NEHA March 2025 Journal of Environmental Health

The March 2025 issue of the Journal of Environmental Health (Volume 87, Number 7), published by the National Environmental Health Association.

Environmental Health To build, sustain, and empower an effective environmental health workforce Volume 87, No. 7 March 2025 Journal of

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Environmental Health To build, sustain, and empower an effective environmental health workforce Volume 87, No. 7 March 2025 Journal of

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ABOUT THE COVER

Enhanced Hotel Room Cleaning: Environmental Microbiological Sampling Investigation in a Midscale Hotel Property..................................................................................8 International Perspectives: Air Pollution and COVID-19: Exploring the Link Between Pandemic Spread and Pollutants ..................................................................................... 20

Historically, hotel rooms have been linked with major bacterial and viral outbreaks. While safety measures implemented dur- ing the COVID-19

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pandemic are no longer enforced by federal, state, or local agencies, many hotels continue to place emphasis on enhanced cleaning processes. This month’s cover article explored the efficacy of enhanced cleaning pro- cesses by quantifying generic and pathogenic microorganisms before and after cleaning in hotel rooms. The results showed that several areas of the room had higher counts after cleaning, indicating that a more effective method needs to be assessed to ensure environ- mental safety in hotel rooms. See page 8. Cover images © iStockphoto: iLexx, bodym, M-Production, dima_sidelnikov, Thai Liang Lim

Charting the Path: Impact of the National Environmental Public Health Internship Program on Educational and Career Choices............................................................................28

Direct From EHAC: Past, Present, and Future ............................................................................. 36

Spotlight on Emerging Professionals: My Summer in Nome: A Journey of Environmental Public Health in Rural Alaska . .................................................................................................... 40

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Environmental Health Calendar................................................................................................42

JEH Quiz #5...............................................................................................................................43

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Spotlight on NEHA Resources: Vectors and Pests ....................................................................... 44

AAS Wagner Award................................................ 5 CDP, Inc..................................................................7 EHAC-Accredited Programs.................................39 EHLR Certificate Program....................................35 Hedgerow Software...............................................59 HS GovTech..........................................................60 Inspect2GO............................................................. 2 JEH Advertising..................................................... 35 NEHA Endowment Foundation Donors..............57 NEHA Job Board...................................................51 NEHA Membership................................................4 NEHA REHS/RS Credential..................................51 NEHA REHS/RS Study Guide...............................26 NEHA/AAS Scholarship Fund Donors..................27 NEHA/NSF Snyder Award......................................5

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President’s Message: The Promise of Artificial Intelligence in the Field of Environmental Health ............ 6

Special Listing............................................................................................................................46

NEHA Second Vice-Presidential Candidate Profile...................................................................48

NEHA Regional Vice-Presidential Candidate Profiles................................................................49

NEHA 2025 AEC.......................................................................................................................52

NEHA News...............................................................................................................................54

NEHA Member Spotlight...........................................................................................................58

March 2025 • Journal of Environmental Health

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An open access journal published monthly (except bimonthly in January/ February and July/August) by the National Environmental Health Association (NEHA), 1400 S. Colorado Blvd., Ste. 325, Denver, CO 80222-9998. Phone: (303) 802-2200; Internet: www.neha.org. E-mail: jeh@neha.org. Volume 87, Number 7. Yearly print subscription rates: $160 (U.S.) and $200 (international). Single print copies: $15, if available. Claims must be filed within 30 days domestic, 90 days foreign, © Copyright 2025, NEHA (no refunds). Opinions and conclusions expressed in articles, columns, and other contributions are those of the authors only and do not reflect the policies or views of NEHA. NEHA and the Journal of Environmental Health are not liable or responsible for the accuracy of, or actions taken on the basis of, any information stated herein. NEHA and the Journal of Environmental Health reserve the right to reject any advertising copy. Advertisers and their agencies will assume liability for the content of all advertisements printed and also assume responsibility for any claims arising therefrom against the publisher. Advertising rates available at www.neha.org/jeh. The Journal of Environmental Health is indexed by Clarivate, EBSCO (Applied Science & Technology Index), Elsevier (Current Awareness in Biological Sciences), Gale Cengage, and ProQuest. The Journal of Environmental Health is archived by JSTOR (www.jstor.org/journal/ jenviheal). Full electronic issues from present to 2012 available at www.neha.org/jeh. All technical manuscripts submitted for publication are subject to peer review. Visit www.neha.org/jeh for submission guidelines and instructions for authors. To submit a manuscript, visit https://jeh.msubmit.net. Direct all questions to jeh@neha.org. Periodicals postage paid at Denver, Colorado, and additional mailing offices. POSTMASTER: Send address changes to Journal of Environmental Health , 1400 S. Colorado Blvd., Ste. 325, Denver, CO 80222-9998.

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Honoring a history of advancing environmental health. Walter F. Snyder was a pioneer in our field and was the cofounder and first executive director of NSF. He embodied outstanding accomplishments, notable contributions, demonstrated capacity, and leadership within environmental health. Do you know someone like that? Nominate them for the Walter F. Snyder Award for outstanding contributions to the advancement of environmental health. This award is cosponsored by NSF and NEHA. Nomination Deadline: May 1 nsf.org/about-nsf/annual-awards/ snyder-award Walter F. Snyder Award

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Do you know an exceptional diplomate of the American Academy of Sanitarians (AAS) who is a leader who shows professional commitment, outstanding resourcefulness, dedication, and accomplishments in advancing the environmental health profession? Nominate them to be recognized with the AAS Davis Calvin Wagner Sanitarian Award.

