==== Front PLoS One PLoS One plos PLOS ONE 1932-6203 Public Library of Science San Francisco, CA USA 10.1371/journal.pone.0279610 PONE-D-22-33938 Research Article Biology and Life Sciences Nutrition Diet Food Medicine and Health Sciences Nutrition Diet Food Medicine and Health Sciences Medical Conditions Infectious Diseases Viral Diseases Covid 19 Biology and Life Sciences Nutrition Diet Medicine and Health Sciences Nutrition Diet Medicine and Health Sciences Epidemiology Pandemics Biology and Life Sciences Agriculture Biology and Life Sciences Nutrition Medicine and Health Sciences Nutrition Social Sciences Sociology Education Schools Biology and Life Sciences Agriculture Crop Science Crops The COVID-19 pandemic and its impacts on diet quality and food prices in sub-Saharan Africa COVID-19 impacts on prices and diet quality in Africa Ismail Abbas Conceptualization Formal analysis Methodology Writing – original draft Writing – review & editing 1 https://orcid.org/0000-0001-7226-5997 Madzorera Isabel Conceptualization Data curation Formal analysis Methodology Writing – original draft Writing – review & editing 2 3 * https://orcid.org/0000-0003-4877-5123 Apraku Edward A. Conceptualization Methodology Writing – review & editing 4 Tinkasimile Amani Conceptualization Methodology Writing – review & editing 5 Dasmane Dielbeogo Conceptualization Methodology Writing – review & editing 6 Zabre Pascal Data curation Writing – review & editing 7 Ourohire Millogo Conceptualization Methodology Writing – review & editing 7 https://orcid.org/0000-0003-0341-2329 Assefa Nega Conceptualization Methodology Writing – review & editing 8 https://orcid.org/0000-0001-5596-573X Chukwu Angela Conceptualization Methodology Writing – review & editing 9 Workneh Firehiwot Conceptualization Methodology Writing – review & editing 10 Mapendo Frank Data curation Writing – review & editing 5 Lankoande Bruno Data curation Writing – review & editing 6 https://orcid.org/0000-0001-6986-9474 Hemler Elena Conceptualization Project administration Writing – review & editing 10 Wang Dongqing Conceptualization Methodology Writing – review & editing 10 Abubakari Sulemana W. Conceptualization Methodology Writing – review & editing 4 Asante Kwaku P. Conceptualization Writing – review & editing 4 Baernighausen Till Conceptualization Funding acquisition Methodology Writing – review & editing 11 Killewo Japhet Conceptualization Methodology Writing – review & editing 12 Oduola Ayoade Conceptualization Methodology Writing – review & editing 13 Sie Ali Conceptualization Writing – review & editing 7 Soura Abdramane Methodology Writing – review & editing 7 Vuai Said Conceptualization Methodology Writing – review & editing 1 Smith Emily Conceptualization Funding acquisition Methodology Writing – review & editing 14 15 Berhane Yemane Conceptualization Methodology Writing – original draft 10 ‡ Fawzi Wafaie W. Conceptualization Funding acquisition Methodology Writing – review & editing 3 16 17 ‡ * 1 College of Natural and Mathematical Sciences, University of Dodoma, Dodoma, Tanzania 2 Division of Community Health Sciences, School of Public Health, University of California, Berkeley, CA, United States of America 3 Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States of America 4 Kintampo Health Research Center, Research and Development Division, Ghana Health Service, Kintampo, Bono East Region, Ghana 5 Africa Academy for Public Health, Dar es Salaam, Tanzania 6 Institut Supérieur des Sciences de la Population, University of Ouagadougou, Ouagadougou, Burkina Faso 7 Nouna Health Research Center, Nouna, Burkina Faso 8 College of Health and Medical Sciences, Haramaya University, Harar, Ethiopia 9 Department of Statistics, University of Ibadan, Ibadan, Nigeria 10 Addis Continental Institute of Public Health, Addis Ababa, Ethiopia 11 Heidelberg Institute of Global Health, University of Heidelberg, Heidelberg, Germany 12 Department of Epidemiology and Biostatistics, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania 13 University of Ibadan Research Foundation, University of Ibadan, Ibadan, Nigeria 14 Department of Global Health, Milken Institute School of Public Health, George Washington University, Washington, DC, United States of America 15 Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, George Washington University, Washington, DC, United States of America 16 Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States of America 17 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States of America Al-Mahish Mohammed Editor King Faisal University, SAUDI ARABIA Competing Interests: The authors have declared that no competing interests exist. ‡ YB and WWF also contributed equally to this work as last authors. * E-mail: imadzorera@berkeley.edu (IM); mina@hsph.harvard.edu (WWF) 29 6 2023 2023 29 6 2023 18 6 e027961011 12 2022 9 6 2023 © 2023 Ismail et al 2023 Ismail et al https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background Sub-Saharan Africa faces prolonged COVID-19 related impacts on economic activity, livelihoods and nutrition, with recovery slowed down by lagging vaccination progress. Objective This study investigated the economic impacts of COVID-19 on food prices, consumption and dietary quality in Burkina Faso, Ethiopia, Ghana, Nigeria, and Tanzania. Methods We conducted a repeated cross-sectional study using a mobile platform to collect data from July-December, 2021 (round 2). We assessed participants’ dietary intake of 20 food groups over the previous seven days and computed the primary outcome, the Prime Diet Quality Score (PDQS), and Dietary Diversity Score (DDS), with higher scores indicating better quality diets. We used generalized estimating equation (GEE) linear regression models to assess factors associated with diet quality during COVID-19. Results Most of the respondents were male and the mean age was 42.4 (±12.5) years. Mean PDQS (±SD) was low at 19.4(±3.8), out of a maximum score of 40 in this study. Respondents (80%) reported higher than expected prices for all food groups. Secondary education or higher (estimate: 0.73, 95% CI: 0.32, 1.15), medium wealth status (estimate: 0.48, 95% CI: 0.14, 0.81), and older age were associated with higher PDQS. Farmers and casual laborers (estimate: -0.60, 95% CI: -1.11, -0.09), lower crop production (estimate: -0.87, 95% CI: -1.28, -0.46) and not engaged in farming (estimate: -1.38, 95% CI: -1.74, -1.02) were associated with lower PDQS. Conclusion Higher food prices and lower diet quality persisted during the COVID-19 pandemic. Economic and social vulnerability and reliance on markets (and lower agriculture production) were negatively associated with diet quality. Although recovery was evident, consumption of healthy diets remained low. Systematic efforts to address the underlying causes of poor diet quality through transforming food system value chains, and mitigation measures, including social protection programs and national policies are critical. http://dx.doi.org/10.13039/100005293 Harvard School of Public Health https://orcid.org/0000-0001-7226-5997 Madzorera Isabel Harvard University Center for African Studies https://orcid.org/0000-0001-7226-5997 Madzorera Isabel Heidelberg Institute of Global HealthSozial- und Präventivmedizin, Universität Heidelberg https://orcid.org/0000-0001-7226-5997 Madzorera Isabel George Washington University Milken Institute of Public Health https://orcid.org/0000-0001-7226-5997 Madzorera Isabel This work was supported by institutional support from Harvard T.H. Chan School of Public Health, Boston, MA (WF); Harvard University Center for African Studies, Boston, MA (WF); Heidelberg Institute of Global Health, Germany (TB), and the George Washington University Milken Institute of Public Health, Washington, DC (ES). The funders have no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data AvailabilityIndividual participant data cannot be shared publicly. A data transfer agreement between Harvard T.H. Chan School of Public Health, Africa Academy for Public Health, and participating institutions (including Addis Continental Institute of Public Health, Nouna Health Research Center, Muhimbili University of Health and Allied Sciences, University of Dodoma, University of Ibadan, and Heidelberg Institute of Global Health) stipulates that data will be kept confidential and will not be shared beyond the research teams without prior permission. The de-identified dataset supporting this research may be made available following a request submitted to ghp@hsph.harvard.edu and be granted after obtaining permission from each participating institution. OutbreaksCOVID-19 Data Availability Individual participant data cannot be shared publicly. A data transfer agreement between Harvard T.H. Chan School of Public Health, Africa Academy for Public Health, and participating institutions (including Addis Continental Institute of Public Health, Nouna Health Research Center, Muhimbili University of Health and Allied Sciences, University of Dodoma, University of Ibadan, and Heidelberg Institute of Global Health) stipulates that data will be kept confidential and will not be shared beyond the research teams without prior permission. The de-identified dataset supporting this research may be made available following a request submitted to ghp@hsph.harvard.edu and be granted after obtaining permission from each participating institution. ==== Body pmcIntroduction The coronavirus disease 2019 (COVID-19) continues to affect the social, economic and health status of individuals and communities globally and has exposed significant inequalities by income, socio-demographic factors and geographic location [1]. Despite the persistent improvement of health globally, some settings continue to face greater threats to health and well-being during public health emergencies due to prevailing social, economic, political, and environmental conditions, and the COVID-19 pandemic is not an exception with greater impacts on the socially disadvantaged, including on the African continent [2, 3]. The absolute number of reported cases and mortality due to COVID-19 in Africa has been lower than in other regions, with 8.4 million cases and 170,300 deaths reported by March 2022 [4]. However, challenges with emerging variants are likely to continue in the region due to vaccine hesitancy and low vaccination coverage, in contrast to declining global cases and recovery of economies [5, 6]. Further, Sub-Saharan Africa (SSA) was already grappling with economic and health challenges before the pandemic, so the impacts of COVID-19 could be more long-lasting than in developed regions [7]. Poverty had been increasing globally before the COVID-19 pandemic and 768 million people were hungry in 2020 [8]. The African continent contributed more than one-third (282 million) of the hungry, and it has been projected that due to the COVID-19 pandemic, hunger will increase globally and even more in SSA [8]. Additionally, further increases in child stunting and wasting in SSA are anticipated [9]. The impact of COVID-19 on economic growth in SSA remains severe [8]. SSA economies are expected to continue to experience recession and slower economic growth, disruptions of agriculture and production, and trade-related constraints if efforts to control COVID-19 remain limited [10]. Further, trade and fiscal deficits are likely to further adversely affect health and nutrition [11]. Countries and communities in SSA also continue to recover from COVID-19 related disruptions to livelihoods and access to key nutrition and food security services [12]. Prior to the COVID-19 pandemic, SSA food systems were already vulnerable to the influence of global markets, changing desirability, and poor food environments [13]. Studies early during the pandemic anticipated declines in food affordability, due to disruptions of food value chains, at the production, processing and distribution stages, and changes in consumer demand, with prices for major crops most affected [13, 14]. COVID-19 has also impacted vendors, markets and regulations with downstream effects on food availability, quality and prices [15]. Further, impacts on food security and dietary quality were anticipated due to market and manufacturing sector closures, restricted geographic access, and food price increases [13]. In our previous work in Burkina Faso, Ethiopia and Nigeria, we found evidence of increasing prices for key food groups, which may have contributed to lower dietary intake early during the COVID-19 pandemic [16]. We also found that higher pulse prices during the COVID-19 pandemic were associated with the consumption of less diverse diets [16]. It is important that as the COVID-19 pandemic persists we continue to assess its impact on the nutrition and health of Africans [12]. We investigate the continued impacts of COVID-19 on diet quality and food prices, using data collected from the African Research, Implementation Science, and Education (ARISE) Network cross-sectional COVID-19 studies in five SSA countries, Burkina Faso, Ethiopia, Ghana, Nigeria and Tanzania. This study contributes to understanding the indirect impacts of COVID-19 on diets in a region where data is limited. Data from several countries allows us to understand the diverse pathways through which COVID-19 affected nutrition. We also have repeated cross-sectional studies in 3 countries, and this allows us to track changes in food security and diet intake at different times during the COVID-19 pandemic. Material and methods Study setting This study design was a repeated cross-sectional study with two rounds of data collection. The first round of the survey took place between July and November 2020. The study included six study sites from three countries, namely Nouna and Ouagadougou in Burkina Faso, Kersa and Addis Ababa in Ethiopia, and Ibadan and Lagos in Nigeria. In each country, one rural site and one urban site were selected. Rural sites included Nouna and Kersa, and a rural sub-area in Ibadan. As determined during the initial study design, a second-round survey was conducted to allow the assessment of the continuous effects of COVID-19 on many aspects of public health. The second survey took place between July and December 2021 and involved all sites involved in round 1 and an additional three sites from two countries, that is the rural sites of Kintampo in Ghana, the rural sub-area of Dodoma in Tanzania, and Dar es Salaam (urban) in Tanzania. The additional sites were included given additional access to resources and access to sites where ethical approval had taken longer to receive in round 1. The survey sites are shown in Fig 1. 10.1371/journal.pone.0279610.g001 Fig 1 Map of ARISE sites for Round 2 of the COVID-19 studies. The study countries and areas were selected based on existing collaborations and infrastructure available through the ARISE Network [17] and represent diverse settings across sub-Saharan Africa. More detailed information on the geographical and socio-demographic characteristics of the ARISE Network sites has been provided elsewhere [17, 18]. Study design The study used a mobile phone platform and computer-assisted telephone interviewing (CATI) to collect data. Research assistants obtained verbal informed consent before starting the interview. They conducted interviews from study call centers. Each country surveyed a minimum of 300 healthcare workers, 600 adults and 600 adolescents (except Ghana, where we surveyed 300 adults and 300 adolescents) in round 2. This analysis involved data from the adult surveys. We randomly selected a larger number of phone numbers of households from the sampling frame, to allow for non-response, refusal or dropout. For the household surveys, 2,500 households were selected from each urban and rural site in Round 1, assuming that 60% of the households would respond to the survey. The assumption allowed us to reach the minimum number of 300 adults per site. From each household, an adult aged 20 years or above was selected for the interview. The second round of data collection targeted the same adults previously surveyed in Round 1 (Burkina Faso, Ethiopia and Nigeria) and additional new respondents to account for non-response and drop-outs. For new countries, the sample was obtained from existing sampling frames. The sampling frames were obtained from existing surveillance systems, including the Health and Demographic Surveillance Systems (HDSS) in Burkina Faso, Ghana, Nigeria and rural Ethiopia (Kersa), and from a household survey in Addis Ababa (Ethiopia) that was established in the first round of data collection. In Tanzania, the sampling frames were obtained from the Dar es Salaam Urban Cohort Study (DUCS) and HDSS and the Dodoma HDSS. Table 1 shows the number of participants in surveys 1 and 2. We ensured consistency in study design and questions across all sites to ensure that differences between the two rounds would not cause issues in comparisons (across countries and rounds if applicable) in the analysis. 10.1371/journal.pone.0279610.t001 Table 1 Demographic characteristics of the study individuals and households in Burkina Faso, Ethiopia, Nigeria, Tanzania and Ghana (N = 2,829).   Overall Burkina Faso Ethiopia Nigeria Tanzania Ghana   Nouna Ouagadougou Kersa Addis Ababa Ibadan Lagos Dar es Salaam Dodoma Kintampo Location Rural Urban Rural Urban Rural Urban Urban Rural Rural N 2829 324 300 298 289 373 290 307 347 301 Sociodemographic characteristics Female sex 1232 (43.6) 50(15.4) 103(34.3) 44(14.8) 202(69.9) 164(44.0) 131(45.2) 146(47.6) 232(66.9) 160(53.2) Age of respondent (Mean ±SD) years 42.4±12.5 47.6±12.8 47.4±10.1 36.6±9.4 38.0±12.2 40.4±13.1 39.5±11.8 48.6±12.5 43.0±10.1 40.0±13.6     20–29 428 (15.1) 15(4.6) 8(2.7) 66(22.1) 72(24.9) 88(23.6) 55(19.0) 16(5.2) 32(9.2) 76(25.3)     30–39 764 (27.0) 66(20.4) 52(17.4) 116(38.9) 112(38.8) 91(24.4) 99(34.1) 53(17.3) 96(27.7) 79(26.3)     ≥ 40 1636 (57.9) 243(75.0) 239(79.9) 116(38.9) 105(36.3) 194(52.0) 136(46.9) 238(77.5) 219(63.1) 146(48.5) Education     None or incomplete primary 1060 (37.9) 254(79.3) 220(74.1) 207(69.5) 83(28.7) 20(5.5) 5(1.8) 15(4.9) 112(32.3) 144(47.8)     Primary school or incomplete secondary 826 (29.6) 53(16.6) 60(20.2) 61(20.5) 77(26.6) 51(14.1) 18(6.5) 211(69.2) 229(66.0) 66(21.9)     Secondary school or higher 909 (32.5) 13(4.1) 17(5.7) 30(10.1) 129(44.6) 290 (80.3) 254(91.7) 79(25.9) 6(1.7) 91(30.2) Household Head of Household 1868 (66.0) 244(75.3) 237(79.0) 265(88.9) 211(73.0) 187(50.1) 155(53.5) 224(73.0) 184(53.0) 161(53.5) Household size (Mean ±SD) 6.4±3.9 10.4±5.9 7.9±3.3 6.7±2.6 4.7±2.4 4.7±2.8 4.4±2.7 5.5±2.5 6.3±2.1 7.3±3.9 Occupation     Unemployed 318(12.2) 18(6.2) 56(22.3) 22(7.4) 118(40.8) 15(5.2) 4(1.6) 50(16.3) 2(0.6) 33(11.1)     Farmer or casual labor 1027 (39.4) 239(82.7) 32(12.8) 250(84.5) 1(0.4) 11(3.8) 5(2.0) 24(7.8) 342(98.6) 123(41.3)     Employed 418(16.0) 13(4.5) 35(13.9) 14(4.7) 59(20.4) 100(34.7) 103(41.9) 49(16.0) 4(0.6) 43(14.4)     Student, self-employed or other 847 (32.5) 19(6.6) 128(51.0) 10(3.4) 111(38.4) 162(56.3) 134(54.5) 183(59.8) 1(0.3) 99(33.2) Religion     Catholic 355(12.6) 94(29.0) 83(27.7) 0(0.0) 2(0.7) 22(6.0) 12(4.2) 88(28.7) 20(5.8) 34(11.3)     Muslim 1185(42.0) 203(62.7) 201(67.0) 291(97.7) 36(12.5) 110(29.9) 60(20.8) 153(49.8) 0(0.0) 131(43.5)     Orthodox Christian 653(23.2) 1(0.3) 0(0) 6(2.0) 227(79.0) 175(47.6) 205(71.2) 6(2.0) 12(3.5) 21(7.0)     Protestant 5858(20.0) 23(7.1) 15(4.9) 1(0.3) 19(7.3) 54(14.7) 6(2.1) 59(19.2) 311(89.9) 97(32.2)     None or other 40(1.4) 3(0.9) 1(0.3) 0(0) 2(0.7) 7(1.9) 5(1.7) 1(0.3) 3(0.9) 18(6.0) PDQS 19 19 18 18 18 21 22 20 18 19     (Median, IQR) (17, 22) (17, 21) (16, 21) (15, 20) (15, 20) (19, 24) (20, 24) (17, 22) (16, 20) (16, 22) Data are shown as mean ±SD or N (percent), IQR: Interquartile range The detailed design and field methods of the round 1 ARISE Network COVID-19 rapid monitoring survey have been published elsewhere [18]. The design and methods of the round 2 survey are detailed on the Harvard University Center for African Studies website [19]. Standardized tools used for the first survey round were updated for the second survey round. The local teams from each site reviewed the tools and updated the questions accordingly. Questionnaires were translated into the local languages. The sites recruited male and female research assistants who then received extensive training on data collection procedures, including the use of telephones and tablets to obtain verbal informed consent and input participant data into an electronic data collection system (Open Data Kit). Research assistants collected information on socio-demographic characteristics, including age, sex, head of household, household size, education, and occupation of respondents. Questions regarding food pricing, food security, and the dietary intake of respondents were also asked. Outcome variables The primary outcome of interest for this study was the Prime Diet Quality Score (PDQS). We also computed the diet diversity score (DDS) as a secondary outcome. The computation of the dietary indices, the PDQS, and DDS considered both Rounds of data collection. Briefly, respondents were asked about their frequency of consumption of 20 food groups commonly consumed in study areas over the preceding seven days. Respondents were asked to recall the number of days they consumed food from a list of 20 food groups over the past 7 days period before the COVID-19 emergency and during the COVID-19 pandemic (in the round 1 survey) [20] and in round 2 we asked respondents to recall their consumption of the same 20 food groups over the previous 7 days. Prime diet quality score (PDQS) We computed the PDQS, a measure of diet quality based on reported dietary intake. Previous studies have found associations between PDQS and birth outcomes, pregnancy-related morbidities, diabetes and cardiovascular disease [21–24]. In a study using an alternative classification of the PDQS in the United States, the PDQS was negatively associated with food insecurity [25]. We classif d from the 20 food groups foods into 14 healthy food groups (dark green leafy vegetables, other vitamin A-rich vegetables including carrots, cruciferous vegetables, other vegetables, whole citrus fruits, other fruits, fish, poultry, legumes, nuts, low-fat dairy, whole grains, eggs and liquid vegetable oils) and 6 unhealthy food groups (red meat, processed meats, refined grains and baked goods, sugar-sweetened beverages (SSBs), desserts and ice cream and fried foods obtained away from home and potatoes) based on PDQS criteria determined by previous studies [21, 23]. Points were assigned for consumption of healthy food groups as: 0–1 serving/week (0 points), 2–3 servings/week (1 point), and ≥4 servings/week (2 points). Scoring for unhealthy food groups was assigned as: 0–1 serving/week (2 points), 2–3 servings/week (1 point) and ≥4 servings/week (0 points). Points for each food group were then summed to give an overall score (maximum score of 40). Dietary diversity score (DDS) We also computed the Dietary diversity score (DDS) based on the classification suggested for the Food and Agriculture Organization [FAO)’s Minimum Dietary Diversity for Women (MDD-W) index. The MDD-W has been validated and is considered a good measure of micronutrient adequacy among women [26, 27]. We grouped the food consumed by study participants into 10 food groups as follows: 1) grains, white roots and tubers and plantains, 2) legumes (beans, peas and lentils), 3) nuts and seeds, 4) dairy, 5) meats, poultry and fish, 6) eggs, 7) vitamin A rich dark green vegetable, 8) other vitamin A rich fruits and vegetables, 9) other vegetables, and 10) other fruits. We divided the reported weekly consumption of the food groups by seven to obtain the daily frequency of consumption. If the food was eaten at least once daily during the previous week, it was considered to contribute to DDS, with a higher number indicating higher dietary diversity. Exposure variables We considered as exposures of interest changes in food pricing by comparing the time before and late during the COVID-19 pandemic for staples (maize, rice, cassava and teff), pulses (beans, lentils, peas, chickpeas), fruits (e.g. bananas, oranges, any locally available fruits), vegetables (e.g. spinach, cabbage, tomatoes, onions, any locally available vegetables) and animal source foods (e.g. beef, chicken, dairy, eggs, fish). We created a binary indicator indicating an increase compared to a decrease or no change in food prices (yes or no). The changes in food pricing were determined before and after March 2020 (first round), when the first case of COVID-19 was reported and also in the second round. Changes in food prices were self-reported by the participants. We also considered food security status (e.g., went without eating for a whole day), and the impact of COVID-19 on income, employment and crop production. We considered respondent age (20–29, 30–39, ≥ 40 years), sex (female or male), education status (none or incomplete primary, primary school or incomplete secondary, secondary school or higher), respondent is head of household (yes or no), occupation (unemployed, farmer or casual laborer, employed, student, self-employed or other), religion (none, Catholic, Muslim, Orthodox Christian, Protestant or other). Other exposures included household characteristics such as household size and a wealth index score (tertiles). We computed the wealth index based on factor analysis on ownership of common household assets and other wealth-related indicators such as donkey cart, radio, television, bicycle, motorcycle ownership, access to grid electricity, improved water, fuel and roof within each country. We classified respondents in each country into wealth tertiles based on results from the country specific factor analysis findings. Statistical analysis Descriptive statistics were used to describe numerical, tabular and graphical presentations of the data. We used means and standard deviations for continuous data and frequencies and percentages for categorical data to summarize social demographic characteristics, nutrition and food security across sites and rounds. We used Generalized Estimating Equation (GEE) linear regression models [28], to assess factors associated with the PDQS at the round 2 endpoint. For those respondents with both round 1 and round 2 data, we evaluated the factors affecting diet quality in round 2 of the study. We conducted sensitivity analysis and assessed among respondents with both rounds 1 and 2, if adjusting for diet quality before the COVID-19 pandemic would change observed associations. We used GEE linear regression models to assess factors associated with the PDQS at the round 2 endpoint accounting for diet quality at round 1. Predictors of dietary quality were determined based on univariate selection. Variables associated with the outcomes at p<0.20 were included in the model. The final models include all selected covariates. Significance was determined at p<0.05. We used SAS version 9.4 for all analyses. Ethical approval and consent The study obtained ethical approval from the Institutional Review Board at Harvard T.H. Chan School of Public Health, the Kintampo Health Research Centre Institutional Ethics Committee (Ghana), Nouna Health Research Center Ethical Committee and National Ethics Committee (Burkina Faso), the Institutional Ethical Review Board of Addis Continental Institute of Public Health (Ethiopia), University of Ibadan Research Ethics Committee and National Health Research Ethics Committee (Nigeria), and the Muhimbili University of Health and Allied Sciences and National Institute for Medical Research (Tanzania). Results We analyzed data from 2,829 adults from five countries. Overall, the study included 44% female respondents. Female participation was low in Nouna (15.4%), Ouagadougou (34.3%) and Kersa (14.8%). The mean (±SD) age of the respondents was 42.4 (±12.5) years. Most of the study respondents had no education or incomplete primary school education (37.9%), and 32.5% had secondary school education or higher. The mean (±SD) household size was 6 (±4) people. Most respondents were farmers or casual laborers (39.4%) and 32.5% were students or self-employed. A more detailed description of the demographic characteristics of the study individuals can be found in Table 1. Changes in income, employment, crop production and food security status Table 2 shows the impact of COVID-19 on various factors that affect diet quality in the round 2 survey. Most respondents (49%) reported no change in income, and 37% reported loss or reduced income from farming, entrepreneurial activities, or business activities during the COVID-19 pandemic. Reported income losses from agriculture, entrepreneurial activities, or formal or informal business were highest in Kintampo (58%), Dar es Salaam (50%), and Ibadan (42%). Lost or reduced salaries were reported most in Ouagadougou (37%) and Lagos (26%). The majority of respondents, however, reported that COVID-19 had not affected their employment status (only 7% reported lost employment). Loss of employment was relatively higher in Addis Ababa (16%), Ouagadougou (14%) and Ibadan and Nouna (at least 10%). Approximately 19% of all respondents across all sites reported lower agriculture production during the COVID-19 pandemic, with the highest declines reported in the rural sites of Kersa (64%), Nouna (37%) and Kintampo (27%). Food insecurity also affected respondents, with 45% reporting that they worried about food, and 30% said they had skipped a meal. Almost 10% had gone for an entire day without eating. These three questions are components of a metric for the assessment of food insecurity, the Household Food Insecurity Access Scale (HFIAS). The number of people reporting skipping a meal was highest in Ibadan (57%), Lagos (52%) and Kintampo (46%). Going for an entire day without eating was reported in Lagos (20%) and Ouagadougou (17%). Finally, social protection was limited, with less than 1% of the study sample reporting access to food aid, and 2% reporting access to cash transfers (results not shown in tables or figures). 10.1371/journal.pone.0279610.t002 Table 2 Impact of COVID-19 on income, employment, crop production, and food security across five countries in the Round 2 study. Overall Burkina Nouna Burkina Ouaga Ethiopia Addis Ethiopia Kersa Nigeria Ibadan Nigeria Lagos Tanzania Dar es Salaam Tanzania Dodoma Ghana Kintampo     Income is unchanged 49.4 61.6 29.7 48.1 82.1 43.3 37.7 40.9 78.1 34.8     Lost/reduced salary 12.1 4.3 36.8 9.1 0.7 11.9 26.1 7.9 0.6 6.4     Lost/reduced income 36.8 34.1 33.5 36.6 16.9 41.9 33.8 49.6 21.3 57.9     Increased salary 1.7 0.0 0.0 6.3 0.3 2.8 2.5 1.7 0.0 1.0 COVID-19 impact on employment     None, unemployed 31.1 46.8 30.0 43.9 10.4 16.5 13.6 41.0 55.9 20.7     None, no change in employment status 52.6 42.4 44.6 35.0 83.6 62.3 71.0 21.5 39.7 68.9     Lost employment 7.1 10.2 13.5 15.9 0.7 10.6 3.9 3.9 1.2 4.4     Changed occupation 3.1 0.3 2.3 4.8 1.7 3.3 5.2 4.3 1.5 5.0     Other (specify) 6.2 0.3 9.7 0.4 3.7 7.3 6.3 29.3 1.7 1.0 Crop production affected by COVID-19     No 36.6 44.3 23.7 4.2 32.7 6.5 31.4 67.4 91.9 22.2     Yes, production has decreased 18.6 36.6 9.4 1.0 63.6 11.1 7.7 4.6 8.1 26.9     Yes, production has increased 1.8 1.0 3.2 1.4 0.3 3.5 1.4 0.7 0.0 4.7     No, I don’t grow any crops 43.0 18.2 63.7 93.4 3.4 78.9 59.6 27.4 0.0 46.3 Food insecurity     Worried about food 45.4 22.5 47.0 50.2 82.6 59.3 52.3 21.5 19.7 57.7     Skipped a meal 30.3 2.5 33.6 28.4 21.5 56.7 55.6 14.7 14.1 45.5     Went for entire day without eating 9.8 2.2 17.0 11.8 4.7 10.5 20.3 2.9 8.7 12.0 Respondents in rural regions of all sites were less likely to report changes in income or loss of salary compared to those residing in urban areas. Rural respondents in Ethiopia and Tanzania were also less likely to report reduced income, however, in other sites there were no rural-urban differences or income reductions affected a greater proportion of rural respondents. Across all sites except Nigeria, rural respondents were more likely to report no change in employment status. In rural sites more people reported worrying about food (except in Dodoma and Nouna), however, going for an entire day without eating was more frequently reported in urban sites (except in Tanzania). Among respondents that were in rounds 1 and 2, reports of disruptions to agricultural production were more prevalent in round 1 as at least 44% indicated that crop production had decreased. Additionally, more respondents had worried about running out of food (57%) and fewer had skipped a meal (21%) in round 1 (results not shown in tables or figures). Changes in prices for key food groups In round 2 of the study, respondents reported that for all food groups, prices reported later during the COVID-19 pandemic were higher than typical prices in the previous year (Fig 2). Higher than expected prices were noted for all food groups across all sites for at least 80% of respondents, except in Dar es Salaam and Dodoma. 10.1371/journal.pone.0279610.g002 Fig 2 The proportion of people reporting changes in prices of key food groups during the COVID-19 pandemic, as compared to this time of the year in previous years. Among respondents that were in rounds 1 and 2, at least 82% had reported increases in staple prices in round 1 compared to the time before the COVID-19 pandemic. For pulses, 77% reported increased prices, for fruits 66% reported higher prices, and 74% and 73% for vegetables and animal foods respectively. For all food groups except vegetables, higher prices were most frequently reported in round 1 (early during the COVID-19 pandemic) compared to round 2 (results not shown in tables or figures). The food groups where there were the largest differences in prevalence of higher prices were fruits and animal-source foods. Effects on food consumption and diet quality Consumption of healthy foods Table 3 shows the frequency of consumption of healthy PDQS food groups across study sites in the five countries (Round 2). Consumption of dark green vegetables was high in Dar es Salaam, Kintampo and Dodoma, where 62%, 44%, and 38% of the respondents reported consuming these food groups four or more times a week, respectively. However, in Ethiopia, at least 69% reported consumption of dark green vegetables on one day or not at all in the previous week. Consumption of other vitamin A-rich vegetables was equally low in Burkina Faso, Ethiopia, Kintampo and Dodoma. Respondents reported low consumption of citrus and other fruits across most sites, except in Dar es Salaam, Ibadan, Lagos and Addis Ababa, where at least 22% of respondents reported citrus fruit consumption four or more times a week. In Addis Ababa, Kersa and Dodoma, most respondents reported consuming fish only once or not at all in the previous week. Poultry consumption was also low across the majority of sites, with more than 70% of respondents reporting consumption less than twice in the prior week, although intake was relatively higher in Lagos and Kintampo. In Addis Ababa (46%), Dar es Salaam (40%) and Dodoma (37%), respondents reported legume consumption