A framework for sample size calculations in longitudinal surveys to measure net and gross changes.

In longitudinal surveys, repeated measurements are collected from the same sample units over time to measure gross change (i.e., change at the level of individual sample members). Longitudinal samples are sometimes supplemented by fresh sample to measure net change (i.e., change at the aggregate level). That is, in each measurement wave, while one part of the sample is newly recruited (fresh), another part overlaps with previously interviewed sample (repeated interviews). Many aspects of survey design of longitudinal surveys have been studied extensively, such as definition of target population, sample design, survey weighting, intervals between interviews, nonresponse, and panel attrition. Although the impact of the overlap between samples on the statistical power has been studied, sample size determination lacks a formulation that takes account of these factors in longitudinal surveys that aim to measure net and gross changes simultaneously. In this study, we propose a framework for sample size calculation to measure net and gross changes in estimated means or proportions concurrently in longitudinal surveys. We present a framework to compute panel and fresh sample sizes for varying levels of net and gross change. Finally, we illustrate the framework using nchange, an R package we developed to execute the algorithm of the proposed framework. The framework and the R package will support researchers to determine sample sizes targeting specific power of analysis with respect to measuring net and gross changes in rotating- or split-panel surveys.

LivWell: a sub-national Dataset on the Living Conditions of Women and their Well-being for 52 Countries.

Data on women's living conditions and socio-economic development are important for understanding and addressing the pronounced challenges and inequalities faced by women worldwide. While such information is increasingly available at the national level, comparable data at the sub-national level are missing. We here present the LivWell global longitudinal dataset, which includes a set of key indicators on women's socio-economic status, health and well-being, access to basic services and demographic outcomes. It covers 447 regions in 52 countries and includes a total of 265 different indicators. The majority of these are based on 199 Demographic and Health Surveys (DHS) for the period 1990-2019 and are complemented by extensive information on socio-economic and climatic conditions in the respective regions. The resulting dataset offers various opportunities for policy-relevant research on gender inequality, inclusive development and demographic trends at the sub-national level.

Infant and child health status ahead of implementation of an integrated intervention to improve nutrition and survival: a cross-sectional baseline assessment.

BACKGROUND: Burundi has one of the poorest child health outcomes in the world. With an acute malnutrition rate of 5% and a chronic malnutrition rate of 56%, under five death is 78 per 1000 live births and 47 children for every 1000 children will live until their first birthday. In response to this grim statistics, Village Health Works, a Burundian-American organisation has invested in an integrated clinical and community intervention model to improve child health outcomes. The aim of this study is to measure and report on child health indicator ahead of implementing this model. METHODS: A cross sectional design was employed, adopting the Demographic Health Survey methodology. We reached out to a sample of 952 households comprising of 2675 birth, in our study area. Mortality data was analysed with R package for mortality computation and other outcomes using SPSS. Principal component analysis was used to classify households into wealth quintiles. Logistic regression was used to assess strength of associations and significance of association was considered at 95% confidence level. RESULTS: The incidence of low birth weight (LBW) was 6.4% at the study area compared to 10% at the national level with the strongest predictor being malnourished women (OR 1.4 95%CI 1.2-7.2 p = 0.043). Fever incidence was higher in the study area (50.5%) in comparison to 39.5% nationally. Consumption of minimum acceptable diet was showed a significant protection against fever (OR 0.64 95%CI 0.41-0.94 p = 0.042). Global Acute Malnutrition rate was 7.6% and this significantly reduced with increasing age of child. Under-five mortality rate was 32.1 per 1000 live births and infant mortality was 25.7 per 1000 in the catchment with most deaths happening within the first 28 days of life (57.3%). CONCLUSION: Improving child health status is complex, therefore, investing into an integrated intervention for both mother and child could yield best results. Given that most under-five deaths occurred in the neonatal period, implementing integrated clinical and community newborn care interventions are critical.

Calculating fertility and childhood mortality rates from survey data using the DHS.rates R package.

The Demographic and Health Surveys (DHS) are a major source for many demographic and health indicators in developing countries. Although these indicators are well defined in the literature, using survey data to calculate some of these indicators has never been an easy task for data users. This paper presents the DHS.rates software, a user-friendly R package developed to calculate fertility indicators, such as the total fertility rate, general fertility rate, and age-specific fertility rates, and childhood mortality indicators, such as the neonatal mortality rate, post-neonatal mortality rate, infant mortality rate, child mortality rate, and under-5 mortality rate, from the DHS data. The package allows for national and subnational indicators. In addition, the package calculates sampling error indicators such as standard error, design effect, relative standard error, and confidence interval for each demographic indicator. The package can also be used to calculate the same indicators from other population surveys such as the Multiple Indicator Cluster Survey (MICS).

Increasing cell phone usage among Hispanics: implications for telephone surveys.

OBJECTIVES: We examined whether the widespread assumption that Hispanics are subject to greater noncoverage bias in landline telephone surveys because they are more likely than other ethnic groups to use cell phones exclusively was supported by data. METHODS: Data came from the 2010 National Health Interview Survey and the 2009 California Health Interview Survey. We considered estimates derived from surveys of adults with landline telephones biased and compared them with findings for all adults. Noncoverage bias was the difference between them, examined separately for Hispanics and non-Hispanic Whites. RESULTS: Differences in demographic and health characteristics between cell-only and landline users were larger for non-Hispanic Whites than Hispanics; cell usage was much higher for Hispanics than non-Hispanic Whites. The existence, pattern, and magnitude of noncoverage bias were comparable between the groups. CONCLUSIONS: We found no evidence to support a larger noncoverage bias for Hispanics than non-Hispanic Whites in landline telephone surveys. This finding should be considered in the design and interpretation of telephone surveys.