Nomination deadline: April 15 www.sanitarians.org/Awards

March 2025 • Journal of Environmental Health

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 PRESIDENT’S MESSAGE

The Promise of Artificial Intelligence in the Field of Environmental Health

CDR Anna Khan, MA, REHS/RS

I love science fiction and as a girl grow- ing up watching the original Star Trek TV show, I often wondered if we would have some of those amazing gadgets in real life. I remember in the early 1980s how thrilled my dad was to get the first mobile phone, the video game Pong , and of course, the microwave. Fast forward to the present, I feel I am living on the cusp of amazing technological developments, especially in the morning when I ask Alexa to turn on the lights, tell me the day’s weather and news, or to add shampoo to my Amazon shopping list. This train of thought led me to think about how technology impacts our work- force, particularly artificial intelligence (AI) and how we will perform daily tasks. According to PwC (2025), AI could poten- tially contribute $15.7 trillion to the global economy and provide a 26% boost to the GDP (gross domestic product) for local economies by 2030. When I talk about the transformative impact of AI on work- force development, please note it is not a personal endorsement of AI, but rather my thoughts based on models and projec- tions across most industries that predict the transformative impact AI will have on workforce development. This technology could be like the advent of the internet and its impact on our day-to-day lives. Chris Hyzy, chief investment officer at Merrill and Bank of America Private Bank, stated, “AI should transform the global economy as electricity and the steam engine did in their own time” (Bank of America Private Bank, 2025).

ally, AI could simplify complex regulatory language in reports, making them clearer for business owners and partners, stream- lining communication, and improving food safety standards. Further, generative AI could revolution- ize communication campaigns by stream- lining the creation of tailored commu- nication materials for diverse audiences. With its ability to analyze vast data sets and understand audience preferences, gen- erative AI could produce engaging content such as social media posts, blog articles, videos, and email newsletters. It could enable marketers to quickly generate varia- tions of materials optimized for specific demographics, cultural contexts, or com- munication channels, ensuring relevance and resonance. For instance, an AI model could create persuasive ads targeting young professionals on social media while gener- ating informative brochures for older audi- ences. Additionally, it could personalize content at scale, adapting tone, language, and visuals to align with audience prefer- ences. This capability not only enhances efficiency but also ensures consistency in brand messaging across all platforms, making campaigns more impactful and cost-effective. Another AI-powered tool is video gen- eration. This emerging aspect of AI could change public health communication by creating engaging and accessible videos that break down complex health informa- tion and overcome literacy barriers. Public health organizations could produce visu- ally rich and easy-to-understand videos

The ability to quickly generate

content using AI will allow public health agencies to respond rapidly during crises.

I think that we should look at AI, espe- cially generative AI, and how it can support our work in public health. Generative AI could significantly enhance the efficiency and accuracy of food inspection processes for environmental health practitioners by automating the creation and analysis of inspection forms and reports. AI-powered tools can generate standardized inspection templates tailored to specific regulations and compliance requirements, ensuring consistency across different inspections. During inspections, practitioners could use AI to input observations in real time, with the system automatically generating comprehensive reports that include key findings, risk assessments, and actionable recommendations. Generative AI could also analyze historical data from previous inspections to identify recurring issues or emerging trends, helping practitioners pri- oritize high-risk establishments. Addition-

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tailored to diverse audiences, using ani- mations, voiceovers, and culturally appro- priate visuals to convey critical messages about topics such as disease prevention, vaccination, and hygiene practices. These AI-generated videos can include simplified language, subtitles in multiple languages, and scenarios relatable to specific commu- nities, ensuring inclusivity and clarity. The ability to quickly generate content using AI will allow public health agencies to respond rapidly during crises—such as pandemics or natural disasters—and deliver essential information to populations regardless of literacy levels or language barriers. This approach not only enhances public under- standing but also fosters trust and compli- ance with health guidelines. The National Environmental Health Asso- ciation (NEHA) is thinking about how AI can support environmental health practitioners. In November 2024, Ashish Sharma, NEHA IT director, provided AI webinars. He shared his experience, covered the basic under-

standing of AI and applications in day-to-day work, and provided specific case scenarios for environmental health professionals. To learn more, the recordings are available on the NEHA website at https://www.neha.org/ ai-webinars. I will end this column on a personal note. I mustered up the courage yesterday to try my Tesla’s semi-autonomous driving feature aptly named full self-driving (FSD). It was difficult giving control over to a car even though I am still in the driver’s seat and can take over at any given moment. To my pleas- ant surprise, the ride was smooth and reas- suring. In fact, it made me think about how much safer the roads would be if cars were driving themselves. No road rage, no inex- perienced drivers struggling to learn road rules, no drivers overcome by their emo- tional state, and no drivers worried about getting around at night due to their night vision or other eyesight issues. We continue to grow and advance society with science and technology. So, just like

when computers and the internet brought about incredible positive change, I encourage you to be prepared and to educate and equip yourself with the knowledge to support our communities through advances that once felt straight out of science fiction.

akhan@neha.org

References Bank of America Private Bank. (2025). Arti- ficial intelligence: A real game changer . https://www.privatebank.bankofamerica. com/articles/economic-impact-of-ai.html PwC. (2025). Sizing the prize—PwC’s Global Artificial Intelligence Study: Exploiting the AI revolution . https://www.pwc.com/gx/en/ issues/artificial-intelligence/publications/ artificial-intelligence-study.html

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Enhanced Hotel Room Cleaning: Environmental Microbiological Sampling Investigation in a Midscale Hotel Property

Alberto A. Beiza, PhD Conrad N. Hilton College of Global Hospitality Leadership, University of Houston Zahra H. Mohammad, PhD Conrad N. Hilton College of Global Hospitality Leadership,

