key: cord-0959316-c56qufai
authors: Kingsbury Lee, Sophie-An; Laefer, Debra F.
title: Spring 2020 COVID-19 Community Transmission Behaviours Around New York City Medical Facilities
date: 2021-06-26
journal: Infection prevention in practice
DOI: 10.1016/j.infpip.2021.100158
sha: b37ba2f6cbc372b4da80084f4931f0b15c212d72
doc_id: 959316
cord_uid: c56qufai

BACKGROUND Epidemiological studies have long been used to develop infection transmission prevention, but exact patterns of touch behaviours and transportation choices (COVID-19 community spread contributors) were previously unknown. AIM To investigate individual risk behaviour levels with respect to local COVID-19 infection levels. METHODS A longitudinal field study recorded behaviours of individuals leaving medical facilities following the New York State’s PAUSE order. A subset of that data was analyzed herein (4,793 records, 16 facilities, 23rd March – 17th May 2020). Touched objects and transportation choices were compared over time using chi-square tests (p < 0.05 significance threshold). FINDINGS Over eight weeks, touching progressively decreased but for-hire vehicle usage increased. In week 1, 60.4% of subjects touched at least one object: a building’s door handle (21.8%); traffic light, railing, or parking meter (5.6%); shared object (19.7%, e.g. vehicle’s door handle); personal object (13.9%, e.g. cell phone); or themselves (0.7%). Certain touch points, however, remained. Throughout the study, public transportation ridership remained steady (about 20%); for-hire car usage increased from 0% in week one to 7% in week eight, mirroring a 7% decrease in personal vehicle usage (from 34% to 27%). Touching and transportation patterns varied significantly by facility. CONCLUSIONS Inconsistent trends in risk related behaviour were documented in the eight weeks following a NYC PAUSE order. Namely, while overall touching decreased 25%, there was no appreciable change in cell phone usage, and for-hire vehicles usage increased 7%.

On 1 st March 2020, the first COVID-19 case was confirmed in New York City (NYC) [1] . Over the ensuing months, NYC became one of the first COVID-19 hotspots in the United States, with an average of over 4,300 new cases a day from late-March to mid-April [1] . Based on New York State's issuance of a PAUSE order, most of NYC was shuttered, starting the evening of 22 nd March, 2020 [2] . Additionally, in an effort to minimize infection transmission, medical facilities were only accessible to patients, health care workers, and delivery drivers [3] . On the first day of the PAUSE order, NYC had 771 new COVID-19 hospital admissions. Within one week, that figure more than doubled to 1,503 [1] . With shortages of personal protective equipment (PPE), health care workers became infected with COVID-19 at high rates (with that group comprising nearly 11% of all reported COVID-19 cases in May 2020) [4] . Furthermore, because of hospital capacity constraints, COVID-19 positive patients with mild to acute symptoms were sent home to reserve beds for the sickest patients [5] .

At this time, the full nature of COVID-19 was unknown, although person-to-person transmission by respiratory droplets -like other coronaviruses -was suspected from the beginning. Early studies showed that the virus could spread via surfaces when a non-infected person touched an infected individual or a contaminated inanimate object and then touched their eyes, nose, or mouth [6] , with laboratory studies showing the virus's ability to survive on common materials for a period of a few hours to multiple days depending upon the material, thereby demonstrating the risk of transmission via surfaces [7] . Other community transmission mechanisms were linked to transportation choices. The NYC subway system was said to be one of the largest disseminators of COVID-19 "if not the principal transmission vehicle", in the first quarter of 2020 [8] .

Developing Epidemiology mechanisms in Three-dimensions to Enhance Response (DETER), a field study funded by the National Science Foundation (NSF) collected over 5,100 records of observed touch behaviours and transportation choices of people exiting select NYC medical facilities over the 8.5 weeks (22 nd March to 19 th May 2020) immediately following New York State's PAUSE order, which was coincident with NYC's spring COVID-19 peak. This paper analyses those data to identify behaviours that may have facilitated COVID-19's spread. Specifically, it examines how people interacted with the built environment and their transportation choices, with the aim of providing insights that may inform the understanding of risk and related public health policies.

