Childhood mortality in sub-Saharan Africa : cross-sectional insight into small-scale geographical inequalities from Census data

Objectives To estimate and quantify childhood mortality, its spatial correlates and the impact of potential correlates using recent census data from three sub-Saharan African countries (Rwanda, Senegal and Uganda), where evidence is lacking. Design Cross-sectional. Setting Nation-wide census samples from three African countries participating in the 2010 African Census round. All three countries have conducted recent censuses and have information on mortality of children under 5 years. Participants 111 288 children under the age of 5 years in three countries. Primary and secondary outcome measures Under-five mortality was assessed alongside potential correlates including geographical location (where children live), and environmental, bio-demographic and socioeconomic variables. Results Multivariate analysis indicates that in all three countries the overall risk of child death in the first 5 years of life has decreased in recent years (Rwanda: HR=0.04, 95% CI 0.02 to 0.09; Senegal: HR=0.02 (95% CI 0.02 to 0.05); Uganda: HR=0.011 (95% CI 0.006 to 0.018). In Rwanda, lower deaths were associated with living in urban areas (0.79, 0.73, 0.83), children with living mother (HR=0.16, 95% CI 0.15 to 0.17) or living father (HR=0.38, 95% CI 0.36 to 0.39). Higher death was associated with male children (HR=1.06, 95% CI 1.02 to 1.08) and Christian children (HR=1.14, 95% CI 1.05 to 1.27). Children less than 1 year were associated with higher risk of death compared to older children in the three countries. Also, there were significant spatial variations showing inequalities in children mortality by geographic location. In Uganda, for example, areas of high risk are in the south-west and north-west and Kampala district showed a significantly reduced risk. Conclusions We provide clear evidence of considerable geographical variation of under-five mortality which is unexplained by factors considered in the data. The resulting under-five mortality maps can be used as a practical tool for monitoring progress within countries for the Millennium Development Goal 4 to reduce under-five mortality in half by 2015

Lost in Translation: Piloting a Novel Framework to Assess the Challenges in Translating Scientific Uncertainty From Empirical Findings to WHO Policy Statements.

BACKGROUND:Calls for evidence-informed public health policy, with implicit promises of greater program effectiveness, have intensified recently. The methods to produce such policies are not self-evident, requiring a conciliation of values and norms between policy-makers and evidence producers. In particular, the translation of uncertainty from empirical research findings, particularly issues of statistical variability and generalizability, is a persistent challenge because of the incremental nature of research and the iterative cycle of advancing knowledge and implementation. This paper aims to assess how the concept of uncertainty is considered and acknowledged in World Health Organization (WHO) policy recommendations and guidelines. METHODS:We selected four WHO policy statements published between 2008-2013 regarding maternal and child nutrient supplementation, infant feeding, heat action plans, and malaria control to represent topics with a spectrum of available evidence bases. Each of these four statements was analyzed using a novel framework to assess the treatment of statistical variability and generalizability. RESULTS:WHO currently provides substantial guidance on addressing statistical variability through GRADE (Grading of Recommendations Assessment, Development, and Evaluation) ratings for precision and consistency in their guideline documents. Accordingly, our analysis showed that policy-informing questions were addressed by systematic reviews and representations of statistical variability (eg, with numeric confidence intervals). In contrast, the presentation of contextual or "background" evidence regarding etiology or disease burden showed little consideration for this variability. Moreover, generalizability or "indirectness" was uniformly neglected, with little explicit consideration of study settings or subgroups. CONCLUSION:In this paper, we found that non-uniform treatment of statistical variability and generalizability factors that may contribute to uncertainty regarding recommendations were neglected, including the state of evidence informing background questions (prevalence, mechanisms, or burden or distributions of health problems) and little assessment of generalizability, alternate interventions, and additional outcomes not captured by systematic review. These other factors often form a basis for providing policy recommendations, particularly in the absence of a strong evidence base for intervention effects. Consequently, they should also be subject to stringent and systematic evaluation criteria. We suggest that more effort is needed to systematically acknowledge (1) when evidence is missing, conflicting, or equivocal, (2) what normative considerations were also employed, and (3) how additional evidence may be accrued