University of Houston Cathy Cheatham, MS

Conrad N. Hilton College of Global Hospitality Leadership,

University of Houston Sujata A. Sirsat, PhD

Conrad N. Hilton College of Global Hospitality Leadership, University of Houston

pandemic (Li et al., 2021; Qi & Chen, 2023; Sawang et al., 2023). Further, Jiang and Wen (2020) emphasize the need to integrate hotel hygiene and cleanliness into marketing and management strategies and consider how guests perceive the cleanliness of specific ho- tel surfaces. They also recommend further re- search into the evolving relationship between the hospitality and healthcare sectors. Many hotels have placed an emphasis on cleaning practices and have incorporated im- ages related to cleanliness and safety messag- ing into their marketing strategies (Herédia- Colaço & Rodrigues, 2021; Jiménez-Barreto et al., 2021; Peco-Torres et al., 2021). To re- store and maintain consumer confidence in safe travel, several leading hotel brands have partnered with sanitation companies to implement enhanced cleaning programs that target high-touch surfaces in lodging operations (Ecolab, 2021; Hilton, 2021; IHG, 2021; Marriott, 2021; Reckitt, 2021; Wynd- ham Hotels, 2021). Although this enhanced cleaning is a step in the right direction, hotels still rely only on visual assessment as a measure of cleanliness and do not take into consideration the bac- teria or organic matter that could be present on surfaces found in the guest rooms (Al- manza et al., 2015). Moreover, studies have highlighted the ineffectiveness of relying on visual assessment as an indicator of environ- mental and surface cleanliness (Almanza et al., 2015; Inkinen et al., 2020; Shimoda et al., 2015; Xu, Weese, et al., 2015). Studies that have identified and quanti- fied biological contaminants (e.g., bacteria)

Abstract The purpose of our study was to illustrate the efficacy of an enhanced cleaning process during a pandemic by quantifying generic and pathogenic microorganisms before and after cleaning in hotel rooms. The objectives of our study were to: 1) sample high-touch surfaces before and after the rooms were cleaned and 2) swab high-touch surfaces using an adenosine triphosphate (ATP) meter before and after the rooms were cleaned. Overall, 10 rooms and 37 surfaces per room were sampled before and after cleaning. The results showed that several areas of the room had a higher Staphylococcus aureus , aerobic plate, and E.coli /coliform counts after cleaning, suggesting that contaminated towels were being used to clean various surfaces in the rooms. In addition, we found no correlation between microbial counts and ATP meter readings. These results show that a more effective method needs to be assessed for practitioners to effectively quantify environmental safety in hotel rooms. Keywords: pandemic, lodging industry, cleaning, microbiology, environment, housekeeping, public health, hotel

Introduction Historically, hotel rooms have been linked with major bacterial and viral outbreaks, including outbreaks of Legionnaires’ disease in 2015 (Ahmed et al., 2019) and 2017 (Yack- ley et al., 2018), Norwalk-like virus in 1996 (Cheesbrough et al., 2000) and 2012 (Raj et al., 2017), severe acute respiratory syndrome (SARS) in 2003 (Bell, 2004), and severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2) in 2021 (Leong et al., 2021). At the beginning of May 2023, the World Health Organization (WHO, 2025) declared that

the global pandemic caused by COVID-19 was considered well-established and ongo- ing and no longer classified as a public health emergency of international concern. As such, federal, state, and local officials have stopped enforcing pandemic safety measures, includ- ing the requirement of face masks and social or physical distancing. Several studies, however, have reported that consumers still value and might even have higher expectations for cleaning and safety protocols that have been implemented by hospitality operations in response to the

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on high-touch surfaces have relied on micro- biological techniques such as aerobic plate counts (APCs) and coliform counts to assess the cleanliness of surfaces in various environ- ments. For example, APCs are used to assess levels of generic bacteria in a given sample (Maturin & Peeler, 2021), which is why this technique is commonly used as an indicator of environmental cleanliness. Additionally, E. coli and coliforms are commonly used as indicators of fecal contamination in the en- vironment (Feng et al., 2020). Although not all strains of E. coli are inherently pathogenic, they can act as opportunistic pathogens that present potential risks for immunocompro- mised hotel guests who come in contact with contaminated surfaces (Feng et al., 2020). Another environmental microorganism of note is Staphylococcus aureus , which is a potential concern in the hotel room setting because the pathogen is naturally produced by the human body, has a high transmission rate, can survive on a variety of surfaces for months at a time, and is highly resistant to desiccation (Lutz et al., 2013). Further, S. au- reus is an opportunistic pathogen and a major cause of nosocomial infections (Otto, 2009), meaning that if the skin or mucous mem- branes (which serve as barriers against infec- tion) are breached, S. aureus might gain ac- cess to underlying tissues or the bloodstream and cause infection (Minnesota Department of Health, 2010). Due to the nature of this pathogen and its ability to persist on surfaces, a major risk for travelers could be present if they have open wounds or if they belong to an immunosuppressed population. To date, we know of no research that has evaluated the microbiological efficacy of the enhanced cleaning procedures implemented in response to the COVID-19 pandemic since 2020. General cleaning practices in hotel rooms follow common cleaning practices such as removing linens, making the bed with fresh linens, clearing the trash, and cleaning the bathroom (Kline et al., 2014). In response to the COVID-19 pandemic, lodging operators have placed a much greater emphasis on wiping and disinfecting surfaces to enhance consumer perceptions of hotel and lodging safety. A common method used in lodging and food service establishments to assess surface cleanliness is using an adenos- ine triphosphate (ATP) meter. ATP meters provide a practical means of assessing surface

