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Table 1 Summary of the characteristics for the selected studies

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

  All studies Study outcomesa
Malaria Child mortality Malnutrition Vaccination Other outcomes
Number of studies 82 34 14 11 8 19
Covariatesa
 Agriculture and livestock 17 3 3 5 4 5
 Climate 43 28 5 5 4 5
 Health-related interventions/outcomes 24 9 5 5 1 7
 Remoteness 43 19 7 5 5 11
 Satellite indices 19 4 5 3 4 7
 Sociodemographic 53 17 6 11 6 17
 Topography/land cover 59 30 7 10 4 12
 No covariates 15 4 7 1 2 2
Geographic coverage
 Single country 50 26 6 7 2 9
 Multi-country 32 8 8 5 6 10
Temporal component
 No 46 19 2 8 7 11
 Yes 36 15 12 4 1 8
Spatial resolutiona
 Less than 5x5km 23 18 0 1 3 2
 5x5 to 10x10km 20 6 5 5 3 4
 Lower admin. level 30 2 8 6 1 14
 Not reported 12 10 1 1 0 0
Uncertaintya
 Standard deviation map 14 6 1 1 5 2
 Interval map/table 28 12 8 2 0 10
 Relative map 7 0 2 3 0 2
 Other metrics 13 9 2 0 1 1
 Not reported 22 7 3 6 2 4
Modeling techniquea
 Bayesian–MCMC 35 24 3 3 3 2
 Bayesian–INLA 28 4 7 6 3 12
 Classical GLM 17 5 2 2 2 6
 Spatial interpolation 2 0 1 1 0 0
 Ensemble models 12 1 5 4 1 5
Out-of-sample pred.
 Cross-validation 22 3 7 5 4 6
 Hold-out 24 18 2 1 0 4
 Not reported 36 13 5 6 4 9
Model fit metricsa
 Bias 34 12 7 6 4 9
 RMSE/MSE 30 3 7 6 6 12
 Coverage 24 8 6 4 4 5
 DIC/AIC 19 6 3 3 1 6
 MAE 16 7 2 3 2 3
 Correlation 15 11 0 2 1 2
 Other metrics 31 15 4 3 1 9
 None reported 11 5 2 3 1 1
  1. aThese characteristics allow studies to be classified in more than one subgroup
  2. MCMC Markov Chain Monte Carlo, INLA Integrated Nested Laplace Approximation, GLM Generalized Linear Models, RMSE Root Mean Squared Error, MSE Mean Squared Error, DIC Deviance Information Criterion, AIC Akaike Information Criterion, MAE Mean Absolute Error