most frequently (≥4 times a week). Whole-grain intake was higher in rural sites of Dodoma (55%), Kersa (52%), Kintampo (46%) and Nouna (42%). Consumption of nuts and seeds was low, Dairy intake was also low with at least 58% of the respondents reporting very low consumption in Ouagadougou, Addis Ababa, Kintampo and Tanzania. Egg consumption was low in Tanzania, Burkina Faso and Ethiopia with 68% of the respondents reporting consumption less than twice in the previous week. 10.1371/journal.pone.0279610.t003 Table 3 Frequency of consumption of healthy PDQS food groups in Burkina Faso, Ethiopia, Nigeria, Tanzania and Ghana in the Round 2 study. Frequency (%) Burkina Nouna Burkina Ouaga Ethiopia Addis Ethiopia Kersa Nigeria Ibadan Nigeria Lagos Tanzania Dar es Salaam Tanzania Dodoma Ghana Kintampo Dark green vegetables < = 1 per week 31.4 48.7 86.5 69.1 18.2 22.8 3.9 16.1 28.6 2–3 times per week 52.2 43.7 11.1 24.2 55.2 50.0 33.9 45.8 27.6 4 or more times per week 16.4 7.7 2.4 6.7 26.5 27.2 62.2 38.0 43.9 Other Vit A vegetables < = 1 per week 60.2 68.7 60.2 77.5 20.4 36.9 26.1 49.3 67.8 2–3 times per week 37.0 25.7 29.8 20.5 58.7 42.8 45.9 31.7 20.9 4 or more times per week 2.8 5.7 10.0 2.0 20.9 20.3 28.0 19.0 11.3 Cruciferous vegetables < = 1 per week 45.4 41.3 63.7 40.9 48.8 43.8 48.2 62.0 71.8 2–3 times per week 51.5 48.7 31.1 42.3 35.7 36.2 42.7 28.2 16.3 4 or more times per week 3.1 10.0 5.2 16.8 15.6 20.0 9.1 9.8 12.0 Other vegetable < = 1 per week 9.9 9.7 2.1 6.4 4.8 7.9 15.3 18.7 2.0 2–3 times per week 49.1 39.3 2.1 14.4 30.6 30.3 30.6 38.3 6.3 4 or more times per week 41.1 51.0 95.9 79.2 64.6 61.7 54.1 42.9 91.7 Citrus < = 1 per week 65.1 66.3 54.7 89.6 28.4 28.6 42.4 85.0 73.8 2–3 times per week 32.4 26.0 22.8 9.4 46.1 44.1 35.5 11.2 14.6 4 or more times per week 2.5 7.7 22.5 1.0 25.5 27.3 22.2 3.8 11.6 Other fruit < = 1 per week 57.5 69.7 65.4 89.9 30.6 34.1 22.5 74.6 70.8 2–3 times per week 40.1 25.3 24.9 10.1 52.8 43.1 41.7 22.5 19.3 4 or more times per week 2.5 5.0 9.7 0.0 16.6 22.8 35.8 2.9 10.0 Fish < = 1 per week 5.3 20.3 99.0 99.0 4.8 12.1 47.6 76.7 23.3 2–3 times per week 56.2 44.7 1.0 0.7 31.1 25.2 39.4 17.8 18.3 4 or more times per week 38.6 35.0 0.0 0.3 64.1 62.8 13.0 5.5 58.5 Poultry < = 1 per week 85.2 92.3 93.4 99.0 54.7 40.3 70.7 89.6 45.2 2–3 times per week 14.5 7.3 4.8 1.0 31.6 30.0 26.7 8.9 20.6 4 or more times per week 0.3 0.3 1.7 0.0 13.7 29.7 2.6 1.4 34.2 Legumes < = 1 per week 57.4 67.3 22.5 69.1 17.7 20.7 13.7 29.7 51.2 2–3 times per week 41.1 26.0 31.5 22.8 62.2 50.0 45.9 33.4 28.6 4 or more times per week 1.5 6.7 46.0 8.1 20.1 29.3 40.4 36.9 20.3 Nuts and seeds < = 1 per week 26.9 63.0 89.6 72.2 32.4 31.4 49.2 77.8 27.9 2–3 times per week 64.5 24.7 7.6 15.8 57.4 44.8 21.5 19.3 30.9 4 or more times per week 8.6 12.3 2.8 12.1 10.2 23.8 29.3 2.9 41.2 Dairy < = 1 per week 38.0 76.7 58.8 38.9 17.7 22.4 59.3 74.1 61.1 2–3 times per week 53.4 18.7 20.8 29.2 60.6 43.8 21.5 15.3 18.9 4 or more times per week 8.6 4.7 20.4 31.9 21.7 33.8 19.2 10.7 19.9 Eggs < = 1 per week 68.2 73.7 73.7 74.5 16.6 19.0 73.0 87.6 52.8 2–3 times per week 27.2 23.7 19.4 22.5 49.9 34.1 18.6 9.5 24.3 4 or more times per week 4.6 2.7 6.9 3.0 33.5 46.9 8.5 2.9 22.9 Whole grains < = 1 per week 16.4 41.3 66.4 12.4 26.8 35.5 44.0 34.6 31.9 2–3 times per week 41.3 37.0 19.4 35.9 53.6 49.7 28.3 10.4 22.3 4 or more times per week 42.3 21.7 14.2 51.7 19.6 14.8 27.7 55.0 45.9 Liquid vegetable oils < = 1 per week 16.4 42.0 19.0 17.5 4.6 9.3 57.0 9.8 31.2 2–3 times per week 56.2 28.7 0.7 15.8 23.1 22.8 7.8 25.7 31.6 4 or more times per week 27.5 29.3 80.3 66.8 72.4 67.9 35.2 64.6 37.2 * Ouga—Ouagadougou; Burkina—Burkina Faso; Addis–Addis Ababa. Consumption of unhealthy foods Table 4 describes the frequency of consumption of unhealthy foods. In Kintampo (36%), Ibadan (29%) and Lagos (27%) respondents reported higher red meat consumption (four or more times a week). Respondents in urban sites (Addis Ababa 22%, Lagos 22% and Dar es Salaam 18%) had relatively higher consumption of sugar-sweetened beverages, compared to rural sites. Processed meat was not commonly consumed, and sweets intake was low across all sites but slightly higher in Nigeria and Dar es Salaam. Finally, intake of refined grains was frequent across all study sites, and the majority of respondents in Kintampo (67%) reported consuming potatoes, roots and tubers four or more times a week. 10.1371/journal.pone.0279610.t004 Table 4 Frequency of consumption of unhealthy PDQS food groups in Burkina Faso, Ethiopia, Nigeria, Tanzania and Ghana in the Round 2 study. Frequency (%) Burkina Nouna Burkina Ouaga Ethiopia Addis Ethiopia Kersa Nigeria Ibadan Nigeria Lagos Tanzania Dar es Salaam Tanzania Dodoma Ghana Kintampo Red meat 4 or more times per week 6.2 6.3 11.8 0.0 29.0 27.3 6.8 8.9 35.6 2–3 times per week 59.6 24.3 25.6 0.7 44.2 46.2 34.9 26.5 23.3 < = 1 per week 34.5 69.3 62.6 99.3 26.8 26.6 58.3 64.6 41.2 Processed meats 4 or more times per week 0.0 0.3 0.0 0.0 3.8 6.9 0.3 0.0 4.0 2–3 times per week 5.9 0.3 0.0 0.0 27.4 29.0 4.2 0.0 9.6 < = 1 per week 94.1 99.3 100.0 100.0 68.9 64.1 95.4 100.0 86.4 Refined grains 4 or more times per week 20.7 30.7 39.1 46.0 53.4 54.8 52.8 31.4 59.8 2–3 times per week 49.1 42.0 29.8 41.3 38.9 36.2 28.3 21.9 21.9 < = 1 per week 30.3 27.3 31.1 12.8 7.8 9.0 18.9 46.7 18.3 SSBs 4 or more times per week 2.8 2.0 21.5 2.4 16.9 22.4 18.2 5.2 12.6 2–3 times per week 42.0 20.3 13.8 22.2 46.1 36.2 29.6 12.7 21.9 < = 1 per week 55.3 77.7 64.7 75.5 37.0 41.4 52.1 82.1 65.5 Sweets and ice cream 4 or more times per week 8.6 5.0 3.5 3.4 12.1 12.4 3.3 14.1 6.3 2–3 times per week 38.6 16.3 6.2 11.4 41.8 32.1 35.8 23.1 11.6 < = 1 per week 52.8 78.7 90.3 85.2 46.1 55.5 60.9 62.8 82.1 Potatoes, roots and tubers 4 or more times per week 1.9 0.3 10.7 18.8 23.9 29.0 37.1 14.4 66.5 2–3 times per week 29.6 14.0 35.6 14.4 60.3 46.2 34.9 43.8 17.3 < = 1 per week 68.5 85.7 53.6 66.8 15.8 24.8 28.0 41.8 16.3 *SSBs—Sugar Sweetened Beverages; Ouga—Ouagadougou; Burkina—Burkina Faso; Addis–Addis Ababa. Changes in overall diet quality Fig 3 shows changes in the frequency of consumption of PDQS food groups comparing the time before the COVID-19 pandemic to the first (July to November 2020) and second (July to December 2021) round surveys. The frequency of consumption of both healthy and unhealthy PDQS food groups was lower in Round 1 and Round 2 surveys (during the COVID-19 pandemic), compared to recalled consumption prior to the pandemic (except for SSB). In general, consumption declined most during the Round 1 survey compared with the period before COVID-19 and started to improve in Round 2. Data were captured around the same months for both surveys, which may decrease the influence of seasonality. However, there were exceptions. The frequency of consumption of dark green vegetables declined by 1 day and liquid vegetable oil consumption by 0.5 days per week on average in Round 2 compared to Round 1. Other food groups for which consumption was lower in Round 2 include poultry, nuts, and seeds. 10.1371/journal.pone.0279610.g003 Fig 3 Changes in frequency of consumption of PDQS food groups comparing the time before the time of COVID-19 to the first and second round surveys. Overall diet quality. The median PDQS (IQR) was 19 [17, 22], and PDQS was highest in Nigerian sites and lowest in Ouagadougou, Addis Ababa, Kersa and Dodoma. Fig 4 shows mean PDQS before COVID-19, early during the time of COVID-19 in 2020 (Round 1), and later during the COVID-19 pandemic in 2021 (Round 2). Overall, PDQS had returned to pre-COVID-19 levels across sites by Round 2 of the survey but remained low. There are, however, site-specific differences. In Nouna and Addis Ababa, PDQS was slightly higher in Round 2 of the survey compared to previous times. In all other sites, PDQS was lower in Round 2, compared to before the pandemic. Tanzania and Ghana did not participate in round 1 data collection. 10.1371/journal.pone.0279610.g004 Fig 4 Mean Prime Diet Quality Scores (PDQS) before, early and later in the COVID-19 pandemic across five countries. For the DDS food groups, the majority of respondents reported decreased consumption, with notable declines for dark green vegetables, other vegetables, staples, meats, beans and peas and fruits (S1 Fig). Further, the DDS for all sites at the different survey rounds are shown in S2 Fig. Overall DDS was lower in the first survey in 2020 and improved at the time of the second survey in 2021 in most sites, to levels closest to pre-COVID estimates. However, in Burkina Faso sites, in Kersa (Ethiopia) and Ibadan (Nigeria) DDS was lower in the second round compared to pre-COVID times (S2 Fig). Table 5 shows the factors associated with diet quality across the five countries in Round 2 of the ARISE study. In a multivariate analysis, respondents residing in Nigeria (estimate: 2.84, 95% CI: 2.26,3.41) and Ghana (estimate: 0.93, 95% CI: 0.37,1.49) had higher PDQS compared to those residing in Burkina Faso. Respondents aged 30–39 years (estimate: 0.77, 95% CI: 0.35,1.19) and those 40 years or older (estimate: 0.72, 95% CI: 0.30,1.13) had higher PDQS compared to those aged 29 years or younger. Male respondents (estimate: -0.54, 95% CI: -0.88, -0.20) and those with no education had poorer diet quality (estimate: -0.40, 95% CI: -0.76, -0.03), and secondary school or higher education was associated with a higher PDQS (estimate: 0.73, 95% CI: 0.32,1.15) compared to having primary school education. Respondents who identified as Catholic (estimate: 0.66, 95% CI: 0.12,1.19) or Muslim (estimate: 0.51, 95% CI: 0.10,0.93) reported higher PDQS compared to Orthodox Christian respondents. Farmers and those engaged in casual labor reported lower PDQS (estimate: -0.60, 95% CI: -1.11, -0.09) compared to those that were formally employed. 10.1371/journal.pone.0279610.t005 Table 5 Factors associated with PDQS in Round 2 of the ARISE study across five countries.   Univariate Multivariate modela Multivariate secondary analysisb Estimate (95% CI) Estimate (95% CI) Estimate (95% CI) PDQS before COVID-19 0.16(0.11,0.22)*** - 0.09(0.03,0.14)* Country     Burkina Faso ref Ref ref     Ethiopia -0.78(-1.17,-0.39)*** -0.37(-0.86,0.11) -0.85(-1.54,-0.15)*     Nigeria 3.11(2.72,3.49)*** 2.84(2.26,3.41)*** 1.93(0.82,3.04)**     Tanzania 0.53 (0.14,0.91)* -0.13(-0.74,0.49) -     Ghana 0.82(0.34,1.30)** 0.93(0.37,1.49)** - Location     Rural area -0.11(-0.39,0.11) -0.13(-0.51,0.24) 0.11(-0.66,0.88)     Urban ref ref ref Staple prices     No change or decreased ref ref ref     Increased 0.32(-0.01,0.65) -0.01(-0.63,0.62) 0.05(-1.28,1.37) Pulse prices     No change or decreased ref ref ref     Increased 0.32(-0.01,0.66) 0.21(-0.37,0.79) 0.78(-0.37,1.94) Fruits prices No change or decreased ref ref ref Increased 0.27(-0.04,0.58) -0.45(-0.98,0.08) -1.43(-2.33,-0.52) ** Vegetable prices     No change or decreased ref ref ref     Increased 0.27(-0.03,0.60) -0.15(-0.71,0.41) 0.38(-0.64,1.39) Animal-source foods prices     No change or decreased ref ref ref     Increased 0.49(0.13,0.84)* 0.41(-0.10,0.92) -0.31(-1.42,0.81) Age (years)     20–29 ref ref ref     30–39 0.56(0.11,1.01)* 0.77(0.35,1.19)*** 0.35(-0.53,1.22)     ≥ 40 0.43(0.02,0.82)* 0.72(0.30,1.13)** -0.04(-0.91,0.84) Sex     Female ref ref ref     Male -0.36(-0.64,-0.08)* -0.54(-0.88,-0.20)** 0.30(-0.36,0.96) Education     None or incomplete primary -0.65(-0.99,-0.33)*** -0.40(-0.76,-0.03)* -0.17(-0.77,0.43)     Primary school or incomplete secondary ref ref ref     Secondary school or higher 1.72(1.37,2.06)*** 0.73(0.32,1.15)** 1.25(0.47,2.03)* Household head -0.64(-0.93,-0.35)*** 0.12(-0.24,0.47) 0.08(-0.58,0.74) Household size -0.06(-0.10,-0.02)** 0.02(-0.01,0.07) 0.01(-0.04,0.07) Religion     None 