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The following two metadata fields were edited - Document ID and Survey ID."}},"study_desc":{"title_statement":{"idno":"ZAF_2020_CRAM-MATCH_v01_M","title":"Coronavirus Rapid Mobile Survey of Maternal and Child Health 2020","alt_title":"CRAM-MATCH 2020"},"authoring_entity":[{"name":"University of Stellenbosch","affiliation":""}],"distribution_statement":{"contact":[{"name":"DataFirst Support","affiliation":"University of Cape Town","email":"support@data1st.org","uri":"www.support.data1st.org"}]},"series_statement":{"series_name":"Other Household Survey [hh\/oth]"},"version_statement":{"version":"Version 1: Edited, anonymised data for public distribution.","version_date":"2021"},"study_info":{"abstract":"The Coronavirus Rapid Mobile Survey of Maternal and Child Health (CRAM-MATCH) was a rapid SMS (Short Message Service) survey conducted in South Africa conducted among pregnant women and mothers registered with the MomConnect mhealth platform in South Africa. This national survey was conducted in June (n=3140) with a follow up in July (n=2287). The survey collected data from pregnant women and new mothers in South Africa on how the Coronavirus pandemic has affected their health including their access to health care.","coll_dates":[{"start":"2020-06-02","end":"2020-06-05","cycle":"Wave 1"},{"start":"2020-06-24","end":"2020-06-30","cycle":"Wave 2"}],"nation":[{"name":"South Africa","abbreviation":"ZAF"}],"geog_coverage":"National coverage","geog_unit":"The data is at the national level","analysis_unit":"Individuals","universe":"The survey collected data from pregnant women and new mothers in South Africa.","data_kind":"Sample survey data [ssd]","notes":"The surveys collected data on nutrition, depressive symptoms, access to antenatal care, vaccinations, and ART. Data on impediments to access was collected by asking the respondents who did not access care or collect their medicine, why they did not do so. Responses included fears about contracting COVID-19, problems with available transport, long waiting times, or stock-outs at the facility."},"method":{"data_collection":{"sampling_procedure":"The sample was drawn from the Momconnect mhealth platform created by the South African National Department of Health (NDOH) in 2014. MomConnect is a mobile health (mHealth) solution created to improve and promote maternal health services in South Africa by providing pregnant mothers with free messaging facility and a helpdesk. The mobile health application also created a national pregnancy registry which has excellent coverage of pregnant women and new mothers. By 2017 more than half of the women attending public sector antenatal care services in South Africa were registered on the Momconnect platform. By 2019 there were over 2 million registered MomConnect users. \n\nA self-weighted sample of 15 000 pregnant women and mothers with children under 12 months was drawn from the database of MomConnect users. The sample was stratified based on province, gestational age or age of their baby and their type of phone.  The 15 000 women all received an invitation to join the SMS survey on the afternoon of 24 June 2020. They could respond by SMS with \"JOIN\" to participate in the survey, by SMSing \"STOP\" to not participate or to reply with \"MORE\" if they needed more information. Those who participated in the survey received R10 in airtime. The wave 1 survey was completed on June 30, 2020. The wave 2 survey invitation was sent on the 2nd of July 2020 and the survey ended on the 5th of July 2020.\n\nPoverty Quintiles\nTwo sets of poverty quintiles were created for respondents by constructing poverty quintiles for primary care public health facilities. The first poverty quintile measures the wealth quintile of the small area place where the facility that the respondent last visited is located. The second poverty quintile measures the average wealth quintile of the catchment area that the facility covers. Because of the focus on access to primary care and because the Momconnect moms' registrations are at their local primary care facility, only data related to public sector primary care facilities was extracted from the government database of facilities (clinics, community health centres and community day centres).\n\nThe richest 15% of areas was also excluded since these individuals are unlikely to make use of public facilities. This implies that the 'wealthiest' quintile only represents the wealthiest of the 85% poorest South Africans. Each small area place in Census was then linked to their closest public primary care facility, using the GIS codes in both the Census and the national facility database to create a catchment area for each facility.Poverty quintiles were created by deriving a measure of living standards and wealth measures via Principal Component Analysis (PCA), using data on employment status, education level, earnings, household size, and cell phone and car ownership of the residents of the area collected during the 2011 census. PCA was used to calculate wealth scores and these were aggregated over the entire catchment area, weighted by the population size of each Small Area place in the Census 2011. The sample of respondents was matched to these poverty quintiles via the Momconnect facility identifier, which captures the facility where the mother was registered.","coll_mode":["Other [oth]"],"research_instrument":"Two questionnaires were used, one for the Wave 1 Survey and another for the Wave 2 Survey.","act_min":"The Project received permission from the National Department of Health to survey their patients. The Team obtained ethics approval for data collection from the University of Stellenbosch's Research Ethics Committee for Social, Behavioural and Education Research [project 14926 on 15 June 2020] as part of the rapid Coronavirus research stream."},"analysis_info":{"response_rate":"Assuming a response rate of 20%, from the targeted sample of 15 000 women, the project aimed to achieve a survey sample of 3000 and realised a sample of 3140 for wave 1 and thus had an effective response rate of 21%. Of the 3140 individuals who responded to wave 1, 2287 also responded in wave 2. 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