cleanliness for hospitality professionals; the devices are user-friendly, require minimal training, and offer immediate results in rela- tive light units (RLUs). These meters, how- ever, measure ATP that is present in all types of organic material—including food, soil, and bacteria—and thus are unable to distinguish the difference between microbial and nonmi- crobial contamination (Bellamy, 2012). Moreover, studies have shown mixed results as to the true reliability of ATP meters as indicators of surface cleanliness, particu- larly in correlation with traditional microbial plate count methods (Deshpande et al., 2020; Omidbakhsh et al., 2014; Snyder et al., 2013). This gap in knowledge presents a health and safety risk for consumers and lodging opera- tors, as travelers have been found to spread diseases caused by bacterial and viral micro- organisms such as E. coli O157:H7 (King et al., 2020), Staphylococcus spp. (Xu, Mkrt- chyan, et al., 2015), Streptococcus (Weiser et al., 2018), hepatitis (Centers for Disease Control and Prevention [CDC], 2019), rhi- novirus (Winther et al., 2011), rotavirus (Kribs-Zaleta et al., 2011), and influenza (CDC, 2024a) through indirect contact via high-touch fomites, which are objects capa- ble of transmitting infection. A key output of these studies showed the importance of creating and maintaining effective cleaning programs designed to enhance not only guest perceptions of the lodging property but also guest safety by reducing potentially patho- genic microorganisms to safe levels. To address this gap, microbiological sam- pling of high-touch surfaces in addition to the use of an ATP meter can offer a more robust assessment of enhanced cleaning practices (ECPs) by identifying microorganisms com- monly used as indicators of environmental cleanliness in addition to the organic residue detected by the ATP meter. According to the Centers for Disease Control and Prevention (CDC, 2024b), microbiological sampling of the environment is an effective approach for quality assurance purposes to evaluate the ef- fects of a change in infection control practic- es or to ensure that systems perform accord- ing to specifications and expected outcomes. Although routine microbiological sampling of hotel rooms might not be practical for lodging operations due to costs and the time required for laboratory testing (Park et al., 2019), having a microbiologic comparator is

appropriate when assessing changes in infec- tion control practices (Snyder et al., 2013), especially considering that the goal of ECPs implemented during a pandemic is to ensure guest safety by enhancing hygiene and infec- tion prevention practices. Therefore, the objectives of our study were 2-fold: 1. To assess current ECPs specific to 37 high- touch surfaces found in hotel rooms by collecting 740 environmental samples and 740 ATP meter readings in 10 hotel rooms to quantify the number of microorganisms and organic matter, respectively, present on high-touch surfaces in hotel rooms. 2. To analyze the data collected to determine if there were statistically significant differ- ences in the number of logs for the micro- organisms detected during microbiological sampling and organic matter detected with the ATP meter before and after the rooms were cleaned.

Methods

Study Setting and Surfaces A microbial sampling of high-touch surfaces was conducted in 10 hotel guest rooms. These rooms are representative of standard guest rooms in midscale hotel properties in the U.S. A total of 37 surfaces were sampled in our study and divided into 3 areas of use: 1. Guest traffic area, which included the doorknobs inside of the bedroom, closet door handles, drapery pull handles, table- top, table handles, dresser handles, cli- mate control panels, iron handles, coffee makers, safety latches, peepholes, trash cans, entry carpet, and the light switch for the bedroom. 2. Bed area, which included the lamp switches, telephone handset, telephone keypad, remote control keypad, clock (which doubled as a phone dock), pil- lowcases, top of the nightstand, and night- stand handles. 3. Bathroom area, which included the toi- let handles, toilet seats, amenity trays, bathroom sink, bathroom faucet handles, bathroom floor, shower floor, toilet paper holder, bathroom light switch, doorknob on the inside of the bathroom, doorknob on the outside of the bathroom, toilet bowl, hair dryer, vanity surface, and the shower handle.

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The guest traffic area in our study is defined as the area where the guest is likely to interact with high-touch surfaces while moving around the room during their stay. The bed area fo- cuses on high-touch surfaces that the guest is likely to interact with at arm’s length from the bed. The bathroom area centers around high- touch surfaces that typically are found in bath- rooms of midscale lodging properties. To determine the list of high-touch surfaces, publicly available information was gathered from websites of leading hotel brands (Hil- ton, 2021; IHG, 2021; Marriott, 2021; Wyn- dham Hotels, 2021) that have implemented ECPs and the cleaning companies they have partnered with (Ecolab, 2021; Reckitt, 2021) and information gathered from health organi- zations (CDC, 2024a; WHO, 2021) that have identified high-touch surfaces in various set- tings. Additionally, the American Automobile Association (AAA, 2021) has implemented the Inspected Clean Program that is used when evaluating their Diamond Rating of ho- tels and specifies the surfaces that have been included in this list. Before the start of the sample collection, each standard guest room was visually in- spected to confirm all of the established sur- faces were present in the room and to identify where the sampling would occur. We deter- mined that all 37 surfaces were present in the room. Many of the surfaces in the hotel room had two of the same surfaces (e.g., two lamps, two nightstands, two closet handles), for which one was designated for the ATP meter measurement and the other was used to collect microbial samples. For surfaces that had only one surface to sample, the surface area was large enough that it could be eas- ily divided to allow for a 5 × 5 cm 2 sampling area for both the ATP meter and the microbial samples. Environmental sampling took place at the beginning of July 2021 and continued until the end of January 2022. A total of 740 microbial samples and 740 ATP meter read- ings were collected from high-touch surfaces in hotel rooms. Additionally, a hygrometer was taken into the hotel rooms and recorded an average room temperature of 68.8 °F and an average relative humidity of 55%. Laboratory Preparation Aluminum foil was cut into 5 × 5 cm 2 tem- plates to control for the surface area that was swabbed during the sample collection