0.01(-1.18,1.21) -0.48(-0.64,1.61) -2.52(-5.23,0.20)     Catholic -0.56(-1.05,-0.08)* 0.66(0.12,1.19)* -0.79(-1.80,0.21)     Muslim -0.89(-1.25,-0.54)*** 0.51(0.10,0.93)* -0.49(-1.30,0.33)     Orthodox Christian ref ref ref     Protestant or other -0.84(-1.26,-0.42)*** 0.27(-0.25,0.78) -0.59(-1.67,0.48) Occupation     Unemployed -2.20(-2.69,-1.70)*** -0.39(-0.93,0.16) 0.21(-0.69,1.12)     Farmer or casual labor -2.06(-2.43,-1.70)*** -0.60(-1.11,-0.09)* 0.52(-0.35,1.39)     Employed ref ref ref     Student, self-employed or other -0.65(-1.03,-0.27)** 0.01(-0.37,0.40) 0.98(0.27,1.70)* Effects of COVID-19     None, unemployed -0.94(-1.25,-0.62)*** -0.38(-0.69,-0.06)* -0.33(-0.90,0.23)     None, no change in employment status ref ref ref     Lost employment -0.01(-0.55,0.57) 0.44(-0.09,0.98) -0.31(-0.48,1.11)     Changed occupation 0.18(-0.64,1.00) 0.12(-0.64,0.88) 0.20(-1.35,1.76)     Other (specify) 0.82(0.23,1.42)* 0.42(-0.15,1.00) -0.58(-1.94,0.79) COVID-19 Impact on income     Income is unchanged ref ref ref     Lost or reduced salary from employer 0.86(0.40,1.32)** -0.19(-0.67,0.30) -0.85(-1.62,-0.08)*     Lost or reduced income farming business 0.37(0.06,0.68) -0.13(-0.45,0.19) -0.51(-1.07,0.05)     Increased salary from employer or farming business 0.03(-1.11,1.17) -0.36(-1.40,0.69) -0.39(-2.16,1.37) Food security     Went without eating for a whole day (past month)     No ref ref ref     Yes -0.71(-1.17,-0.24)** -1.10(-1.53,-0.67)*** -1.28(-2.03,-0.52)** Own crop production affected     Unchanged ref ref ref     Production has decreased -1.06(-1.46,-0.67)*** -0.87(-1.28,-0.46)*** -1.04(-1.68,-0.41)**     Production has increased 1.16(0.10,2.22)* 0.18(-0.81,1.17) 0.58(-1.13,2.29)     Does not farm -0.22(-0.53,0.09) -1.38(-1.74,-1.02)*** -1.23(-1.84,-0.62)*** Wealth index     Wealth tertile 1 0.02(-0.31,0.35) -0.21(-0.51,0.10) 0.14(-0.39,2.26)     Wealth tertile 2 1.07(0.71,1.42)*** 0.48(0.14,0.81)* 0.43(-0.16,1.02)     Wealth tertile 3 ref ref ref Acronyms: PDQS: Prime Diet Quality Score * <0.05 **<0.01 ***<0.001 a/Based on Round 2 data only b/ Model restricted to individuals with round 1 and 2 data (N = 929) and adjusted for PDQS score prior to the COVID-19 pandemic (measured at Round 1). Unemployed respondents had on average lower diet quality (estimate: -0.38, 95% CI: -0.69, -0.06) compared to those whose employment status was unchanged during the COVID-19 pandemic. Those reporting going for an entire day without eating had lower PDQS than those who did not (estimate: -1.10, 95% CI: -1.53, -0.67). Lower crop production (estimate: -0.87, 95% CI: -1.28, -0.46) or not engaging in farming (estimate: -1.38, 95% CI: -1.74, -1.02) were associated with lower PDQS compared to having unchanged levels of crop production. Being in the middle tertile of the wealth index (estimate: 0.48, 95% CI: 0.14,0.81) was associated with higher PDQS compared to being in the highest tertile. In sensitivity analysis controlling for PDQS before the COVID-19 pandemic and restricted to the three countries with Round 1 data, PDQS before the COVID-19 pandemic was positively associated with PDQS later in the Round 2 survey (estimate 0.09, 95% CI: 0.03,0.14) after adjusting for other factors. We found country-level differences in PDQS, with respondents in Nigeria having higher PDQS (estimate 1.93, 95% CI: 0.82,3.04) and those in Ethiopia having lower PDQS (estimate: -0.85, 95% CI: -1.54, -0.15) compared to those residing in Burkina Faso. Higher fruit prices were associated with lower PDQS (estimate: -1.43, 95% CI: -2.33, -0.52). Secondary school education or higher was associated with higher diet quality (estimate: 1.25, 95% CI: 0.47,2.03) compared to primary school education, as was being self-employed or a student (estimates: 0.98, 95% CI:0.27,1.70) compared to being employed. Lower or reduced salary (estimate -0.85, 95% CI: -1.62, -0.08), lower crop production (estimate: -1.04, 95% CI: -1.68, -0.41) and not farming (estimate -1.23, 95% CI: -1.84, -0.62) were associated with lower diet quality after adjusting for other factors. Discussion We evaluated the impacts of COVID-19 on factors known to influence food consumption such as food prices, food security and crop production, and how these and other factors affected diet quality two years into the COVID-19 pandemic in Burkina Faso, Ethiopia, Ghana, Nigeria and Tanzania. We found that food prices remained higher than expected during the pandemic, consumption of healthy food groups remained low, and that diet quality showed signs of slight recovery since 2020 but remained poor. Food insecurity, low agriculture production, and some occupations were negatively associated with PDQS, while higher socioeconomic status was positively associated with diet quality. In this study, we found evidence of the continued impact of COVID-19 on food prices, with the majority of respondents indicating that food prices were higher than before the pandemic. In a study conducted across several countries early in the COVID-19 emergency in June 2020, there was evidence that the prices of maize, sorghum, imported rice and rice in SSA were higher than expected [29]. Price increases were attributed to movement restrictions and lockdowns, with economic factors such as exchange rate, and inflation also contributing [29]. Other studies indicated that a global slowdown due to COVID-19 and its related impact on Gross Domestic Product (GDP) could lead to impacts on food security and consumption, through increased unemployment, reduced trade and decreased production [30]. In this study, we found that lower agricultural production, not participating in crop production, as well as food insecurity, were associated with the consumption of lower-quality diets. The impacts of COVID-19 on agricultural production and food security have also been documented in previous studies. In one study, the COVID-19 pandemic was shown to impact bean production by small-scale farmers in SSA, with disruptions in access to seeds, inputs, and labor, among other things [31]. Lower agricultural production can impact the consumption of quality diets by decreasing the availability of foods and increasing prices for nutritious foods. In Nigeria, disruptions due to COVID-19 restrictions early in the pandemic included lockdowns, curfews and travel restrictions in urban areas and these affected the ability of local authorities to support agricultural production, disrupted trade and decreased access to healthy diets [32]. Although restrictions across countries have been lifted, the lasting impact may be due to impacts on regional and international food trade that have affected food prices [32]. We found that diet quality and diversity scores showed initial dips and later showed small increases later during the COVID-19 crisis; sometimes returning closer to pre-COVID states. However, despite these increments, it was still clear that consumption of key micronutrient-rich food groups such as dark green vegetables, other vegetables, poultry, nuts and seeds, and liquid vegetable oils had not recovered to pre-COVID-19 levels. Similar observations were noted for red meat consumption. Further, the noted recovery of diet quality scores is small and both diet quality and diversity remain low across all the countries assessed. A study in Mexico also investigated the factors associated with diet quality early and later during the COVID-19 pandemic and found that the consumption of healthy foods and food security declined, however, consumption of unhealthy foods increased [33]. In another study in the United States, individuals reporting food insecurity also had lower consumption of both healthy and unhealthy foods [34]. In our study, there were country-specific differences with greater recovery in diet quality experienced in sites in Nigeria and Addis Ababa. In contrast, other sites continued to experience declining diet quality and diversity. These differences could reflect the differences in the states’ capacity to mitigate against the COVID-19 impacts and the availability of social protection. In this study, however, the availability of social protection to address the impacts of COVID-19 was very limited. The impacts of COVID-19 on the African continent, particularly on food security and diets, are expected to be dire, given that purchasing power and social safety nets are already limited [35]. In several countries, the impacts of COVID-19 on food prices and quality have been due to decreased economic activity, lower income and declines in employment and income. We found that loss of employment was infrequently reported. However, nearly 50% of the respondents indicated that they had lost income from employment, farming or business activities. The impacts of the loss of income from employment and income-generating activities on diets can be through loss of purchasing power [12]. The effects of COVID-19 related income shocks on food security and diets have been reported in Uganda and Kenya, with poor households most affected [36, 37]. For example, loss of income during COVID-19 was reported by farmers in Burundi (36%), Uganda (20%), and Kenya (3%). In our study, loss of income from farming and business was greatest in Kintampo, Dar es Salaam and Ibadan, with rural areas more likely to experience a loss due to impacts on agriculture. Previous studies have indicated rural areas and farming communities might be more resilient to the impacts of COVID-19 due to shorter value chains and reliance by smallholder farmers on their production [12]. In our study, the impacts on rural and urban locations depend on the country context; for example, the largest declines in DDS were observed in urban sites such as Ouagadougou and rural sites of Kersa and Nouna. We found that the older or more educated respondents with higher socioeconomic status had higher diet quality in the study. We also found that those engaged in farming or casual labor in our study tended to have lower diet quality, reinforcing this observation. These individuals are less likely to have enough savings to cope with shocks such as the COVID-19 pandemic and may have been highly vulnerable even before the shock. Economic stimulus packages and investment in sectors such as agriculture have been proposed as possible solutions to address the challenges posed by the COVID-19 pandemic in Africa and to promote recovery [31]. However, these were not readily available to communities and respondents in our study settings. Overall, our study findings are that during the COVID-19 pandemic respondents reported food prices increased and lower diet quality diets. Those living in urban areas were affected more compared to rural communities in terms of price increases. In rural areas more participants engaged in agricultural production and if production was maintained, the COVID-19 effects were less likely to be reported. Those living in rural areas may be protected from market disruptions. However, those that reported lower agriculture production or no agriculture production had poorer diets. Furthermore, COVID-19 related disruptions differed among the countries and locations surveyed, indicating an important role of prevailing local conditions prior to the pandemic. Moreover, those who were already vulnerable, for example with low education, farmers and causal laborers were more likely to have poorer diets during the pandemic. Studies have suggested that the greatest impact of COVID-19 in the African context has been felt by those who were already vulnerable such as those of low social economic status eg informal workers, those reliant on daily income or those facing social and economic vulnerability [12]. This was evident in our study. Our findings are also consistent with the findings of a systematic