Protective Behaviors and Secondary Harms Resulting From Nonpharmaceutical Interventions During the COVID-19 Epidemic in South Africa: Multisite, Prospective Longitudinal Study

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Abstract

Background

In March 2020, South Africa implemented strict nonpharmaceutical interventions (NPIs) to contain the spread of COVID-19. Over the subsequent 5 months, NPI policies were eased in stages according to a national strategy. COVID-19 spread throughout the country heterogeneously; the disease reached rural areas by July and case numbers peaked from July to August. A second COVID-19 wave began in late 2020. Data on the impact of NPI policies on social and economic well-being and access to health care are limited.

Objective

We aimed to determine how rural residents in three South African provinces changed their behaviors during the first COVID-19 epidemic wave.

Methods

The South African Population Research Infrastructure Network nodes in the Mpumalanga (Agincourt), KwaZulu-Natal, (Africa Health Research Institute) and Limpopo (Dikgale-Mamabolo-Mothiba) provinces conducted up to 14 rounds of longitudinal telephone surveys among randomly sampled households from rural and periurban surveillance populations every 2-3 weeks. Interviews included questions on the following topics: COVID-19–related knowledge and behaviors, the health and economic impacts of NPIs, and mental health. We analyzed how responses varied based on NPI stringency and household sociodemographics.

Results

In total, 5120 households completed 23,095 interviews between April and December 2020. Respondents’ self-reported satisfaction with their COVID-19–related knowledge and face mask use rapidly rose to 85% and 95%, respectively, by August. As selected NPIs were eased, the amount of travel increased, economic losses were reduced, and the prevalence of anxiety and depression symptoms fell. When the number of COVID-19 cases spiked at one node in July, the amount of travel dropped rapidly and the rate of missed daily medications doubled. Households where more adults received government-funded old-age pensions reported concerns about economic matters and medication access less often.

Conclusions

South Africans complied with stringent, COVID-19–related NPIs despite the threat of substantial social, economic, and health repercussions. Government-supported social welfare programs appeared to buffer interruptions in income and health care access during local outbreaks. Epidemic control policies must be balanced against the broader well-being of people in resource-limited settings and designed with parallel support systems when such policies threaten peoples’ income and access to basic services.

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Most cited references 33

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An interactive web-based dashboard to track COVID-19 in real time

Ensheng Dong, Hongru Du, Lauren Gardner (2020)

In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.

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Impact of COVID-19 Pandemic on Mental Health in the General Population: A Systematic Review

Jiaqi Xiong, Orly Lipsitz, Flora Nasri … (2020)

Highlights • The Coronavirus disease 2019 (COVID-19) pandemic has resulted in unprecedented hazards to mental health globally. • Relatively high rates of anxiety, depression, post-traumatic stress disorder, psychological distress, and stress were reported in the general population during the COVID-19 pandemic in eight countries. • Common risk factors associated with mental distress during the COVID-19 pandemic include female gender, younger age group (≤40 years), presence of chronic/psychiatric illnesses, unemployment, student status, and frequent exposure to social media/news concerning COVID-19. • Mitigation of COVID-19 induced psychological distress requires government intervention and individual efforts.

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Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study

Marius Gilbert, Giulia Pullano, Francesco Pinotti … (2020)

Summary Background The novel coronavirus disease 2019 (COVID-19) epidemic has spread from China to 25 countries. Local cycles of transmission have already occurred in 12 countries after case importation. In Africa, Egypt has so far confirmed one case. The management and control of COVID-19 importations heavily rely on a country's health capacity. Here we evaluate the preparedness and vulnerability of African countries against their risk of importation of COVID-19. Methods We used data on the volume of air travel departing from airports in the infected provinces in China and directed to Africa to estimate the risk of importation per country. We determined the country's capacity to detect and respond to cases with two indicators: preparedness, using the WHO International Health Regulations Monitoring and Evaluation Framework; and vulnerability, using the Infectious Disease Vulnerability Index. Countries were clustered according to the Chinese regions contributing most to their risk. Findings Countries with the highest importation risk (ie, Egypt, Algeria, and South Africa) have moderate to high capacity to respond to outbreaks. Countries at moderate risk (ie, Nigeria, Ethiopia, Sudan, Angola, Tanzania, Ghana, and Kenya) have variable capacity and high vulnerability. We identified three clusters of countries that share the same exposure to the risk originating from the provinces of Guangdong, Fujian, and the city of Beijing, respectively. Interpretation Many countries in Africa are stepping up their preparedness to detect and cope with COVID-19 importations. Resources, intensified surveillance, and capacity building should be urgently prioritised in countries with moderate risk that might be ill-prepared to detect imported cases and to limit onward transmission. Funding EU Framework Programme for Research and Innovation Horizon 2020, Agence Nationale de la Recherche.

