NEHA September 2024 Journal of Environmental Health

The September 2024 issue of the Journal of Environmental Health (Volume 87, Number 2), published by the National Environmental Health Association.

JOURNAL OF Environmental Health Dedicated to the advancement of the environmental health professional Volume 87, No. 2 September 2024

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ADVANCEMENT OF THE SCIENCE Air Pollution and COVID-19 Mortality: A Case-Control Study of COVID-19 Cases Reported in Indiana in 2020........................................................................................................ 8 Special Report: Navigating Community-Engaged Research to Understand How Drought Aects Water Quality .................................................................................................... 14 ADVANCEMENT OF THE PRACTICE International Perspectives: International Environmental Health Skills, Knowledge, and Qualifications: Enhancing Professional Practice Through Agreements Between Countries ............ 20 Building Capacity: Building Capacity With the Right Mobile Inspection Device .......................... 24 Direct From AEHAP: Environmental Health Academic Programs: Looking Back and Moving Forward .................................................................................................................. 26 Direct From ATSDR: Public Health Assessment Course: A Modernized Training for In-Depth Instruction on Evaluating Health Impacts of Community Exposure to Hazardous Substances ............................................................................................................. 28 Direct From CDC/Environmental Health Services: Resources for Conducting Foodborne Outbreak Environmental Assessments: From Just-in-Time to Anytime .......................................... 32 ADVANCEMENT OF THE PRACTITIONER Spotlight on NEHA Resources: Emergency and Disaster Readiness ............................................ 36 Spotlight on NEHA Resources: Food Safety ............................................................................... 38 Environmental Health Calendar ...............................................................................................40 YOUR ASSOCIATION President’s Message: Environmental Health: A Clear Identity .................................................................. 6 NEHA Annual Financial Statement......................................................................................................41 Special Listing ........................................................................................................................... 42 In Memoriam............................................................................................................................. 44 NEHA News .............................................................................................................................. 46 NEHA 2025 AEC....................................................................................................................... 49

ABOUT THE COVER

Community- engaged research is becoming more prevalent in envi- ronmental health. With a spectrum of commu- nity involvement characterizing this research, it

has the potential to foster a diverse set of collaborative dynamics. This month’s cover article, “Navigating Community-Engaged Research to Understand How Drought Aects Water Quality,” explores how a research team facilitated a regional community-engaged research eort that focused on the eects of drought driven by climate change. The article outlines lessons learned and project manage- ment strategies used to facilitate large-scale community-engaged environmental science. See page 14. Cover images © iStockphoto: skodonnell / Eva Almqvist

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September 2024 • Journal of Environmental Health

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September 2024 • Journal of Environmental Health

YOUR ASSOCIATION

Open Access

 PRESIDENT’S MESSAGE

Environmental Health: A Clear Identity

CDR Anna Khan, MA, REHS/RS

S eptember is National Preparedness Month and serves as an annual re- minder for individuals, families, and communities to assess their readiness and take proactive steps to mitigate risks. As environmental public health practitioners, we are not strangers to emergency prepared- ness and response work. We play a critical role by planning for and addressing the im- pact of disasters on public health. Climate change, however, threatens to alter the way we approach emergency preparedness and response in our communities. For example, there are parts of our country that used to have a distinct wildfire season. Wildfires can now strike during any month throughout the year in some parts of our country due to unpredictable and erratic weather sys- tems. With the changes in weather systems, we also have changes in the risk of other is- sues such as disease vectors and outbreaks, which are becoming more widespread. There is, however, another uncertain aspect of our profession that we need to face—a clear identity. In addition to all the challenges posed by climate change now and in the near future, we are at a critical crossroads in our profes- sion. If environmental health is the corner- stone of public health, have we positioned ourselves to rise to this challenge now and in the future? Does the public understand our role in public health preparedness and response? We need to ensure environmen- tal health is understood and included by default in public health guidance and policy just like essential ingredients are “baked in” when preparing a recipe.

