Temporal trends in spatial inequalities of maternal and newborn health services among four east African countries, 1999-2015

Uncovering the causes and socio-demographic constructs of stillbirths and neonatal deaths in an urban slum of Karachi

INTRODUCTION

Neonatal deaths and stillbirths are significant public health concerns in Pakistan, with an estimated stillbirth rate of 43 per 1,000 births and a neonatal mortality rate of 46 deaths per 1,000 live births. Limited access to obstetric care, poor health seeking behaviors and lack of quality healthcare are the leading root causes for stillbirths and neonatal deaths. Rehri Goth, a coastal slum in Karachi, faces even greater challenges due to extreme poverty, and inadequate infrastructure. This study aims to investigate the causes and pathways leading to stillbirths and neonatal deaths in Rehri Goth to develop effective maternal and child health interventions.

METHODS

A mixed-method cohort study was nested with the implementation of large maternal, neonatal and child health program, captured all stillbirths and neonatal death during the period of May 2014 till June 2018. The Verbal and Social Autopsy (VASA) tool (WHO 2016) was used to collect primary data from all death events to determine the causes as well as the pathways. Interviews were conducted both retrospectively and prospectively with mothers and caregivers. Two trained physicians reviewed the VASA form and the medical records (if available) and coded the cause of death blinded to each other. Descriptive analysis was used to categorize stillbirth and neonatal mortality data into high- and low-mortality clusters, followed by chi-square tests to explore associations between categories, and concluded with a qualitative analysis.

RESULTS

Out of 421 events captured, complete VASA interviews were conducted for 317 cases. The leading causes of antepartum stillbirths were pregnancy-induced hypertension (22.4%) and maternal infections (13.4%), while obstructed labor was the primary cause of intrapartum stillbirths (38.3%). Neonatal deaths were primarily caused by perinatal asphyxia (36.1%) and preterm birth complications (27.8%). The qualitative analysis on a subset of 40 death events showed that health system (62.5%) and community factors (37.5%) contributing to adverse outcomes, such as delayed referrals, poor triage systems, suboptimal quality of care, and delayed care-seeking behaviors.

CONCLUSION

The study provides an opportunity to understand the causes of stillbirths and neonatal deaths in one of the impoverished slums of Karachi. The data segregation by clusters as well as triangulation with qualitative analysis highlight the needs of evidence-based strategies for maternal and child health interventions in disadvantaged communities.

Predicting the drop out from the maternal, newborn and child healthcare continuum in three East African Community countries: application of machine learning models

BACKGROUND

For optimal health, the maternal, newborn, and child healthcare (MNCH) continuum necessitates that the mother/child receive the full package of antenatal, intrapartum, and postnatal care. In sub-Saharan Africa, dropping out from the MNCH continuum remains a challenge. Using machine learning, the study sought to forecast the MNCH continuum drop out and determine important predictors in three East African Community (EAC) countries.

METHODS

The study utilised Demographic Health Surveys data from the Democratic Republic of Congo (DRC) (2013/14), Kenya (2014) and Tanzania (2015/16). STATA 17 was used to perform the multivariate logistic regression. Python 3.0 was used to build five machine learning classification models namely the Logistic Regression, Random Forest, Decision Tree, Support Vector Machine and Artificial Neural Network. Performance of the models was assessed using Accuracy, Precision, Recall, Specificity, F1 score and area under the Receiver Operating Characteristics (AUROC).

RESULTS

The prevalence of the drop out from the MNCH continuum was 91.0% in the DRC, 72.4% in Kenya and 93.6% in Tanzania. Living in the rural areas significantly increased the odds of dropping out from the MNCH continuum in the DRC (AOR:1.76;95%CI:1.30-2.38), Kenya (AOR:1.23;95%CI:1.03-1.47) and Tanzania (AOR:1.41;95%CI:1.01-1.97). Lower maternal education also conferred a significant increase in the DRC (AOR:2.16;95%CI:1.67-2.79), Kenya (AOR:1.56;95%CI:1.30-1.84) and Tanzania (AOR:1.70;95%CI:1.24-2.34). Non exposure to mass media also conferred a significant positive influence in the DRC (AOR:1.49;95%CI:1.15-1.95), Kenya (AOR:1.46;95%CI:1.19-1.80) and Tanzania (AOR:1.65;95%CI:1.13-2.40). The Random Forest exhibited superior predictive accuracy (Accuracy = 75.7%, Precision = 79.1%, Recall = 92.1%, Specificity = 51.6%, F1 score = 85.1%, AUROC = 70%). The top four predictors with the greatest influence were household wealth, place of residence, maternal education and exposure to mass media.

CONCLUSIONS

The MNCH continuum dropout rate is very high in the EAC countries. Maternal education, place of residence, and mass media exposure were common contributing factors to the drop out from MNCH continuum. The Random Forest had the highest predictive accuracy. Household wealth, place of residence, maternal education and exposure to mass media were ranked among the top four features with significant influence. The findings of this study can be used to support evidence-based decisions in MNCH interventions and to develop web-based services to improve continuity of care retention.