Infant mortality in South Africa - distribution, associations and policy implications, 2007: an ecological spatial analysis

<p>Abstract</p> <p>Background</p> <p>Many sub-Saharan countries are confronted with persistently high levels of infant mortality because of the impact of a range of biological and social determinants. In particular, infant mortality has increased in sub-Saharan Africa in recent decades due to the HIV/AIDS epidemic. The geographic distribution of health problems and their relationship to potential risk factors can be invaluable for cost effective intervention planning. The objective of this paper is to determine and map the spatial nature of infant mortality in South Africa at a sub district level in order to inform policy intervention. In particular, the paper identifies and maps high risk clusters of infant mortality, as well as examines the impact of a range of determinants on infant mortality. A Bayesian approach is used to quantify the spatial risk of infant mortality, as well as significant associations (given spatial correlation between neighbouring areas) between infant mortality and a range of determinants. The most attributable determinants in each sub-district are calculated based on a combination of prevalence and model risk factor coefficient estimates. This integrated small area approach can be adapted and applied in other high burden settings to assist intervention planning and targeting.</p> <p>Results</p> <p>Infant mortality remains high in South Africa with seemingly little reduction since previous estimates in the early 2000's. Results showed marked geographical differences in infant mortality risk between provinces as well as within provinces as well as significantly higher risk in specific sub-districts and provinces. A number of determinants were found to have a significant adverse influence on infant mortality at the sub-district level. Following multivariable adjustment increasing maternal mortality, antenatal HIV prevalence, previous sibling mortality and male infant gender remained significantly associated with increased infant mortality risk. Of these antenatal HIV sero-prevalence, previous sibling mortality and maternal mortality were found to be the most attributable respectively.</p> <p>Conclusions</p> <p>This study demonstrates the usefulness of advanced spatial analysis to both quantify excess infant mortality risk at the lowest administrative unit, as well as the use of Bayesian modelling to quantify determinant significance given spatial correlation. The "novel" integration of determinant prevalence at the sub-district and coefficient estimates to estimate attributable fractions further elucidates the "high impact" factors in particular areas and has considerable potential to be applied in other locations. The usefulness of the paper, therefore, not only suggests where to intervene geographically, but also what specific interventions policy makers should prioritize in order to reduce the infant mortality burden in specific administration areas.</p

Juvenile rank acquisition is associated with fitness independent of adult rank

Social rank is a significant determinant of fitness in a variety of species. The importance of social rank suggests that the process by which juveniles come to establish their position in the social hierarchy is a critical component of development. Here, we use the highly predictable process of rank acquisition in spotted hyenas to study the consequences of variation in rank acquisition in early life. In spotted hyenas, rank is ‘inherited’ through a learning process called ‘maternal rank inheritance.’ This pattern is very consistent: approximately 80% of juveniles acquire the exact rank expected under the rules of maternal rank inheritance. The predictable nature of rank acquisition in these societies allows the process of rank acquisition to be studied independently from the ultimate rank that each juvenile attains. In this study, we use Elo-deviance scores, a novel application of the Elo-rating method, to calculate each juvenile’s deviation from the expected pattern of maternal rank inheritance during development. Despite variability in rank acquisition among juveniles, most of these juveniles come to attain the exact rank expected of them according to the rules of maternal rank inheritance. Nevertheless, we find that transient variation in rank acquisition in early life is associated with long-term fitness consequences for these individuals: juveniles ‘underperforming’ their expected ranks show reduced survival and lower lifetime reproductive success than better-performing peers, and this relationship is independent of both maternal rank and rank achieved in adulthood. We also find that multiple sources of early life adversity have cumulative, but not compounding, effects on fitness. Future work is needed to determine if variation in rank acquisition directly affects fitness, or if some other variable, such as maternal investment or juvenile condition, causes variation in both of these outcomes. (Includes Supplemental Materials and Reviewers\u27 Comments.