our public health impact is and came up with a slogan for us: “Live fully with clean air, water, and food.” When I think about messages that can accompany our slogan, I think about how to summarize our wide-ranging and complex contributions into a few words. While I was at the IFEH World Congress, I attended a presentation about environmen- tal health and what environmental health practitioners do. The presenter stated four main practice areas to help environmental health practitioners discuss their role in environmental health: 1) leading and inno- vating, 2) collaborating, 3) helping, and 4) protecting. These critical ingredients are tenets of environmental health practice. Whether we are in the field (in the private or public sector) or in academia, we can all find ourselves working in at least one of these practice areas. I would love to hear your stories with one of these elements as the theme. Over the next year, I hope to capture our perspectives and experiences in these areas. I am convinced that we will all learn that despite our wide- ranging areas of practice, we have more in common in the service of public health than we realize. I hope that you will provide feed- back because it aects all of us as well as our families, friends, and future generations. I want our slogan and our stories to ade- quately stress the impact and benefit we bring to our communities, locally and glob- ally. Then, we can build on this promise by sharing our impact through metrics and numbers. Each of us has our own uniquely personal story that can help paint a more

I think we have a branding issue in envi- ronmental health. In fact, this issue is shared by our colleagues across the globe. I recently returned from Australia where I was attend- ing and presenting at the International Fed- eration of Environmental Health (IFEH) World Congress on Environmental Health. The issue I heard over and over—both in my workshop as well as in other presentations— was that not enough people understand our contribution to public health. I have been thinking about this issue since then. How can we change this lack of awareness? Some industries and groups have a catchy slogan or motto to first gain your interest while getting their core values across. Then they follow up with a main message included in a personal story with impactful metrics. For example, common slogans include: “Doc- tors save lives” and “GE brings good things to life.” I thought about how all-encompassing We need to ensure environmental health is understood and included by default in public health guidance and policy.

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complete picture of our profession for our communities. I know some of you may be pessimistic about this approach and think that we cannot change the course we are on and how environmental health will continue to be invisible. Professional basketball player and sportscaster Bill Walton said it best: “If you think you’re too small to make a dier- ence, you’ve never spent a night in bed with a mosquito.”

With this optimistic look to the future, where do we go next? I do not want us to just talk about the changes but actually have some strategies in place to support and lead envi- ronmental health into the future. During my presidency, I will be working on the existential issue of our meaning and our purpose. I will be reaching out to you—our subject matter experts—for your perspectives and recommen- dations because it will take all of us to identify

who we are as a group and what we stand for. And then—and only then—can we move in the right direction. Together. And united.

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September 2024 • Journal of Environmental Health

ADVANCEMENT OF THE SCIENCE

Open Access

(8tra)t It has been hypothesized that air pollution can increase an individual’s susceptibility to COVID-19. Our study sought to investigate if short-term exposure to a high average level of Air Quality Index (AQI), ozone (O 3 ), particulate matter (PM 2.5 , PM 10 ), sulfur dioxide (SO 2 ), carbon monoxide (CO), and nitrogen dioxide (NO 2 ) are risk factors for death from COVID-19. We conducted an unmatched case-control study to compare the risk of mortality among reported COVID-19 cases within metropolitan statistical areas in Indiana from March 31, 2020, to December 31, 2020 ( N = 53,459). Air pollution concentration data at the county level were retrieved from the U.S. Environmental Protection Agency. Pollutant concentrations for the 90 days before disease development were averaged. Data were analyzed using stepwise logistic regression accounting for median air temperature, race, ethnicity, and preexisting conditions as confounders. At the population level, individuals who were exposed to a greater average of PM 2.5 in the 90 days before the development of COVID-19 had an increased risk of death: OR = 2.16, 95% confidence interval (CI) [1.12, 18.57]. Individuals with chronic lung disease had an increased risk of death in relation to PM 10 : OR = 2.72, 95% CI [1.15, 6.43]. Short-term exposure to particulate matter may serve as a risk factor for COVID-19 mortality. Keywords: COVID-19, air pollution, Indiana, particulate matter Air Pollution and COVID-19 Mortality: A Case-Control Study of COVID-19 Cases Reported in Indiana in 2020