process. Test tubes (37 for each surface) containing 9 mL of 1% peptone water (PW; Difco) were prepared before each sampling event to collect samples from high-touch surfaces before and after cleaning for each room sampled in our study. To control for any residual sanitizers that could be present during the sample collection, 0.9 ml (10%) of Dey-Engley Neutralizing Broth was added to each sample collection tube that was used to collect samples after the room was cleaned. All materials were sterilized by autoclave at 15 lb (7 kg) of pressure (121 °C) for 15 min. Sample Collection For the bacterial profile, each surface was swabbed aseptically before the room was cleaned using sterile cotton-tipped plastic swabs moistened with PW in a 5 × 5 cm 2 area on each surface. Sterile 5 × 5 cm 2 aluminum foil templates were used to control for vari- ability in surface area sampling sizes. After the sampling of each surface, the untouched ends of the cotton-tipped plastic swabs were broken off to prevent possible contamination and placed in sterile test tubes containing 9 ml of PW. Samples were then placed in a por- table cooler with ice (<10 °C) and transferred to a microbiology laboratory for analysis. Following the hotel room cleaning, sam- pling was conducted again 1 hr after the room was cleaned to allow an adequate amount of time for cleaners and surface sanitizers to air- dry. As an added control, a neutralizing agent was added to the sample collection tubes to negate the effect of any residual sanitizers on sampled surfaces. For sampling conducted with the ATP me- ter, surfaces were swabbed to determine if the presence of ATP was detected. Sterile tem- plates were placed on surfaces in the room and swabbed using the Hygiena UltraSnap Surface Swabs per manufacturer instructions. Results from each sample were generated in real time within 15 s and recorded for each surface before and after cleaning. The ATP meter was recalibrated per manufacturer in- structions prior to each sampling event. A to- tal of 10 rooms were sampled over 6 months from July 2021 to January 2022.

laboratory for microbiological analysis. Next, 10-fold serial dilutions were made by pass- ing 1 ml of each collected sample into a test tube containing 9 ml of PW. Then 1 ml from each dilution (3 dilutions) was placed onto the appropriate 3M Petrifilm Plates to quan- tify APCs, E. coli /coliforms, and S. aureus for each sample and the plates were incubated at 37 °C for 24–48 hr. The total viable count was determined based on the distinct color of the microorganisms on each of the plates. Micro- bial colonies were manually counted using a Quebec colony counter to quantify the presence of aerobic bacterial contamination, generic E. coli and coliforms, and S. aureus as indicators of microbial contamination for each surface. Bacterial counts were converted by using the log formula in Microsoft Excel before the statistical analysis. The data from the 10 rooms were then transferred into IBM SPSS Statistics version 28 and the mean log CFU/ cm 2 and RLU/cm 2 were compared using a paired samples t -test to determine if there were statistically significant differences (p < .05) based on the microorganism and area of the hotel room.

Results

Sample Analysis Before and After Cleaning

The 37 sampled high-touch surfaces were divided into the guest traffic area, bed area, and bathroom area; surfaces were further grouped by the log CFU/cm 2 for APCs, E. coli /coliform counts, S. aureus , and RLU/cm 2 from the ATP meter readings for analysis. A paired samples t -test (95% confidence inter- vals) was conducted to determine if there were statistically significant differences in the samples taken before and after the hotel rooms were cleaned. In general, S. aureus counts decreased in each of the three areas on all high-touch sur- faces sampled in the hotel room. Over the course of our study, E. coli was not detected in any of the guest rooms. Coliform counts, however, increased in all three of the sampled areas on all high-touch surfaces, except for the closet handle, drapery pull handle, and shower floor. For the APCs, a majority of the high-touch surfaces included in the guest traffic area increased after the room was cleaned, whereas all surfaces in the bed area

Microbiological and Statistical Analysis

After the surface samples were collected, they were transported to a Biosafety Level 2

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and bathroom area decreased except for the nightstand handle and hair dryer. Lastly, the ATP meter results found that RLUs detected decreased after the room was cleaned for all high-touch surfaces, with the exception of the carpet, telephone dial pad, doorknob inside the bathroom, and hair dryer. The following sections discuss the results of the paired samples t -test for the microbial and ATP data collected. Guest Traffic Area The guest traffic area consisted of high-touch surfaces the guest was likely to interact with in the hotel room that were farther than an arm’s reach away from the bed area and not in the bathroom. The results of the paired samples t -test concluded that for total via- ble APCs, the log CFU/cm 2 before cleaning ( M = 1.14, SD = 0.57) did not significantly decrease after the room was cleaned ( M = 1.11, SD = 0.37, t (13) = 0.348, p = .367). A majority of the surfaces included in the guest traffic area increased in the number of logs detected after the room was cleaned, except for the tabletop, dresser handle, coffee maker, and entry carpet. The only surface that dem- onstrated a statistically significant decrease in the logs detected for APCs was the entry carpet ( M = 0.67, SD = 0.72, p = .008), which had an average of 2.68 log CFU/cm 2 before the room was cleaned and an average of 2.01 log CFU/cm 2 after the room was cleaned ( t (9) = 2.938, p = .008). Overall, the log concentrations of the sam- ples collected for coliforms before cleaning ( M = 0.36, SD = 0.09) increased significantly after the room was cleaned ( M = 0.55, SD = 0.11) in the guest traffic area ( t (13) = -4.656, p < .001). Although specific high-touch surfaces did not significantly increase, the doorknob inside the bedroom, tabletop, table drawer handle, dresser handle, climate control panel, iron, coffee maker, door safety latch, door peep- hole, trash can, entry carpet, and bedroom light switch all increased an average of 0.25 log CFU/cm 2 after the room was cleaned. Only the closet handle and drapery pull handle decreased an average of 0.14 log CFU/cm 2 and 0.10 log CFU/cm 2 , respectively. For S. aureus , the average log concentra- tions of the collected samples before cleaning ( M = 1.08, SD = 0.48) significantly decreased after the room was cleaned ( M = 0.63, SD = 0.38) for the guest traffic area ( t (13) = 7.900,

