Dataset Information

The Comparative Panel File: Harmonized Household Panel Surveys from Seven Countries.

ABSTRACT: The Comparative Panel File (CPF) harmonizes the world's largest and longest-running household panel surveys from seven countries: Australia (HILDA), Germany (SOEP), United Kingdom (BHPS and UKHLS), South Korea (KLIPS), Russia (RLMS), Switzerland (SHP), and the United States (PSID). The project aims to support the social science community in the analysis of comparative life course data. The CPF builds on the Cross-National Equivalent File but offers a larger range of variables, larger and more recent samples, an easier and more flexible workflow, and an open science platform for development. The CPF is not a data product but an open-source code that integrates individual and household panel data from all seven surveys into a harmonized three-level data structure. The CPF allows analysing individual trajectories, time trends, contextual effects, and country differences. The project is organized as an open science platform. The CPF version 1.0 contains 2.7 million observations from 360,000 respondents, covering the period from 1968 to 2019 and up to 40 panel waves per respondent. In this data brief, we present the background, design, and content of the CPF.

SUBMITTER: Turek K

PROVIDER: S-EPMC8152217 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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The Comparative Panel File: Harmonized Household Panel Surveys from Seven Countries.

Turek Konrad K Kalmijn Matthijs M Leopold Thomas T

European sociological review 20210312 3

The Comparative Panel File (CPF) harmonizes the world's largest and longest-running household panel surveys from seven countries: Australia (HILDA), Germany (SOEP), United Kingdom (BHPS and UKHLS), South Korea (KLIPS), Russia (RLMS), Switzerland (SHP), and the United States (PSID). The project aims to support the social science community in the analysis of comparative life course data. The CPF builds on the Cross-National Equivalent File but offers a larger range of variables, larger and more re ...[more]

PMID: 34054348

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Project description:In most low- and middle-income countries (LMICs), household out-of-pocket (OOP) health spending constitutes a major source of healthcare financing. Household surveys are commonly used to monitor OOP health spending, but are prone to recall bias and unable to capture seasonal variation, and may underestimate expenditure-particularly among households with long-term chronic health conditions. Household expenditure diaries have been developed as an alternative to overcome the limitations of surveys, and pictorial diaries have been proposed where literacy levels may render traditional diary approaches inappropriate. This study compares estimates for general household and chronic healthcare expenditure in South Africa, Tanzania and Zimbabwe derived using survey and pictorial diary approaches. We selected a random sub-sample of 900 households across urban and rural communities participating in the Prospective Urban and Rural Epidemiology study. For a range of general and health-specific categories, OOP expenditure estimates use cross-sectional survey data collected via standardised questionnaire, and data from these same households collected via two-week pictorial diaries repeated four times over 2016-2019. In all countries, average monthly per capita expenditure on food, non-food/non-health items, health, and consequently, total household expenditure reported by pictorial diaries was consistently higher than that reported by surveys (each p<0.001). Differences were greatest for health expenditure. The share of total household expenditure allocated to health also differed by method, accounting for 2% in each country when using survey data, and from 8-20% when using diary data. Our findings suggest that the choice of data collection method may have significant implications for estimating OOP health spending and the burden it places on households. Despite several practical challenges to their implementation, pictorial diaries offer a method to assess potential bias in surveys or triangulate data from multiple sources. We offer some practical guidance when considering the use of pictorial diaries for estimating household expenditure.

Project description:We combine new data from high-frequency surveys with data on the stringency of containment measures to examine the short-term impacts of the COVID-19 pandemic on households in developing countries. This paper is one of the first to document the impacts of COVID-19 on households across a large number of developing countries and to do so for a comparable time-period, corresponding to the peak of the pandemic-induced drop in human mobility, and the first to systematically analyze the cross- and within-country effects on employment, income, food security and learning. Using representative data from 31 countries, accounting for a combined population of almost 1.4 billion, we find that in the average country 36 percent of respondents stopped working in the immediate aftermath of the pandemic, 65 percent of households reported decreases in income, and 30 percent of children were unable to continue learning during school closures. Pandemic-induced jobs and income losses translated into heightened food insecurity at the household level. The more stringent the virus containment measures, the higher the likelihood of jobs and income losses. The pandemic's effects were widespread and regressive, disproportionally affecting vulnerable segments of the population. Women, youth, and workers without higher education - groups disadvantaged in the labor market before the COVID-19 shock - were significantly more likely to lose their jobs and experience decreased incomes. Self-employed and casual workers - the most vulnerable workers in developing countries - bore the brunt of the pandemic-induced income losses. Interruptions in learning were most salient for children from lower-income countries, and within countries for children from lower-income households with lower-educated parents and in rural areas. The unequal impacts of the pandemic across socio-economic groups risk cementing inequality of opportunity and undermining social mobility and calls for policies to foster an inclusive recovery and strengthen resilience to future shocks.

