key: cord-0782972-pscxq780
authors: Nawrocki, Jeff; Olin, Katherine; Holdrege, Martin C; Hartsell, Joel; Meyers, Lindsay; Cox, Charles; Powell, Michaela; Cook, Camille V; Jones, Jay; Robbins, Tom; Hemmert, Andrew; Ginocchio, Christine C
title: The Effects of Social Distancing Policies on non-SARS-CoV-2 Respiratory Pathogens
date: 2021-03-17
journal: Open Forum Infect Dis
DOI: 10.1093/ofid/ofab133
sha: c6e2f306c76c19c4af1fa19678c6d5de40d3dfd9
doc_id: 782972
cord_uid: pscxq780

BACKGROUND: The initial focus of the US public health response to COVID-19 was the implementation of numerous social distancing policies. While COVID-19 was the impetus for imposing these policies, it is not the only respiratory disease affected by their implementation. This study aimed to assess the impact of social distancing policies on non-SARS-CoV-2 respiratory pathogens typically circulating across multiple US states. METHODS: Linear mixed-effect models were implemented to explore the effects of five social distancing policies on non-SARS-CoV-2 respiratory pathogens across nine states from January 1 through May 1, 2020. The observed 2020 pathogen detection rates were compared week-by-week to historical rates to determine when the detection rates were different. RESULTS: Model results indicate that several social distancing policies were associated with a reduction in total detection rate, by nearly 15%. Policies were associated with decreases in pathogen circulation of human rhinovirus/enterovirus and human metapneumovirus, as well as influenza A, which typically decrease after winter. Parainfluenza viruses failed to circulate at historical levels during the spring. Total detection rate in April 2020 was 35% less than historical average. Many of the pathogens driving this difference fell below historical detection rate ranges within two weeks of initial policy implementation. CONCLUSION: This analysis investigated the effect of multiple social distancing policies implemented to reduce transmission of SARS-CoV-2 on non-SARS-CoV-2 respiratory pathogens. These findings suggest that social distancing policies may be used as an impactful public health tool to reduce communicable respiratory illness.

Acute respiratory infections are associated with significant mortality and societal disruption.

Influenza accounts for approximately 20,000 deaths annually in the United States (US) and has been as high as 80,000 deaths in recent years [1, 2] . Non-influenza respiratory infections lead to approximately $40 billion annually in direct and indirect costs in the US due to missed work and health-care treatment [3] . The COVID-19 pandemic illustrates these negative impacts at epic proportions. As of March 2021, over 515,000 US deaths are attributed to COVID-19, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [4] . In the second quarter of 2020, the US economy experienced a 31% decrease in real GDP [5] .

To reduce transmission and population exposure to SARS-CoV-2, the initial focus of the US public health response was to implement numerous social distancing policies. While COVID-19 was the impetus for imposing these policies, it is not the only respiratory disease affected by their implementation. The wide-scale adoption of multiplex diagnostic tests and pathogen surveillance platforms, such as BioFire® Syndromic Trends, enables the analysis of respiratory diseases throughout the pandemic [6, 7] . While there may be many contributing factors that affect respiratory disease incidence, the initial interventions to prevent the spread of SARS-CoV-2 provide a unique opportunity to evaluate their secondary effects on non-SARS-CoV-2 pathogens.

Previous studies examined the effects of social distancing policies on mobility within a population and the growth rates of COVID-19, concluding that the policies resulted in a measurable decrease for both outcomes [8] [9] [10] [11] [12] [13] [14] [15] . Other studies have concluded that the implementation of social distancing policies reduced the incidence of non-SARS-CoV-2 respiratory pathogens such as influenza, respiratory syncytial virus, and Mycoplasma pneumoniae [16] [17] [18] [19] [20] [21] .

This study aimed to assess the impact of social distancing policies intended to limit the spread of SARS-CoV-2 on non-SARS-CoV-2 respiratory pathogens typically circulating at the time of A c c e p t e d M a n u s c r i p t 6 implementation. This was achieved using current and historical detection rates from a large-scale epidemiological network which includes results from 21 respiratory pathogens across multiple states. Understanding the effects of the policies on non-SARS-CoV-2 respiratory pathogens may better inform future policies to reduce the incidence and societal impacts of respiratory infections.

