key: cord-1056055-n2j81x1y
authors: Luo, W.; Baldwin, E.; Jiang, A. Y.; Li, S.; Yang, B.; Li, H.
title: Built environment's impact on COVID-19 transmission and mental health revealed by COVID-19 Participant Experience data from the All of Us Research Program
date: 2022-04-07
journal: nan
DOI: 10.1101/2022.04.05.22273358
sha: 918a30d00963d9b0a6bd91332e677a75e815433c
doc_id: 1056055
cord_uid: n2j81x1y

Objectives: The coronavirus disease 2019 (COVID-19) pandemic has led to millions of deaths. Effectively cutting the transmission of COVID-19 is essential to reduce the impact. Previous studies have observed the potential relationship between the built environment and COVID-19 transmission, however, to date, stringent studies investigating these relationships at the individual level are still insufficient. Here, we aim to examine the relationship between household types and COVID-19 infection (or mental health) during the early stages of the pandemic by using the All of Us Research Program COVID-19 Participant Experience (COPE) Survey data. Design: Based on 62,664 participants' responses to COPE from May to July 2020, we matched the cases of self-reported COVID-19 status, anxiety, or stress, with controls of the same race, sex, age group, and survey version. We conducted multiple logistic regressions between one of the outcomes and household type under the adjustment of other related covariates, such as ethnicity, age, social distancing behavior, and house occupancy. Results: Household type with a shared component was significantly associated with COVID-19 infection (OR=1.19, 95% CI 1.1 to 1.3; p=2x10-4), anxiety (OR=1.26, 95% CI 1.1 to 1.4; p=1.1x10-6), and stress (OR=1.29, 95% CI 1.2 to 1.4, p=4.3x10-10) as compared to free-standing houses after adjusted for the abovementioned confounding factors. Further, frequent nonessential shopping or outings, another indicator of built environment, was also associated with COVID-19 infection (OR=1.36, 95% CI 1.1 to 1.8; p=0.02), but not associated with elevated mental health conditions. Conclusion: Our study demonstrated that the built environment of houses with a shared component tends to increase the risk of COVID-19 transmission, which consequently led to more anxiety and stress for their dwellers. It also suggested the necessity to improve the quality of the built environment through planning, design, and management toward a more resilient society in coping with future pandemics.

Since the start of the COVID-19 pandemic in December of 2019, it has resulted in more than six million reported deaths. The virus of SARS-CoV-2 is transmitted via inhalation of the virus in air that has been contaminated with the respiratory fluids of infected persons which are released as particles and droplets 1 . It can also be circulated in aerosols 2 if the air ventilation rate is insufficient 3 or if the air is highly recycled in a closed setting, such as a plane or a cruise ship, as seen on the Diamond Princess Cruise Ship 4 . On the other hand, the virus can remain on the surface of objects, such as doorknobs, stairs, and elevator panel buttons, for hours to days 5 . Thus, the virus can be transmitted by soiled hands which have been contaminated by touching the surface of objects and then by touching the mucous membranes of their bodies (e.g., noses). In both cases, SARS-CoV-2 is transmitted within the built environment, which makes the impact of the built environment on COVID-19 transmission a critical issue. This is particularly true during the initial phase of a pandemic when the knowledge of the transmission approaches for the public is very limited.