p < .001). While all high-touch surfaces in the guest traffic area decreased an average of 0.36 log CFU/cm 2 for this specific microorganism, the surfaces that demonstrated a significant decrease in the number of logs detected after the room was cleaned were the doorknob inside of the bedroom ( M = 0.43, SD = 0.61, p = .026); drapery pull handle ( M = 0.21, SD = 0.34, p = .041); tabletop ( M = 0.63, SD = 0.90, p = .027); climate control panel ( M = 0.51, SD = 0.82, p = .041); iron handle ( M = 0.60, SD = 0.82, p = .045); and coffee maker ( M = 0.78, SD = 1.00, p = .018). For the ATP meter data, the results from the paired samples t -test found that the detected RLUs on high-touch surfaces before cleaning ( M = 91.8, SD = 112.77) signifi- cantly decreased after the room was cleaned ( M = 49.76, SD = 51.22) in the guest traffic area ( t (13) = 2.471, p = .014). Specifically, the table drawer handle, climate control panel, door safety latch, and bedroom light switch significantly decreased in number of detected RLUs. All other surfaces in this area showed decreases in the organic matter detected, ranging from 3 to 231 RLUs, except for the entry carpet, which increased by 1 RLU on average. The results for the paired samples t -test in the guest traffic area are shown in Table 1. Bed Area Results from the statistical analysis for the bed area found that APCs before cleaning ( M = 1.67, SD = 0.46) significantly decreased after the room was cleaned ( M = 1.24, SD = 0.19, t (7) = 2.452, p = .022). Specifically, the bedside lamp switch ( p = .006); bedside clock/phone dock ( M = 0.88, SD = 0.88, p = .032); and pillowcases ( M = 0.74, SD = 1.05, p = .026) significantly decreased in the average number of log CFU/cm 2 for these high-touch surfaces. All other surfaces decreased an aver- age of 0.39 log CFU/cm 2 after the room was cleaned except for the nightstand handle, which increased an average of 0.51 log CFU/ cm 2 after cleaning. For coliforms, the log concentrations before cleaning ( M = 0.46, SD = 0.11) also significantly increased after the room was cleaned ( M = 0.81, SD = 0.13, t (7) = -8.925, p < .001). For this microorganism, microbial counts significantly increased on the phone dial pad ( M = -0.44, SD = 0.74, p = .046) and TV remote control ( M = -0.54, SD = 0.65, p

= .013). All other surfaces that were tested for the presence of coliforms in the bed area increased an average of 0.33 log CFU/cm 2 . Contrary to the results for coliforms, microbial counts for S. aureus before cleaning ( M = 1.44, SD = 0.37) significantly decreased after the bed area was cleaned ( M = 0.89, SD = 0.13, t (7) = 5.688, p < .001). Specifically, the high-touch surfaces that had significant log reductions for S. aureus in this area were the bedside clock/phone dock ( M = 0.90, SD = 1.15, p = .017); pillowcases ( M = 0.72, SD = 0.70, p = .005); and nightstand top ( M = 0.87, SD = 1.20, p = .023). All other surfaces in this area had a mean log reduction of 0.31 log CFU/cm 2 after the room was cleaned. For the ATP meter data, our results indi- cate that the average RLUs detected on high- touch surfaces in the bed area before clean- ing ( M = 159.03, SD = 129.68) significantly decreased after the room was cleaned ( M = 66.26, SD = 42.70, t (7) = 1.948, p = .046). Overall, high-touch surfaces were deemed cleaner according to the ATP meter read- ings; however, the surface of the phone dial pad increased an average of 7 RLU/cm 2 . The only surface that had a significant decrease in the average RLU detected was the bedside clock/phone dock ( M = 57.3, SD = 60.50, p = .008), whereas all other surfaces decreased an average of 116 RLU/cm 2 . Results for the paired samples t -test for the bed area are shown in Table 2. Bathroom Area For the data collected from the bathroom area, results indicated that the APCs detected before cleaning ( M = 1.88, SD = 0.70) sig- nificantly decreased after the bathroom was cleaned ( M = 1.37, SD = 0.37, t (14) = 3.878, p < .001). Specifically, the sink faucet handles ( M = 0.87, SD = 1.22, p = .033); bathroom floor ( M = 1.06, SD = 1.59, p = .032); shower floor ( M = 1.48, SD = 0.87, p < .001); and van- ity surface in front of the sink ( M = 1.26, SD = 0.75, p = .002) all had significantly lower log CFU/cm 2 following the room cleaning. The hair dryer was the only high-touch surface in the bathroom that increased in APCs; it increased by an average of 0.42 log CFU/cm 2 after cleaning. Every other high-touch sur- face sampled in the bathroom decreased an average of 0.35 log CFU/cm 2 after cleaning. Consistent with our findings from the guest traffic area and bed area, coliform

