Abstract
Data collection commenced under the auspices of a collaborative global health partnership by the Child Health and Mortality Prevention Surveillance (CHAMPS) network to investigate the impact of the COVID-19 pandemic and related lockdowns on child and maternal health, economic hardships, and access to care for children and pregnant women. Survey data were collected between January and August 2022 within a Health and Demographic Surveillance System (HDSS) in Western Kenya. The data were gathered using a survey instrument designed to measure household knowledge and awareness of COVID-19, experiences of economic and social hardships, changes in food availability, and challenges to accessing healthcare during the pandemic. The data are drawn from two communities in Western Kenya, one rural (Karemo) and one urban (Manyatta) and consist of a survey of 28,677 households.
Keywords: SARS-CoV-2, Kenya, Lockdown, Resilience
Specifications Table
| Subject | Health Sciences, Medical Sciences & Pharmacology |
| Specific subject area | Household economic and social circumstances during the COVID-19 pandemic. |
| Type of data | Raw, Table |
| Data collection | The data were collected from two sites in Western Kenya. One is a predominantly rural area, Karemo, in Siaya County and the other is an urban area, Manyatta, in the Kisumu County [1,2]. The Karemo area consists of 168 villages (distinct locally, culturally, and geographically defined division constituting the sub county), spans an area of approximately 200 km2, and comprises a population of 99,173 individuals. The urban area comprises 121 villages, covers an area of 5 km2, and has a population of 70,743 individuals. These two HDSS catchment areas and their respective populations have been described elsewhere in detail [1,2]. The data were collected between January and August 2022. All the households in the HDSS were selected for participation. Data were acquired using the survey instrument “Harmonized COVID-19 Impact Questions for the CHAMPS HDSS Network.” Protocols for data collection procedure followed the duly approved Study Protocol approved by KEMRI’s Scientific and Ethics Research Unit (SERU); approval reference number Ref.No.KEMRI/RES/7/3/1. |
| Data source location | Institution: KEMRI, Centre for Global Health Research, Kisian, Kenya City/Town/Region: Siaya and Kisumu, Nyanza Province Country: Kenya |
| Data accessibility | Repository name: CHAMPS Population Surveillance Dataverse Data identification number: https://doi.org/10.15139/S3/PDIY33 Direct URL to data: https://dataverse.unc.edu/dataset.xhtml?persistentId=doi:10.15139/S3/PDIY33 Instructions for accessing these data: The data are publicly available through the CHAMPS Population Surveillance Dataverse. |
| Related research article | None |
1. Value of the Data
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Information Related to COVID-19 Awareness: These data include information concerning household awareness of and familiarity with SARS-CoV-2 and COVID-19.
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Knowledge Concerning Socioeconomic Changes Concurrent with COVID-19: The data offer potential insights with regards to socioeconomic shocks experienced during pandemic related lockdowns and policies executed to minimize the pace of disease spread during the pandemic.
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Implications of COVID-19 for Maternal and Child Health: These data highlight potential consequences for child and maternal health, elucidating challenges to accessing healthcare in a resource-limited setting during the pandemic.
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Implications for Policy Development: The data may aid policy makers in developing future interventions, have benefit for public health researchers in addressing challenges stemming from the COVID-19 pandemic and associated lockdowns, particularly in resource-limited settings.
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Broader Analytic Potential: The COVID-19-related data are robust as a standalone dataset, but researchers can analyze the data in combination with the HDSS data from the Kenya study settings, or as part of cross-site analyses with comparable data from other HDSS sites in the CHAMPS network [[3], [4], [5], [6], [7]].
2. Background
By July 2022, over 6 million people had died due to Covid-19 and over 500 million people had contracted the disease. Apart from these direct health effects, the pandemic had broader implications as lockdowns and related measures were implemented by governments to mitigate the pace and severity of disease spread [8,9]. These unprecedented restrictions exacted an economic toll by affecting industries such as hospitality, retail, travel, and tourism and culminating in increased unemployment and business closures [[9], [10], [11], [12], [13], [14]]. In Africa, lockdowns and related restrictions disrupted diminished economic activities, supply chains, and led to declines in GDP growth [15].
To better understand broader impacts of the pandemic, we collected the data described herein under the auspices of a collaborative global health partnership by the CHAMPS network to investigate the impact of the COVID-19 pandemic and related lockdowns on child and maternal health, economic hardships, and access to care for children and pregnant women. From January to August 2022, a survey was conducted in Western Kenya that involved 28,677 households in two communities, Karemo (rural) and Manyatta (urban), and aimed to assess their understanding of COVID-19, the economic and social difficulties they faced, changes in food access, and challenges encountered in obtaining healthcare during the pandemic.
3. Data Description
The data, a data dictionary, and the survey instrument are publicly accessible through an open access data repository hosted by Dataverse, titled “CHAMPS Population Surveillance Dataverse.”