A handful of studies have reported the connection between various types of built environments and COVID-19 transmission, as summarized in a review study 6 . These studies spanned different types of built environments from multiple countries in a variety of cities, for instance, trains in and between cities in Hubei Province (e.g., Wuhan) 7 , restaurants and public markets in Hong Kong 8, 9 , transportation infrastructure in Huangzhou 10 , apartment air ducts in Seoul 11 , house quality and crowding in Washington D.C. 12 , building values, units, and membership in New York City 13 , and intervention of the built environment in cities of Turkey 14 . These studies found that housing quality and living conditions were strong predictors for the ward level COVID-19 death count, such as in Washington D.C. 12 . Other studies have corroborated the results. For instance, a study in King County, Washington, demonstrated that built environment density was positively associated with COVID-19 incidence rates 15 . Indeed, the mitigation of viral transmission through the air delivery system can reduce the transmission of the virus. Nevertheless, current studies on the relationships between the built environment and COVID-19 transmission are still plagued by small sample sizes 13 , lack of precision, and relying on reports at county 16 , city 7 , ZIP code 15 , and community levels 12 . Stringent studies at the individual level are still lacking, most likely due to the high cost of acquiring data and the difficulty of controlling confounding covariates in prior methods.

Recent advances in data-driven projects such as the All of US Research Program (AllofUsRP) 17 , the largest biobank project in the United States, have provided unique opportunities to investigate the impact of the built environment on COVID-19 transmission. AllofUsRP has conducted six versions of large-scale, comprehensive survey studies for the COVID-19 Participant Experience (COPE) in 2020 and 2021 18 . The first three versions of COPE collected household type information and were conducted in May, June, and July of 2020, the starting period of the pandemic. These versions involved 62,664 participants with individual-level household building type information, ranging from free-standing houses to various types of apartments and studios. More . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted April 7, 2022. ; https://doi.org/10.1101/2022.04.05.22273358 doi: medRxiv preprint importantly, COPE also tracked the mental health of the participants, plus their social distancing behaviors during 2020-2021, providing further opportunities to examine the impact of the built environment on the stress that dwellers experienced. Thus, in the current investigation, we perform a stringent association study between the built environment and COVID-19 transmission, plus its impact on residents' mental health 19 . Higher levels of stress and anxiety have been reported due to COVID-19 20 ; thus, the relevance to household type is worth investigating. We will control various types of confounders while studying the interwoven factors, taking advantage of the rich information collected by the survey. Unveiling these relationships will not only demonstrate the importance of mitigation strategies for COVID-19 transmission but will also provide adaptive and resilient design solutions focusing on the built environment to respond to potential airborne and contagious viruses in the future 21 .

As follows, we will briefly discuss the methodology of the study in Section 2, followed by detailed results in Section 3. We will then discuss the results and relationships to related studies and conclude at the end.

We chose the AllofUsRP dataset because of its large cohort, diversity, and availability of household type data in the COPE survey. AllofUsRP aims to recruit adults (18 years and older) who live in the United States from all backgrounds. To date, the project has enrolled over 300,000 participants, which are from diversified backgrounds (e.g., ethnicities, social behaviors, geographic locations, medical conditions) and represent their community in research studies 22 . The transparency, diversity, and inclusion of the AllofUsRP provide researchers with a unique opportunity to investigate the potential roles that the built environment might play on mental and physical health 23 . AllOfUsRP shares the data assets collected from the participants in a common cloud environment, the All of Us Researcher Workbench 22 . Qualified researchers have access to the dataset and can retrieve and analyze the data through web-based tools and interactive cloudbased computing environments 17 . Access to de-identified individual-level data requires registration and training, which has been fulfilled by all authors directly analyzing the data. The Institutional Review Board of the University of Arizona waived ethical approval for this work due to using de-identified data.

The AllofUsRP COVID-19 Participant Experience survey is an online survey that began in May 2020 and ended in February 2021, with the aim to better understand how COVID-19 affects participants' daily lives and health conditions, especially their mental health 24 . This survey is about 20-to 30-minutes long and covers topics about social distancing experiences, self-reported COVID-19 status, well-being, basic participant's information, mental health, COVID-19 induced socioeconomic changes (e.g., work and financial changes), and physical activity, among many others 18 . The survey had six . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted April 7, 2022. ; https://doi.org/10.1101/2022.04.05.22273358 doi: medRxiv preprint versions and had asked the participants to answer the most recent version of questions, enabling researchers to examine the changing effects of COVID-19 over time. At the time of our analysis, we took advantage of the first three versions (May, June, and July 2020) of the COPE data consisting of 62,664 participants in AllofUsRP Dataset v4 where household type information was available. Our analysis focuses on the responses of these participants about the topics of anxiety, stress, house types, social distancing behaviors, and their COVID-19 infection conditions. We used Structural Query Language (SQL) to extract the data guided by the concept ids of the variables (outcomes and covariates; details shown below) of our interest.