DETER was an Institutional Review Board (IRB)-approved project to gather information about randomly selected subjects (patients, health care workers, and delivery workers) leaving various hospitals and urgent care clinics in NYC. The observed route of each subject was recorded in a Keyhole Markup Language/Zipped (KML/KMZ) compatible mapping program J o u r n a l P r e -p r o o f along with observations related to gender, time, date, objects touched, and destinations and transportation means selected (Supplemental Figure 1 ). Subsequently, the data were manually transferred to a spreadsheet and coded (Google Sheets/Microsoft Excel). This paper analyses a subset of that publicly available dataset (4,793 records from 16 facilities from 23 rd March to 17 th May 2020).

While the study began on 22 nd March and ended 19 th May 2020 (and captured 5,124 records), the greatest number of observers were in the field during eight weeks (23/03/20-17/05/20) producing 4,949 records. Of those, 144 subjects returned to the hospital. Since this study considered transportation choices, those records were excluded. Of the 4,805 records, 12 were excluded due to incompleteness. The final dataset considered contained 4,793 records (2,442 females and 2,351 males). Cumulatively, across all facilities, the daily collection averaged 87.5 subjects (range 0-165). In week one (23/03-29/03/20), there were only six observers at five medical facilities in the field, three hospitals and two urgent care clinics. In weeks two through seven (30/03-17/05/20), this expanded to 16 observers regularly observing behaviours of randomly selected subjects (patients, health care workers, and delivery workers) leaving 16 medical facilities (eight hospitals and eight urgent care clinics) in Brooklyn, the Bronx, Queens, and Manhattan. For a more nuanced understanding of the data, four facilities were analysed individually (see Supplemental Figures 2-3) .

In this paper, objects touched were clustered into five categories: (1) "Personal Objects," including cell phones, cigarettes, personal care items (such as makeup, tissues, and hand sanitizer), and clothing; (2) "Environmental Objects," fixed objects such as a building's door handle, bench, fence, mailbox, surface, traffic light post, trash can, or wall; (3) "Self," the individual's face, hair, and head; (4) "Shared Objects," objects that were not fixed and that multiple people could touch, including a vehicle's door handles and delivery packages; and (5) "Other." The number and percentage of people who touched objects in each category were calculated for each week by gender and facility. The total percentage of people who touched each category of objects across all the facilities was also calculated by week and gender. To contextualize this information, these data are plotted together with daily numbers of new NYC COVID-19 hospitalizations [1] .

For this analysis, transportation was categorized into five groups: using public transportation (bus and subway), driving/riding in a personal vehicle, riding in a for-hire vehicle, walking, and riding a bicycle. The number and percentage of people who used each mode of transportation were calculated for each week by gender and facility. The total percentage of people who used each transportation choice for all the facilities combined was calculated by week and gender.

Differences between week one and week eight (in the percentage of people who touched objects or who touched no objects during observation) was compared using a chi-square analysis. Similarly, the differences in the percentage of people who used different modes of transportation were also compared using a chi-square analysis. For all analyses, p < 0.05 was used as the threshold for significance.

On average, across all 16 facilities and eight weeks of observation, 44.2% of subjects touched one or more objects: one touch (35.6%), two touches (7.1%), or three or more touches (1.5%). As shown in Table 1 , people most frequently touched personal objects (20.7%), most commonly cell phones (10.4%), cigarettes (3.8%), and personal care items (1.5%). Of the 11.6% of people who touched environmental objects, the majority touched a building's door handle (9.5%), while others touched traffic light posts (0.8%) and trash cans (0.5%). A total of 11.2% touched a vehicle's door handle, and the majority of those vehicles (81.1%) were personal vehicles. Only 69 people (1.4%) touched themselves during the observation period, making "self" the least frequently touched category.

Over the eight-week observation period, the level of touching significantly decreased (p < 0.001) from over 60% of subjects touching at least one object in week one to less than 35% in week eight ( Figure 1 ). The greatest reduction in objects touched was for environmental objects like a building's door handles (from 29.2% to 9.1%, p < 0.001) and shared objects, such as a vehicle's door handle (from 21.5% to 14.1% , p < 0.01). The percentage of people touching their faces, hair, and clothing remained consistently low (< 1.7%). For some personal objects, the frequency of touching increased slightly during the observation period. For example, in week one, 4.2% touched their cell phones, and by week eight, that figure was 7.9% (p < 0.05).