Preconception care in sub-Saharan Africa: A systematic review and meta-analysis on the prevalence and its correlation with knowledge level among women in the reproductive age group

Objective

Preconception care is aimed to promote optimal health in women before conception to reduce or prevent poor pregnancy outcomes. Although there are several published primary studies from sub-Saharan African countries on preconception care, they need to quantify the extent of preconception care utilization, the knowledge level about preconception care, and the association among women in the reproductive age group in this region. This systematic review and meta-analysis aimed to estimate the pooled utilization of preconception care, pooled knowledge level about preconception care, and their association among women in the reproductive age group in sub-Saharan Africa.

Methods

Databases including PubMed, Science Direct, Hinari, Google Scholar, and Cochrane library were systematically searched for relevant literature. Additionally, the references of included articles were checked for additional possible sources. The Cochrane Q test statistics and I ² tests were used to assess the heterogeneity of the included studies. A random-effect meta-analysis model was used to estimate the pooled prevalence of preconception care, knowledge level of preconception care, and their correlation among reproductive-aged women in sub-Saharan African countries.

Results

Of the identified 1593 articles, 20 studies were included in the final analysis. The pooled utilization of preconception care and good knowledge level about preconception care among women of reproductive age were found to be 24.05% (95% confidence interval: 16.61, 31.49) and 33.27% (95% confidence interval: 24.78, 41.77), respectively. Women in the reproductive age group with good knowledge levels were greater than two times more likely to utilize the preconception care than the women with poor knowledge levels in sub-Saharan African countries (odds ratio: 2.35, 95% confidence interval: 1.16, 4.76).

Conclusion

In sub-Saharan African countries, the utilization of preconception care and knowledge toward preconception care were low. Additionally, the current meta-analysis found good knowledge level to be significantly associated with the utilization of preconception care among women of reproductive age. These findings indicate that it is imperative to launch programs to improve the knowledge level about preconception care utilization among women in the reproductive age group in sub-Saharan African countries.

Socioeconomic inequity in coverage and quality of maternal postnatal care in Ethiopia

OBJECTIVE

High-quality postnatal care is vital for improving maternal health. This study examined the relationship between household socioeconomic status and both coverage and quality of postnatal care in Ethiopia.

METHOD

Cross-sectional household survey data were collected in October-November 2013 from 12 zones in 4 regions of Ethiopia. Women reporting a live birth in the 3-24 months prior to the survey were interviewed about the care they received before, during and after delivery and their demographic characteristics. Using mixed effect logistic and linear regression, the associations between household socioeconomic status and receiving postnatal care, location of postnatal care (health facility vs. non-health facility), cadre of person providing care and the number of seven key services (including physical checks and advice) provided at a postnatal visit, were estimated.

RESULTS

A total of 16% (358/2189) of women interviewed reported receiving at least one postnatal care visit within 6 weeks of delivery. Receiving a postnatal care visit was strongly associated with socioeconomic status with women from the highest socioeconomic group having twice the odds of receiving postnatal care compared to women in the poorest quintile (OR [95% CI]: 1.98 [1.29, 3.05]). For each increasing socioeconomic status quintile there was a mean increase of 0.24 postnatal care services provided (95% CI: 0.06-0.43, p = 0.009) among women who did not give birth in a facility. There was no evidence that number of postnatal care services was associated with socioeconomic status for women who gave birth in a facility. There was no evidence that socioeconomic status was associated with the provider or location of postnatal care visits.

CONCLUSION

Postnatal care in Ethiopia shows evidence of socio-economic inequity in both coverage and quality. This demonstrates the need to focus on quality improvement as well as coverage, particularly among the poorest women who did not deliver in a facility.

Identifying geographical inequalities of maternal care utilization in Ethiopia: a Spatio-temporal analysis from 2005 to 2019

INTRODUCTION

Inequalities in maternal care utilization pose a significant threat to maternal health programs. This study aimed to describe and explain the spatial variation in maternal care utilization among pregnant women in Ethiopia. Accordingly, this study focuses on identifying hotspots of underutilization and mapping maternal care utilization, as well as identifying predictors of spatial clustering in maternal care utilization.

METHODS

We evaluated three key indicators of maternal care utilization: pregnant women who received no antenatal care (ANC) service from a skilled provider, utilization of four or more ANC visits, and births attended in a health facility, based the Ethiopian National Demographic and Health Survey (EDHS5) to 2019. Spatial autocorrelation analysis was used to measure whether maternal care utilization was dispersed, clustered, or randomly distributed in the study area. Getis-Ord Gi statistics examined how Spatio-temporal variations differed through the study location and ordinary Kriging interpolation predicted maternal care utilization in the unsampled areas. Ordinary least squares (OLS) regression was used to identify predictors of geographic variation, and geographically weighted regression (GWR) examined the spatial variability relationships between maternal care utilization and selected predictors.