March 2025 • Journal of Environmental Health

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TABLE 1

Results of t -Tests Comparing High-Touch Surfaces in the Guest Traffic Area

Surface

Aerobic Plate Count

Coliforms

Staphylococcus aureus M SD SEM t

ATP Meter

M SD SEM t

0.61 0.41 0.13 -1.412 0.30 0.00 0.00 -0.100 0.80 0.59 0.19 2.232 * 113 134 42 1.510

Bedroom doorknob before

Bedroom doorknob after 0.91 0.72 0.23

0.40 0.32 0.10

0.37 0.22 0.07

49 25

Closet handle before

1.02 1.17 0.37 -0.046 0.54 0.56 0.18 0.796 1.00 1.21 0.38 1.312 59 30

9 0.251

Closet handle after 55 37 12 Drapery pull handle before 0.51 0.47 0.15 -0.891 0.47 0.54 0.17 0.524 0.51 0.34 0.11 1.964 * 18 16 5 0.338 Drapery pull handle after 0.81 0.83 0.26 0.37 0.22 0.07 0.30 0.00 0.00 16 16 5 Tabletop before 1.23 1.17 0.37 0.639 0.30 0.00 0.00 -1.796 1.27 1.05 0.33 2.206 * 447 693 219 1.151 Tabletop after 0.92 0.81 0.26 0.64 0.60 0.19 0.64 0.55 0.17 216 477 151 Table handle before 0.89 0.82 0.26 -0.284 0.30 0.00 0.00 -1.838 0.93 0.77 0.24 0.889 102 87 27 1.856 * Table handle after 1.01 0.82 0.26 0.65 0.60 0.19 0.61 0.52 0.16 59 36 12 Dresser handle before 1.37 1.00 0.32 0.872 0.30 0.00 0.00 -1.479 1.29 1.09 0.35 1.587 56 36 11 0.966 Dresser handle after 0.95 0.78 0.25 0.63 0.71 0.22 0.76 0.64 0.20 42 39 12 Climate control panel before 0.80 0.90 0.29 -0.668 0.30 0.00 0.00 -1.819 0.81 0.82 0.26 1.964 * 59 34 11 2.209 * Climate control panel after 1.06 0.74 0.23 0.66 0.63 0.20 0.30 0.00 0.00 39 15 5 Iron handle before 1.03 0.82 0.26 -0.172 0.30 0.00 0.00 -1.796 0.90 0.82 0.26 2.328 * 19 21 7 0.664 Iron handle after 1.11 0.84 0.27 0.64 0.60 0.19 0.30 0.00 0.00 15 15 5 Coffee maker before 1.84 1.09 0.34 1.353 0.51 0.34 0.11 -1.034 1.71 1.13 0.36 2.473 * 72 90 28 1.283 Coffee maker after 1.29 0.87 0.28 0.76 0.64 0.20 0.93 0.60 0.19 36 19 6 Door safety latch before 0.74 0.63 0.20 -0.444 0.30 0.00 0.00 -1.000 0.64 0.60 0.19 0.271 184 209 66 1.967 * Door safety latch after 0.90 0.85 0.27 0.48 0.57 0.18 0.57 0.46 0.15 63 41 13 Door peephole before 0.73 0.57 0.18 -0.863 0.40 0.32 0.10 -1.281 0.65 0.60 0.19 0.285 26 14 4 1.363 Door peephole after 1.06 0.77 0.24 0.62 0.68 0.21 0.60 0.50 0.16 20 14 4 Trash can rim before 1.40 1.14 0.36 -0.299 0.47 0.37 0.12 -0.532 1.34 1.06 0.33 0.023 25 16 5 1.049 Trash can rim after 1.54 0.78 0.25 0.61 0.66 0.21 1.33 0.69 0.22 18 13 4 Carpet floor before 2.68 0.63 0.20 2.938 ** 0.30 0.00 0.00 -1.000 2.30 1.03 0.32 0.861 11 12 4 -0.509 Carpet floor after 2.01 1.01 0.32 0.47 0.54 0.17 1.95 1.05 0.33 12 16 5 1.04 0.98 0.31 0.40 0.32 0.10 0.50 0.42 0.13

Bedroom light switch before Bedroom light switch after

1.17 0.99 0.31 0.446 0.30 0.00 0.00 -1.441 0.94 1.01 0.32 0.179 95 55 18 1.853 *

0.96 0.88 0.28

0.57 0.59 0.19

0.85 1.05 0.33

58 55 18

* p < .05, ** p < .01, n = 10, df = 9. Note. ATP = adenosine triphosphate.

counts detected before cleaning ( M = 0.46, SD = 0.44) significantly increased after the room was cleaned ( M = 0.81, SD = 0.14, t (14) = -3.369, p = .002). High-touch surfaces that significantly increased in microbial counts following the bathroom cleaning include the

amenity tray ( M = -0.68, SD = 0.96, p = .026) located next to the bathroom sink that is used to store hand soap, lotion, and shampoo; sink faucet handles ( M = -0.55, SD = 0.63, p = .011); doorknob inside bathroom ( M = -0.58, SD = 0.90, p = .036); and hair dryer ( M = -0.74, SD

= 0.93, p = .016). Although coliform counts on most surfaces increased after cleaning, the shower floor was the only surface that had an average reduced log CFU/cm 2 and was found to be significantly cleaner after cleaning ( M = 0.90, SD = 1.49, p = .044). All other surfaces

Volume 87 • Number 7

TABLE 2

Results of t -Tests Comparing High-Touch Surfaces in the Bed Area

Surface

Aerobic Plate Count

Coliforms

Staphylococcus aureus M SD SEM t

ATP Meter

M SD SEM t

1.97 0.49 0.15 3.179 ** 0.51 0.34 0.11 -0.980 1.67 0.80 0.25 1.552 81 64 20 0.872