Women's Education Level, Maternal Health Facilities, Abortion Legislation and Maternal Deaths: A Natural Experiment in Chile from 1957 to 2007

The aim of this study was to assess the main factors related to maternal mortality reduction in large time series available in Chile in context of the United Nations' Millennium Development Goals (MDGs).Time series of maternal mortality ratio (MMR) from official data (National Institute of Statistics, 1957-2007) along with parallel time series of education years, income per capita, fertility rate (TFR), birth order, clean water, sanitary sewer, and delivery by skilled attendants were analysed using autoregressive models (ARIMA). Historical changes on the mortality trend including the effect of different educational and maternal health policies implemented in 1965, and legislation that prohibited abortion in 1989 were assessed utilizing segmented regression techniques.During the 50-year study period, the MMR decreased from 293.7 to 18.2/100,000 live births, a decrease of 93.8%. Women's education level modulated the effects of TFR, birth order, delivery by skilled attendants, clean water, and sanitary sewer access. In the fully adjusted model, for every additional year of maternal education there was a corresponding decrease in the MMR of 29.3/100,000 live births. A rapid phase of decline between 1965 and 1981 (-13.29/100,000 live births each year) and a slow phase between 1981 and 2007 (-1.59/100,000 live births each year) were identified. After abortion was prohibited, the MMR decreased from 41.3 to 12.7 per 100,000 live births (-69.2%). The slope of the MMR did not appear to be altered by the change in abortion law.Increasing education level appears to favourably impact the downward trend in the MMR, modulating other key factors such as access and utilization of maternal health facilities, changes in women's reproductive behaviour and improvements of the sanitary system. Consequently, different MDGs can act synergistically to improve maternal health. The reduction in the MMR is not related to the legal status of abortion

Structural violence and maternal healthcare utilisation in sub-Saharan Africa: A Bayesian multilevel analysis

Considerable advances have been made in medical sociology and other population health- related subject areas to understand structural sources of disparities in health outcomes. We know that structural factors are the ‘fundamental causes’ of disease and illness. However, few studies focusing on structural conditions have considered maternal healthcare especially in sub-Saharan Africa. There is a dearth of knowledge regarding the effects of wider structural factors on maternal healthcare utilisation. Specifically, it is not well-known as to which dimension of the social structure is strongly associated with maternal healthcare and what specific combinations of factors influence adequate use of maternal healthcare in sub- Saharan Africa. This study was conceived to fill this gap in literature. The study focuses on community and country-level inequalities in gender relations, human rights violations and globalisation as the three dimensions of structural violence that are consequential to maternal healthcare in sub-Saharan Africa. I also consider individual level maternal characteristics that are associated with maternal healthcare utilisation. The analysis pools data of 245,955 respondents from the most recent Demographic and Health Surveys (DHS) and several other international datasets. I apply separate three-level Bayesian multilevel models on women who have had births five years prior to the recent DHS, nested in 17,000 communities, which are nested in 35 sub-Saharan African countries. On each aspect of structural violence, I estimate four models predicting the odds of having four or more antenatal care visits, institutional delivery and postnatal care based on a set of individual, community and country-level variables. Overall, the results indicate that inequalities in gender relations and disrespect for human rights are negatively associated with adequate use of maternal healthcare in sub-Saharan Africa. The relationship between globalisation and maternal healthcare is conditional on the specific dimension of globalisation. In comparison with other dimensions of globalisation, social globalisation is the most significant predictor of adequate maternal healthcare. These results help to underscore the importance of contextual factors in understanding women’s utilisation of maternal healthcare in sub-Saharan Africa

Spatio-temporal modelling of under-five mortality and associations with malaria-anaemia comorbidity and health interventions in sub-Saharan Africa

