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Denominators vary owing to missing values (assumed to be missing at random).
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We used multiple imputation to impute the missing values assuming that data was missing at random (stata command: ice) [ 22- 24].
For the primary outcome we also undertook a sensitivity analysis that used both repeated measures and multiple imputations for missing values (assuming data were missing at random).
Based on these calibrations, (i.e., sets of item parameter values), latent scores can be estimated for those individuals for which one has either complete data or data with some missing values, assuming these are missing at random.
Missing values were assumed to result from areas falling below limits of detection.
Following Measure DHS convention, missing values are assumed to indicate no visit to that type of facility.
Missing values were assumed to be missing at random.
Missing values were assumed to be within normal limits.
For simplification of analysis, missing values were assumed to be missing at random.
Missing values were assumed to be 'missing at random' (see online supplementary material for details of missing and imputed values).
For these variables, missing values were assumed to be missing at random and were imputed using multiple imputation.
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