Non-SARS-CoV-2 respiratory pathogen detection rates were acquired from BioFire® Syndromic Trends, a cloud-based epidemiological network containing de-identified results from a multiplex diagnostic panel surveying 21 respiratory targets [6] . This diagnostic panel simultaneously tests for adenovirus (AdV), Bordetella pertussis, Bordetella parapertussis, Chlamydia pneumoniae, Mycoplasma pneumoniae, coronaviruses (CoVs) 229E, HKU1, NL63, and OC43, influenza A H1, H1-2009, H3, and influenza A (no subtype), influenza B, human metapneumovirus (hMPV), human rhinovirus/enterovirus (HRV/EV), parainfluenza viruses (PIVs) 1, 2, 3, and 4, and respiratory syncytial virus (RSV). Due to variance in pathogen subtype circulation each year, the bacterial and viral targets were categorized into nine groups: AdV, bacteria, non-SARS-CoV-2 CoVs, influenza A, influenza B, hMPV, HRV/EV, PIVs, and RSV. Pathogen detection rate was defined as positive counts of a given pathogen group divided by the total number of tests. The total detection rate was calculated as the sum of all pathogen detection rates. Daily detection rates from January 2015 through May 2020 for nine US states, including California, Colorado, Illinois, Kansas, Michigan, Missouri, Nebraska, New York, and Ohio, were included in these analyses. States chosen were those with three or more datacontributing institutions and at least three years of data for historical comparisons. National detection rates were calculated by aggregating data from all data-contributing US institutions.

Supplementary orders [24] . The dates of social distancing policies implemented from January through April 2020 were acquired at the county level for the nine states [24] . County-level policy implementation dates were averaged to calculate a single state implementation date.

National and state-level historical mean detection rates and ranges (minimum rate and maximum rate) were calculated using detection rates from 2015-2019 for each epidemiological week. The observed 2020 detection rates were compared week-by-week to the historical rates to determine deviations from historical values. The Deviation Week was marked as the first of three consecutive weeks in which the 2020 rate fell below its respective historical range.

Additionally, to assess the impact of BioFire test utilization on pathogen detection rates, the 2020 weekly test counts was calculated as a proportion of 2019 weekly test counts. The same 2020-2019 comparison was performed for weekly positive tests.

A c c e p t e d M a n u s c r i p t 8

Linear models have been previously used to assess the impact of policy effects on COVID-19 rates and mobility [8, [12] [13] [14] [15] . Linear mixed-effect models were built for each pathogen group detection rate and total detection rate using state-level rates from January 1 through May 1, 2020. The five policy groupings were included as fixed effects: Foreign Travel Ban, Federal Guidelines, Public

Closures, Gatherings Ban, and Stay at Home. States were included as a random intercept to control for variability between states and to assess the impact of the policies themselves. A linear temporal effect was included to account for non-policy effects across states, such as growing awareness of the emerging pandemic. For each pathogen group, and the total detection rate, the model is: Due to the range of pathogen incubation periods and the potential delay for testing, the analysis investigated a one-week and two-week lag [25] . Models were therefore built with no lag, one-week, and two-week lags.

Policy effects on detection rates were assessed by examining the policy coefficients from all models.

Each coefficient represents the shift from the mean detection rate, given that everything else is held constant in the model. A policy grouping effect was considered statistically significant if the policy A c c e p t e d M a n u s c r i p t 9 coefficient's 95% confidence interval (CI) did not overlap with zero. Model performance was evaluated using the coefficient of determination, R 2 .

Decreases in national total detection rate and in many pathogen detection rates were seen in March and April 2020 (Supplementary Figure 1) . Comparisons of national historical and 2020 total detection rate and pathogen group detection rates are shown in Figures 1 and 2 , respectively. The national total detection rate was within the historical range in weeks prior to the Foreign Travel Ban implementation on March 11, 2020 and exhibited a reduction in subsequent weeks ( Figure 1 ). Both national and state detection rates' Deviation Weeks compared to the Foreign Travel Ban are outlined in Supplementary Table 2 .

Based on observed Deviation Weeks, the decrease in the national total detection rate may be attributed to decreases in AdV, hMPV, and HRV/EV, as well as the lack of increase in PIV ( Figure 2 ).

The detection rates for AdV and PIV decreased nationally during the week preceding the Foreign Travel Ban's implementation. Conversely, HRV/EV and hMPV detection rates both deviated within two weeks following the policy implementation.