De-identified data can be downloaded through the repository file titled “COVID_LOCKDOWN_DATA.” The data are organized into sections aligning with survey instrument modules:
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Respondent and household information: Data from this module includes information related to respondent age, sex, residence, marital status, educational level, occupation, and family size.
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Knowledge of COVID-19: This module includes data concerning household awareness of COVID-19 preventive measures (e.g., hygiene, mask wearing, staying home, and social distancing) and governmental efforts related to reducing COVID-19 transmission (e.g., movement restriction, public awareness, isolation, and disinfection of public places).
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Food security: The data from this module covers variables related to food availability, delivery services, affordability, and fears associated with COVID-19 that may have created barriers to purchasing food.
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COVID-19 related economic shocks and coping: Example variables include household member job loss, business closure, and agricultural disruptions as well as household observation of price increases for food, agricultural inputs, and/or business inputs.
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Healthcare services for children and pregnant women: Variables from this module include routine ANC, PNC, & delivery services; COVID-19 testing; malaria and HIV treatment; malnutrition services; routine vaccination; and services for chronic illness.
Variable names adhere to the conventions outlined in the data dictionary. For survey questions that prompted respondents to select multiple options (“select all that apply”), responses were disaggregated into individual variables in the raw data file during the data cleaning process. The variable labels associated with these variables are designed to facilitate clear identification of the represented information.
The data dictionary is available for download as an Excel spreadsheet under the title “COVID Impact Data Dictionary.” This document organizes information about each variable in the data into 5 columns (Labels, Variable_Name, Column_Name, Is_Nullable, and Data_Type). It is a representation of the structure and content of the electronic survey conducted in the field using tablets. The field labels, representing variable descriptions, are presented in English. Additionally, the field attribute column provides details about the variable type, including variable values and associated labels, all in English.
The survey instrument, titled “COVID_CHAMPS_Instrument,” includes the original survey instrument designed for paper-based data collection. This form was adapted into an electronic format, presented in English, and is available for reference in the data dictionary. The electronic version incorporates additional revisions tailored for effective implementation within local contexts.
A total of 28,677 surveys were collected with an overall response rate of 60 %; 19,461 surveys were completed in Karemo (74 % response rate) and 9216 surveys were completed in Manyatta (response rate of 43 %). Of the 28,677 surveys collected, 28,031 surveys were complete and have sufficient information to be linked to the broader HDSS data. The modal age category of household heads was 30–39 years (29.2 %); most households (60.9 %) were headed by a female household member and most household heads (87.6 %). A little over half of household heads had a primary level education and another 37.4 % had a secondary level education—only 8.9 % reported no education. The most common occupation category was subsistence agriculture (42.6 %) followed by skilled labor (14.2 %); a little over 1 % of household heads were unemployed. Roughly 70 % of households resided in rural Karemo with the remaining 30 % resided in urban Manyatta.
4. Experimental Design, Materials and Methods
The data presented herein were collected in the Karemo and Manyatta catchment areas as a component of an international health initiative within the auspices of the CHAMPS network to understand implications of COVID-19 lockdowns and associated social distancing protocols on child health and mortality in resource-limited settings [1,[3], [4], [5], [6],[10], [11], [12],[16], [17], [18]].
The study had two key aims:
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Examine household awareness: Investigate level of knowledge and awareness about COVID-19, disease prevention, and community-level interventions aimed at reducing disease transmission.
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Explore socioeconomic impacts: Evaluate potential contributions of lockdowns and related policies to broader economic and social impacts on individuals and households.
Data were gathered with the intention of enhancing knowledge of repercussions stemming from the pandemic and ensuing lockdown measures on the well-being of individual, households, and communities. This encompassed assessing the effects on livelihoods, food availability, and healthcare services within the geographical catchment areas[1]. Survey interviews were conducted, prompting respondents to reflect on their experiences since March 2020. Data collection did not aim to ascertain COVID-19 cases formally; laboratory tests for SARS-CoV-2 were not included as part of this data collection process.
Data were collected in Western Kenya, focusing on two distinct areas: Karemo in Siaya County, a predominantly rural region; and Manyatta in Kisumu County, a peri‑urban area [1]. The rural area covers 200 km2, which is divided into 168 villages, and is the home of 99,173 individuals residing in 26,353 households. The urban area is home to a population of 70,743 individuals residing in 21,512 households; it spans across 5km2 and is partitioned into 121 villages.
The Siaya HDSS site was established in 2007 as an expansion of the larger HDSS, which was established in 2001. The Manyatta – Kisumu HDSS site began in 2016 with the establishment of an urban HDSS to collect demographic and health-related information. For the Covid-19 Lockdown module, integrated into the HDSS’s regular rounds data collection, the entire HDSS populations in the two catchment areas were selected. Originally, the scope of data collection was designed for a simple random sample of 440 households from Manyatta - Kisumu and another 440 households from Karemo – Siaya; however, the scope of data collection was ultimately expanded to cover the entire population.
