Dataset Information

GLD-SAIPAR Covid-19 survey in Zambia dataset.

ABSTRACT: In March 2020, Zambia reported their first cases of Covid-19, with the government restricting non-essential travel, banning large public gatherings, and establishing emergency committees to spearhead efforts to contain the pandemic at a national level. The Governance and Local Development Institute (GLD) in collaboration with South African Institute for Policy and Research (SAIPAR) implemented a survey in 2020 to generate a dataset to provide better understanding of citizens' responses to such measures, and the impact of the pandemic on their individual lives and communities. To generate as complete a picture as possible of the local situations over time, we conducted two survey rounds. These rounds resulted in a total of 3925 completed surveys (1022 respondents took both rounds), gathering information on issues including knowledge of Covid-19; attitudes and fears surrounding health and economic impacts; social, economic, and health vulnerabilities; social distancing practices and other preventative measures. Particular attention was paid to the local variation in concerns over the social stigma, levels of enforcement, and engagement of different authorities.

SUBMITTER: Metheney EA

PROVIDER: S-EPMC10641148 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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GLD-SAIPAR Covid-19 survey in Zambia dataset.

Metheney Erica Ann EA Lust Ellen E

Data in brief 20231026

In March 2020, Zambia reported their first cases of Covid-19, with the government restricting non-essential travel, banning large public gatherings, and establishing emergency committees to spearhead efforts to contain the pandemic at a national level. The Governance and Local Development Institute (GLD) in collaboration with South African Institute for Policy and Research (SAIPAR) implemented a survey in 2020 to generate a dataset to provide better understanding of citizens' responses to such m ...[more]

PMID: 37965609

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Project description:Although data about COVID-19 cases and deaths in the United States are readily available at the county-level, datasets on smaller geographic areas are limited. County-level data have been used to identify geospatial patterns of COVID-19 spread and, in conjunction with sociodemographic variables, have helped identify population health disparities concerning COVID-19 in the US. Municipality-level data are essential for advancing more targeted and nuanced understanding of geographic-based risk and resilience associated with COVID-19. We created a dataset that tracks COVID-19 cases and deaths by municipalities in the state of New Jersey (NJ), US, from April 22, 2020 to December 31, 2020. Data were drawn primarily from official county and municipality websites. The dataset is a spreadsheet containing cumulative case counts and case rates in each municipaly on three target dates, representing the peak of the first wave, the summer trough after the first wave, and the outbreak of the second wave in NJ. This dataset is valuable for four main reasons. First, the dataset is unique, because New Jersey's Health Department does not release COVID-19 data for the 77% (433/565) of municipalities with populations smaller than 20,000 individuals. Second, especially when combined with other data sources, such as publicly available sociodemographic data, this dataset can be used to advance epidemiological research on geographic differences in COVID-19, as well as to inform decision-making concerning the allocation of resources in response to the pandemic (e.g., strategies for targeted vaccine outreach campaigns). Third, county-level data mask important variations across municipalities, so municipality-level data permit a more nuanced exploration of health disparities related to local demographics, socioeconomic conditions, and access to resources and services. New Jersey is a good state to explore these patterns, because it is the most densely-populated and racially/ethnically diverse state in the US. Fourth, New Jersey was one of the few locations in the US with a high prevalence of COVID-19 during the first wave of the pandemic in the US. Thus, this dataset permits exploration of whether sociodemographic variables predicted COVID-19 differently as time progressed. To summarize, this unique municipality-level dataset in a diverse state with high COVID-19 cases is valuable for scholars and policy analysts to explore social and environmental factors related to the prevalence and transmission of COVID-19 in the US.

Dataset Information

GLD-SAIPAR Covid-19 survey in Zambia dataset.

Publications

GLD-SAIPAR Covid-19 survey in Zambia dataset.

Similar Datasets

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