Anna E. Heilers, MPH, CPH Indiana Department of Health Sarah A. Bennett Indiana Department of Health

Because air quality can be regulated through policy, the regulation of air pollution could play a key role in preventing excess morbidity and mortality from COVID-19 (Xu et al., 2022). We conducted an unmatched case-control study to assess short-term air pollution and approximate the risk of COVID-19 mortality. As more comprehen- sive data are now available for COVID-19 cases from 2020, we sought to contribute a more complete assessment of the potential impact of air pollution on COVID-19 mortal- ity in Indiana. Methods An unmatched case-control study was con- ducted comparing the risk of mortality among reported COVID-19 cases within metropolitan statistical areas (MSAs) in Indi- ana between March 31, 2020, and December 31, 2020 ( N = 53,459), in relation to short- term air pollution exposure. Short-term air pollution exposure was defined as a 90-day mean exposure concentration. Data analysis was conducted in SAS 9.4 via IUanyWare Citrix Receiver, Microsoft Oce Excel, and R-4.2.1 via RStudio Desktop 2022.07.2+576. COVID-19 data used for our study were compiled within the National Electronic Dis- ease Surveillance System (NEDSS), Indiana’s infectious disease surveillance system. Pol- lutant data files from the U.S. Environmental Protection Agency were used to obtain mea- sures of air pollution and air temperature. Using a systematic approach, air pollution and COVID-19 case data were integrated and stratified to retain COVID-19 cases within counties associated with MSAs and within the specified time frame. The 90-day analysis period for assess- ing short-term exposure to air pollutants was determined by referencing valid dates of first symptom onset with the preceding

Introduction In 2020, the third leading cause of death in Indiana was COVID-19 (Centers for Disease Control and Prevention, 2024). Since 2020, preliminary studies have demonstrated a rela- tionship between COVID-19 incidence, hospi- talization, and mortality with elevated levels of air pollution (Copat et al., 2020; Zang et al., 2022). Elevated levels of air pollutants have been shown to increase the risk of developing chronic conditions, including chronic lung diseases such as chronic obstructive pulmo- nary disease (COPD) and asthma (Weaver et al., 2022). As chronic conditions are risk fac- tors for severe COVID-19 outcomes, research-

ers have theorized that long-term exposure to pollutants, which increases the risk of developing chronic respiratory diseases in a population, could also lead to increased risk of COVID-19 in these populations (Weaver et al., 2022). Researchers have also theorized that short-term air pollution exposure could lead to an increased risk of mortality through immunological mechanisms and reduced viral clearance (Karan et al., 2020). Additionally, several studies have identified the presence of SARS-CoV-2 in particulate matter pollution, suggesting particulate matter pollution might act as a carrier of viral particles into the respi- ratory tract (Meo et al., 2021).