Based on participants' responses to COPE, we conducted a retrospective case-control study and started by identifying all positives and negatives for each outcome, specifically COVID-19, anxiety, and stress status. COVID-19 status was self-reported by the question 'Do you think you have had COVID-19'. The status was considered as the binary outcome variable ('Yes': positive; 'No': negative); we removed the participants who were not sure about their COVID-19 status and reported 'Maybe' for convenience. We treated 'more than half of days' and 'nearly every day' as positive anxiety cases, and 'not at all' as negative controls for whether participants felt nervous, anxious, or on edge during the last two weeks. We removed missing data and the response of 'several days' due to not being distinctly positive or negative. Similarly, we treated 'fairly often' and 'very often' as positive stress cases versus 'never' and 'almost never' as negative controls in the question of feeling nervous and stressed in the last month, excluding 'sometimes' from the analysis.

We matched each positive outcome cohort with a control cohort, with the same race, sex, age group (per 10 years), and survey version, similar to our previous COVID-19 study 25 . This is because these factors are related to social behaviors that directly underline COVID-19 transmission. We divided the available cases and controls of an outcome (e.g., COVID-19 infections) in COPE into strata of the same race, sex, age group, and survey version. Then, we randomly selected the same number of control individuals without replacement as the number of cases from the same stratum. In a few scenarios without enough controls, we loosened the matched field in the order of survey version, race, sex, and age group. Cases in the strata without any matched controls (including partial matches) were excluded from the analysis. Of note, if the confounding factors are fully matched, they will not be expected to be significant in the model even if they are significant before matching.

We fitted the multiple logistic regression model using the COPE survey to determine the relationship between household types and one of the influenced outcomes, COVID-19 infection, anxiety, or stress during the early stages of the COVID-19 pandemic. Three models were tested with potential confounders to adjust the association explained . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 7, 2022. ; https://doi.org/10.1101/2022.04.05.22273358 doi: medRxiv preprint by the covariates that we focused on. 1) Model A used in Section 3.1 analyzed COVID-19 status as the outcome given household type as the major explanatory variable (covariate), which was adjusted for other confounding covariates such as household occupancy, race/ethnicity, sex, age, social distancing, hygiene behaviors (e.g., shopping/outings), and mental health (anxiety and stress). 2) Model B and C in Section 3.2 used mental health (anxiety and stress) as the outcome to study the contribution from household types, which is adjusted for the other covariates, similar to Model A.

For household type, we examined individual types (e.g., free-standing houses and twobedroom apartments), in addition to combining those with a shared component for a summarized analysis. This specifically included the townhouse, three-(or more), two-, and one-bedroom apartments, studio, and nursing home or rehab facilities. We also included the number of occupants in a house as a confounding covariate. For social distancing behavior, we included the number of days with the following behaviors in the last five days: staying at home, working or volunteering outside the home, attending social gatherings outside of more than 10 people, and having close contact with somebody in a risk group. We also included hygiene practices in the analysis. We treated each variable as numeric (e.g., age) or categorical data, particularly for those with missing values (missing value as a special level). The details of the studied questions and multichoice response options can be referred to on the AllofUsRP website 24 .

Odds ratios of the outcome by the major covariates were calculated from the contrasts that used conventionally selected reference groups (i.e., the free-standing house for house types; none of the days (0 days) of just for fun shopping and outings) for nonessential behavior in all models. The covariates that were irrelevant after ANOVA analysis (p>0.25 with Chi-square test) were excluded from the final logistic regression model for further analysis. All statistical tests were 2-sided, and a p-value < 0.05 was considered statistically significant. Analyses were implemented in the All of Us Researcher Workbench with R.