RESULT

A total of 26,702 pregnant women were included, maternal care utilization varies geographically across surveys. Overall, statistically significant low maternal care utilization hotspots were identified in the Somali region. Low hotspot areas were also identified in northern Ethiopia, stretching into the Amhara, Afar, and Beneshangul-Gumuz regions; and the southern part of Ethiopia and the Gambella region. Spatial regression analysis revealed that geographical variations in maternal care utilization indicators were commonly explained by the number of under-five children, the wealth index, and media access. In addition, the mother's educational status significantly explained pregnant women, received no ANC service and utilized ANC service four or more times. Whereas, the age of a mother at first birth was a spatial predictor of pregnant who received no ANC service from a skilled provider.

CONCLUSION

In Ethiopia, it is vital to plan to combat maternal care inequalities in a manner suitable for the district-specific variations. Predictors of geographical variation identified during spatial regression analysis can inform efforts to achieve geographical equity in maternal care utilization.

Geographic inequalities in health intervention coverage – mapping the composite coverage index in Peru using geospatial modelling

Background

The composite coverage index (CCI) provides an integrated perspective towards universal health coverage in the context of reproductive, maternal, newborn and child health. Given the sample design of most household surveys does not provide coverage estimates below the first administrative level, approaches for achieving more granular estimates are needed. We used a model-based geostatistical approach to estimate the CCI at multiple resolutions in Peru.

Methods

We generated estimates for the eight indicators on which the CCI is based for the departments, provinces, and areas of 5 × 5 km of Peru using data from two national household surveys carried out in 2018 and 2019 plus geospatial covariates. Bayesian geostatistical models were fit using the INLA-SPDE approach. We assessed model fit using cross-validation at the survey cluster level and by comparing modelled and direct survey estimates at the department-level.

Results

CCI coverage in the provinces along the coast was consistently higher than in the remainder of the country. Jungle areas in the north and east presented the lowest coverage levels and the largest gaps between and within provinces. The greatest inequalities were found, unsurprisingly, in the largest provinces where populations are scattered in jungle territory and are difficult to reach.

Conclusions

Our study highlighted provinces with high levels of inequality in CCI coverage indicating areas, mostly low-populated jungle areas, where more attention is needed. We also uncovered other areas, such as the border with Bolivia, where coverage is lower than the coastal provinces and should receive increased efforts. More generally, our results make the case for high-resolution estimates to unveil geographic inequities otherwise hidden by the usual levels of survey representativeness.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-14371-7.

Perceptions and Experiences of Health Care Workers on Accountability Mechanisms for Enhancing Quality Improvement in the Delivery of Maternal Newborns and Child Health Services in Mkuranga, Tanzania

Background

Maternal mortality estimates globally show that by 2017 there were still 211 deaths per 100,000 live births; more strikingly, 99% of them happen in low and middle-income countries, including Tanzania. There has been insufficient progress in improving maternal and newborn health despite the efforts to strengthen the health systems, to improve the quality of maternal health in terms of training and deploying human resources for health, constructing health facilities, and supplying medical products. However, fewer efforts are invested in enhancing accountability toward the improvement of the quality of maternal health care. This the study was conducted to explore the perceptions of healthcare workers regarding accountability mechanisms for enhancing quality improvement in the delivery of maternal newborn and child health services in Tanzania.

Methods

We adopted phenomenology as a study design to understand how health workers perceive accountability and data were collected using semi-structured interviews. We then used thematic analysis to analyze themes and sub- themes.

Results

The study revealed four categories of perceptions namely, differences in the conceptualization of accountability and accountability mechanisms, varied opinions about the existing accountability mechanisms, perceived the usefulness of accountability mechanisms, together with perceived challenges in the enforcement of accountability mechanisms.

Conclusion

Perceived variations in the understanding of accountability among healthcare workers signaled a proper but fragmented understanding of accountability in maternal care. Accountability mechanisms are perceived to be useful for enhancing hard work in the provision of maternal health services. Moreover, inadequate motivation resulting from health system bottlenecks tend to constrain enforcement of accountability in the provision of maternal care services. Thus, we recommend that the government should deal with health system constraints and enforce regular monitoring and supervision.

Identifying exposure pathways mediating adverse birth outcomes near active surface mines in Central Appalachia

Background:

Previous work has determined an association between proximity to active surface mining within Central Appalachia and an increased risk of preterm birth (PTB) and low birthweight (LBW). Multiple potential exposure pathways may exist; however, including inhalation of particulate matter (airshed exposure), or exposure to impacted surface waters (watershed exposure). We hypothesize that this relationship is mediated by exposure to contaminants along one or both of these pathways.

Methods:

We geolocated 194,084 birth records through health departments in WV, KY, VA, and TN between 1990 and 2015. We performed a mediation analysis, iteratively including within our models: (a) the percent of active surface mining within 5 km of maternal residence during gestation; (b) the cumulative surface mining airshed trajectories experienced during gestation; and (c) the percent of active surface mining occurring within the watershed of residency during gestation.

