Domestic Food Prices After COVID-19

Analyzing Impact on Developing Countries' Food Security

By [source]

About this dataset

This dataset looks at the effect of the COVID-19 pandemic on food prices in both domestic and international markets, particularly in developing countries. It contains data on monthly changes in food prices, categorised by country, market, price type (domestic or international) and commodities. In particular, this dataset provides insight into how the pandemic has impacted food security for those living in poorer countries where price increases may be more acutely felt. This dataset gives us a greater understanding of these changing dynamics of global food systems to enable more efficient interventions and support for those who are most vulnerable

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How to use the dataset

This dataset is an excellent resource for anyone looking to analyze the impact of COVID-19 on domestic food prices in developing countries. With this dataset, you can get an up-to-date overview of changes in the costs of various commodities in a given market and by a given price type. Additionally, you can filter data by commodity, country and price type.

In order to use this dataset effectively, here are some steps: - Identify your research question(s) - Filter the dataset by selecting specific columns that best answer your research question (ex: month, country, commodity) - Analyze the data accordingly (for example: Sorting the results then calculating averages). - Interpret results into actionable insights or visualizations

Research Ideas

• Analyzing trends in the cost of food items across different countries to understand regional disparities in food insecurity.
• Comparing pre- and post-COVID international food prices to study how nations altered their trade policies in response to the pandemic, indicating a shift towards or away from trading with other nations for food procurement.
• Using sentiment analysis to study consumer sentiment towards purchasing certain items based on their market prices, allowing businesses and governments alike to better target interventions aimed at improving access and availability of food supplies

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: dom_clean_data.csv | Column name | Description | |:---------------|:---------------------------------------------------------------------------| | month | The month in which the data was collected. (Date) | | country | The country in which the data was collected. (String) | | price_type | The type of price (domestic or international) that was collected. (String) | | market | The market in which the data was collected. (String) | | commodity | The type of commodity that was collected. (String) |

File: int_clean_data.csv | Column name | Description | |:---------------|:---------------------------------------------------------------------------| | country | The country in which the data was collected. (String) | | commodity | The type of commodity that was collected. (String) | | price_type | The type of price (domestic or international) that was collected. (String) | | time | The month in which the data was collected. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

The way in which people report the coronavirus (COVID-19) pandemic has affected their household finances in the past seven days, if people report their costs of living has changed in the last month and why, and people’s financial situation in the last month – indicators from the Opinions and Lifestyle Survey (OPN).

Context

Covid-19 Impact on Humanitarian Operations Data Viz inputs. This dataset is part of COVID-19 Pandemic

It's updated daily in the site.: https://data.humdata.org/dataset/covid-19-data-visual-inputs

Content

International restrictions:
Some Governments have suspended all international commercial passenger flights until 30 September.
It will then review the situation. New tourist visa applications are currently suspended. These arrangements are subject to change and at short notice. There are temperature checks for all arrivals. Arrivals must provide evidence of a negative COVID-19 test result. Testing requirements ahead of entry are subject to change. Confirmation should be sought from your local well in advance of your departure.
Arrivals must enter government-arranged quarantine. You will be allotted your quarantine facility on arrival. You may have no choice but will be placed in the same government quarantine facility or hotel as all the other people on your flight. You will be provided with food for which you will be charged.
The standard quarantine period for new arrivals is 28 days (21 days in a government-arranged facility followed by 7 days of home quarantine). However request permission to undergo a shorter quarantine period. In this case the quarantine requirements are as follows: Complete 7 days home quarantine prior to the date of travel. During these 7 days you may only leave your place of quarantine to take a COVID test. Provide evidence of a negative COVID test result. Complete 7 days quarantine in a government facility or government approved hotel on arrival (allocated on arrival). Undertake a COVID test after 7 days (through the National Health Laboratory at a cost is MMK 200 000). If you test negative you must complete a further 7 days of home quarantine and will then be able to leave quarantine. If you test positive you will be transferred to a designated government hospital for COVID patients. You will be required to remain for 28 days in hospital after which you can leave if you have tested negative for COVID for two consecutive weeks prior.
Even for those who have been granted permission to undergo a shorter quarantine period. This includes if any person on a flight tests positive for COVID-19. If hospitalised with coronavirus patients are obliged to use a government facility even if they have private insurance. Patients in government hospitals are generally expected to make their own arrangements for bringing in food and other essential supplies. Lone travellers will not be allowed out of isolation to purchase food or make phone calls. Arrivals will be expected to provide contact details. They are not aware of any requirement to download an app. Public health requirements for humanitarian flighs [https://humanitarianbooking.wfp.org/en/wfp-aviation/]