Non-SARS-CoV-2 CoVs, influenza A, and RSV had detection rates that fell outside their historical range during the time of the analysis. Influenza B also fell outside its historical range but had increased detection rates earlier than in past years. Bacteria was the only group with consistent national detection rates through the time frame of the analysis (Figure 2 ). 

The effects of social distancing policies on non-SARS-CoV-2 respiratory pathogen detection rates were estimated using linear mixed-effect models. Figure 4 outlines the policies' mean effect sizes on total detection rate and 95% CI. By including the temporal effect, policy effect sizes are measures of the change in the slope of the pathogen rate. Positive effects suggest an increase in total detection rate potentially associated with a given policy, while negative effects indicate decreases. Individual social distancing policies decreased total detection rate, in some cases by nearly 15%. The one-week lag yielded the best model fit, with the highest R 2 value, for total detection rate. Supplementary been an additional factor contributing to increases in voluntary social distancing [27] . Distinguishing between the effects of Federal Guidelines, the Gatherings Ban, and Public Closures was difficult as they were implemented within a narrow time frame and had some overlap in recommendation components. Additionally, New York and Michigan implemented state-wide mask mandates on April 15 and 26, respectively [28, 29] . Because these were implemented towards the end of the study period, mask mandates were excluded from the analysis. Several studies suggested the presence of one pathogen may deter a secondary pathogen infection, as was observed with the disappearance of seasonal influenza and delayed onset of RSV during the 2009 influenza A H1N1 pandemic [30] [31] [32] [33] . Another study found that the presence of HRV interfered with influenza A infections by stimulating an immune response [34] . Conversely, studies have suggested that infection with one pathogen may also encourage opportunistic secondary infections [32, 35, 36] . During this study period, the BioFire® System did not contain a SARS-CoV-2 assay, which made it challenging to assess pathogen interactions. However, the HRV/EV detection rate and the with seasonal respiratory viruses after data is available from the 2020-2021 respiratory season.

A limitation of this investigation was the restricted resolution into institutions' testing procedures and testing algorithms. To rule out the use of testing as a confounder, the proportions of 2020

weekly tests run as well as 2020 weekly positive tests compared to 2019 were evaluated. The increase in early 2020 national test utilization from 150% to nearly 300% may have been due to

BioFire test use as a rule-out diagnostic for COVID-19 while the availability of COVID-19 tests was still limited [37, 38] . However, the change in test utilization was not proportional to the rapid decrease in detection rates. Additionally, weekly positive tests increased between January and March of 2020 before drastically decreasing in April, suggesting that the decreases in detection rates may be due to declining pathogen circulation rather than solely an increase in testing.

An additional limitation of this analysis is the time frame over which pathogen detection rates were being investigated. A prior study suggests M. pneumoniae may be affected by social distancing pneumoniae detection rates did decrease in the second half of 2020. The decreases' potential connections to social distancing policies will be investigated in a further analysis.

A similar decrease in detection rates was apparent for communicable gastrointestinal viruses (Supplementary Figure 6) . Between the beginning of March and the end of April, there was an 82%

decrease in combined detection rates of adenovirus, astrovirus, norovirus, rotavirus, and sapovirus.

Gastrointestinal bacterial pathogen detection rates did not decrease. This suggests the social distancing policies during the COVID-19 pandemic may be associated with declines in viral pathogens of non-respiratory syndromes.

While the social distancing policies were implemented to reduce transmission of SARS-CoV-2, their implementation was effective at decreasing detection rates for non-SARS-CoV-2 respiratory pathogens. This analysis investigated social distancing policy effects on respiratory pathogens in nine US states and identified multiple policies that may have resulted in a decrease in pathogen circulation. When spring 2020 rates were compared against the historical averages, there was a significant decline in detection rates. These findings suggest that social distancing measures may be an impactful public health tool for reducing the incidence and burden of communicable respiratory illness. Further research should evaluate the effects of social distancing fatigue, states re-opening, and the long-term effects of the COVID-19 pandemic on respiratory disease. 

The data from Syndromic Trends used in this study are de-identified according to HIPAA standards.

Additionally, BioFire enters into a Data Use Agreement with each institution to protect against reidentification of any individual and to control the use of the data. Therefore, patient consent was neither required nor applicable due to the nature of this study.

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