review of the impact of COVID-19 on diet quality and nutrition in LMICs (including Ethiopia and Nigeria) in 2021 that found that impacts varied in intensity and duration and were informed by the stringency and duration of national COVID-19 shutdowns and extent of reliance on local compared to international food markets [12]. Thus urban areas which are more integrated with external markets may be more vulnerable to price fluctuations, Additionally, locations in rural areas and those with shorter value chains such as small-scale farming households were more resilient and were less adversely affected by COVID-19 related disruptions [12]. Our study has several strengths. First, we conducted a repeated cross-sectional study in 3 countries, allowing us to track changes in food security and diet intake at different times during the COVID-19 pandemic. Further, we collected data on the same individuals in some contexts. A limitation of the study is that we had a loss to follow-up in round 2 of the survey, limiting our ability to look at changes over time prospectively for more households. Loss to follow-up is, however, expected in telephone surveys, especially in our study contexts. Also, there may be measurement errors as a result of self-reported dietary intake and food pricing. However, we do not think that measurement error will vary by the outcomes. Conclusion Our study finds that households in study countries reported higher food prices, and lower diet quality during the COVID-19 pandemic. We found that effects differed by country and local contexts, suggesting the need for a nuanced understanding of the influence of COVID-19 on diets. Those who were already vulnerable before the pandemic such as casual laborers and the unemployed were more likely to be affected, as well as those not engaged in farming and more reliant on markets, although this differed by country and rural and urban location. Those whose agriculture production was unaffected and likely to subsist on their own production may be protected from market disruptions due to the COVID-19 pandemic. Further, while there was some recovery in the quality of diets consumed as the COVID pandemic continued, recovery lagged behind for the consumption of some nutrient-rich foods. Dietary quality and diversity were already low prior to the pandemic and remain a cause for concern. All these factors including higher than usual food prices and poor diet quality experienced in the study have health implications. Efforts should continue to improve diet quality through mitigation measures, including social protection for people for sustained nutrition recovery. More importantly, systematic efforts to improve the underlying causes of poor diet quality through transforming food system value chains through relevant programs and national policies are critical. Supporting information S1 Fig Change in consumption of MDDW food groups prior to and in Round 2 of the COVID-19 study. (TIF) Click here for additional data file. S2 Fig Mean diet diversity scores (DDS) prior to, early during COVID-19 and later in the COVID-19 pandemic across 5 countries. (PUB) Click here for additional data file. We thank all study participants and data collectors for contributing to this study. The survey team in Ghana is grateful for support from the Kintampo Health Research Centre of Ghana Health Service, and the community leadership of Kintampo North Municipality and Kintampo South District. We acknowledge institutional support from Harvard T.H. Chan School of Public Health, Boston, MA; Harvard University Center for African Studies, Boston, MA; Heidelberg Institute of Global Health, Germany and the George Washington University Milken Institute of Public Health, Washington, DC. Abbreviations ARISE African Research, Implementation Science and Education Network DDS Dietary diversity score DUCS Dar es Salaam Urban Cohort Study HDSS Health and Demographic Surveillance System MDD-W Minimum Dietary Diversity for Women PDQS Prime Diet Quality Score SSA Sub-Saharan Africa SSBs sugar-sweetened beverages 10.1371/journal.pone.0279610.r001 Decision Letter 0 Al-Mahish Mohammed Academic Editor © 2023 Mohammed Al-Mahish 2023 Mohammed Al-Mahish https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version0 23 Jan 2023 PONE-D-22-33938The COVID-19 pandemic and its prolonged impacts on food prices, food consumption and diet quality in sub-Saharan AfricaPLOS ONE Dear Dr. Madzorera, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. In addition to addressing reviewers'' comments, please address the following comments as well:The link in line 183 is not working. It is suggested to move this link in the reference list, write the accessed date, and cite the needed information accordingly. There is a complete absence of literature review section. Please add a literature review section. The results of the study should be compared and contrasted to other published studies. The authors are urged to use paired t-test/Wilcoxon test to compare their variables of interest before and after COVID pandemic.  The mathematical equations of the estimated models should be clearly specified in the statistical analysis section. 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Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ********** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ********** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: Yes ********** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ********** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: • I think the manuscript focused more on the diet quality rather than food price, as stated in Line 196. Therefore, I suggest to change the title to: “The COVID-19 pandemic and its impacts on diet quality and food prices in sub-Saharan Africa” • Line 69: in abstract, Dietary diversity score (DDS), should be mentioned in the Method used • Line 69-70 could be done as one sentence, no need for repetition (data collected) • Line 76-84: there is no need to write the numbers here, as they are mentioned inside the text, additional study results could be stated here • Introduction: was written systematically • Line 122-125: Reference order would be 12-13-14, not 12-14-13 • Line 150-151: All nine sites from the five countries (Figure 1) were included in the second round of data collection. (No need for repetition) • Line 160: Study design, is clearly described and detailed, still a sentence is needed to clearly stated the total sample size in each round as stated in line 273 • Line 195 and 217: It is mentioned that the diet diversity score (DDS) was computed, still the DDS results are not shown on the results and discussion sections • Line 204-205: In addition to mention previous study using PDQS, it is recommended to mention some studies used the same dependent variables to compare results • Line 242: head of household (yes or no) what does (Yes) indicates? More explanation is needed • Line 246: Wealth index, more elaboration is needed of factor analysis • Line 252: Why GEE was adopted as an analytical tool? A brief justification is needed, with model specification • Line 282 Table 1: for easy follow up and understanding, it is better to write only the percentage not numbers inside the Table • Line 299-303 and line 239: These questions (worried about food, skipped a meal, gone for an entire day without eating.) are related to food consumption behaviors which is one of the types of food security measurement. Therefore, it is better to rephrase the sentences to reflect that. The same comment is applicable to Table 2 • Line 314: Table 2: - There is no need to repeat “Impact of COVID-19” on the subheading as it was reflected of Table title. - Table title it is better to write five rather than 5. - Is there any significance difference between these percentages???? may be another test is required • Line 324: All figures numbers should be revised to match the figures attached at the end of the manuscript • Line 335: It is better to shorten the Fig 2 title • Lines 340 and 360: Also applicable to line 373 - “consumption of healthy PDQS food groups”, is it a right scientific terminology???? - Unit time is required with PDQS (week or 24 hrs) • Line 378-380: What is the source for data for” consumption of food groups at the time before the COVID-19 pandemic”, it should be stated in material and method section • Line 608: “2020;4:1401-.” Please delete the dash (-) at the end • Line 637: The publication year is missing Reviewer #2: The paper addresses an important recent topic and covered sub-researched countries in Africa. The writing is good and easy to follow; however, there is a need to improve some parts as discussed below. It would be also helpful if page number has been added to the document. Introduction: The introduction should clearly state the main contributions of the study to strengthen the argument and ensure the importance of this work related to other relevant studies. A clearly stated and well-defined contribution is the merit of any research. Authors only indicated “Therefore, it is important that as the COVID-19 pandemic persists we continue to assess its impact on the nutrition and health of the households in SSA (12).” Then, the authors have details the strengths of the work on page 39 in the discussion. These strengths /contributions should also be indicated in the introduction. Please add to the study design justification that the differences between the two samples/rounds will not cause issues in comparisons (across countries, across rounds if applicable) in the results section, especially that you conducted country-level differences in PDQS. Conclusion should include a summary of what the study is about, not only the outcomes. Minor comments are as follows: Table font is different than the text font and style. Texts in tables and figures should be in black to be more readable, it is in gray now. Page 22: “Loss of or reduced salaries” I think you meant “lost or reduced salaries” Page 23 and 24: Not sure what do “(not shown) and “(results not shown)” mean? If you meant the results are not shown in the Table, please state that clearly. ********** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. 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Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. 