Dataset Information

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

ABSTRACT: 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).

SUBMITTER: Elkasabi M

PROVIDER: S-EPMC6534308 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Elkasabi Mahmoud M

PloS one 20190524 5

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 ...[more]

PMID: 31125337

Similar Datasets

Project description:IntroductionIn smoking cessation clinical trials, timeline followback (TLFB) interviews are widely used to track daily cigarette consumption. However, there are no standard tools for calculating abstinence based on TLFB data. Individual research groups have to develop their own calculation tools, which is not only time- and resource-consuming but might also lead to variability in the data processing and calculation procedures.Aims and methodsTo address these issues, we developed a novel open-source Python package named abstcal to calculate abstinence using TLFB data. This package provides data verification, duplicate and outlier detection, missing-data imputation, integration of biochemical verification data, and calculation of a variety of definitions of abstinence, including continuous, point-prevalence, and prolonged abstinence.ResultsWe verified the accuracy of the calculator using data derived from a clinical smoking cessation study. To improve the package's accessibility, we have made it available as a free web app.ConclusionsThe abstcal package is a reliable abstinence calculator with open-source access, providing a shared validated online tool to the addiction research field. We expect that this open-source abstinence calculation tool will improve the rigor and reproducibility of smoking and addiction research by standardizing TLFB-based abstinence calculation.ImplicationsAbstinence calculation is an essential task in any smoking intervention study. However, there have not been standard open-source tools available to the researchers. This commentary describes a Python-based package called abstcal that can calculate abstinence from TLFB data, a common methodology to collect smoking consumption data in research settings. The package supports the calculation of point-prevalence, prolonged, and continuous abstinence. Importantly, the package has a web app interface that allows researchers to use the tool without any coding experience. This tool will facilitate smoking research by providing a standardized and easy-to-use abstinence calculation tool.

Project description:BackgroundMany definitions are being used to conceptualize child health problems. With survey data, commonly used indicators for identifying children with health problems have included chronic condition checklists, measures of activity limitations, elevated service use, and health utility thresholds. This study compares these different indicators in terms of the prevalence rates elicited, and in terms of how the subgroups identified differ.MethodsSecondary data analyses used data from the National Longitudinal Survey of Children and Youth, which surveyed a nationally representative sample of Canadian children (n = 13,790). Descriptive analyses compared healthy children to those with health problems, as classified by any of the key indicators. Additional analyses examined differences between subgroups of children captured by a single indicator and those described as having health problems by multiple indicators.ResultsThis study demonstrates that children captured by any of the indicators had poorer health than healthy children, despite the fact that over half the sample (52.2%) was characterized as having a health problem by at least one indicator. Rates of child ill health differed by indicator; 5.6% had an activity limitation, 9.2% exhibited a severe health difficulty, 31.7% reported a chronic condition, and 36.6% had elevated service use. Further, the four key indicators captured different types of children. Indicator groupings differed on child and socio-demographic factors. Compared to children identified by more than one indicator, those identified only by the severe health difficulty indicator displayed more cognitive problems (p < 0.0001), those identified only by the chronic condition checklist had a greater likelihood of reporting allergies or asthma (p < 0.0001), and those identified as having elevated service use only were more affluent (p = 0.01) and showed better overall health (p < 0.0001). Children identified by only a single indicator were less likely to have serious health problems than those identified by two or more indicators.ConclusionWe provide information useful to researchers when selecting indicators from survey data to identify children with health problems. Researchers and policy makers need to be aware of the impact of such definitions on prevalence rates as well as on the composition of children classified as being in poor health.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets