Parameters associated with design effect of child anthropometry indicators in small-scale field surveys

Abstract Background Cluster surveys provide rapid but representative estimates of key nutrition indicators in humanitarian crises. For these surveys, an accurate estimate of the design effect is critical to calculate a sample size that achieves adequate precision with the minimum number of sampling units. This paper describes the variability in design effect for three key nutrition indicators measured in small-scale surveys and models the association of design effect with parameters hypothesized to explain this variability. Methods 380 small-scale surveys from 28 countries conducted between 2006 and 2013 were analyzed. We calculated prevalence and design effect of wasting, underweight, and stunting for each survey as well as standard deviations of the underlying continuous Z-score distribution. Mean cluster size, survey location and year were recorded. To describe design effects, median and interquartile ranges were examined. Generalized linear regression models were run to identify potential predictors of design effect. Results Median design effect was under 2.00 for all three indicators; for wasting, the median was 1.35, the lowest among the indicators. Multivariable linear regression models suggest significant, positive associations of design effect and mean cluster size for all three indicators, and with prevalence of wasting and underweight, but not stunting. Standard deviation was positively associated with design effect for wasting but negatively associated for stunting. Survey region was significant in all three models. Conclusions This study supports the current field survey guidance recommending the use of 1.5 as a benchmark for design effect of wasting, but suggests this value may not be large enough for surveys with a primary objective of measuring stunting or underweight. The strong relationship between design effect and region in the models underscores the continued need to consider country- and locality-specific estimates when designing surveys. These models also provide empirical evidence of a positive relationship between design effect and both mean cluster size and prevalence, and introduces standard deviation of the underlying continuous variable (Z-scores) as a previously unexplored factor significantly associated with design effect. The magnitude and directionality of this association differed by indicator, underscoring the need for further investigation into the relationship between standard deviation and design effect

Use of Rapid Ascertainment Process for Institutional Deaths (RAPID) to identify pregnancy-related deaths in tertiary-care obstetric hospitals in three departments in Haiti

Abstract Background Accurate assessment of maternal deaths is difficult in countries lacking standardized data sources for their review. As a first step to investigate suspected maternal deaths, WHO suggests surveillance of “pregnancy-related deaths”, defined as deaths of women while pregnant or within 42 days of termination of pregnancy, irrespective of cause. Rapid Ascertainment Process for Institutional Deaths (RAPID), a surveillance tool, retrospectively identifies pregnancy-related deaths occurring in health facilities that may be missed by routine surveillance to assess gaps in reporting these deaths. Methods We used RAPID to review pregnancy-related deaths in six tertiary obstetric care facilities in three departments in Haiti. We reviewed registers and medical dossiers of deaths among women of reproductive age occurring in 2014 and 2015 from all wards, along with any additional available dossiers of deaths not appearing in registers, to capture pregnancy status, suspected cause of death, and timing of death in relation to the pregnancy. We used capture-recapture analyses to estimate the true number of in-hospital pregnancy-related deaths in these facilities. Results Among 373 deaths of women of reproductive age, we found 111 pregnancy-related deaths, 25.2% more than were reported through routine surveillance, and 22.5% of which were misclassified as non-pregnancy-related. Hemorrhage (27.0%) and hypertensive disorders (18.0%) were the most common categories of suspected causes of death, and deaths after termination of pregnancy were statistically significantly more common than deaths during pregnancy or delivery. Data were missing at multiple levels: 210 deaths had an undetermined pregnancy status, 48.7% of pregnancy-related deaths lacked specific information about timing of death in relation to the pregnancy, and capture-recapture analyses in three hospitals suggested that approximately one-quarter of pregnancy-related deaths were not captured by RAPID or routine surveillance. Conclusions Across six tertiary obstetric care facilities in Haiti, RAPID identified unreported pregnancy-related deaths, and showed that missing data was a widespread problem. RAPID is a useful tool to more completely identify facility-based pregnancy-related deaths, but its repeated use would require a concomitant effort to systematically improve documentation of clinical findings in medical records. Limitations of RAPID demonstrate the need to use it alongside other tools to more accurately measure and address maternal mortality