10.1371/journal.pone.0279610.r002 Author response to Decision Letter 0 Submission Version1 17 May 2023 PONE-D-22-33938 The COVID-19 pandemic and its prolonged impacts on food prices, food consumption and diet quality in sub-Saharan Africa PLOS ONE Dear Dr. Madzorera, Comment: Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Response: We would like to thank the Editor and Reviewers for your review of the manuscript and feedback provided. We have found the review process and insights provided helpful and it has helped us improve the quality of our manuscript. We think our audience will certainly benefit from the improved paper. Comment: In addition to addressing reviewers'' comments, please address the following comments as well: 1. The link in line 183 is not working. It is suggested to move this link in the reference list, write the accessed date, and cite the needed information accordingly. Response: We thank the Editor for this comment. As suggested, we have replaced the link with a reference and in the manuscript. We have updated the text as follows, “The design and methods of the round 2 survey are detailed on the Harvard University Center for African Studies website (1).” (Lines 696-697, page 10) Comment: 2. There is a complete absence of literature review section. Please add a literature review section. Response: We acknowledge the Editor’s comments and agree that this is important. To address this concern, we have added additional literature to the manuscript, mainly in the discussion section. The changes are tracked in the resubmitted manuscript. Because there was limited literature on the topic prior to and during our study, we are limited in the literature we can add to the introduction section. Comment: 3. The results of the study should be compared and contrasted to other published studies. Response: We thank the reviewers for this comment. Comparisons to other studies were provided where available in the discussion section. It is important to note that unlike in other regions, studies on this topic have been limited in the African context and the literature does not cover in detail all the topics presented in the paper. Therefore, our review of literature is limited to studies available at the time. We have also added a few additional reports that we could find on the topic in the discussion section. Comment: 4. The authors are urged to use paired t-test/Wilcoxon test to compare their variables of interest before and after COVID pandemic. Response: We thank the Reviewers for this suggestion. We have carefully considered this suggestion and do agree that what the authors suggest is an interesting research question. However, because of our study design and the nature of data collected, we believe it may not be advisable to apply the suggested method. We asked respondents to recall their usual consumption of foods prior to the COVID-19 pandemic and in the first and second round (or before and during COVID). With the suggested approach we would be evaluating differences in consumption prior to COVID-19 and later during the pandemic without considering possible confounding. However, given that we rely on self-reported/recalled data ( with a longer recall period for pre-COVID 19 diets), we worry about recall bias and confounding. This analysis proposed is possible if we wanted to restrict respondents who appeared in both rounds of data collection, calculate PDQS as a continuous variable, and compare between rounds using paired t-tests. However, we believe that the secondary analysis in table 5 is even better because we as we controlled for other variables. We believe this sufficiently addresses the concerns raised. Comment: 5. The mathematical equations of the estimated models should be clearly specified in the statistical analysis section. Response: We thank the reviewer for this comment. In the field of epidemiology, it is not the norm to include the mathematical models in the manuscript itself. Our target audience would not be expecting this. Nevertheless, we present the model for the main analysis shown in table 5 below. YMean PDQS=�+�1X1+�2X2+�3X3+�4X4+�5X5+�nXn+� Comment: 6. The conclusion is very brief and weak, and hence It should be improved. Response: We have noted this feedback. We have strengthened the conclusion as follows, Overall, our study reveals that many on the African continent experienced higher foods prices, and lower diet quality during the COVID-19 pandemic. We found that effects differed by country and local contexts, suggesting the need for nuanced understanding of the influence of COVID-19. Further it was evident that those who were already vulnerable before the pandemic were more likely to be affected, as well as those not engaged in farming and more reliant on markets, although this differed by country and rural and urban location. Those whose agriculture production was unaffected and likely to subsist on their own production may be protected from market disruptions due to the COVID-19 pandemic. We found that while there was some recovery for diet quality, recovery lagged behind for consumption of some nutrient rich foods and that dietary diversity and quality were already low prior to the pandemic and remain a cause for concern. All these factors including higher than usual food prices and poor diet quality experienced in the study have health implications. Efforts should continue to improve diet quality through mitigation measures, including social protection for people for sustained nutrition recovery. Further, more systematic efforts to improve the underlying causes of poor diet quality through transforming food systems value chains through relevant programs and national policies is critical. (lines 1120-1141, pages 32-33) Comment: 7. Please make sure the article meets PLOS ONE’s style including the referencing style. Please submit your revised manuscript by 25/2/2023. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols. Response: We thank the Editor for this additional guidance. We are submitting the documents required and will ensure that all documents adhere to the provided guidelines. We look forward to receiving your revised manuscript. Kind regards, Mohammed Al-Mahish Academic Editor PLOS ONE Journal Requirements: When submitting your revision, we need you to address these additional requirements. Comment: 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf Response: We thank the reviewer for this feedback. We have formatted the manuscript using the guidelines provided. Comment: 2. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match. When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section. Response: We thank the reviewer. This comment has been addressed above. We have removed all information on funding sources from the manuscript as advised. We consent to the use of the following statement as our Funding statement: "This work was supported by institutional support from Harvard T.H. Chan School of Public Health, Boston, MA (WF); Harvard University Center for African Studies, Boston, MA (WF); Heidelberg Institute of Global Health, Germany (TB), and the George Washington University Milken Institute of Public Health, Washington, DC (ES). The funders have no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." Comment: 3. Thank you for stating the following in the Funding Source Section of your manuscript: "This work was supported by institutional support from Harvard T.H. Chan School of Public Health, Boston, MA; Harvard University Center for African Studies, Boston, MA; Heidelberg Institute of Global Health, Germany, and the George Washington University Milken Institute of Public Health, Washington, DC." We note that you have provided funding information that is not currently declared in your Funding Statement. However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form. Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows: "This work was supported by institutional support from Harvard T.H. Chan School of Public Health, Boston, MA (WF); Harvard University Center for African Studies, Boston, MA (WF); Heidelberg Institute of Global Health, Germany (TB), and the George Washington University Milken Institute of Public Health, Washington, DC (ES). The funders have no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." Response: We thank the Reviewers for this feedback. Please see response above. Our funding statement has been adjusted as follows: "This work was supported by institutional support from Harvard T.H. Chan School of Public Health, Boston, MA (WF); Harvard University Center for African Studies, Boston, MA (WF); Heidelberg Institute of Global Health, Germany (TB), and the George Washington University Milken Institute of Public Health, Washington, DC (ES). The funders have no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." Comment: Please include your amended statements within your cover letter; we will change the online submission form on your behalf. Response: We have included the statement in our cover letter as suggested. Comment: 4. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. 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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ________________________________________ 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Comment: Reviewer #1: • I think the manuscript focused more on the diet quality rather than food price, as stated in Line 196. Therefore, I suggest to change the title to: “The COVID-19 pandemic and its impacts on diet quality and food prices in sub-Saharan Africa” Response: We have changed the title of the manuscript as advised. Comment: • Line 69: in abstract, Dietary diversity score (DDS), should be mentioned in the Method used Response: We have added the Dietary diversity score (DDS) to the methods section. Comment: • Line 69-70 could be done as one sentence, no need for repetition (data collected) Response: We have removed the repeated text “ data collected”. Comment: • Line 76-84: there is no need to write the numbers here, as they are mentioned inside the text, additional study results could be stated here Response: We thank the Reviewer for this suggestion. However, in our field of study of epidemiology, it is expected to include some numbers in the results section. We have edited the text to remove numbers when deemed not necessary. Comment: • Introduction: was written systematically Response: We thank the Reviewer for this comment. Comment: • Line 122-125: Reference order would be 12-13-14, not 12-14-13 Response: We thank the Reviewer for noticing this. We have fixed this issue with references. Comment: • Line 150-151: All nine sites from the five countries (Figure 1) were included in the second round of data collection. (No need for repetition) Response: We have deleted the statement as correctly noted by the Reviewer. Comment: • Line 160: Study design, is clearly described and detailed, still a sentence is needed to clearly stated the total sample size in each round as stated in line 273 Response: We thank the reviewer for this point. The samples collected in round 2 of the study for each site are shown in Table 1 page 14. Participation in round 1 and 2 of the survey is shown in the table below. We have included this table as a supplementary table 1 in the manuscript and added text on this. “S Table 1 shows the number of participants in surveys 1 and 2 of the study.” (Line 386, page 9) S Table 1: Proportion of participants participating in survey rounds Proportion of participants participating in survey rounds Country/Region Burkina Faso Nouna Burkina Faso Ouaga Ethiopia Addis Ethiopia Kersa Nigeria Ibadan Nigeria Lagos Tanzania Dar es Salaam Tanzania Dodoma Ghana Kintampo Total Round 2 109 33.64 125 41.53 122 42.21 117 39.26 226 60.59 129 44.48 307 100.00 347 100.00 301 100.00 1783 63.00 Round 1 215 66.36 176 58.47 167 57.79 181 60.74 147 39.41 161 55.52 0 0.00 0 0.00 0 0.00 1047 37.00 Total 324 11.45 301 10.64 289 10.21 298 10.53 373 13.18 290 10.25 307 10.85 347 12.26 301 10.64 2830 100.00 Comment: • Line 195 and 217: It is mentioned that the diet diversity score (DDS) was computed, still the DDS results are not shown on the results and discussion sections Response: We thank the Reviewer for this observation. The findings from the DDS analysis were shown in the supplementary tables but not described in the methods section. We have included the findings of the analysis in the results section as shown below. As it is a secondary analysis we have not spend much time describing the findings in the discussion section. We hope this addresses the reviewer’s concerns. “For the DDS food groups, the majority of respondents reported decreased consumption, with notable declines for dark green vegetables, other vegetables, staples, meats and fruits (S Fig 1). Further, the DDS for all sites at the different survey rounds are shown in S Fig 2. Overall DDS was lower in the first survey in 2020 and improved at the time of the second survey in 2021 in most sites, to levels closet to pre-COVID estimates. However, in Burkina Faso sites, in Kersa (Ethiopia) and Ibadan (Nigeria) DDS was lower in the second round compared