study in Mozambique to better understand women's experiences of participating in mobile phone surveys. METHODS:This study was part of the Rapid Mortality Mobile Phone Survey (RaMMPS) project implemented in Mozambique to test the use of mobile phone interviews for childhood mortality measurement at the national level. We conducted a qualitative study with 32 women who had previously participated in the RAMMPS mobile phone survey. Interviews were conducted both in-person and over the phone. Thematic analysis was done manually using the Framework approach. RESULTS:Gender-related considerations that emerged from the data regarding women's participation included women's access to mobile phones, the reduced time burden and convenience of participating in mobile phone interviews compared to in-person interviews, difficulties ensuring privacy in mobile phone surveys, the effect of the interviewer's gender on participant responses, and women's safety concerns. CONCLUSION:Important considerations for including women in mobile phone surveys relate to efforts to reduce response bias and mitigate harm, such as ensuring privacy and considering the gender of the data collector. Addressing these issues is crucial to improving women's participation and experience in mobile phone surveys.

Background:Most child deaths can be averted through prompt and appropriate treatment of child illnesses such as pneumonia, diarrhoea, and malaria. However, research has suggested that increases in care seeking do not necessarily mean that quality care is being received. We assessed the service readiness and process quality of curative healthcare during childhood and determined whether children are receiving health services with sufficient quality across countries. Methods:We linked data from household surveys including the standard Demographic and Health Survey and the Multiple Indicator Cluster Survey to data from facility surveys including the Service Provision Assessment and Health Facility Assessment in Bangladesh, the Democratic Republic of Congo, Haiti, Kenya, Malawi, Nepal, Senegal and Tanzania to estimate the effective coverage of child illness treatment. We assessed the gaps in service availability and coverage, lack of service readiness, missed care opportunities, and inadequate service process, where service readiness and process quality were defined according to global standards with country-specific adaptations. We analysed the service readiness, quality of care, and effective coverage by individual illness and combined illnesses accounting for equity dimensions. Results:Seven to 42% of children experienced at least one illness. An integrated management of child illnesses (IMCI) service was available in 58-85% of facilities. We found that 55-66% of health facilities in the countries were ready to deliver treatment to sick children. However, the readiness-adjusted contact suggested that child healthcare was mostly sought in facilities with low readiness score, ranging from 15% (Nepal) to 46.0% (Malawi). Health facilities had low diagnostics, supervision, and trained personnel capacity to manage child illnesses. Concerning the quality of care, only 51-60% of the procedures during clinical encounters were in line with standards. Counselling of caretakers had the lowest score, while treatment components had the highest process quality score. Hospitals had higher readiness and process quality scores compared to primary facilities and the private sector. There were, however, large gaps in service readiness and significant inadequate service processes in all countries; 35% (Haiti) to 79% (Bangladesh) of sick children sought care from a health facility, with only 7% (Nepal) to 29% (Malawi) of them actually receiving appropriate treatment. We found large inequalities in care seeking, quality of care, and effective coverage across levels of education and poverty, and places of residence. Conclusions:A large proportion of facilities did not meet the required capacity to provide IMCI services. The provision of health services has major quality gaps, highlighting the need for strengthening health service access, capacity and quality of care to reach universal child health coverage.

In sub-Saharan Africa, maternal and newborn deaths remain disproportionately higher among low-income populations, and they are associated with delivery in poorly equipped facilities and a shortage of staff to manage birth complications. We measured facility readiness to provide essential maternal and newborn health services and its association with women’s experience of person-centered maternity care (PCMC), and we compared facilities serving and not serving informal settlements in Nairobi, Lusaka and Ouagadougou cities. We conducted a health facility assessment in public and private facilities serving select urban informal settlements in Nairobi, and we used existing data in Lusaka and Ouagadougou. We computed readiness indices for labor and delivery care, and small and/or sick newborn care (SSNC) in each city, and used t-tests to compare them across facilities serving and not serving informal settlements. We linked women’s self-reported PCMC scores to the labor and delivery readiness score of the facility they attended and ran 2-level linear regression models testing the association between facility readiness and PCMC scores. Facility readiness scores were computed among 18, 38 and 138 facilities offering delivery services in Nairobi, Lusaka and Ouagadougou respectively. Mean labor and delivery readiness scores in facilities serving informal settlements ranged from 55.9% in Ouagadougou to 73.6% in Lusaka; SSNC readiness ranged from 37.2% in Ouagadougou to 61.3% in Nairobi. While facilities serving informal settlements had statistically significantly poorer readiness in Lusaka and Ouagadougou, key items such as newborn caps, registers, guidelines, and staff trained in Kangaroo Mother Care were lacking across both areas. We found no significant association between facility readiness and PCMC. All facilities have substandard readiness for essential maternal and newborn health services, but those serving informal settlements are more disadvantaged. Investments in service readiness and quality of care remain critical.