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89 days, forming the analysis interval. Valid dates of the first positive specimen collec- tion were referenced if a patient was asymp- tomatic or if the date of symptom onset was missing or invalid. Patients without a valid date of symptom onset or date of first positive specimen collection were excluded from the analysis. Air pollution data were merged onto the individual patient-level COVID-19 dataset through an inner join by Federal Information Processing Standard (FIPS) county codes. Average air pollutant concentrations and the median air tempera- ture during the defined 90-day interval were calculated for each COVID-19 case accord- ing to the patient’s county of residence. To account for intra-county variation, the study was limited to MSAs, which have a greater number of air pollution reporting sites within each county. The cases for this study were defined as anyone with a lab-confirmed positive PCR or antigen test result for SARS-CoV-2 reported in Indiana who died as a result of COVID-19 ( n = 2,564). Controls were defined as those who had a lab-confirmed positive PCR or antigen test for SARS-CoV-2 reported in Indi- ana who did not die as a result of COVID-19 ( n = 45,368). Two backward stepwise logistic regression models were performed in R-4.2.1 via RStudio Desktop 2022.07.2+576 to calculate OR s and 95% confidence intervals (CIs) with adjust- ment for race, ethnicity, and the presence of preexisting conditions as confounders. These models included the potential exposures— mean sulfur dioxide (SO 2 ), mean Air Quality Index (AQI), mean nitrogen dioxide (NO 2 ), and particulate matter (PM 2.5 and PM 10 ) con- centrations—that were calculated during a 90-day interval from each patient’s first sign of infection. Mean ozone (O 3 ) and mean carbon monoxide (CO) concentrations were excluded from the model due to a lack of variability in the data. The binary outcome of interest for these models was a reported COVID-19 death. An Akaike information criterion (AIC) for both models is reported in the results section. Model 1 examined the risk of death among individuals with a positive test for SARS- CoV-2 reported to the Indiana Department of Health ( N = 53,459). This analysis assessed the entire study population. The model accounted for median air temperature during the 90-day exposure interval, race, ethnicity,

age, and the presence of at least one preexist- ing condition before the positive test result. The final regression equation for Model 1 is: ln ( p ) = 5.5667 – 0.4007 x NO 2 + 1 – p 0.8272 x PM 2.5 – 0.1940 x AQI + 0.0052 x Age – 0.0193 x Race – 0.0163 x ° F represents the concentration of NO 2 (ppb), x PM 2.5 represents the concentration of PM 2.5 (μg/m 3 ), and x AQI represents the con- where x NO 2 centration calculated mean AQI during the exposure interval. Additionally, the model retained age ( x Age ), race ( x Race ), and medial air temperature ( x °F ) during the interval. Model 2 examined the risk of death among reported COVID-19 cases who had a previ- ously diagnosed chronic lung disease ( n = 3,672) at the time of COVID-19 illness onset. This model accounted for median air temper- ature during the 90-day exposure interval, as well as race, ethnicity, and age. The final regression equation for Model 2 is: ln ( p ) = 13.8044 – 1.7762 x NO 2 + 1 – p 0.6070 x PM 2.5 + 0.9709 x PM 10 + 0.0038 x Age – 0.4616 x ° F where x NO 2 represents the concentration of NO 2 (ppb), x PM 2.5 represents the concentra- tion of PM 2.5 (μg/m 3 ), and x PM10 represents the concentration of PM 10 (μg/m 3 ) during the exposure interval. Additionally, the model retained age ( x Age ) and medial air temperature ( x °F ) during the interval as covariates. Results Study Population The study population consisted of all reported confirmed and probable COVID-19 cases in Indiana between March 31, 2020, and December 31, 2020. There were 53,459 patients with positive test results for SARS- CoV-2 reported who met our study inclu- sion criteria. Of the 53,459 patients, 2,564 patients died (4.7%) as a result of COVID- 19 infection; these cases are the ones whose data we used for our analysis. The remaining 45,368 patients in the sample who did not have a reported COVID-19 death were the cases we used as the control group for our analysis (Table 1).

Model 1 had an AIC of -19.71 and produced a statistically significant association between the 90-day average concentration of PM 2.5 and the risk of mortality from COVID-19. For every 1 μg/m 3 increase in PM 2.5 , there was a 4.5990 greater risk of death from COVID-19. No other statistically significant association between air pollution and mortality risk was observed from this model (Table 2). Model 2 had an AIC of 5.68. This model examined the approximate risk of mortality for individuals with previously diagnosed chronic lung disease. Two statistically signifi- cant associations were observed. The concen- tration of NO 2 appears to have a protective association with mortality from COVID-19 ( OR = 0.1609). For every 1-unit increase in the 90-day average ppb concentration of NO 2 , mortality from COVID-19 for individu- als with chronic lung conditions decreased by a factor of approximately 0.16. Additionally, PM 10 was found to have a statistically signifi- cant association with increased COVID-19 mortality. For every 1 μg/m 3 increase in the 90-day average concentration of PM 10 , there was a 2.7235 increased risk of mortality from COVID-19 (Table 3).