Results:

Our baseline models found that active surface mining was associated with an increased odds of PTB (1.09, 1.05–1.13) and LBW (1.06, 1.02–1.11), controlling for individual-level predictors. When mediators were added to the baseline model, the association between active mining and birth outcomes became nonsignificant (PTB: 0.48, 0.14–1.58; LBW 0.78, 0.19–3.00), whereas the association between PTB and LBW remained significant by airshed exposure (PTB: 1.14, 1.11–1.18; LBW: 1.06, 1.03–1.10).

Conclusions:

Our results found that surface mining airsheds at least partially explained the association between active mining and adverse birth outcomes, consistent with a hypothesis of mediation, while mediation via the watershed pathway was less evident.

Trends and determinants of late antenatal care initiation in three East African countries, 2007-2016: A population based cross-sectional analysis

Early antenatal care is critical for the mother and newborn's health. Antenatal care is often delayed in Sub-Saharan Africa. The study aims to examine the trends and determinants of late antenatal care initiation in the Democratic Republic of Congo, Kenya, and Tanzania from 2007-2016. The study employed Demographic Health Surveys data of reproductive-age women seeking antenatal care in the Democratic Republic of Congo (2007-2013/14), Kenya (2008-2014), and Tanzania (2010-2015/16). Bivariate and multivariate analysis was conducted per survey, taking sampling weights into account. The determinants of late antenatal care initiation were measured using multivariate logistic regression models and the trends were assessed using prediction scores. Late antenatal care initiation declined in Tanzania (60.9%-49.8%) and Kenya (67.8%-60.5%) but increased in the Democratic Republic of Congo (56.8%-61.0%) between surveys. In the Democratic Republic of Congo, higher birth order was associated with antenatal care initiation delays from 2007-2014, whilst rural residency (AOR:1.28;95%CI:1.09-1.52), lower maternal education (AOR:1.29;95%CI:1.13-1.47) and lower-income households (AOR:1.30;95%CI:1.08-1.55) were linked to antenatal care initiation delays in 2014. In Kenya, lower maternal education and lower-income households were associated with antenatal care initiation delays from 2008-2014, whilst rural residency (AOR:1.24;95%CI:1.11-1.38) and increased birth order (AOR:1.12; 95%CI:1.01-1.28) were linked to antenatal care initiation delays in 2014. In Tanzania, higher birth order and larger households were linked to antenatal care initiation delays from 2010-2016, whilst antenatal care initiation delays were associated with lower maternal education (OR:1.51;95%CI:1.16-1.97) in 2010 and lower-income households (OR:1.45;95%CI:1.20-1.72) in 2016. Except for the Democratic Republic of Congo, the sub-region is making progress in reducing antenatal care delays. Women from various geographic, educational, parity, and economic groups exhibited varying levels of delayed antenatal care uptake. Increasing women's access to information platforms and strengthening initiatives that enhance female education, household incomes, and localise services may enhance early antenatal care utilisation.

Disrupted Service Delivery? The Impact of Conflict on Antenatal Care Quality in Kenya

Introduction: African countries facing conflict have higher levels of maternal mortality. Understanding the gaps in the utilization of high-quality maternal health care is essential to improving maternal survival in these states. Few studies have estimated the impact of conflict on the quality of health care. In this study, we estimated the impact of conflict on the quality of health care in Kenya, a country with multiple overlapping conflicts and significant disparities in maternal survival.

Materials and Methods: We drew on data on the observed quality of 553 antenatal care (ANC) visits between January and April 2010. Process quality was measured as the percentage of elements of client–provider interactions performed in these visits. For structural quality, we measured the percentage of required components of equipment and infrastructure and the management and supervision in the facility on the day of the visit. We spatially linked the analytical sample to conflict events from January to April 2010. We modeled the quality of ANC as a function of exposure to conflict using spatial difference-in-difference models.

Results: ANC visits that occurred in facilities within 10,000 m of any conflict event in a high-conflict month received 18–21 percentage points fewer components of process quality on average and had a mean management and supervision score that was 12.8–13.5 percentage points higher. There was no significant difference in the mean equipment and infrastructure score at the 5% level. The positive impact of conflict exposure on the quality of management and supervision was driven by rural facilities. The quality of management and supervision and equipment and infrastructure did not modify the impact of conflict on process quality.

Discussion: Our study demonstrates the importance of designing maternal health policy based on the context-specific evidence on the mechanisms through which conflict affects health care. In Kenya, deterioration of equipment and infrastructure does not appear to be the main mechanism through which conflict has affected ANC quality. Further research should focus on better understanding the determinants of the gaps in process quality in conflict-affected settings, including provider motivation, competence, and incentives.

