Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HE2AT Center study protocol.

INTRODUCTION: African cities, particularly Abidjan and Johannesburg, face challenges of rapid urban growth, informality and strained health services, compounded by increasing temperatures due to climate change. This study aims to understand the complexities of heat-related health impacts in these cities. The objectives are: (1) mapping intraurban heat risk and exposure using health, socioeconomic, climate and satellite imagery data; (2) creating a stratified heat-health forecast model to predict adverse health outcomes; and (3) establishing an early warning system for timely heatwave alerts. The ultimate goal is to foster climate-resilient African cities, protecting disproportionately affected populations from heat hazards. METHODS AND ANALYSIS: The research will acquire health-related datasets from eligible adult clinical trials or cohort studies conducted in Johannesburg and Abidjan between 2000 and 2022. Additional data will be collected, including socioeconomic, climate datasets and satellite imagery. These resources will aid in mapping heat hazards and quantifying heat-health exposure, the extent of elevated risk and morbidity. Outcomes will be determined using advanced data analysis methods, including statistical evaluation, machine learning and deep learning techniques. ETHICS AND DISSEMINATION: The study has been approved by the Wits Human Research Ethics Committee (reference no: 220606). Data management will follow approved procedures. The results will be disseminated through workshops, community forums, conferences and publications. Data deposition and curation plans will be established in line with ethical and safety considerations.

Measuring geographic access to emergency obstetric care: a comparison of travel time estimates modelled using Google Maps Directions API and AccessMod in three Nigerian conurbations.

Google Maps Directions Application Programming Interface (the API) and AccessMod tools are increasingly being used to estimate travel time to healthcare. However, no formal comparison of estimates from the tools has been conducted. We modelled and compared median travel time (MTT) to comprehensive emergency obstetric care (CEmOC) using both tools in three Nigerian conurbations (Kano, Port-Harcourt, and Lagos). We compiled spatial layers of CEmOC healthcare facilities, road network, elevation, and land cover and used a least-cost path algorithm within AccessMod to estimate MTT to the nearest CEmOC facility. Comparable MTT estimates were extracted using the API for peak and non-peak travel scenarios. We investigated the relationship between MTT estimates generated by both tools at raster celllevel (0.6 km resolution). We also aggregated the raster cell estimates to generate administratively relevant ward-level MTT. We compared ward-level estimates and identified wards within the same conurbation falling into different 15-minute incremental categories (<15/15-30/30-45/45-60/+60). Of the 189, 101 and 375 wards, 72.0%, 72.3% and 90.1% were categorised in the same 15- minute category in Kano, Port-Harcourt, and Lagos, respectively. Concordance decreased in wards with longer MTT. AccessMod MTT were longer than the API's in areas with ≥45min. At the raster cell-level, MTT had a strong positive correlation (≥0.8) in all conurbations. Adjusted R2 from a linear model (0.624-0.723) was high, increasing marginally in a piecewise linear model (0.677-0.807). In conclusion, at <45-minutes, ward-level estimates from the API and AccessMod are marginally different, however, at longer travel times substantial differences exist, which are amenable to conversion factors.

Geographical accessibility to functional emergency obstetric care facilities in urban Nigeria using closer-to-reality travel time estimates: a population-based spatial analysis.