Internal restrictions:
In some areas there may be local requirements for visitors from other parts of the country to quarantine or self-isolate for up to at least 28 days on arrival. Further local preventative measures may also be in effect in regions townships wards and village tracks . Additional movement restrictions may be imposed in specific townships if COVID-19 cases are found. You should check with local authorities for information on possible local preventative measures.

A curfew from 9pm until 4am each day is currently in effect in all townships with reported COVID-19 cases.

Those living in an area under a Stay at Home order should stay in their homes. Unless they have permission from the local ward administrator to go outside. Permission to go outside is generally only granted to those who are preforming specific tasks recognised by the government or for one person per household to go shopping or for two persons per household to go to a medical hospital or clinic. If you are in an area under a Stay at Home order you should contact the local ward administrators for details including any requirements that may be specific to that location.
Anyone wishing to travel outside of a township subject to a Stay at Home order will need permission from the State Government.
Since 13 May it has been compulsory for anyone going out in public to wear a facemask. Failure to wear one will result in a fine. This requirement is being enforced strictly and has led to the arrest of some offenders.

Acknowledgements

https://data.humdata.org/dataset/covid-19-data-visual-inputs

Photo by United Nations COVID-19 Response

Inspiration

The Covid-19 Pandemic

Pandemics and natural disasters are recognized to cause major disruptions. The main objective of this study was to explore the impacts of COVID-19 and supertyphoon Odette in Cebu, Philippines. A total of 2630 participants were interviewed exploring the impacts of COVID-19 and supertyphoon Odette. The majority of the respondents (2486/2630; 94.5%) had financial problems due to COVID-19. Almost three out of four respondents (1962/2630; 74.6%) experienced moderate to severe impact on their mental health. Almost a third of the respondents (874/2630; 33.2%) reported moderate to severe impact on their physical well-being, mostly related to weight-related disorders. Almost half of the respondents (1248/2630; 47.5%) experienced moderate to severe impacts on their relationships with family members, relatives, friends and neighbors. More than two-thirds of the respondents (1673/2360; 63.6%) reported moderate to severe financial problems due to supertyphoon Odette. Households who were financially impacted by Supertyphoon Odette were more likely not have recently migrated to their current residence (p

The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2023, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2023 than in 2005.

Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Housing Tenure.

[1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

[2] Ibid.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; 16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

In 2018-19 the GLA first undertook a Survey of Londoners. At the time it provided vital evidence on Londoners that had never been collected before in such detail. In 2021-22, the GLA conducted another Survey of Londoners, following the same methodology as the Survey of Londoners 2018-19, an online and paper self-completion survey of adults aged 16 and over in London. The survey, which received responses from 8,630 Londoners, aimed to assess the impact of COVID-19 and associated restrictions on key social outcomes for Londoners, not available from other data sources. It is important to understand the context in which the Survey of Londoners 2021-22 took place. Survey fieldwork began in November 2021; so, up to that point, it had been four months since most legal limits on social contact had been removed. However, after fieldwork had started, some restrictions due to the emergence of the Omicron variant were introduced. This may or may not have had some effect on the data. Given these changing circumstances, caution should be applied when interpreting the results. The Survey of Londoners 2021-22 also took place just before the full effects of the cost-of-living crisis began to set in. It is highly likely that the situations of Londoners have changed while analysis was taking place. On this page there is a headline findings report, published on 30 September 2022, which provides descriptive results for the key headline measures and supporting demographic data collected by the survey. Accompanying this report are more detailed tables documenting the key results of the survey by a range of demographic and other characteristics, a short summary document presenting key findings from the survey, and a technical report for those interested in the survey’s methodology. Further to these, a series of pen portraits, providing snapshots of particular groups of Londoners, as captured at the time of the Survey of Londoners 2021-22, were first added on 31 October 2022. Also on this page, there is an initial findings report, that was published on 2 September 2022. This was published to provide timely evidence from the survey to support the case for further targeted support to help low-income Londoners with the cost-of-living crisis. We have launched an online explorer where users can interrogate the data collected from the two surveys, conducted in 2018-19 and 2021-22. This is the first iteration, so we welcome any feedback on it - GO TO THE EXPLORER The record-level Survey of Londoners dataset can be accessed via the UK Data Service, University of Essex. The dataset is available for not-for-profit educational and research purposes only. Finally, as the North East London (NEL) NHS funded a 'boost' in their sub-region to enable a more detailed analysis to be conducted within, they produced an analytical report in September 2022. This is also available for download from this page.