Spatial and socioeconomic inequalities in the availability of community health centres in the Jakarta region, Indonesia

In the late 1960s, Indonesia established community health centres (CHCs) throughout the country to provide basic healthcare services for the poor. However, CHC expenditures and investments vary widely at the sub-provincial level, among administrative areas known as cities and regencies, raising concern that facilities and services do not correspond to population needs. This study aimed to examine spatial and socioeconomic inequalities in the availability of CHCs in the Jakarta region. We used spatial and statistical analysis methods at the village level to investigate these inequalities based on CHC data from the Ministry of Health and socioeconomic data from Indonesia Statistics. Results show that CHCs and the healthcare workers within them are unevenly distributed. In areas with high need, the availability of CHCs and healthcare workers were found to be low. There is a mismatch in healthcare services and delivery for low-income, unemployed populations at the village level that needs to be addressed. The findings discussed in this paper suggest that Jakarta Department of Health should coordinate with local public health districts to determine locations for new CHCs and assign healthcare workers to each CHC based on need as this would improve access to essential health services for the low-income population.

A review of geospatial methods for population estimation and their use in constructing reproductive, maternal, newborn, child and adolescent health service indicators

Background

Household survey data are frequently used to measure reproductive, maternal, newborn, child and adolescent health (RMNCAH) service utilisation in low and middle income countries. However, these surveys are typically only undertaken every 5 years and tend to be representative of larger geographical administrative units. Investments in district health management information systems (DHMIS) have increased the capability of countries to collect continuous information on the provision of RMNCAH services at health facilities. However, reliable and recent data on population distributions and demographics at subnational levels necessary to construct RMNCAH coverage indicators are often missing. One solution is to use spatially disaggregated gridded datasets containing modelled estimates of population counts. Here, we provide an overview of various approaches to the production of gridded demographic datasets and outline their potential and their limitations. Further, we show how gridded population estimates can be used as alternative denominators to produce RMNCAH coverage metrics in combination with data from DHMIS, using childhood vaccination as examples.

Methods

We constructed indicators on the percentage of children one year old for diphtheria, pertussis and tetanus vaccine dose 3 (DTP3) and measles vaccine dose (MCV1) in Zambia and Nigeria at district levels. For the numerators, information on vaccines doses was obtained from each country’s respective DHMIS. For the denominators, the number of children was obtained from 3 different sources including national population projections and aggregated gridded estimates derived using top-down and bottom-up geospatial methods.

Results

In Zambia, vaccination estimates utilising the bottom-up approach to population estimation substantially reduced the number of districts with > 100% coverage of DTP3 and MCV1 compared to estimates using population projection and the top-down method. In Nigeria, results were mixed with bottom-up estimates having a higher number of districts > 100% and estimates using population projections performing better particularly in the South.

Conclusions

Gridded demographic data utilising traditional and novel data sources obtained from remote sensing offer new potential in the absence of up to date census information in the estimation of RMNCAH indicators. However, the usefulness of gridded demographic data is dependent on several factors including the availability and detail of input data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06370-y.

Practical geospatial and sociodemographic predictors of human mobility

Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.

Modelling geographical accessibility to urban centres in Kenya in 2019

BACKGROUND

Access to major services, often located in urban centres, is key to the realisation of numerous Sustainable Development Goals (SDGs). In Kenya, there are no up-to-date and localised estimates of spatial access to urban centres. We estimate the travel time to urban centres and identify marginalised populations for prioritisation and targeting.

METHODS

Urban centres were mapped from the 2019 Kenya population census and combined with spatial databases of road networks, elevation, land use and travel barriers within a cost-friction algorithm to compute travel time. Seven travel scenarios were considered: i) walking only (least optimistic), ii) bicycle only, iii) motorcycle only, iv) vehicle only (most optimistic), v) walking followed by motorcycle transport, vi) walking followed by vehicle transport, and vii) walking followed by motorcycle and then vehicle transport (most pragmatic). Mean travel time, and proportion of the population within 1-hour and 2-hours of the urban centres were summarized at sub-national units (counties) used for devolved planning. Inequities were explored and correlations between the proportion of the population within 1-hour of an urban centre and ten SDG indicators were computed.

RESULTS

A total of 307 urban centres were digitised. Nationally, the mean travel time was 4.5-hours for the walking-only scenario, 1.0-hours for the vehicle only (most optimistic) scenario and 1.5-hours for the walking-motorcycle-vehicle (most pragmatic) scenario. Forty-five per cent (21.3 million people) and 87% (41.6 million people) of Kenya's population resided within 1-hour of the nearest urban centre for the least optimistic and most pragmatic scenarios respectively. Over 3.2 million people were considered marginalised or living outside the 2-hour threshold in the pragmatic scenario, 16.0 million Kenyans for walking only, and 2.2 million for the most optimistic scenario. County-level spatial access was highly heterogeneous ranging between 8%-100% and 32%-100% of people within the 1-hour threshold for the least and most optimistic scenarios, respectively. Counties in northern and eastern parts of Kenya were generally most marginalised. The correlation coefficients for nine SDG indicators ranged between 0.45 to 0.78 and were statistically significant.

CONCLUSION

Travel time to urban centres in Kenya is heterogeneous. Therefore, marginalised populations should be prioritised during resource allocation and policies should be formulated to enhance equitable access to public services and opportunities in urban areas.