BACKGROUND: Better accessibility for emergency obstetric care facilities can substantially reduce maternal and perinatal deaths. However, pregnant women and girls living in urban settings face additional complex challenges travelling to facilities. We aimed to assess the geographical accessibility of the three nearest functional public and private comprehensive emergency obstetric care facilities in the 15 largest Nigerian cities via a novel approach that uses closer-to-reality travel time estimates than traditional model-based approaches. METHODS: In this population-based spatial analysis, we mapped city boundaries, verified and geocoded functional comprehensive emergency obstetric care facilities, and mapped the population distribution for girls and women aged 15-49 years (ie, of childbearing age). We used the Google Maps Platform's internal Directions Application Programming Interface to derive driving times to public and private facilities. Median travel time and the percentage of women aged 15-49 years able to reach care were summarised for eight traffic scenarios (peak and non-peak hours on weekdays and weekends) by city and within city under different travel time thresholds (≤15 min, ≤30 min, ≤60 min). FINDINGS: As of 2022, there were 11·5 million girls and women aged 15-49 years living in the 15 studied cities, and we identified the location and functionality of 2020 comprehensive emergency obstetric care facilities. City-level median travel time to the nearest comprehensive emergency obstetric care facility ranged from 18 min in Maiduguri to 46 min in Kaduna. Median travel time varied by location within a city. The between-ward IQR of median travel time to the nearest public comprehensive emergency obstetric care varied from the narrowest in Maiduguri (10 min) to the widest in Benin City (41 min). Informal settlements and peripheral areas tended to be worse off compared to the inner city. The percentages of girls and women aged 15-49 years within 60 min of their nearest public comprehensive emergency obstetric care ranged from 83% in Aba to 100% in Maiduguri, while the percentage within 30 min ranged from 33% in Aba to over 95% in Ilorin and Maiduguri. During peak traffic times, the median number of public comprehensive emergency obstetric care facilities reachable by women aged 15-49 years under 30 min was zero in eight (53%) of 15 cities. INTERPRETATION: Better access to comprehensive emergency obstetric care is needed in Nigerian cities and solutions need to be tailored to context. The innovative approach used in this study provides more context-specific, finer, and policy-relevant evidence to support targeted efforts aimed at improving comprehensive emergency obstetric care geographical accessibility in urban Africa. FUNDING: Google.

Socio-spatial equity analysis of relative wealth index and emergency obstetric care accessibility in urban Nigeria.

BACKGROUND: Better geographical accessibility to comprehensive emergency obstetric care (CEmOC) facilities can significantly improve pregnancy outcomes. However, with other factors, such as affordability critical for care access, it is important to explore accessibility across groups. We assessed CEmOC geographical accessibility by wealth status in the 15 most-populated Nigerian cities. METHODS: We mapped city boundaries, verified and geocoded functional CEmOC facilities, and assembled population distribution for women of childbearing age and Meta's Relative Wealth Index (RWI). We used the Google Maps Platform's internal Directions Application Programming Interface to obtain driving times to public and private facilities. City-level median travel time (MTT) and number of CEmOC facilities reachable within 60 min were summarised for peak and non-peak hours per wealth quintile. The correlation between RWI and MTT to the nearest public CEmOC was calculated. RESULTS: We show that MTT to the nearest public CEmOC facility is lowest in the wealthiest 20% in all cities, with the largest difference in MTT between the wealthiest 20% and least wealthy 20% seen in Onitsha (26 vs 81 min) and the smallest in Warri (20 vs 30 min). Similarly, the average number of public CEmOC facilities reachable within 60 min varies (11 among the wealthiest 20% and six among the least wealthy in Kano). In five cities, zero facilities are reachable under 60 min for the least wealthy 20%. Those who live in the suburbs particularly have poor accessibility to CEmOC facilities. CONCLUSIONS: Our findings show that the least wealthy mostly have poor accessibility to care. Interventions addressing CEmOC geographical accessibility targeting poor people are needed to address inequities in urban settings.

Access to critical obstetric care can be lifesaving for pregnant women and their offspring. However, socioeconomic factors are known to affect accessibility to health services across different groups. Here, we assessed peak and off-peak travel times to functional health facilities for women from 15 Nigerian cities, using travel time estimates produced by Google Maps and stratified by wealth status. Travel time to the nearest hospital and the number of hospitals reachable within 60 min varied across cities. The wealthiest 20% across all cities had the shortest travel time and vice versa for the least wealthy 20%. Women who live in the suburbs particularly have poor accessibility. Tailored action is needed to improve access for vulnerable populations living in urban settings.

Protocol of an individual participant data meta-analysis to quantify the impact of high ambient temperatures on maternal and child health in Africa (HE2AT IPD).

