Does adjusting for recall in trend analysis affect coverage estimates for maternal and child health indicators? An analysis of DHS and MICS survey data.
BACKGROUND
The Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) are the major data sources in low- and middle-income countries (LMICs) for evaluating health service coverage. For certain maternal and child health (MCH) indicators, the two surveys use different recall periods: 5 years for DHS and 2 years for MICS.


OBJECTIVE
We explored whether the different recall periods for DHS and MICS affect coverage trend analyses as well as missing data and coverage estimates.


METHODS
We estimated coverage, using proportions with 95% confidence intervals, for four MCH indicators: intermittent preventive treatment of malaria in pregnancy, tetanus vaccination, early breastfeeding and postnatal care. Trends in coverage were compared using data from 1) standard 5-year DHS and 2-year MICS recall periods (unmatched) and 2) DHS restricted to 2-year recall to match the MICS 2-year recall periods (matched). Linear regression was used to explore the relationship between length of recall, missing data and coverage estimates.


RESULTS
Differences in coverage trends were observed between matched and unmatched data in 7 of 18 (39%) comparisons performed. The differences were in the direction of the trend over time, the slope of the coverage change or the significance levels. Consistent trends were seen in 11 of the 18 (61%) comparisons. Proportion of missing data was inversely associated with coverage estimates in both short (2 years) and longer (5 years) recall of the DHS (r=-0.3,p=0.02 andr=-0.4,p=0.004, respectively). The amount of missing information was increased for longer recall compared with shorter recall for all indicators (significant odds ratios ranging between 1.44 and 7.43).


CONCLUSIONS
In a context where most LMICs are dependent on population-based household surveys to derive coverage estimates, users of these types of data need to ensure that variability in recall periods and the proportion of missing data across data sources are appropriately accounted for when trend analyses are conducted.
Health Systems Research Unit, South African Medical Research Council, Cape Town, South Africa; Nobubelo.Ngandu@mrc.ac.za.
Nobubelo K
NK
Ngandu
Biostatistics Research Unit, South African Medical Research Council, Pretoria, South Africa.|School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa.
Samuel
S
Manda
Health Systems Research Unit, South African Medical Research Council, Cape Town, South Africa.
Donela
D
Besada
Health Systems Research Unit, South African Medical Research Council, Cape Town, South Africa.
Sarah
S
Rohde
Programme Division, Health, UNICEF, New York, NY, USA.
Nicholas P
NP
Oliphant
Health Systems Research Unit, South African Medical Research Council, Cape Town, South Africa.|School of Public Health, University of the Western Cape, Cape Town, South Africa.
Tanya
T
Doherty
indicator definitions; missing data; population surveys; statistical considerations