The impact of child health interventions and risk factors on child survival in Kenya, 1993-2014: a Bayesian spatio-temporal analysis with counterfactual scenarios

BACKGROUND

During the millennium development goals period, reduction in under-five mortality (U5M) and increases in child health intervention coverage were characterised by sub-national disparities and inequities across Kenya. The contribution of changing risk factors and intervention coverage on the sub-national changes in U5M remains poorly defined.

METHODS

Sub-national county-level data on U5M and 43 factors known to be associated with U5M spanning 1993 and 2014 were assembled. Using a Bayesian ecological mixed-effects regression model, the relationships between U5M and significant intervention and infection risk ecological factors were quantified across 47 sub-national counties. The coefficients generated were used within a counterfactual framework to estimate U5M and under-five deaths averted (U5-DA) for every county and year (1993-2014) associated with changes in the coverage of interventions and disease infection prevalence relative to 1993.

RESULTS

Nationally, the stagnation and increase in U5M in the 1990s were associated with rising human immunodeficiency virus (HIV) prevalence and reduced maternal autonomy while improvements after 2006 were associated with a decline in the prevalence of HIV and malaria, increase in access to better sanitation, fever treatment-seeking rates and maternal autonomy. Reduced stunting and increased coverage of early breastfeeding and institutional deliveries were associated with a smaller number of U5-DA compared to other factors while a reduction in high parity and fully immunised children were associated with under-five lives lost. Most of the U5-DA occurred after 2006 and varied spatially across counties. The highest number of U5-DA was recorded in western and coastal Kenya while northern Kenya recorded a lower number of U5-DA than western. Central Kenya had the lowest U5-DA. The deaths averted across the different regions were associated with a unique set of factors.

CONCLUSION

Contributions of interventions and risk factors to changing U5M vary sub-nationally. This has important implications for targeting future interventions within decentralised health systems such as those operated in Kenya. Targeting specific factors where U5M has been high and intervention coverage poor would lead to the highest likelihood of sub-national attainment of sustainable development goal (SDG) 3.2 on U5M in Kenya.

Subnational estimates of factors associated with under-five mortality in Kenya: a spatio-temporal analysis, 1993-2014

BACKGROUND

To improve child survival, it is necessary to describe and understand the spatial and temporal variation of factors associated with child survival beyond national aggregates, anchored at decentralised health planning units. Therefore, we aimed to provide subnational estimates of factors associated with child survival while elucidating areas of progress, stagnation and decline in Kenya.

METHODS

Twenty household surveys and three population censuses conducted since 1989 were assembled and spatially aligned to 47 subnational Kenyan county boundaries. Bayesian spatio-temporal Gaussian process regression models accounting for inadequate sample size and spatio-temporal relatedness were fitted for 43 factors at county level between 1993 and 2014.

RESULTS

Nationally, the coverage and prevalence were highly variable with 38 factors recording an improvement. The absolute percentage change (1993-2014) was heterogeneous ranging between 1% and 898%. At the county level, the estimates varied across space and over time with a majority showing improvements after 2008 which was preceded by a period of deterioration (late-1990 to early-2000). Counties in Northern Kenya were consistently observed to have lower coverage of interventions and remained disadvantaged in 2014 while areas around Central Kenya had and historically have had higher coverage across all intervention domains. Most factors in Western and South-East Kenya recorded moderate intervention coverage although having a high infection prevalence of both HIV and malaria.

CONCLUSION

The heterogeneous estimates necessitates prioritisation of the marginalised counties to achieve health equity and improve child survival uniformly across the country. Efforts are required to narrow the gap between counties across all the drivers of child survival. The generated estimates will facilitate improved benchmarking and establish a baseline for monitoring child development goals at subnational level.

High-resolution mapping of reproductive tract infections among women of childbearing age in Bangladesh: a spatial-temporal analysis of the demographic and health survey

BACKGROUND

Reproductive tract infections (RTIs) have become major but silent public health problems devastating women's lives in Bangladesh. Accurately and precisely identifying high-risk areas of RTIs through high-resolution risk maps is meaningful for resource-limited settings.

METHODS

We obtained data reported with RTI symptoms by women of childbearing age in the years 2007, 2011 and 2014 from Bangladesh Demographic and Health Survey. High-spatial Environmental, socio-economic and demographic layers were downloaded from different open-access data sources. We applied Bayesian spatial-temporal models to identify important influencing factors and to estimate the infection risk at 5 km spatial resolution across survey years in Bangladesh.

RESULTS

We estimated that in Bangladesh, there were approximate 11.1% (95% Bayesian credible interval, BCI: 10.5-11.7%), 13.9% (95% BCI: 13.3-14.5%) and 13.4% (95% BCI: 12.8-14.0%) of women of childbearing age reported with RTI symptoms in 2007, 2011 and 2014, respectively. The risk of most areas shows an obvious increase from 2007 to 2011, then became stable between 2011 and 2014. High risk areas were identified in the southern coastal areas, the western Rajshahi Division, the middle of Khulna Division, and the southwestern Chittagong Division in 2014. The prevalence of Rajshahi and Nawabganj District were increasing during all the survey years.