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Author and article information

Contributors

Guy Harling:

ORCID: https://orcid.org/0000-0001-6604-491X

Africa Health Research InstituteP.O. Box 198, Mtubatuba 3935KwaZulu-NatalSouth Africa27 0355507500guy.harling@ahri.org

Journal

Journal ID (nlm-ta): JMIR Public Health Surveill

Journal ID (iso-abbrev): JMIR Public Health Surveill

Journal ID (publisher-id): JPH

Title: JMIR Public Health and Surveillance

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2369-2960

Publication date Collection: May 2021

Publication date (Electronic): 13 May 2021

Publication date PMC-release: 13 May 2021

Volume: 7

Issue: 5

Electronic Location Identifier: e26073

Affiliations

[1 ] Africa Health Research Institute KwaZulu-Natal South Africa

[2 ] Institute for Global Health University College London London United Kingdom

[3 ] Medical Research Council/Wits Rural Public Health and Health Transitions Research Unit (Agincourt) School of Public Health, Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa

[4 ] Department of Epidemiology Harvard T.H. Chan School of Public Health Harvard University Boston, MA United States

[5 ] Center for Population and Development Studies Harvard T.H. Chan School of Public Health Harvard University Boston, MA United States

[6 ] School of Nursing and Public Health University of KwaZulu-Natal Durban South Africa

[7 ] International Network for the Demographic Evaluation of Populations and Their Health Network Accra Ghana

[8 ] Dikgale-Mamabolo-Mothiba Population Health Research Centre School of Health Care Sciences, Faculty of Health Sciences University of Limpopo Mankweng South Africa

[9 ] Department of Computer Science School of Mathematical and Computer Sciences, Faculty of Science and Agriculture University of Limpopo Mankweng South Africa

[10 ] Department of Science and Innovation-Medical Research Council South African Population Research Infrastructure Network Johannesburg South Africa

[11 ] Division of Infectious Diseases University of Alabama, Birmingham Birmingham, AL United States

[12 ] Harvard Medical School and the Medical Practice Evaluation Center Massachusetts General Hospital Boston, MA United States

[13 ] Department of Public Health School of Health Care Sciences, Faculty of Health Sciences University of Limpopo Mankweng South Africa

Author notes

Corresponding Author: Guy Harling guy.harling@ 123456ahri.org

Author information

Guy Harling https://orcid.org/0000-0001-6604-491X

Francesc Xavier Gómez-Olivé https://orcid.org/0000-0002-4876-0848

Joseph Tlouyamma https://orcid.org/0000-0003-4635-3234

Tinofa Mutevedzi https://orcid.org/0000-0002-1749-3089

Chodziwadziwa Whiteson Kabudula https://orcid.org/0000-0002-5867-0336

Ruth Mahlako https://orcid.org/0000-0002-2406-9294

Urisha Singh https://orcid.org/0000-0001-6905-7599

Daniel Ohene-Kwofie https://orcid.org/0000-0001-7107-9498

Rose Buckland https://orcid.org/0000-0002-3947-6685

Pedzisai Ndagurwa https://orcid.org/0000-0001-5628-4901

Dickman Gareta https://orcid.org/0000-0002-4004-5655

Resign Gunda https://orcid.org/0000-0003-1404-9879

Thobeka Mngomezulu https://orcid.org/0000-0002-4142-2432

Siyabonga Nxumalo https://orcid.org/0000-0001-5931-1784

Emily B Wong https://orcid.org/0000-0003-4755-5380

Kathleen Kahn https://orcid.org/0000-0003-3339-3931

Mark J Siedner https://orcid.org/0000-0003-3506-842X

Eric Maimela https://orcid.org/0000-0003-2843-4663

Stephen Tollman https://orcid.org/0000-0003-0744-7588

Mark Collinson https://orcid.org/0000-0002-8205-7099

Kobus Herbst https://orcid.org/0000-0002-5436-9386

Article

Publisher ID: v7i5e26073

DOI: 10.2196/26073

PMC ID: 8121138

PubMed ID: 33827046

SO-VID: cdc35af3-7089-4d11-862a-d5a15eaee55c

Copyright © ©Guy Harling, Francesc Xavier Gómez-Olivé, Joseph Tlouyamma, Tinofa Mutevedzi, Chodziwadziwa Whiteson Kabudula, Ruth Mahlako, Urisha Singh, Daniel Ohene-Kwofie, Rose Buckland, Pedzisai Ndagurwa, Dickman Gareta, Resign Gunda, Thobeka Mngomezulu, Siyabonga Nxumalo, Emily B Wong, Kathleen Kahn, Mark J Siedner, Eric Maimela, Stephen Tollman, Mark Collinson, Kobus Herbst. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 13.05.2021.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.

History

Date received : 26 November 2020

Date revision requested : 12 February 2021

Date revision received : 25 March 2021

Date accepted : 31 March 2021

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