A remarkable reduction of the total number of under-five deaths was achieved between 1990 and 2018 in the African setting, as pre-school mortality fell to 5.3 million deaths compared to 12.5 million in 1990. The bulk share of this reduction is attributed to the Millennium Development Goals (MDGs) era, during which time the under-five mortality rate has been declining with an annual rate of 3.8% across Africa. Despite these important achievements, the sub-Sahara African region did not meet the fourth target of the MDGs and still has an unacceptably high under-five mortality rate. Crucially, limiting the under-five mortality rate to a maximum of 25 deaths per 1,000 live births by 2030 lies at the heart of the Sustainable Development Goals (SDGs) and a recent report from the United Nations has warned that based on current trends, the African continent will not meet the SDG target for under-five mortality. Hence, providing useful insights from the associations between under-five mortality, the leading causes of disease and preventative or curative health interventions could make available valuable information to decision makers in order the African countries to achieve the SDGs on pre-school mortality. Malaria is a major contributor to under-five mortality in sub-Saharan Africa, accounting for 400,000 deaths, approximately 60% of which are in children below the age of five. At global scale, the disability-adjusted life-years for the malaria disease are 45 million. An important aspect of the disease is that infection by malaria parasites does not necessarily lead to mortality and it is rather conditions that follow infection or other comorbidities that produce severe forms of the disease with increased mortality risk. Apart from malaria, pneumonia and diarrhea account for the most frequent causes of pre-school deaths. An interesting feature of all these three leading causes of under-fives in Africa, i.e. pneumonia, diarrhea and malaria, is that they share febrile response as their main clinical manifestation. Against the leading causes of under-five mortality, preventative or curative health interventions have been widely adopted in Africa, with their spatial coverage being on a significant rise, particularly due to the so-called scaling-up of health interventions during the last five years of the MDGs. For instance, ownership of Insecticide-Treated nets against malaria rose from 50 to 80 percent between 2010 and 2015, while their utilization averted 663 million clinical malaria cases over the MDGs era. Yet, the coverage of health interventions and the subsequent reduction in under-five deaths has happened in an unequal way across sub- Saharan Africa, raising concerns about health inequities at sub-national level. The overall aim of the present PhD thesis is to develop, implement and interpret Bayesian geostatistical models with spatially varying coefficients in order to analyze approximately one million, cross-sectional mortality related-data in Africa and associate under-five mortality with malaria and health interventions. The point-by-point objectives of our work are as follows: 1. To develop a novel indicator for quantifying malaria-related mortality for children under the age of five in sub-Saharan Africa, namely the malaria-anemia comorbidity prevalence indicator (chapter 2); 2. To identify health inequities experienced by sub-national populations due to the geographical variation in the association between curative or preventive health interventions and under-five mortality in sub-Saharan Africa (chapter 3); 3. To assess the contribution of the leading causes of under-five mortality in sub- Saharan Africa on febrile response by associating the prevalence of malaria parasitaemia, diarrhoea and ARI with fever. (chapter 4); 4. To estimate the association between health interventions and under-five mortality on changes in mortality risk between two time points across Africa (chapter 5); 5. To compare Bayesian variable selection methods for spatially varying coefficient models, given that these approaches are at the forefront of analyzing geolocated mortality data in Africa (chapter 6). In chapter 2, we estimated the association of malaria parasitaemia, anemia, and malaria- anemia comorbidity with all-cause under-five mortality and evaluated the potential of malaria-anemia comorbidity prevalence to quantify malaria-related deaths in sub-Saharan Africa. Additionally, we estimated within-country variation of the association between comorbidity and under-5 mortality, using spatially varying coefficient models. We presented our results at high spatial resolution, including model-based risk maps of malaria, anemia, and malaria-anemia comorbidity. In chapter 3, we modeled the geographical variation in the association between health interventions and all-cause, under-five mortality in order to identify health inequities experienced by sub-national populations within a given country. To achieve that, we developed Bayesian geostatistical Weibull survival models with spatially varying coefficients for the effect of health