The COVID Social Mobility & Opportunities study (COSMO) is a national cohort study of more than 12,000 young people from across England, who were in Year 11 in the academic year 2020-21.The study aims to examine the short-, medium- and long-term impacts of the COVID-19 pandemic on educational inequality and social mobility.Data collectionThere have been two waves of data collection so far.Wave 1 and Wave 2 data are now available via UK Data Service. To learn more about the COSMO study design and data collection please visit the COSMO study website.Who funds the study?COSMO is a partnership between the UCL Centre for Education Policy & Equalising Opportunities (CEPEO), the Sutton Trust, and CLS, with CLS providing expertise on design and management of longitudinal studies.Wave 1 (begun in 2021) was funded by UK Research and Innovation as part of its COVID-19 rapid response fund. Wave 2 (begun in 2022) was funded by the Economic and Social Research Council.Fieldwork is being carried out by Verian (previously Kantar Public).Data from Wave 2 of the COVID Social Mobility and Opportunities (COSMO) study is now available to researchers interested in exploring how COVID-19 and the cost of living crisis has affected the lives of 17–18-year-olds across England.Nearly two fifths of 17-18 year olds from the most disadvantaged areas have struggled to receive the mental health support they need in the past year, according to new COSMO study research.Explore the latest findings and news on the COSMO website.COSMO is the largest study of its kind into the unequal effects of COVID-19 on a generation of young people.The study aims to capture the extent to which the pandemic shaped educational trajectories, and how this varies across different groups.When the pandemic hit the UK in 2020, Year 11 pupils were beginning to make important decisions about their futures. They subsequently faced two years of serious disruption to their education, including the ultimate cancellation of their GCSEs.The upheaval was unprecedented, with the consequences felt more deeply by those from disadvantaged backgrounds.Initial findings from COSMO are already providing valuable outputs about the differential effects of the pandemic. This will continue as young people transition to higher education and the labour market.COSMO uses an area-stratified random probability sample from the National Pupil Database, with additional independent school sampling, successfully recruiting more than 13,000 young people who were in Year 11 in 2020-21. The study oversampled young people from disadvantaged, ethnic minority and other often-excluded groups to ensure it reflects the full range of experiences of the pandemic.Wave 1 data collection involved web-first fieldwork (initial invite to an online survey, with targeted face-to-face follow up) with young people and parents.Wave 2 also followed a web-first approach with young people and parents, with face-to-face, telephone and further online follow-up. It was completed in April 2023. All young people who took part in Wave 1 (along with their main parent) were invited to take part in Wave 2.A proposed Wave 3 of the study is planned subject to availability of funding.The study covers how the disruption to schooling during the pandemic has affected young people’s educational attainment and wellbeing, as well as their longer-term educational and career outcomes.In Wave 1, topics covered across questionnaires included:In Wave 2, the emphasis shifted towards different paths young people might be taking, covering:A consultation on the content of Wave 2 was carried out in February 2022. For more information about the consultation, visit the COSMO website.Study partner Sutton Trust has commissioned an additional sample of young people from disadvantaged backgrounds who showed academic potential before the pandemic, to look in more depth at the impact on their chances for social mobility.The study has been designed for linkage to administrative data from the National Pupil Database, the Longitudinal Educational Outcomes (LEO) dataset, as well as other sources, such as:Researchers can access Wave 1 and Wave 2 COSMO data and documentation through the UK Data Service.To learn more about the COSMO study design and data collection please visit the COSMO study website. Jake’s research focuses on better understanding the causes and consequences of educational inequalities, evaluating policies and programmes aiming to reduce these inequalities, and how best to do this evaluation. In addition to leading the COSMO study, Jake’s other work includes research projects for multiple UK government departments, such as work for the Department for Education into the transition from education into work, as well as leading multiple randomised evaluations, such as Education Endowment Foundation-funded work focused on improving teachers’ use of formative assessment. His doctoral research consisted of three linked studies considering aspects of socio-economic inequality in access to higher education in England, considering both the point of entry to university but also its precursors.Find the latest developments and insights from across all our longitudinal studies.The CLS Bibliography is a searchable database of published work based on our cohort studies. Search by keyword, author, date range and journal.Data from our studies are mainly available through the UK Data Service. We run training to support researchers who are interested in using our studies in their work. Centre for Longitudinal Studies UCL Social Research Institute20 Bedford Way London WC1H 0ALEmail: clsdata@ucl.ac.uk