Touch patterns varied by site (Supplemental Figure 2 ). For example, CityMD Fresh Meadows, an urgent care clinic in Queens, had the highest percentage of people who touched nothing over the eight weeks, never falling below 92.2%. In contrast, at Wyckoff Heights Medical Center in Brooklyn, only 15.1% touched nothing, while 23.7% touched a personal object, and 51.6% touched an environmental object, most commonly a door handle. During the week beginning 27 th April, that hospital modified the observed exit to have an automatic door, which instantly decreased touch rates (Supplemental Figure 2d) . As the observations from the DETER study were not shared with the medical facilities during this period, the change of door mechanism was an internal decision.

When analysed by gender, the percentages of men and women who were observed touching at least one object were similar (44.9% and 43.7%, respectively), and they touched the same number of objects: one object (35.3% of men and 35.8% of women), two objects (7.4% of men and 6.8% of women), or three or more objects (1.9% of men and 1.1% of women). The level of touches over time was also similar between men and women, although the objects they touched differed. Over the observation period, both men and women touched cell phones (9.3% and 10.8%, respectively) and building door handles (10.4% and 8.1%, respectively) at similar rates, but men touched cigarettes more than twice as much as women (5.0% and 2.4%, respectively), which is largely reflective of the current gender breakdown of NYC smokers (17% male and 9% female) [9] . Figure 1 . Patterns of touch behaviour for individuals leaving select NYC medical facilities from 23 rd March to 17 th May 2020. As a reference, the daily COVID-19 hospitalizations levels in NYC are also plotted). * The asterisk represents the percentage of people in week one who did not touch any objects, including people who only touched the door handle at Wyckoff Heights Medical Center, to show a predicted value for the percent of people who would not touch an object had the medical centre switched to an automated door prior to the COVID-19 pandemic.

Of the initial 4,805 individuals analysed for touch behaviour, a further 1,180 records were excluded because final destinations were considered ambiguous: "Other" (227 records), "Parking Lot" (397 records), "Tent (Hospital)" (30 records), "Street" (370 records), or not recorded (156 records). Because bicycles were not clearly labelled as "Personal Bicycle" vs "Shared/rented Bicycle," the 21 records that noted bicycle usage were also removed, leaving 3,604 records (1,887 females and 1,717 males) for transportation analysis.

Averaged across all facilities and the eight-week study, the most common transportation choices were walking (41.9%) and personal vehicle (32.5%) (Figure 2) . A further 19.6% used public transportation (10% subway and 9.7% bus) and 5.9% chose a for-hire vehicle. Transportation choices also changed over the eight-week period (Figure 2 ). During week one, 34.3% chose personal vehicles, which decreased to 26.9% in week eight, a change that did not reach statistical significance (p < 0.1). In contrast, the percentage of people choosing for-hire vehicles increased from 0% in week one to 7.2% in week eight. The percentage walking did not change significantly (45.9% in week one vs 48.8% in week eight; p > 0.1). Public transportation had a small downturn with 19.9% in week one (9.4% subway, 10.5% bus) versus 16.5% in week eight (7.5% subway, 9.1% bus). Individual transportation choices varied by gender. Over the whole observation period, more men departed in personal vehicles than women (36.3% vs 29.2%), and women were 1.3 times more likely to ride public transportation than men (21.7% versus 16.3%) [p < 0.001]. Not only did transportation choice vary longitudinally and by gender, but there were also notable differences by facility (Supplemental Figure 3) . For example, at CityMD Fresh Meadows J o u r n a l P r e -p r o o f Urgent Care clinic in Queens, initially 93.1% left the urgent care clinic in a personal vehicle in week one, but this dropped to 73.7% by week eight, with more walking (21.1% in week eight) and little change in public transportation. In contrast, at NYU Langone Brooklyn hospital, in week one 74.1% left in personal vehicles. This figure dropped to 38.9% in week eight. At that site, while walking increased (7.4% in week one to 22.9% in week eight), public transportation ridership also increased dramatically (18.5% in week one to 31.3% in week eight). So the change was not wholly explainable by an improvement in the weather.

With Governor Andrew Cuomo's mandatory PAUSE order on 22 nd March 2020, New York became one of the first states in the U.S. to go into lockdown to reduce the transmission of COVID-19 [2]. According to Google's mobility trend data [10] , the percentage of New Yorkers visiting retail and recreation facilities, grocery stores and pharmacies, parks, transit stations, and workplaces declined precipitously. For example, by 23 rd March, the percentage of people going to transit stations and workplaces in Manhattan had fallen to 23% and 25% of baseline, respectively [10] . Despite these changes, over the next four weeks (23/03-19/04/20), New York City saw an average of over 1,000 new COVID-19 hospitalizations each day [1] . Hospitals and urgent care clinics became epicentres of COVID-19 over the next two months.