Project description:The 2011 United Nations (UN) General Assembly Political Declaration on Prevention and Control of Non-Communicable Diseases (NCDs) brought NCDs to the global health agenda. Essential medicines are central to treating chronic diseases such as hypertension and diabetes. Our study aimed to quantify access to essential medicines for people with chronic conditions in five low- and middle-income countries and to evaluate how household socioeconomic status and perceptions about medicines availability and affordability influence access. We analysed data for 1867 individuals with chronic diseases from national surveys (Ghana, Jordan, Kenya, Philippines and Uganda) conducted in 2007-10 using a standard World Health Organization (WHO) methodology to measure medicines access and use. We defined individuals as having access to medicines if they reported regularly taking medicine for a diagnosed chronic disease and data collectors found a medicine indicated for that disease in their homes. We used logistic regression models accounting for the clustered survey design to investigate determinants of keeping medicines at home and predictors of access to medicines for chronic diseases. Less than half of individuals previously diagnosed with a chronic disease had access to medicines for their condition in every country, from 16% in Uganda to 49% in Jordan. Other than reporting a chronic disease, higher household socioeconomic level was the most significant predictor of having any medicines available at home. The likelihood of having access to medicines for chronic diseases was higher for those with medicines insurance coverage [highest adjusted odds ratio (OR) 3.12 (95% confidence intervals (CI): 1.38, 7.07)] and lower for those with past history of borrowing money to pay for medicines [lowest adjusted OR 0.56 (95% CI: 0.34, 0.92)]. Our study documents poor access to essential medicines for chronic conditions in five resource-constrained settings. It highlights the importance of financial risk protection and consumer education about generic medicines in global efforts towards improving treatment of chronic diseases.

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Project description:BACKGROUND:Malaria is one of the major causes of childhood death in sub-Saharan countries. A reliable estimation of malaria prevalence is important to guide and monitor progress toward control and elimination. The aim of the study was to estimate the true prevalence of malaria in children under five in the Democratic Republic of the Congo, Uganda and Kenya, using a Bayesian modelling framework that combined in a novel way malaria data from national household surveys with external information about the sensitivity and specificity of the malaria diagnostic methods used in those surveys-i.e., rapid diagnostic tests and light microscopy. METHODS:Data were used from the Demographic and Health Surveys (DHS) and Malaria Indicator Surveys (MIS) conducted in the Democratic Republic of the Congo (DHS 2013-2014), Uganda (MIS 2014-2015) and Kenya (MIS 2015), where information on infection status using rapid diagnostic tests and/or light microscopy was available for 13,573 children. True prevalence was estimated using a Bayesian model that accounted for the conditional dependence between the two diagnostic methods, and the uncertainty of their sensitivities and specificities obtained from expert opinion. RESULTS:The estimated true malaria prevalence was 20% (95% uncertainty interval [UI] 17%-23%) in the Democratic Republic of the Congo, 22% (95% UI 9-32%) in Uganda and 1% (95% UI 0-3%) in Kenya. According to the model estimations, rapid diagnostic tests had a satisfactory sensitivity and specificity, and light microscopy had a variable sensitivity, but a satisfactory specificity. Adding reported history of fever in the previous 14 days as a third diagnostic method to the model did not affect model estimates, highlighting the poor performance of this indicator as a malaria diagnostic. CONCLUSIONS:In the absence of a gold standard test, Bayesian models can assist in the optimal estimation of the malaria burden, using individual results from several tests and expert opinion about the performance of those tests.

Dataset Information

The Comparative Panel File: Harmonized Household Panel Surveys from Seven Countries.

Publications

The Comparative Panel File: Harmonized Household Panel Surveys from Seven Countries.

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