INTRODUCTION: Globally, recognition is growing of the harmful impacts of high ambient temperatures (heat) on health in pregnant women and children. There remain, however, major evidence gaps on the extent to which heat increases the risks for adverse health outcomes, and how this varies between settings. Evidence gaps are especially large in Africa. We will conduct an individual participant data (IPD) meta-analysis to quantify the impacts of heat on maternal and child health in sub-Saharan Africa. A detailed understanding and quantification of linkages between heat, and maternal and child health is essential for developing solutions to this critical research and policy area. METHODS AND ANALYSIS: We will use IPD from existing, large, longitudinal trial and cohort studies, on pregnant women and children from sub-Saharan Africa. We will systematically identify eligible studies through a mapping review, searching data repositories, and suggestions from experts. IPD will be acquired from data repositories, or through collaboration with data providers. Existing satellite imagery, climate reanalysis data, and station-based weather observations will be used to quantify weather and environmental exposures. IPD will be recoded and harmonised before being linked with climate, environmental, and socioeconomic data by location and time. Adopting a one-stage and two-stage meta-analysis method, analytical models such as time-to-event analysis, generalised additive models, and machine learning approaches will be employed to quantify associations between exposure to heat and adverse maternal and child health outcomes. ETHICS AND DISSEMINATION: The study has been approved by ethics committees. There is minimal risk to study participants. Participant privacy is protected through the anonymisation of data for analysis, secure data transfer and restricted access. Findings will be disseminated through conferences, journal publications, related policy and research fora, and data may be shared in accordance with data sharing policies of the National Institutes of Health. PROSPERO REGISTRATION NUMBER: CRD42022346068.

A geospatial database of close-to-reality travel times to obstetric emergency care in 15 Nigerian conurbations.

Travel time estimation accounting for on-the-ground realities between the location where a need for emergency obstetric care (EmOC) arises and the health facility capable of providing EmOC is essential for improving pregnancy outcomes. Current understanding of travel time to care is inadequate in many urban areas of Africa, where short distances obscure long travel times and travel times can vary by time of day and road conditions. Here, we describe a database of travel times to comprehensive EmOC facilities in the 15 most populated extended urban areas of Nigeria. The travel times from cells of approximately 0.6 × 0.6 km to facilities were derived from Google Maps Platform's internal Directions Application Programming Interface, which incorporates traffic considerations to provide closer-to-reality travel time estimates. Computations were done to the first, second and third nearest public or private facilities. Travel time for eight traffic scenarios (including peak and non-peak periods) and number of facilities within specific time thresholds were estimated. The database offers a plethora of opportunities for research and planning towards improving EmOC accessibility.

Leveraging big data for improving the estimation of close to reality travel time to obstetric emergency services in urban low- and middle-income settings.

Maternal and perinatal mortality remain huge challenges globally, particularly in low- and middle-income countries (LMICs) where >98% of these deaths occur. Emergency obstetric care (EmOC) provided by skilled health personnel is an evidence-based package of interventions effective in reducing these deaths associated with pregnancy and childbirth. Until recently, pregnant women residing in urban areas have been considered to have good access to care, including EmOC. However, emerging evidence shows that due to rapid urbanization, this so called "urban advantage" is shrinking and in some LMIC settings, it is almost non-existent. This poses a complex challenge for structuring an effective health service delivery system, which tend to have poor spatial planning especially in LMIC settings. To optimize access to EmOC and ultimately reduce preventable maternal deaths within the context of urbanization, it is imperative to accurately locate areas and population groups that are geographically marginalized. Underpinning such assessments is accurately estimating travel time to health facilities that provide EmOC. In this perspective, we discuss strengths and weaknesses of approaches commonly used to estimate travel times to EmOC in LMICs, broadly grouped as reported and modeled approaches, while contextualizing our discussion in urban areas. We then introduce the novel OnTIME project, which seeks to address some of the key limitations in these commonly used approaches by leveraging big data. The perspective concludes with a discussion on anticipated outcomes and potential policy applications of the OnTIME project.

Implementation of a Community Transport Strategy to Reduce Delays in Seeking Obstetric Care in Rural Mozambique.