We first investigated whether household type (e.g., apartment) had an impact on COVID-19 transmission using the matched cases and controls (Methods 2.2). Of the 62,664 participants in the COPE Survey, 4,870 COVID-19 cases were reported from 3,700 participants (some participants reported multiple times in different versions of the survey during May, June, and July 2020). We thus matched 4,870 negative controls with the same race, sex, age group (per 10 years), and survey version (month) as the cases (four failed to match survey version and nine failed to match survey version and race). The majority of participants lived in the free-standing house (single house): 59.3% (2,886 out of 4,870) of the self-claimed cases (Covid = 1), which is close to the . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 7, 2022. ; https://doi.org/10.1101/2022.04.05.22273358 doi: medRxiv preprint 6 national average. The characteristics of the cases and matched controls are summarized in Table 1 . Since free-standing houses are assumed to be less risky, we used it as the reference level to compute the risk for participants living in other house types. Using a multiple logistic regression model, we examined the association between household type and COVID-19 status, with additional controls for ethnicity, birth year, social distancing behavior, and household occupancy, plus anxiety and stress status. Skip, prefer not to say, or no answer 38 32 *Including another single population, none of these, none indicated, and no matching concept

The distribution of house types conditioned on the COVID-19 status is depicted in Fig.  1 , showing the probability of each household type given a COVID-19 status. The figure demonstrated that participants who became infected had a lower probability of living in a free-standing house as compared to the non-infected. Participants with positive cases were more likely to live in household types with shared components (See the . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 7, 2022. ; https://doi.org/10.1101/2022.04.05.22273358 doi: medRxiv preprint reverse trend in Figure 1 for apartments). A multiple logistic regression further confirmed the trend. Participants living in household types with a shared component (e.g., townhouse and apartment) had a statistically significant higher risk of infection (OR=1.19 95% CI 1.1 to 1.3; p=0.0002) as compared to those living in a free-standing house. Among all the house types with a shared component, the odds of contracting COVID-19 for participants who live in a nursing home or rehab facility were more than seven-fold greater than participants who live in a free-standing house (OR = 7.13 95% CI 1.5 to 33.7; p = 0.01). Other significantly higher odds appear among the respondents who live in three-bedroom (or more) apartments (OR = 1.37, 95% CI 1.1 to 1.7, p= 0.001). This effect was not confounded by age which had a different infection risk, likely due to distinct social behaviors. Even when controlled by age group (using both cases and controls within the same age group), the trends of odds ratios were similar (data not shown). Some household types lacked significance, such as those depleted due to age groups (e.g., nursing home facilities for ages 40 and below). Besides, race, sex, age, ethnicity, survey version, and household occupancy were not associated with COVID-19 infection in the matched cohort, but social distancing behavior (e.g., number of days at home, gatherings with over 10 people, shopping, etc.), and mental health (anxiety and stress) were (discussed in the next sections). is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 7, 2022. ) were found to have a significantly elevated risk of mental health conditions than participants who lived in free-standing houses; homeless had the highest odds ratio of anxiety (OR = 4.13, 95% CI 1.5 to 11.7; p = 0.008), particularly for those above their 50s. Race, sex, and age were confounding factors for the association between household type and mental health (e.g., stress) as all were significantly (p<0.01) associated with mental health in the matched models. COVID-19 status, household occupancy, hygiene, and social distancing habits (close contact, days at home and at work), were associated with both anxiety and stress (p<0.05). Last, stratification studies of sex and age group led to similar trends (data not shown), and anxiety and stress were correlated.

. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 7, 2022. ; https://doi.org/10.1101/2022.04.05.22273358 doi: medRxiv preprint Fig. 2 . Distribution of house build type conditioned on anxiety status. Participants with anxiety are less likely to live in a free-standing house and more likely to live in a house type with shared components compared to those without anxiety. Odds ratios and pvalues of anxiety for each of the house types as compared to free-standing houses are shown above the probability bar graphs for their corresponding type. Fig. 3 . Distribution of house build type conditioned on stress status. Participants with stress are less likely to live in a free-standing house and more likely to live in a house type with shared components compared to those without stress. Odds ratios and p- is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 7, 2022. ; https://doi.org/10.1101/2022.04.05.22273358 doi: medRxiv preprint values of stress for each of the house types as compared to free-standing houses are shown above the probability bar graphs for their corresponding type.

Finally, we examined the association between COVID-19 status and nonessential shopping, or outings, using the same analysis in 3.1 & 3.2. Most shopping was done indoors; thus, it is an issue also related to the built environment. Fig. 4 shows the distribution of nonessential shopping and outings behavior with respect to COVID-19 status, which suggests more shopping and outings were associated with an elevated risk of COVID-19 infection. During the early stages of the pandemic, most participants tended to eliminate nonessential shopping. More than 70% of participants reported none of the days in the last five days, whether they got infected or not. Therefore, we set this shopping type as the reference level. The multiple logistic regression analysis showed that participants who went on outings more often had a higher risk of infection. Participants who shopped most days (more than three days within the last five days) yielded nearly 36% more risk as compared to the participants who shopped none of the days (OR = 1.36, 95% CI 1.1 to 1.8, p = 0.02). Interestingly, participants with frequent shopping behavior were not significantly associated with more anxiety or stress. The results suggest that commercially built environments, such as shopping malls, were likely to contribute to COVID-19 transmission in the initial stage of the pandemic. However, a more precise measurement of the variables is needed for a more robust conclusion. In this study, we applied multiple logistic regression analyses using the COPE survey data and found that household type was associated with COVID-19 infection and mental health (e.g., anxiety) during the early stages of the COVID-19 pandemic. Our analysis also revealed that individuals with more frequent shopping or outings were more likely to get infected with COVID-19. Fig. 5 . illustrates the summarized findings among the household type, COVID-19 infection, mental health, and nonessential shopping.

People who lived in nursing homes or rehab suggested a much higher possibility of becoming infected with COVID-19 compared to those who lived in free-standing houses. This was true even with stratification analysis using the same age group (although they lacked power). For instance, we found OR=3.96, 95% CI 0.7 to 23.2, p=0.13 for those over the age of 60. Reports suggested crowding is a risky factor for infection and mortality 26 , particularly in nursing homes. In addition, apartment dwellers (i.e., three-bedroom (or more) apartments) showed a higher tendency for contracting COVID-19, even when considering the confounding factor of the number of occupants in the house (data not shown). Meanwhile, two-bed or one-bedroom apartments showed inconsistent and underpowered results in some age groups, likely due to lower household occupancy. Although contributions from confounding factors that were not modeled cannot be excluded (e.g., interactions with visitors and workers), the possibility of influence from the built environment is high due to the stringent controls and reproducibility of the results in the stratified analyses. Concerning mental health, people who . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 7, 2022. ; https://doi.org/10.1101/2022.04.05.22273358 doi: medRxiv preprint are homeless and participants who lived in apartments with shared living spaces were at a higher rate of reporting anxiety than inhabitants in free-standing homes. This corroborates prior reports and is partially caused by the congregate living conditions 27 . All the results indicated that built environments with shared components played a role in the transmission of SARS-CoV-2 and imposed mental burdens on dwellers during the COVID-19 pandemic.

Considering the association between social behaviors and COVID-19, such as outings and COVID-19 infection, there is an increasing trend of positive cases with more frequent nonessential shopping behaviors (3 or more days in the last five days) as compared to individuals who shopped for none of the days. As shown in Fig. 5 , shopping and outing behaviors could relate to the built environment due to a large group of shoppers mixing indoors throughout the pandemic, providing common areas for COVID-19 viral transmission 28 .

