Geospatial variations in trends of reproductive, maternal, newborn and child health and nutrition indicators at block level in Bihar, India, during scale-up of Ananya program interventions

J Glob Health. 2020 Dec;10(2):021004. doi: 10.7189/jogh.10.021004.

Abstract

Background: Geographical variations in the levels and trajectory of health indicators at local level can inform the adaptation of interventions and development of targeted approaches for efficient scale-up of intervention impact. We examined the hypothesis that time trends of a set of reproductive, maternal, newborn, and child health and nutrition (RMNCHN) indicators varied at block-level during the statewide scale-up phase of the Ananya program in Bihar, India.

Methods: We used data on 22 selected indicators from four rounds of the Community-based Household Survey carried out between 2014 and 2017. Indicator levels at each round were estimated for each block. We used hierarchical Bayesian spatiotemporal modelling to smooth the raw estimates for each block with the estimates from its neighbouring blocks, and to examine space-time interaction models for evidence of variations in trends of indicators across blocks. We expressed the uncertainty around the smoothed levels and the trends with 95% credible intervals.

Results: There was evidence of variations in trends at block level in all but three indicators: facility delivery, public facility delivery, and age-appropriate initiation of complementary feeding. Fifteen indicators showed trends in opposite directions (increases in some blocks and declines in others). All blocks had at least 97.5% probability of a rise in immediate breastfeeding, early pregnancy registration, and having at least four antenatal care visits. All blocks had at least 97.5% probability of a decline in seeking care for pregnancy complications.

Conclusions: The findings underscore the value of monitoring and evaluation at local level for targeted implementation of RMNCHN interventions. There is a need for identifying systematic factors leading to universal trends, or variable contextual or implementation factors leading to variable trends, in order to optimise primary health care program impact.

Study registration: ClinicalTrials.gov number NCT02726230.

MeSH terms

  • Bayes Theorem
  • Child Health*
  • Cross-Sectional Studies
  • Female
  • Humans
  • India
  • Infant
  • Infant Health*
  • Infant, Newborn
  • Lot Quality Assurance Sampling
  • Maternal Health*
  • Nutritional Status
  • Pregnancy
  • Reproductive Health
  • Spatio-Temporal Analysis

Associated data

  • ClinicalTrials.gov/NCT02726230