INTRODUCTION: Delays due to long distances to health facilities, poor road infrastructure, and lack of affordable transport options contribute to the burden of maternal deaths in Mozambique. This study aimed to assess the implementation and uptake of an innovative community-based transport program to improve access to emergency obstetric care in southern Mozambique. METHODS: From April 2016 to February 2017, a community transport strategy was implemented as part of the Community Level Interventions for Pre-eclampsia Trial. The study aimed to reduce maternal and perinatal mortality and morbidity by 20% in intervention clusters in Maputo and Gaza Provinces, Mozambique, by involving community health workers in the identification and referral of pregnant and puerperal women at risk. Based on a community-based participatory needs assessment, the transport program was implemented with the trial. Demographics, conditions requiring transportation, means of transport used, route, and outcomes were collected during implementation. Data were entered into a REDCap database. RESULTS: Fifty-seven neighborhoods contributed to the needs assessment; of those, 13 (23%) implemented the transport program. Neighborhoods were selected based on their expression of interest and ability to contribute financially to the program (US$0.33 per family per month). In each selected neighborhood, a community management committee was created, training in small-scale financial management was conducted, and monitoring tools were provided. Twenty people from 9 neighborhoods benefited from the transport program, 70% were pregnant and postpartum women. CONCLUSION: These results demonstrate that it was feasible to implement a community-based transport program with no external input of vehicles, fuel, personnel, and maintenance. However, high cost and a lack of acceptable transport options in some communities continue to impede access to obstetric health care services and the ability for timely follow-up. When strengthening capacities of community health workers to promptly assist and refer emergency cases, it is crucial to encourage local transport programs and transportation infrastructure among minimally resourced communities to support access and engagement with health systems.

RoadMApp: a feasibility study for a smart travel application to improve maternal health delivery in a low resource setting in Zimbabwe.

BACKGROUND: Travel time and healthcare financing are critical determinants of the provision of quality maternal health care in low resource settings. Despite the availability of pregnancy-related mHealth and smart travel applications, there is a lack of evidence on their usage to travel to health facilities for routine antenatal care and emergencies. There is a shortage of information about the feasibility of using a custom-made mobile technology that integrates smart travel and mHealth. This paper explores the feasibility of implementing a custom-made geographically enabled mobile technology-based tool (RoadMApp) to counter the adverse effects of long travel times for maternal care in Kwekwe District, Zimbabwe. METHODS: We frame the paper using the first two steps (listen & plan) of the Spiral Technology Action Research (STAR model). The paper uses an exploratory case study design and Participatory Learning Approaches (PLA) with stakeholders (community members) and in-depth interviews with key informants (health care service providers, pregnant women, transport operators). One hundred ninety-three participants took part in the study. We conducted focus group discussions with pregnant women, women of childbearing age, men (household heads), and elderly women. The discussion questions centered on travel time, availability of transport, cellular network coverage, and perceptions of the RoadMApp application. Data were analysed thematically using Nvivo Pro 12. RESULTS: Most parts of rural Kwekwe are far from health facilities and have an inefficient road and telecommunications network. Hence, it is hard to predict if RoadMApp will integrate into the lives of the community - especially those in rural areas. Since these issues are pillars of the design of the RoadMApp mHealth, the implementation will probably be a challenge. CONCLUSION: Communities are keen to embrace the RoadMApp application. However, the feasibility of implementing RoadMApp in Kwekwe District will be a challenge because of maternal health care barriers such as poor road network, poor phone network, and the high cost of transport. There is a need to investigate the social determinants of access to maternity services to inform RoadMApp implementation.

Interactions between the Physical and Social Environments with Adverse Pregnancy Events Related to Placental Disorders-A Scoping Review.

BACKGROUND: Due to different social and physical environments across Africa, understanding how these environments differ in interacting with placental disorders will play an important role in developing effective interventions. METHODS: A scoping review was conducted, to identify current knowledge on interactions between the physical and social environment and the incidence of placental disease in Africa. RESULTS: Heavy metals were said to be harmful when environmental concentrations are beyond critical limits. Education level, maternal age, attendance of antenatal care and parity were the most investigated social determinants. CONCLUSIONS: More evidence is needed to determine the relationships between the environment and placental function in Africa. The results show that understanding the nature of the relationship between social determinants of health (SDH) and placental health outcomes plays a pivotal role in understanding the risk in the heterogenous communities in Africa.

The PRECISE (PREgnancy Care Integrating translational Science, Everywhere) Network's first protocol: deep phenotyping in three sub-Saharan African countries.