CONCLUSION

The high-resolution risk maps of RTIs we produced can guide the control strategies targeted to priority areas cost-effectively. More than one eighth of women of childbearing age reported symptoms suggesting RTIs and the risk of RTIs varies in different geographical area, urging the government to pay more attention to the worrying situation of female RTIs in the country.

Geospatial estimation of reproductive, maternal, newborn and child health indicators: a systematic review of methodological aspects of studies based on household surveys

BACKGROUND

Geospatial approaches are increasingly used to produce fine spatial scale estimates of reproductive, maternal, newborn and child health (RMNCH) indicators in low- and middle-income countries (LMICs). This study aims to describe important methodological aspects and specificities of geospatial approaches applied to RMNCH coverage and impact outcomes and enable non-specialist readers to critically evaluate and interpret these studies.

METHODS

Two independent searches were carried out using Medline, Web of Science, Scopus, SCIELO and LILACS electronic databases. Studies based on survey data using geospatial approaches on RMNCH in LMICs were considered eligible. Studies whose outcomes were not measures of occurrence were excluded.

RESULTS

We identified 82 studies focused on over 30 different RMNCH outcomes. Bayesian hierarchical models were the predominant modeling approach found in 62 studies. 5 × 5 km estimates were the most common resolution and the main source of information was Demographic and Health Surveys. Model validation was under reported, with the out-of-sample method being reported in only 56% of the studies and 13% of the studies did not present a single validation metric. Uncertainty assessment and reporting lacked standardization, and more than a quarter of the studies failed to report any uncertainty measure.

CONCLUSIONS

The field of geospatial estimation focused on RMNCH outcomes is clearly expanding. However, despite the adoption of a standardized conceptual modeling framework for generating finer spatial scale estimates, methodological aspects such as model validation and uncertainty demand further attention as they are both essential in assisting the reader to evaluate the estimates that are being presented.

Accessibility of basic paediatric emergency care in Malawi: analysis of a national facility census

BACKGROUND

Emergency care is among the weakest parts of health systems in low-income countries with both quality and accessibility constraints. Previous studies estimated accessibility to surgical or emergency care based on population travel times to nearest hospital with no assessment of hospital readiness to provide such care. We analysed a Malawi national facility census with comprehensive inventory audits and geocoded facility locations to identify hospitals equipped to provide basic paediatric emergency care with estimated travel times to these hospitals from non-equipped facilities and in relation to Malawi's population distribution.

METHODS

We analysed a Malawi national facility census in 2013-2014 to identify hospitals equipped to manage critically ill children according to an extended version of WHO Emergency Triage, Assessment and Treatment (ETAT) guidelines. These guidelines include 25 components including staff, transport, equipment, diagnostics, medications, fluids, feeds and consumables that defined an emergency-equipped hospital in our study. We estimated travel times to emergency-equipped hospitals from non-equipped facilities and relative to population distributions using geocoded facility locations and an established accessibility mapping approach using global road network datasets from OpenStreetMap and Google.

RESULTS

Four (3.5, 95% CI: 1.3-8.9) of 116 Malawi hospitals were emergency-equipped. Least available items were nasogastric tubes in 34.5% of hospitals (95% CI: 26.4-43.6), blood typing services (40.4, 95% CI: 31.9-49.6), micro nebulizers (50.9, 95% CI: 41.9-60.0), and radiology (54.2, 95% CI: 45.1-63.0). Nationally, the median travel time from non-equipped facilities to the nearest emergency-equipped hospital was 73 min (95% CI: 67-77) ranging 1-507 min. Approximately one-quarter (27%) of Malawians lived over 120 min from an emergency-equipped hospital with significantly better accessibility in Central than North and South regions (16% vs. 38 and 35%, p < 0.001).

CONCLUSIONS

There are unacceptable deficiencies in accessibility of basic paediatric emergency care in Malawi. Reliable supply chains for essential drugs and commodities are needed, particularly nasogastric tubes, asthma drugs and blood, along with improved capacity for time-sensitive referral. Further child mortality reductions will require substantial investments to expand basic paediatric emergency care into all Malawi hospitals for better managing critically ill children at highest mortality risk.

A Scoping Review of Spatial Analysis Approaches Using Health Survey Data in Sub-Saharan Africa

Spatial analysis has become an increasingly used analytic approach to describe and analyze spatial characteristics of disease burden, but the depth and coverage of its usage for health surveys data in Sub-Saharan Africa are not well known. The objective of this scoping review was to conduct an evaluation of studies using spatial statistics approaches for national health survey data in the SSA region. An organized literature search for studies related to spatial statistics and national health surveys was conducted through PMC, PubMed/Medline, Scopus, NLM Catalog, and Science Direct electronic databases. Of the 4,193 unique articles identified, 153 were included in the final review. Spatial smoothing and prediction methods were predominant (n = 108), followed by spatial description aggregation (n = 25), and spatial autocorrelation and clustering (n = 19). Bayesian statistics methods and lattice data modelling were predominant (n = 108). Most studies focused on malaria and fever (n = 47) followed by health services coverage (n = 38). Only fifteen studies employed nonstandard spatial analyses (e.g., spatial model assessment, joint spatial modelling, accounting for survey design). We recommend that for future spatial analysis using health survey data in the SSA region, there must be an improve recognition and awareness of the potential dangers of a naïve application of spatial statistical methods. We also recommend a wide range of applications using big health data and the future of data science for health systems to monitor and evaluate impacts that are not well understood at local levels.

