Evidence of health inequity in child survival: spatial and Bayesian network analyses of stillbirth rates in 194 countries

Abstract

Estimated at 2.6 million annually, stillbirths worldwide have stayed alarmingly high, in contrast to neonatal and under-five mortality rates. It is a neglected public health challenge globally, with less attention to its social determinants. We examined spatial patterns of country-level stillbirth rates and determined the influence of social determinants of health on spatial patterns of stillbirth rates. We also estimated probabilistic relationships between stillbirth rates and significant determinants from the spatial analysis. Using country-level aggregated data from the United Nations databases, it employed ecological spatial analysis and artificial intelligence modeling based on Bayesian network among 194 World Health Organization member countries. From the spatial analysis, thirty-seven countries formed a cluster of high values (hot-spots) for stillbirth and 13 countries formed a cluster of low values (cold-spots). In the multivariate regression, gender inequality and anaemia in pregnancy were significantly associated with spatial patterns of higher stillbirth rates, while higher antenatal care (ANC) coverage and skilled birth attendants during delivery were associated with clusters of lower stillbirth rates. The Bayesian network model suggests strong dependencies between stillbirth rate and gender inequality index, geographic regions and skilled birth attendants during delivery. The Bayesian network predicted that the probability of low stillbirth rate increased from 56% to 100% when the percentage of countries with high skilled birth attendants during delivery increased from 70% to 88%, high ANC coverage increased from 55% to 70%, high prevalence of anaemia in pregnancy decreased from 27% to 11% and high gender inequality index decreased from 43% to 21%. Recognizing the urgency in reducing stillbirths globally, multi-pronged strategies should be designed to promote gender equality and strengthen the reproductive and maternal health services in Africa, Eastern Mediterranean, South Eastern Asia, and other countries with disproportionately high stillbirth rates.

Figure 1

The conceptual model for the determinants of stillbirth (adapted from Social Determinants of Health Framework).

Figure 2

(a) Spatial distribution of stillbirth rates by country, 2015. (b) Univariate local indicators of spatial association (LISA) cluster map of stillbirth rate, 2015.

Figure 3

Adjusted local indicators of spatial association (LISA) cluster map of stillbirth rate and: (a) prevalence of anaemia in pregnancy, (b) ANC coverage, (c) Skilled birth attendants at delivery, (d) Gender inequality index.

Figure 4

(a) Bayesian network of overall (status quo) probabilistic inference of WHO regions, gender inequality, ANC coverage, skilled birth attendants at delivery, anaemia in pregnancy on stillbirth rate. (b) Probabilistic inferences (%) of the key determinants for each WHO region. Note: (i) Strength of influence displayed by weighted normalized width of links. (ii) Green arcs represent strengthening effects, while red arcs and purple arcs represent weakening and unclear effects, respectively (iii) Stillbirth rate (high >12, low ≤12 per 1000 births); Gender inequality index (high >0.4, low ≤0.4); ANC coverage (high >75%, low ≤75%); skilled birth attendants during delivery (high >85%, low ≤85%); anaemia in pregnancy (high >40%, low ≤40%). (iii) WHO region: African (AFR), American (AMR), Eastern Mediterranean (EMR), European (EUR), South East Asian (SEAR), and Western Pacific (WPR).