BACKGROUND: The PRECISE (PREgnancy Care Integrating translational Science, Everywhere) Network is a new and broadly-based group of research scientists and health advocates based in the UK, Africa and North America. METHODS: This paper describes the protocol that underpins the clinical research activity of the Network, so that the investigators, and broader global health community, can have access to 'deep phenotyping' (social determinants of health, demographic and clinical parameters, placental biology and agnostic discovery biology) of women as they advance through pregnancy to the end of the puerperium, whether those pregnancies have normal outcomes or are complicated by one/more of the placental disorders of pregnancy (pregnancy hypertension, fetal growth restriction and stillbirth). Our clinical sites are in The Gambia (Farafenni), Kenya (Kilifi County), and Mozambique (Maputo Province). In each country, 50 non-pregnant women of reproductive age will be recruited each month for 1 year, to provide a final national sample size of 600; these women will provide culturally-, ethnically-, seasonally- and spatially-relevant control data with which to compare women with normal and complicated pregnancies. Between the three countries we will recruit ≈10,000 unselected pregnant women over 2 years. An estimated 1500 women will experience one/more placental complications over the same epoch. Importantly, as we will have accurate gestational age dating using the TraCer device, we will be able to discriminate between fetal growth restriction and preterm birth. Recruitment and follow-up will be primarily facility-based and will include women booking for antenatal care, subsequent visits in the third trimester, at time-of-disease, when relevant, during/immediately after birth and 6 weeks after birth. CONCLUSIONS: To accelerate progress towards the women's and children's health-relevant Sustainable Development Goals, we need to understand how a variety of social, chronic disease, biomarker and pregnancy-specific determinants health interact to result in either a resilient or a compromised pregnancy for either mother or fetus/newborn, or both. This protocol has been designed to create such a depth of understanding. We are seeking funding to maintain the cohort to better understand the implications of pregnancy complications for both maternal and child health.

Harnessing the PRECISE network as a platform to strengthen global capacity for maternal and child health research in sub-Saharan Africa.

It is widely acknowledged across the global health sector that research programmes need to be designed and implemented in a way that maximise opportunities for strengthening local capacity. This paper examines how the United Kingdom Research and Innovation (UKRI) Grand Challenges Research Fund (GCRF) funded PRECISE (PREgnancy Care Integrating translational Science, Everywhere) Network has been established as a platform to strengthen global capacity for research focused on the improvement of maternal, fetal and newborn health in sub-Saharan Africa.Best practice principles outlined in an ESSENCE on Health Research report have been considered in relation to the PRECISE Network capacity-building activities described in this paper. These activities are described at the individual, programmatic and institutional levels, and successes, challenges and recommendations for future work are outlined.The paper concludes that the PRECISE leadership have an opportunity to review and refresh activity plans for capacity building at this stage in the project to build on achievements to date.

The contribution of qualitative research within the PRECISE study in sub-Saharan Africa.

The PRECISE Network is a cohort study established to investigate hypertension, fetal growth restriction and stillbirth (described as "placental disorders") in Kenya, Mozambique and The Gambia. Several pregnancy or birth cohorts have been set up in low- and middle-income countries, focussed on maternal and child health. Qualitative research methods are sometimes used alongside quantitative data collection from these cohorts. Researchers affiliated with PRECISE are also planning to use qualitative methods, from the perspective of multiple subject areas. This paper provides an overview of the different ways in which qualitative research methods can contribute to achieving PRECISE's objectives, and discusses the combination of qualitative methods with quantitative cohort studies more generally.We present planned qualitative work in six subject areas (health systems, health geography, mental health, community engagement, the implementation of the TraCer tool, and respectful maternity care). Based on these plans, with reference to other cohort studies on maternal and child health, and in the context of the methodological literature on mixed methods approaches, we find that qualitative work may have several different functions in relation to cohort studies, including informing the quantitative data collection or interpretation. Researchers may also conduct qualitative work in pursuit of a complementary research agenda. The degree to which integration between qualitative and quantitative methods will be sought and achieved within PRECISE remains to be seen. Overall, we conclude that the synergies resulting from the combination of cohort studies with qualitative research are an asset to the field of maternal and child health.

Is the closest health facility the one used in pregnancy care-seeking? A cross-sectional comparative analysis of self-reported and modelled geographical access to maternal care in Mozambique, India and Pakistan.