Bedside lamp switch before Bedside lamp switch after

1.09 0.91 0.29

0.74 0.67 0.21

1.08 0.90 0.28

56 53 17

Phone handset before 1.21 1.05 0.33 0.461 0.54 0.56 0.18 -1.036 1.04 0.88 0.28 0.718 84 111 35 1.413 Phone handset after 1.00 0.82 0.26 0.84 0.78 0.25 0.77 0.69 0.22 38 22 7 Phone dial pad before 1.21 0.99 0.31 0.079 0.30 0.00 0.00 -1.881 * 1.08 0.92 0.29 0.163 43 35 11 -0.508 Phone dial pad after 1.18 0.91 0.29 0.74 0.74 0.23 1.01 0.86 0.27 49 46 15 TV remote control before 1.75 0.97 0.31 0.987 0.37 0.22 0.07 -2.641 * 1.28 0.95 0.30 0.851 337 436 138 1.806 TV remote control after 1.35 0.93 0.29 0.91 0.68 0.21 0.97 0.71 0.22 125 140 44 Bedside clock/phone dock before 2.19 0.81 0.26 2.118 * 0.56 0.62 0.20 -0.687 1.92 1.02 0.32 2.483 * 118 60 19 2.995 ** Bedside clock/phone dock after 1.37 0.76 0.24 0.82 0.87 0.27 1.02 0.68 0.21 60 41 13 Pillowcase before 1.79 0.73 0.23 2.231 * 0.30 0.00 0.00 -1.321 1.71 0.84 0.26 3.243 ** 383 866 274 1.385 Pillowcase after 1.05 1.01 0.32 0.57 0.65 0.20 0.99 0.94 0.30 4 5 1 Nightstand top before 2.21 1.30 0.41 1.83 0.51 0.66 0.21 -1.168 1.69 1.37 0.43 2.301 * 160 89 28 0.867 Nightstand top after 1.31 0.91 0.29 0.93 0.75 0.24 0.82 0.73 0.23 131 68 21 Nightstand handle before 1.05 1.10 0.35 -1.649 0.47 0.37 0.12 -1.464 1.06 1.08 0.34 1.707 67 67 21 0.043 Nightstand handle after 1.56 0.87 0.27 0.95 0.92 0.29 0.75 0.98 0.31 66 41 13

* p < .05, ** p < .01, n = 10, df = 9. Note. ATP = adenosine triphosphate.

sampled in this area increased an average of 0.37 log CFU/cm 2 . For S. aureus , microbial counts detected before cleaning ( M = 1.61, SD = 0.60) sig- nificantly decreased after the bathroom was cleaned ( M = 0.82, SD = 0.28, t (14) = 6.187, p < .001). While all the high-touch surfaces decreased for this microorganism after the bathroom was cleaned, the surfaces that had significant decreases in the average log CFU/ cm 2 were the toilet handle ( M = 1.00, SD = 0.76, p = .001); toilet seat ( M = 0.84, SD = 0.90, p = .008); sink faucet handles ( M = 0.97, SD = 0.61, p < .001); bathroom floor ( M = 1.13, SD = 1.12, p = .005); shower floor ( M = 1.91, SD = 1.29, p < .001); toilet paper holder ( M = 0.68, SD = 0.79, p = .011); bathroom light switch ( M = 0.82, SD = 0.81, p = .006); doorknob inside bathroom ( M = 1.05, SD = 0.68, p < .001); doorknob outside bathroom ( M = 0.69, SD = 1.12, p = .042); and vanity surface ( M = 1.37, SD = 0.76, p = .002). All

the other surfaces decreased an average of 0.26 log CFU/cm 2 . Our analysis of the ATP meter data col- lected in the bathroom area indicates that the RLUs detected before cleaning ( M = 158.88, SD = 175.47) significantly decreased after the room was cleaned ( M = 51.23, SD = 24.90, t (14) = 2.285, p = .019). The surfaces that had a significant decrease in RLU/cm 2 included the toilet handle ( M = 45.50, SD = 69.50, p = .034); sink faucet handles ( M = 122.30, SD = 63.75, p < .001); shower floor ( M = 76.30, SD = 112.63, p = .030); toilet paper holder ( M = 9.50, SD = 14.80, p = .037); light switch in the bathroom ( M = 84.20, SD = 140.83, p = .046); and doorknob outside bathroom ( M = 46.3, SD = 78.59, p = .048). Our results also indi- cate that the doorknob inside of the bathroom and the hair dryer increased in average RLUs detected after the bathroom was cleaned by 16 RLU/cm 2 and 20 RLU/cm 2 , respectively. All other surfaces decreased an average of

163 RLU/cm 2 after the bathroom was cleaned. The results for the paired samples t -test in the bathroom area are shown in Table 3. Discussion Previous research has addressed public health concerns due to potential sources of community-associated infections resulting from microorganisms found in hotel environ- ments (Jaradat et al., 2020; Xu, Mkrtchyan, et al., 2015; Xu, Weese, et al., 2015). Spe- cifically, high-touch surfaces found in hotel rooms could serve as fomites that can trans- mit potentially pathogenic microorganisms through either direct contact (e.g., surface- to-mouth) or indirect contact (e.g., surface- to-hand-to-eye, surface-to-hand-to-mouth) (Lopez et al., 2013). Although microbio- logical testing of hotel rooms might not be practical for day-to-day operations, the find- ings from our study show that the number of environmental and potentially pathogenic

March 2025 • Journal of Environmental Health

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