In the early stages of the pandemic, increasing positive cases and deaths, a lack of knowledge of the virus, growing financial issues, strict social distancing regulations, and COVID-19 infections affected people's well-being and daily life by contributing to widespread and increased psychological problems 20 . Further, previous studies have shown that people with pre-existing mental health conditions may have a higher tendency of being infected due to medical visits and emotional responses to the COVID-19 pandemic, thereby placing them at an increased risk of COVID-19 infection compared to those without mental health conditions 29 . Specifically, another study pointed out that individuals with mental health disorders are likely to have greater barriers in obtaining timely medical services, exacerbating mental health issues 30 . Our results corroborated previous findings (Fig. 5) , although we could not distinguish the order (causal relationship) of mental disease and COVID-19 infection due to the lack of longitudinal information in the survey.

Although our study didn't investigate them, latent connections also exist between shopping or outings with anxiety and stress. Previous studies have reported that moderate shopping could provide both psychological and therapeutic value 31, 32 . Additionally, experiments about the effectiveness of diversion buying for stress release showed that a certain amount of spending was necessary to release stress 33 . Therefore, more just for fun shopping and outings may be related to anxiety and stress in a positive manner. Demonstrating this, frequent shoppers (every day) in their youth (20s age group) showed a reduction in stress (OR=0.07, p<0.0001).

The study is significant because it provides strong scientific evidence that demonstrates the associations between the built environment and COVID-19 transmission based on large-scale individualized data. While previous studies have suggested the associations based on summarized data (e.g., density of household types, COVID-19 transmission rate at the ZIP code scale 15 ), the current study provided a more stringent investigation by controlling various confounders, such as age and sex.

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The copyright holder for this preprint this version posted April 7, 2022. ; https://doi.org/10.1101/2022.04.05.22273358 doi: medRxiv preprint 13 The results of the study should be interpreted with caution due to various limitations: 1) The associations identified in the study may not indicate causal relationships, particularly from the secondary use of existing COPE data. 2) COVID-19 status was based on self-reporting in lieu of PCR-tested results, which were limited at the early stages of the pandemic. 3) COVID-19 infection is highly related to social behaviors, which vary significantly among age groups. Some of the stratification analyses in particular age groups were underpowered. 4) As shopping status is combined with outings, the impact on the commercial built environment is worth further study. 5) COVID-19, anxiety, and stress present interwoven relationships. Therefore, the order of the phenotypes can hardly be determined and are all confounded by socioeconomic status (e.g., employment status, financial difficulties, and other COVID-19 related impacts). This was not included in the current study due to the option for multiple answers rather than the multiple-choice survey in COPE, which might cause power issues and multicollinearity in regression.

In conclusion, the study demonstrated that the association of the built environment with a shared component tends to increase COVID-19 transmission and that their dwellers experience increased anxiety and stress levels. It is crucial to improve the quality of the built environment through planning, design, and management, pursuing a more resilient society that is able to cope with future pandemics. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 7, 2022. ; https://doi.org/10.1101/2022.04.05.22273358 doi: medRxiv preprint . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted April 7, 2022. 

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Acknowledgements: This research has been conducted using All of Us Researcher Workbench platform The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2

Authors' Contributions: HL, BY, and SL conceived the study. HL and WL conducted the data processing and analysis. WL and HL drafted the manuscript. BY, EB, and AJ revised the manuscript. All authors reviewed the manuscript.Competing Interests: None declared.

The study was supported by seed funding support from the College of Architecture, Planning and Landscape Architecture and partially supported by a startup fund from the College of Agriculture and Life Sciences, University of Arizona.. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 7, 2022. ; https://doi.org/10.1101/2022.04.05.22273358 doi: medRxiv preprint