BACKGROUND: Travel time to care is known to influence uptake of health services. Generally, pregnant women who take longer to transit to health facilities are the least likely to deliver in facilities. It is not clear if modelled access predicts fairly the vulnerability in women seeking maternal care across different spatial settings. OBJECTIVES: This cross-sectional analysis aimed to (i) compare travel times to care as modelled in a GIS environment with self-reported travel times by women seeking maternal care in Community Level Interventions for Pre-eclampsia: Mozambique, India and Pakistan; and (ii) investigate the assumption that women would seek care at the closest health facility. METHODS: Women were interviewed to obtain estimated travel times to health facilities (R). Travel time to the closest facility was also modelled (P) (closest facility tool (ArcGIS)) and time to facility where care was sought estimated (A) (route network layer finder (ArcGIS)). Bland-Altman analysis compared spatial variation in differences between modelled and self-reported travel times. Variations between travel times to the nearest facility (P) with modelled travel times to the actual facilities accessed (A) were analysed. Log-transformed data comparison graphs for medians, with box plots superimposed distributions were used. RESULTS: Modelled geographical access (P) is generally lower than self-reported access (R), but there is a geography to this relationship. In India and Pakistan, potential access (P) compared fairly with self-reported travel times (R) [P (H0: Mean difference = 0)] < .001, limits of agreement: [- 273.81; 56.40] and [- 264.10; 94.25] respectively. In Mozambique, mean differences between the two measures of access were significantly different from 0 [P (H0: Mean difference = 0) = 0.31, limits of agreement: [- 187.26; 199.96]]. CONCLUSION: Modelling access successfully predict potential vulnerability in populations. Differences between modelled (P) and self-reported travel times (R) are partially a result of women not seeking care at their closest facilities. Modelling access should not be viewed through a geographically static lens. Modelling assumptions are likely modified by spatio-temporal and/or socio-cultural settings. Geographical stratification of access reveals disproportionate variations in differences emphasizing the varied nature of assumptions across spatial settings. Trial registration ClinicalTrials.gov, NCT01911494. Registered 30 July 2013, https://clinicaltrials.gov/ct2/show/NCT01911494.

Is the closest health facility the one used in pregnancy care-seeking? A cross-sectional comparative analysis of self-reported and modelled geographical access to maternal care in Mozambique, India and Pakistan

July Travel time to care is known to influence uptake of health services. Generally, pregnant women who take longer to transit to health facilities are the least likely to deliver in facilities. It is not clear if modelled access predicts fairly the vulnerability in women seeking maternal care across different spatial settings. This cross-sectional analysis aimed to (i) compare travel times to care as modelled in a GIS environment with self-reported travel times by women seeking maternal care in Community Level Interventions for Pre-eclampsia: Mozambique, India and Pakistan; and (ii) investigate the assumption that women would seek care at the closest health facility. Women were interviewed to obtain estimated travel times to health facilities (R). Travel time to the closest facility was also modelled (P) (closest facility tool (ArcGIS)) and time to facility where care was sought estimated (A) (route network layer finder (ArcGIS)). Bland-Altman analysis compared spatial variation in differences between modelled and self-reported travel times. Variations between travel times to the nearest facility (P) with modelled travel times to the actual facilities accessed (A) were analysed. Log-transformed data comparison graphs for medians, with box plots superimposed distributions were used. Modelled geographical access (P) is generally lower than self-reported access (R), but there is a geography to this relationship. In India and Pakistan, potential access (P) compared fairly with self-reported travel times (R) [P (H0: Mean difference = 0)] < .001, limits of agreement: [- 273.81; 56.40] and [- 264.10; 94.25] respectively. In Mozambique, mean differences between the two measures of access were significantly different from 0 [P (H0: Mean difference = 0) = 0.31, limits of agreement: [- 187.26; 199.96]].Modelling access successfully predict potential vulnerability in populations. Differences between modelled (P) and self-reported travel times (R) are partially a result of women not seeking care at their closest facilities. Modelling access should not be viewed through a geographically static lens. Modelling assumptions are likely modified by spatio-temporal and/or socio-cultural settings. Geographical stratification of access reveals disproportionate variations in differences emphasizing the varied nature of assumptions across spatial settings

How accurate are modelled birth and pregnancy estimates? Comparison of four models using high resolution maternal health census data in southern Mozambique

