Interesting paper on application of geostatistical models in spatial variation and inequities in antenatal care coverage

[Edwin_Kagereki]

Came across this pre-print paper about using bayesian geostatistical mixed model to predict ANC4+ coverage and compute exceedance probability for target coverage in East Africa countries (i.e Kenya, Uganda and Tanzania). Here’s the abstract:

Pregnant women in sub-Saharan Africa (SSA) experience the highest levels of maternal mortality and stillbirths due to predominantly avoidable causes. Antenatal care (ANC) can prevent, detect, alleviate, or manage these causes. While eight ANC contacts are now recommended, coverage of the previous minimum of four visits (ANC4+) remains low and inequitable in SSA.
We modelled ANC4+ coverage and likelihood of attaining district-level target coverage of 70% across three equity stratifiers (household wealth, maternal education, and travel time to the nearest health facility) based on data from malaria indicator surveys in Kenya (2020), Uganda (2018/19) and Tanzania (2017). Geostatistical models were fitted to predict ANC4+ coverage and compute exceedance probability for target coverage. The number of pregnant women without ANC4+ were then computed. Prediction was at 3 km spatial resolution and aggregated at national- and district-level for sub-national planning.
About six in ten women reported ANC4+ visits, meaning that approximately 3 million women in the three countries had less than ANC4+ visits. The majority of the 366 districts in the three countries had ANC4+ coverage of 50-70%. In Kenya, 13% of districts had <70% coverage, compared to 10% and 27% of the districts in Uganda and mainland Tanzania, respectively. Only one district in Kenya and ten districts in mainland Tanzania likely met the target coverage. Six percent, 38%, and 50% of the districts had at most 5,000 women with <ANC4+ visits in Kenya, Uganda, and mainland Tanzania, respectively, while districts with >20,000 women having <ANC4+ visits were 38%, 1% and 1%, respectively. In many districts, ANC4+ coverage and likelihood of attaining the target coverage was lower among the poor, uneducated and those geographically marginalized from healthcare.