This analysis of an observational study of 4,793 individuals leaving medical care facilities in NYC between 23 rd March to 17 th May 2020 included many people at high risk for infection, including medical facility employees, patients, and essential workers delivering goods and services. This study identified a high frequency of behaviours, such as contact with shared environmental objects like door handles, potentially contaminated personal objects, like cell phones, as well as shared modes of transportation that could increase community transmission. These behaviours decreased over time, but still persisted amongst observed individuals a full eight weeks after the lockdown, despite abundant public health education about COVID-19 transmission.

Studies have shown that individuals may get infected by touching contaminated surfaces then touching their eyes, noses, or mouths before washing their hands [6] , and that COVID-19 can reside on surfaces such as plastics and stainless steel for up to two to three days [7] . While not the primary means for transmission, the US Centers for Disease Control and Prevention (CDC) identified doorknobs, counters, and tabletops as high-risk surfaces, and their contamination leading to infectious transmission [11] . Patterns of touch behaviour of doors, chairs, and horizontal working surfaces have also been observed as possible sources of COVID-19 transmission in medical clinics [12] , with one study showing high levels of surface contamination of a COVID-19 patient's hospital room [13] . In the study herein, in week one, almost one-third of observed individuals leaving a medical care facility touched an object that was commonly touched by others without intervening cleaning. These included door handles, street lights, parking meters, and hand railings. The selection of door mechanisms and door designs can reduce transmission risk. For example, one study showed that the onward transmission of bacteria on door handles in a hospital environment was lower with large push plates than with "pull" door handles [14] .

While the incidence of individuals touching shared and environmental objects decreased during the observation period, contact with personal objects, such as cell phones, remained high. Studies have shown that on average, people pick up their cell phones as often as 96 times a day [15] and rarely wash them [16] . When they put their cell phones to their faces, they can introduce bacteria and viral droplets into their eyes, nose, or mouth. In a medical environment, since people touch their cell phones without washing them or their hands, cell phones can act as a reservoir of transmission of potent pathogens [16] . In fact, when 386 health care workers' phones were swabbed early in the pandemic, 316 of them (81.8%) grew bacterial pathogens [16] .

Outside of medical facilities, public transportation sites have been identified as among the highest risk places for acquiring COVID-19, because of touching shared surfaces such as kiosks, touch screens, handrails, and also the inability to maintain social distancing [11] . In this study, 19.1% of people leaving medical facilities used public transportation. Given that on average, before the COVID-19 lockdown, about 56% of NYC's population used public transportation in a given week [17] . The lower figures in week one of this study presumably reflects some change in behaviour in response to public health warnings early in the pandemic combined with the PAUSE order.

Across New York City, for-hire vehicle use dropped considerably between February and April: the number of taxi trips declined from 217,000 per day to 8,000 per day, and ride-hailing services declined from 749,000 to 144,000 per day [18] . A study of 240 NYC drivers found that one in four had a family member who had coronavirus symptoms, and two-thirds of all drivers reported not having sufficient PPE to work safely [19] . Moreover, not all vehicles were fit for proper social distancing [11] . At the start of this study, ride share service fell to zero and increased only modestly during the study period (reaching 7% by week eight).

These findings must be considered within the limitations of this study, specifically with regard to its timing, duration, and the opportunistic observational method. Specifically, there are no baseline data about behaviours prior to the COVID-19 pandemic. Instead other public data sets including Google Mobility Data [10] and previous published studies serve as baseline references. The study data are observational with limited sampling by 16 different observers, and each subject was observed only by a single observer. To minimize variations in measurement and observation, all observers met on video conference calls throughout the data collection period. All data were checked for consistency by two independent researchers during the data cleaning process. Additionally, observers could not see everyone leaving the facility, so they were instructed to randomly choose people, creating the possibility of sampling bias. Lastly, the observers were instructed not to attempt to identify the subject's potential role (patient or type of employee), which limits the ability to ascertain individual risk of infection.