Background: Existence of inequalities in quality and access to healthcare services at subnational levels has been identified despite a decline in maternal and perinatal mortality rates at national levels, leading to the need to investigate such conditions using geographical analysis. The need to assess the accuracy of global demographic distribution datasets at all subnational levels arises from the current emphasis on subnational monitoring of maternal and perinatal health progress, by the new targets stated in the Sustainable Development Goals. Methods: The analysis involved comparison of four models generated using Worldpop methods, incorporating region-specific input data, as measured through the Community Level Intervention for Pre-eclampsia (CLIP) project. Normalised root mean square error was used to determine and compare the models' prediction errors at different administrative unit levels. Results: The models' prediction errors are lower at higher administrative unit levels. All datasets showed the same pattern for both the live birth and pregnancy estimates. The effect of improving spatial resolution and accuracy of input data was more prominent at higher administrative unit levels. Conclusion: The validation successfully highlighted the impact of spatial resolution and accuracy of maternal and perinatal health data in modelling estimates of pregnancies and live births. There is a need for more data collection techniques that conduct comprehensive censuses like the CLIP project. It is also imperative for such projects to take advantage of the power of mapping tools at their disposal to fill the gaps in the availability of datasets for populated areas.

How accurate are modelled birth and pregnancy estimates? Comparison of four models using high resolution maternal health census data in southern Mozambique.

BACKGROUND: Existence of inequalities in quality and access to healthcare services at subnational levels has been identified despite a decline in maternal and perinatal mortality rates at national levels, leading to the need to investigate such conditions using geographical analysis. The need to assess the accuracy of global demographic distribution datasets at all subnational levels arises from the current emphasis on subnational monitoring of maternal and perinatal health progress, by the new targets stated in the Sustainable Development Goals. METHODS: The analysis involved comparison of four models generated using Worldpop methods, incorporating region-specific input data, as measured through the Community Level Intervention for Pre-eclampsia (CLIP) project. Normalised root mean square error was used to determine and compare the models' prediction errors at different administrative unit levels. RESULTS: The models' prediction errors are lower at higher administrative unit levels. All datasets showed the same pattern for both the live birth and pregnancy estimates. The effect of improving spatial resolution and accuracy of input data was more prominent at higher administrative unit levels. CONCLUSION: The validation successfully highlighted the impact of spatial resolution and accuracy of maternal and perinatal health data in modelling estimates of pregnancies and live births. There is a need for more data collection techniques that conduct comprehensive censuses like the CLIP project. It is also imperative for such projects to take advantage of the power of mapping tools at their disposal to fill the gaps in the availability of datasets for populated areas.

Place-specific factors associated with adverse maternal and perinatal outcomes in Southern Mozambique: a retrospective cohort study.

OBJECTIVES: To identify and measure the place-specific determinants that are associated with adverse maternal and perinatal outcomes in the southern region of Mozambique. DESIGN: Retrospective cohort study. Choice of variables informed by literature and Delphi consensus. SETTING: Study conducted during the baseline phase of a community level intervention for pre-eclampsia that was led by community health workers. PARTICIPANTS: A household census identified 50 493 households that were home to 80 483 women of reproductive age (age 12-49 years). Of these women, 14 617 had been pregnant in the 12 months prior to the census, of which 9172 (61.6%) had completed their pregnancies. PRIMARY AND SECONDARY OUTCOME MEASURES: A combined fetal, maternal and neonatal outcome was calculated for all women with completed pregnancies. RESULTS: A total of six variables were statistically significant (p≤0.05) in explaining the combined outcome. These included: geographic isolation, flood proneness, access to an improved latrine, average age of reproductive age woman, family support and fertility rates. The performance of the ordinary least squares model was an adjusted R2=0.69. Three of the variables (isolation, latrine score and family support) showed significant geographic variability in their effect on rates of adverse outcome. Accounting for this modest non-stationary effect through geographically weighted regression increased the adjusted R2 to 0.71. CONCLUSIONS: The community exploration was successful in identifying context-specific determinants of maternal health. The results highlight the need for designing targeted interventions that address the place-specific social determinants of maternal health in the study area. The geographic process of identifying and measuring these determinants, therefore, has implications for multisectoral collaboration. TRIAL REGISTRATION NUMBER: NCT01911494.