The Family Resources Survey (FRS) has been running continuously since 1992 to meet the information needs of the Department for Work and Pensions (DWP). It is almost wholly funded by DWP.

The FRS collects information from a large, and representative sample of private households in the United Kingdom (prior to 2002, it covered Great Britain only). The interview year runs from April to March.

The focus of the survey is on income, and how much comes from the many possible sources (such as employee earnings, self-employed earnings or profits from businesses, and dividends; individual pensions; state benefits, including Universal Credit and the State Pension; and other sources such as savings and investments). Specific items of expenditure, such as rent or mortgage, Council Tax and water bills, are also covered.

Many other topics are covered and the dataset has a very wide range of personal characteristics, at the adult or child, family and then household levels. These include education, caring, childcare and disability. The dataset also captures material deprivation, household food security and (new for 2021/22) household food bank usage.

The FRS is a national statistic whose results are published on the gov.uk website. It is also possible to create your own tables from FRS data, using DWP’s Stat Xplore tool. Further information can be found on the gov.uk Family Resources Survey webpage.

Secure Access FRS data
In addition to the standard End User Licence (EUL) version, Secure Access datasets, containing unrounded data and additional variables, are also available for FRS from 2005/06 onwards - see SN 9256. Prospective users of the Secure Access version of the FRS will need to fulfil additional requirements beyond those associated with the EUL datasets. Full details of the application requirements are available from http://ukdataservice.ac.uk/media/178323/secure_frs_application_guidance.pdf" style="background-color: rgb(255, 255, 255);">Guidance on applying for the Family Resources Survey: Secure Access.

FRS, HBAI and PI
The FRS underpins the related Households Below Average Income (HBAI) dataset, which focuses on poverty in the UK, and the related Pensioners' Incomes (PI) dataset. The EUL versions of HBAI and PI are held under SNs 5828 and 8503, respectively. The Secure Access versions are held under SN 7196 and 9257 (see above).

FRS 2022-23

The impact of the coronavirus (COVID-19) pandemic on the FRS 2022-23 survey was much reduced when compared with the two previous survey years. Throughout the year, there was a gradual return to pre-pandemic fieldwork practices, with the majority of interviews being conducted in face-to-face mode. The achieved sample was just over 25,000 households. Users are advised to consult the FRS 2022-23 Background Information and Methodology document for detailed information on changes, developments and issues related to the 2022-23 FRS data set and publication. Alongside the usual topics covered, the 2022-2023 FRS also includes variables for Cost of Living support, including those on certain state benefits; energy bill support; and Council Tax support. See documentation for further details.