to pre-COVID times (S Fig 2). (page 910-916, page 24) Comments: • Line 204-205: In addition to mention previous study using PDQS, it is recommended to mention some studies used the same dependent variables to compare results. Response: We have included some additional studies that look at similar outcomes. However, because studies on diet quality have been limited in the African context, we refer to studies in other regions and we present this in the discussion section. Please see below additional text added. “A study in Mexico also investigated the factors with diet quality early and later during the COVID-19 pandemic and found that consumption of healthy foods and food security declined, however consumption of unhealthy foods increased (35). In another study in the United States, individuals reporting food insecurity also lower consumption of both healthy and unhealthy foods (36). (lines 1009-1013, page 29) Comment: • Line 242: head of household (yes or no) what does (Yes) indicates? More explanation is needed Response: The question referred to whether the respondent was the head of household. In the study context the household head may report better dietary intake than other household members, hence the need to consider this as a factor possibly influencing reported dietary intake. • Line 246: Wealth index, more elaboration is needed of factor analysis Response: We have provided additional clarification of the factor analysis approach. “We computed the wealth index based on factor analysis on ownership of common household assets and other wealth-related indicators such as donkey cart, radio, television, bicycle, motorcycle ownership, access to grid electricity, improved water, fuel and roof within each country. We classified respondents in each country based into wealth tertiles based on results from the country specific factors selected.” (Lines 783-789, pages 12-13) Comment: • Line 252: Why GEE was adopted as an analytical tool? A brief justification is needed, with model specification Response: We selected GEE linear regression as the study design included clustering. This use of this modeling approach is supported by literature (2). Comments: • Line 282 Table 1: for easy follow up and understanding, it is better to write only the percentage not numbers inside the Table Response: We appreciate the concern of the reviewer on this. However, this is convention in epidemiology and we would expect that our audience would be interested in seeing both numbers. I hope this clarifies our decision to retain both the n (%). Comments: • Line 299-303 and line 239: These questions (worried about food, skipped a meal, gone for an entire day without eating.) are related to food consumption behaviors which is one of the types of food security measurement. Therefore, it is better to rephrase the sentences to reflect that. The same comment is applicable to Table 2 Response: We agree with the Reviewer. We have added the following sentence to clarify this for the audience. “Food insecurity also affected respondents, with 45% reporting that they worried about food, and 30% said they had skipped a meal. Almost 10% had gone for an entire day without eating. These three questions are components of a metric for the assessment of food insecurity, the Household Food Insecurity Access Scale (HFIAS).”( Lines 838-841, page 17) Comments: • Line 314: Table 2: - There is no need to repeat “Impact of COVID-19” on the subheading as it was reflected of Table title. - Table title it is better to write five rather than 5. Response: We have deleted the subheading and included “ five” in the table heading. Comments: - Is there any significance difference between these percentages???? may be another test is required Response: We determined to use this table as a descriptive table. We decided that even if we show significant differences, without adjusting for possible confounders this may overemphasize findings erroneously. Comments: • Line 324: All figures numbers should be revised to match the figures attached at the end of the manuscript Response: We thank the Reviewer for noticing this error. We have fixed the figure headings to match last minute changes we had made. Comments: • Line 335: It is better to shorten the Fig 2 title Response: We have changed the title for this figure 3 to “Fig 3. Changes in frequency of consumption of PDQS food groups comparing the pre-COVID-19 timepoint to the first and second surveys” Comments: • Lines 340 and 360: Also applicable to line 373 - “consumption of healthy PDQS food groups”, is it a right scientific terminology???? Response: We thank the reviewer for this question. We believe that this is correct terminology since the PDQS also assesses consumption of unhealthy foods/food groups as well. Comments: - Unit time is required with PDQS (week or 24 hrs) Response: We often don’t include the frequency per week for the score. This information is captured in the consumption of the specific food groups. Comments: • Line 378-380: What is the source for data for” consumption of food groups at the time before the COVID-19 pandemic”, it should be stated in material and method section Response: We have provided clarifying information on this in the manuscript. “Respondents were asked to recall the number of days they consumed food from a list of 20 food groups over the past 7 days period before the COVID-19 emergency and during the COVID-19 pandemic (in the round 1 survey) (20) and in round 2 we asked respondents to recall their consumption of the same 20 food groups over the previous 7 days.” (Lines 713-717, page 10) Comments: • Line 608: “2020;4:1401-.” Please delete the dash (-) at the end Response: Thank you for this. We have updated the citation. Comments: • Line 637: The publication year is missing Response: We thank the reviewer for this. We have updated the citation number 37. Comments: Reviewer #2: The paper addresses an important recent topic and covered sub-researched countries in Africa. The writing is good and easy to follow; however, there is a need to improve some parts as discussed below. It would be also helpful if page number has been added to the document. Introduction: The introduction should clearly state the main contributions of the study to strengthen the argument and ensure the importance of this work related to other relevant studies. A clearly stated and well-defined contribution is the merit of any research. Authors only indicated “Therefore, it is important that as the COVID-19 pandemic persists we continue to assess its impact on the nutrition and health of the households in SSA (12).” Then, the authors have details the strengths of the work on page 39 in the discussion. These strengths /contributions should also be indicated in the introduction. Response: The Reviewer raises important points. We have addressed these comments by revising the introduction section as follows: “We investigate the continued impacts of COVID-19 on diet quality and food prices, using data collected from the African Research, Implementation Science and Education (ARISE) Network cross-sectional COVID-19 studies by in five SSA countries, Burkina Faso, Ethiopia, Ghana, Nigeria and Tanzania. This study contributes to understanding the indirect impacts of COVID-19 on diets in a region where data is limited. Data from several countries allows us to understand the diverse pathways through which COVID-19 affected nutrition. We also have repeated cross-sectional studies in 3 countries, and this allows us to track changes in food security and diet intake at different times during the COVID-19 pandemic.” ( Lines 315-322, page 7) Comments: Please add to the study design justification that the differences between the two samples/rounds will not cause issues in comparisons (across countries, across rounds if applicable) in the results section, especially that you conducted country-level differences in PDQS. Response: We have added this information to the Methods section. “We ensured consistency in study design and questions across all sites to ensure that differences between the two samples/rounds would not cause issues in comparisons (across countries and across rounds if applicable) in the analysis.” (Lines 653-655, page 9) Comments: Conclusion should include a summary of what the study is about, not only the outcomes. Response: We have revised the conclusion section of the manuscript to incorporate this and other feedback from Reviewers. Comments: Minor comments are as follows: Comments: Table font is different than the text font and style. Response: This is deliberate to ensure that the contents of the tables fit into the limited space within the manuscript. Comments: Texts in tables and figures should be in black to be more readable, it is in gray now. Response: We have updated all text in tables to be in black font. Comments: Page 22: “Loss of or reduced salaries” I think you meant “lost or reduced salaries” Response: We have updated the manuscript. “Lost or reduced salaries were reported most in Ouagadougou (37%) and Lagos (26%).” ( Lines 828-829, page 16) Comments: Page 23 and 24: Not sure what do “(not shown) and “(results not shown)” mean? If you meant the results are not shown in the Table, please state that clearly. Response: We mean that the findings are not shown in the tables or figures. We have revised all appropriate text to “results not shown in tables or figures” ________________________________________ 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No   References 1. ARISE Covid Survey Round 2 Methods Brief. 2. Pekár S, Brabec M. Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences. Ethology. 2018;124(2):86-93. 10.1371/journal.pone.0279610.r003 Decision Letter 1 Al-Mahish Mohammed Academic Editor © 2023 Mohammed Al-Mahish 2023 Mohammed Al-Mahish https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version1 12 Jun 2023 The COVID-19 pandemic and its  impacts on diet quality and food prices in sub-Saharan Africa PONE-D-22-33938R1 Dear Dr. Madzorera, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. 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For more information, please contact onepress@plos.org. Kind regards, Mohammed Al-Mahish Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed ********** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ********** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ********** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: Yes ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ********** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: (No Response) Reviewer #2: The authors have adequately addressed my comments raised in a previous round of review. Thank you for addressing the comments and suggestions. ********** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No ********** 10.1371/journal.pone.0279610.r004 Acceptance letter Al-Mahish Mohammed Academic Editor © 2023 Mohammed Al-Mahish 2023 Mohammed Al-Mahish https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 19 Jun 2023 PONE-D-22-33938R1 The COVID-19 pandemic and its  impacts on diet quality and food prices in sub-Saharan Africa Dear Dr. Madzorera: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Mohammed Al-Mahish Academic Editor PLOS ONE ==== Refs References 1 Bambra C , Riordan R , Ford J , Matthews F . The COVID-19 pandemic and health inequalities. J Epidemiol Community Health. 2020;74 (11 ):964–8. doi: 10.1136/jech-2020-214401 32535550 2 WHO. COVID-19 responsible for at least 3 million excess deaths in 2020 2021 [cited 2021. Available from: https://www.who.int/news-room/spotlight/the-impact-of-covid-19-on-global-health-goals. 3 McNeely CL , Schintler LA , Stabile B . 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