The data collection instrument was developed using standard survey design methods that included completion of a rapid literature review, identification of key research concepts, generation of core survey questions, and reviewing example questionnaires as preliminary source material from which a subset of standardized survey questions were selected and revised (e.g., the “High Frequency Mobile Phone Surveys of Households to Assess the Impacts of COVID-19″ questionnaire developed by the World Bank as a standardized instrument for assessing social and economic impacts of the pandemic) [[19], [20], [21]]. Internally, the questions were also reviewed by specialists in epidemiological studies for scientific feasibility, logical flow, and contextual relevance. The harmonized instrument, initially generated as a template for use across the CHAMPS network, underwent slight revisions for implementation within local contexts. The finalized data collection instrument comprised into six sections: respondent information, knowledge regarding SARS-CoV-2 transmission, food availability, COVID-19 related shocks and coping mechanisms, under-five child healthcare services, and healthcare services for pregnant women (see supplementary materials). The survey instrument included questions related to hardships encountered during the pandemic period, prompting respondents to reflect on their experiences since March 2020. Data collection was carried out as an extension survey module during the regular HDSS rounds (data collection periods wherein information related to demographic events are gathered) using the HDSS’s inhouse developed application platform for electronic data collection, which is in the English version. Hard copies were translated to Dholuo and Kiswahili.
Given that data collection of the COVID-19 impact survey was carried out as extension of regular HDSS data collection activities, data collectors and supervisors underwent a week-long training session that comprehensively covered the COVID-19 study’s objectives, confidentiality protocols, and data collection techniques prior to commencing with data collection. This training was provided in addition to broader refresher training on data collection methods related to HDSS data collection. Given the regular collection of HDSS data by the HDSS team, all data collectors and supervisors are already well experienced and trained with regards to survey data collection methods—the trainings provided were intended to orient the teams to the specific of the COVID-19 study.
Data cleaning and validation followed standard operating procedures for the HDSS[22]. Quality control measures included pretesting the survey instrument on a sub-sample of respondents not eligible for the study. Insights from the pre-test informed revisions to the research and data collection tools. Data concerns such as implausible values, inconsistencies, and/or incomplete data were flagged for in-field correction by the data collectors. A random sample of questionnaires underwent re-visits by field supervisors and quality control interviewers to verify recorded information. Implementation of the data collection module was approved by KEMRI’s Scientific and Ethics Research Unit (SERU); approval reference number Ref.No.KEMRI/RES/7/3/1.
Limitations
The findings reported here are not generalizable outside of the communities under study—a limitation common to studies using data collected from HDSS sites [14]. Overall, the study collected responses from 28,677 households; response rates were moderate overall and marked limitation for data collected in Manyatta. Reasons for the moderate response rates include selective non-participation of some social groups in the survey, logistical challenges in reaching certain locations or groups, and incomplete or missing data due to migration. As an observational study, other limitations include potential recall bias due to the extended length of time considered in the study or unmeasured variable bias. Given the cross-sectional nature of the data, they are unable to assess variation over time and contribute to findings from other studies that suggest hardships associated with COVID-19 lockdowns are temporarily dynamic [7]. In the context of demographic surveillance systems, additional rounds of data collection using the same survey instrument is easy to implement within subsequent rounds of data collection already being fielded.
Ethics Statement
This study was conducted according to the guidelines in the Declaration of Helsinki; all procedures involving research study participants, including digital data collection using tablets that were programmed with the corresponding survey instruments, were approved by KEMRI’s SERU; approval reference number Ref.No.KEMRI/RES/7/3/1. Written informed consent was obtained for participants who were able to read and write. For participants who were unable to read or write, the informed consent statement was read and oral informed consent from the participant was obtained, recorded, and witnessed. These procedures for obtaining written or oral informed consent were approved by KEMRI’s SERU; approval reference number Ref.No.KEMRI/RES/7/3/1.
CRediT Author Statement
DO, VA, BTB, SM, DOO, RO: project administration. SAC, JM, ZM, DO, VA, GA, BA, TM and BTB: conceptualization and methodology. DO, CK, BA, MD, NA, LO, TG, GM, and GD: data curation. JM, ZM, CK, LA, BO, BA, and EA: validation. DO and JM: writing - original draft. All authors: writing - review & editing. DO, SAC, VA, DOO and BTB: supervision. VA, DOO and BTB: funding acquisition.
Disclaimer
The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the US Centers for Disease Control and Prevention.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This work was supported by grant OPP1126780 from the Bill & Melinda Gates Foundation to Dr Cynthia Whitney. This work was further supported by grant sub-award A1028081 (CHAMPS-Kenya Site Grant number) from the Bill & Melinda Gates Foundation to Dr. Victor Akelo. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission.
Footnotes
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.dib.2025.112283.
Appendix. Supplementary Materials
Data Availability
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