interventions on mortality. Our approach allowed us to calculate the number of statistically important associations between interventions and mortality at regional level and hence to assess if health equity of interventions exists across the regions of a given country. In chapter 4, we assessed the contribution of the leading causes of under-five mortality in sub-Saharan Africa on febrile response by associating the prevalence of malaria parasitaemia, diarrhoea and ARI with fever. Our flexible Bayesian spatial modelling approach allowed evaluating the geographical distribution of disease-exposure effect on fever in space (Administrative level 1). We also calculated the Potential Attributable Fraction (PAF) in order to quantify the contribution of childhood diseases on fever. In chapter 5, we developed a novel methodology to statistically model the effect of health interventions on the changes in under-five mortality risk between two DHS survey time-points for 21 countries in Africa. We used a Bayesian geostatistical Weibull survival modeling approach and implemented rigorous Bayesian variable selection procedures in order to identify the most suitable set of health interventions for subsequent model fit. In chapter 6, we assessed the performance of stochastic search variable selection (SSVS) for the fixed effects of geostatistical models, we compared three different Bayesian variable selection (BVS) methods for conditionally autoregressive (CAR) structured spatially varying coefficient models and finally we assessed the sensitivity of SSVS for the fixed effects when is co-implemented with a BVS procedure. We conducted a simulation study and applied the methods to the Burundi DHS in order to assess the aforementioned selection procedures. The present PhD thesis has contributed to the scientific fields of Epidemiology and Statistics by committing to the spatio-temporal modelling of under-five mortality data in the African setting, using primarily routinely collected, cross-sectional, household-based survey data coming from the Demographic and Health surveys program. The key outcomes of the research conducted in this thesis are as follows: 1. Our work contributed to the development, proposal and validation of a novel indicator for quantifying malaria-mortality using survey data, i.e. the malaria-anemia comorbidity indicator. Our main conclusions were that malaria burden in sub-Saharan Africa is considerably underestimated when anemia in not taken into account and that the malaria-anemia comorbidity prevalence provides a useful measure of the malaria-related deaths; 2. We presented the first study to assess sub-national health inequities, across most countries in Africa, by employing a spatial statistical modelling approach and routinely collected survey data coming from the DHS and MIS. Our results demonstrated strong sub-national health inequities across various regions for 28 African countries; 3. Our estimates confirmed the strong contribution of diarrhoea and acute respiratory infection on febrile response and accounted only one out of five cases to malaria; 4. Our work concluded that the health interventions that are mostly associated with changes in all-cause, under-five mortality risk in sub-Saharan Africa were Bacillus Calmette–Guérin (BCG) immunization, vitamin A supplementation and deworming medication; 5. Our analysis showed that the SSVS method is able to accurately identify the statistically important predictors for the fixed effects of geostatistical models and that SSVS is not sensitive to co-implementation with a BVS procedure for CAR- structured, spatially varying coefficients. We also concluded that one of the three BVS methods for varying coefficients, namely the Global selection method, is able to identify true varying coefficients with 70% success rate

Birth Spacing, Fertility and Neonatal Mortality in India:Dynamics, Frailty and Fecundity

A dynamic panel data model of neonatal mortality and birth spacing is analyzed, accounting for causal effects of birth spacing on subsequent mortality and of mortality on the length of the next birth interval, while controlling for unobserved heterogeneity in mortality (frailty) and birth spacing (fecundity). The model is estimated using micro data on almost 30,000 children of 7,300 Indian mothers, for whom a complete retrospective record of fertility and child mortality is available. Information on sterilization is used to identify an equation for completion of family formation that is needed to account for right-censoring in the data. We find clear evidence of frailty, fecundity, and causal effects of birth spacing on mortality and vice versa, but find that birth interval effects can explain only a limited share of the correlation between neonatal mortality of successive children in a family. We also predict the impact of mortality on total fertility. Model simulations suggest that, for every neonatal death, an additional 0.37 children are born, of whom 0.3 survive.fertility, birth spacing, neonatal mortality, health, dynamic panel data models, siblings