Estimating uncertainty in geospatial modelling at multiple spatial resolutions: the pattern of delivery via caesarean section in Tanzania

Visualising maternal and newborn health (MNH) outcomes at fine spatial resolutions is crucial to ensuring the most vulnerable women and children are not left behind in improving health. Disaggregated data on life-saving MNH interventions remain difficult to obtain, however, necessitating the use of Bayesian geostatistical models to map outcomes at small geographical areas. While these methods have improved model parameter estimates and precision among spatially correlated health outcomes and allowed for the quantification of uncertainty, few studies have examined the trade-off between higher spatial resolution modelling and how associated uncertainty propagates. Here, we explored the trade-off between model outcomes and associated uncertainty at increasing spatial resolutions by quantifying the posterior distribution of delivery via caesarean section (c-section) in Tanzania. Overall, in modelling delivery via c-section at multiple spatial resolutions, we demonstrated poverty to be negatively correlated across spatial resolutions, suggesting important disparities in obtaining life-saving obstetric surgery persist across sociodemographic factors. Lastly, we found that while uncertainty increased with higher spatial resolution input, model precision was best approximated at the highest spatial resolution, suggesting an important policy trade-off between identifying concealed spatial heterogeneities in health indicators.

Expanding access to maternal, newborn and primary healthcare services through private-community-government partnership clinic models in rural Kenya: the Ubuntu-Afya kiosk model

Background

Fifteen counties contribute 98.7% of the maternal and newborn morbidity and mortality in Kenya. The dismal maternal and newborn (MNH) outcomes in these settings are mostly attributable to limited access to skilled MNH services. Public health services are stretched and limited in reach, and many social programmes are not sustainably designed. We implemented a network of 16 self-sustaining community medical centres (Ubuntu-Afya Kiosks) in Homa Bay County, to facilitate access to MNH and other primary health services. We investigated the effect of these centres on MNH access indicators over a 2-year period of initial implementation.

Methods

We conducted a baseline and end-line survey in June 2016 and May 2018 respectively, in 10 community health units (CHU) served by Ubuntu-Afya Kiosks. We targeted women of child bearing age, ensuring equal sample across the 10 CHUs. The surveys were powered to detect a 10% increase in the proportion of women who deliver under a skilled birth attendant from a perceived baseline of 55%. Background characteristics of the respondents were compared using Fisher’s exact test for the categorical data. STATA ‘svy’ commands were used to calculate confidence intervals for the proportions taking into account the clustering within CHU.

Results

The coverage of antenatal care during previous pregnancy was 99% at end-line compared to 81% at baseline. Seventy one percent of mothers attended at least four antenatal care visits, compared to 64% at baseline. The proportion of women who delivered under a skilled birth attendant during previous pregnancy was higher at end-line (90%) compared to baseline (85%). There was an increase in the proportion of women who had their newborns examined within 2 day of delivery from 74 to 92% at end-line. A considerable proportion of the respondents visited private clinics at end-line (31%) compared to 3% at baseline.

Conclusions

Ubuntu-Afya Kiosks were associated with enhanced access to MNH care, with significant improvements observed in newborn examination within 2 days after delivery. More women sought care from private clinics at end-line compared to baseline, indicating potential for private sector in supporting health service delivery gaps in under-served settings.

Modelling the Wealth Index of Demographic and Health Surveys within Cities Using Very High-Resolution Remotely Sensed Information

A systematic and precise understanding of urban socio-economic spatial inequalities in developing regions is needed to address global sustainability goals. At the intra-urban scale, access to detailed databases (i.e., a census) is often a difficult exercise. Geolocated surveys such as the Demographic and Health Surveys (DHS) are a rich alternative source of such information but can be challenging to interpolate at such a fine scale due to their spatial displacement, survey design and the lack of very high-resolution (VHR) predictor variables in these regions. In this paper, we employ satellite-derived VHR land-use/land-cover (LULC) datasets and couple them with the DHS Wealth Index (WI), a robust household wealth indicator, in order to provide city-scale wealth maps. We undertake several modelling approaches using a random forest regressor as the underlying algorithm and predict in several geographic administrative scales. We validate against an exhaustive census database available for the city of Dakar, Senegal. Our results show that the WI was modelled to a satisfactory degree when compared against census data even at very fine resolutions. These findings might assist local authorities and stakeholders in rigorous evidence-based decision making and facilitate the allocation of resources towards the most disadvantaged populations. Good practices for further developments are discussed with the aim of upscaling these findings at the global scale.