FRS 2021-22 and 2020-21 and the coronavirus (COVID-19) pandemic

The coronavirus (COVID-19) pandemic has impacted the FRS 2021-22 and 2020-21 data collection in the following ways:

• In 2020-21, fieldwork operations for the FRS were rapidly changed in response to the coronavirus (COVID-19) pandemic and the introduction of national lockdown restrictions. The established face-to-face interviewing approach employed on the FRS was suspended and replaced with telephone interviewing for the whole of the 2020-21 survey year.

• This change impacted both the size and composition of the achieved sample. This shift in mode of interview has been accompanied by a substantial reduction in the number of interviews achieved: just over 10,000 interviews were achieved this year, compared with 19,000 to 20,000 in a typical FRS year. While we made every effort to address additional biases identified (e.g. by altering our weighting regime), some residual bias remains. Please see the FRS 2020-21 Background Information and Methodology document for more information.
• The FRS team have published a technical report for the 2020-21 survey, which provides a full assessment of the impact of the pandemic on the statistics. In line with the Statistics Code of Practice, this is designed to assist users with interpreting the data and to aid transparency over decisions and data quality issues.

• In 2021-22, the interview mode was largely telephone, with partial return to face-to-face interviews towards end of survey year. The achieved sample was over 16,000 households. This is a return towards the number expected in a normal survey year (around 20,000 households).
• In both survey years, there remain areas where users are advised to exercise caution when making comparisons to other survey years. More details on how the results for the 2020 to 2021 and 2021-22 survey years were affected by the coronavirus (COVID-19) pandemic can be found in the FRS 2020 to 2021 Background Information and Methodology and FRS 2021 to 2022 Background Information and Methodology.

The FRS team are seeking users' feedback on the 2020-21 and 2021-22 FRS. Given the breadth of groups covered by the FRS data, it has not been possible for DWP statisticians to assess or validate every breakdown which is of interest to external researchers and users. Therefore, the FRS team are inviting users to let them know of any insights you may have relating to data quality or trends when analysing these data for your area of interest. Please send any feedback directly to the FRS Team Inbox: team.frs@dwp.gov.uk

Latest edition information

For the second edition (May 2025), the data were redeposited. The following changes have been made:

• An ONS-delivered fix to the highest level of qualification (EDUCQUAL) which for several adults had been erroneously recorded.
• For ESA (benefit 16 on the BENEFITS table) the associated VAR3 has now been populated using ESA admin data, to show whether cases are Support Group etc.
• For Pension Credit recipients (benefit 4 on the BENEFITS table) adding the low-income benefits and tax credits Cost of Living Payment as benefit 124; with its flag CLPAYIRB set on the ADULT table.

The role of religion and politics in the responses to the coronavirus pandemic raises the question of their influence on the risk of other diseases. This study focuses on age-adjusted death rates of cancer, heart disease, and infant mortality per 1000 live births before the pandemic (2018-2019) and COVID-19 in 2020-2021. Eight hypothesized predictors of health effects were analyzed by examining their correlation to age-adjusted death rates among U.S. states, percentage who pray once or more daily, Republican influence on state health policies as indicated by the percentage vote for Trump in 2016, percent of household incomes below poverty, median family income divided by a cost-of-living index, the Gini income inequality index, urban concentration of the population, physicians per capita, and public health expenditures per capita. Since prayer for divine intervention is common to otherwise diverse religious beliefs and practices, the percentage of people claiming to pray daily in each state was used to indicate potential religious influence. All of the death rates were higher in states where more people claimed to pray daily, and where Trump received a larger percentage of the vote. Except for COVID-19, the death rates were consistently lower in states with higher public health expenditures per capita. Only COVID-19 was correlated to physicians per capita, lower where there were more physicians. Corrected statistically for the other factors, income per cost of living explains no variance. Heart disease and COVID-19 death rates were higher in areas with more income inequality. All of the disease rates were in correlation with more rural populations. Correlation of daily prayer with smoking cigarettes, and neglect of public health recommendations for fruit and vegetable consumption and COVID-19 vaccination suggests that prayer may be substituted for preventive practices.