J o u r n a l P r e -p r o o f This study of 4,793 individuals outside 16 medical facilities in New York City in the spring of 2020 demonstrated that despite public health warnings at the onset of COVID-19 in the United States, individuals departing high-risk environments like medical centres demonstrated behaviours that could contribute to COVID-19 community spread, primarily through contact with shared objects and shared modes of transportation. Over the observation period of eight weeks, there were indicators of an increased perception of risk. For example, rates of touch decreased, especially door handles (20% drop from week one to week eight) and vehicle door handles (7.4% drop). However, observed individuals continued to touch certain items, including cell phones at near constant rates, and the percentage of individuals riding in for-hire vehicles increased from 0% in week one to 7% in week eight, which aligns with a 7% decrease in ridership in personal vehicles, an arguably safer transportation choice. These two examples demonstrate that behavioural response to risk was not wholly consistent, as has been observed in some non-COVID-19 studies. As the subjects were not interviewed, the motivations and rationale for their actions could not be fully ascertained (e.g. perhaps individuals were unaware that cell phone usage could be a risk) [20] [21] . In summary, observational results from this DETER data set can be used to inform analyses of infectious disease transmission with epidemiologic models [22] [23] [24] [25] . Furthermore, results can be used to advance more effective public health policies and guidelines that include cleaning regimens for public environmental objects and the removal or relocation of frequently touched objects to help limit the spread of COVID-19. Table   Table I * No Touch indicates the percentage of individuals observed who did not touch any items. Some individuals touched more than one item. ** Had Wyckoff Heights Medical Center implemented an automated door before the PAUSE order, this figure would most likely be closer to 15.5%. Figure 1 . Patterns of touch behaviour for individuals leaving New York City medical facilities from 23 rd March to 17 th May 2020. As a reference, the number of COVID-19 hospitalizations in New York City is also plotted. * The asterisk represents the percentage of people in week one who did not touch any objects, including people who only touched the door handle at Wyckoff Heights Medical Center, to show a predicted value for the percent of people who would not touch an object had the medical center switched to an automated door prior to the COVID-19 pandemic. 

New York: NYC Health Department;c2020

Covid-19: Data -Trends

Governor Cuomo Issues Guidance on Essential Services Under The 'New York State on PAUSE' Executive Order

CDC Estimates of Nurse & Healthcare Worker COVID-19

Cases are Likely Understated

Doctor in New York. None of Us Will Ever Be the Same. The New York Times

An Imperative Need for Research on the Role of Environmental Factors in Transmission of Novel Coronavirus (COVID-19)

Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1

The Subways Seeded the Massive Coronavirus Epidemic

Epi Data Brief

NYC Health; c2018

Mountain View (CA): Google. c2000

Atlanta: CDC; c2020

Protect Yourself When Using Transportation

Hand touches on the surfaces of a healthcare waiting area

Do established infection prevention and control measures prevent spread of SARS-CoV-2 to the hospital environment beyond the patient room?

Hospital Door Handle Design and Their Contamination with Bacteria: A Real Life Observational Study

Are We Pulling against Closed Doors?

Americans Don't Want to Unplug From Phones While on Vacation, Despite Latest Digital Detox Trend

Accessed 27

Mobile phones: Reservoirs for the transmission of nosocomial pathogens

Taxi and Ridehailing Usage in New York City

I'm a New York City Uber driver. The pandemic shows that my industry needs fundamental change or drivers will never recover. Business Insider

Behavioral inconsistencies do not imply inconsistent strategies. Front Psychol

Reconceptualizing the determinants of risk behavior

Emerging Infectious Diseases

Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy

Infectious disease policymaking: Lessons for the COVID-19 pandemic

Mathematical analysis of COVID-19 via new mathematical model

Supplemental Figure 2. Patterns of touch behaviour from individuals leaving four medical facilities in New York City (a) City MD Fresh Meadows Urgent Care Clinic (Queens)

As a reference, the number of COVID-19 hospitalizations in New York City is also plotted. Week one data are not available for City MD Fresh Meadows and City

Supplemental Figure 3. Modes of transportation used by individuals leaving three medical facilities in New York City (a) City MD Fresh Meadows Urgent Care Clinic (Queens)

As a reference, the number of COVID-19 hospitalizations in New York City is overlaid. Week one data are not available for City MD Fresh Meadows Urgent Care Clinic

The authors have no conflicts to declare Funding This work was funded by the U.S. National Science Foundation (Award No. 2027293).

The study was an Institutional Review Board (IRB)-approved project.J o u r n a l P r e -p r o o f