Discussion

Findings In both of the models, there was at least one statistically significant exposure variable. In Model 1, which examined risk of mortality from COVID-19 in relation to air pollution exposure, a statistically significant associa- tion between 90-day average PM 2.5 concen- tration and mortality from COVID-19 was observed. In Model 2, which examined the risk of air pollution exposure among COVID- 19 patients who had a previously diagnosed chronic lung disease, there was a statistically significant association between 90-day aver- age PM 10 concentration and mortality from COVID-19. Model 2 did not show any sig- nificance between the relationship of average 90-day PM 2.5 concentration and COVID-19 mortality, though. Interpretation We hypothesize that an average 90-day PM 2.5 concentration can increase the risk of mortal- ity from COVID-19 in the general population. Other studies have also observed this rela- tionship, and previous researchers have sug-

September 2024 • Journal of Environmental Health

ADVANCEMENT OF THE SCIENCE

TABLE 1

Characteristics of and Air Pollutant Concentrations for Indiana Residents With a Positive SARS-CoV-2 Test Result in 2020 Who Died ( n = 2,564) or Had No Associated Death ( n = 45,368)

Characteristic

Cases* ( n = 2,564) # (%)

Controls* ( n = 45,368) # (%)

p -Value

Age (years) 0–<18

2 (0.1)

3,284 (7.2)

<.0001 a

18–<50 50–<65

81 (3.2)

24,421 (53.8) 9,799 (21.6) 7,864 (17.3)

297 (11.6) 2,184 (85.2)

≥65

Gender

Male

1,263 (49.3) 1,287 (50.2)

21,109 (46.3) 23,980 (52.9)

.0003 b

Female

Unknown

14 (0.5)

279 (0.6)

Race

White

1,717 (67.0) 327 (12.8)

25,980 (57.3) 5,183 (11.4)

<.0001 b

Black or African American

Asian

21 (0.8)

1,025 (2.3)

Native Hawaiian or Pacific Islander Native American or Alaska Native

6 (0.2) 3 (0.1)

97 (0.2) 51 (0.1) 858 (1.9)

Multiracial Other race Unknown

41 (1.6) 170 (6.6) 279 (10.9)

3,349 (7.4) 8,825 (19.5)

Ethnicity

Hispanic or Latinx

105 (4.1)

5,251 (11.6) 26,247 (57.9) 13,870 (30.6)

<.0001 b

Non-Hispanic or Latinx

1,848 (72.1) 611 (23.8)

Unknown

2019 novel coronavirus case type Confirmed

2,482 (96.8)

43,202 (95.2)

.0002 b

Probable

82 (3.3)

2,116 (4.8)

Significant medical history (i.e., ≥1 preexisting condition)

1,735 (98.1) 561 (21.9) 306 (11.9)

12,834 (28.3) 3,014 (6.6) 2,113 (4.7) 8,353 (18.4) Controls* ( n = 45,368) Median (IQR)

<.0001 b <.0001 b

History of chronic lung disease

Former smoker Current smoker

.0385 b

47 (1.8)

<.0001 b p -Value

Air Pollutant or Measure

Cases* ( n = 2,564) Median (IQR)

Average AQI

42.64 (37.73–48.31) 0.28 (0.19–1.12) 9.79 (2.66–10.28) 9.54 (8.39–10.23) 17.17 (16.66–17.73)

44.24 (38.48–48.33)

<.0001 a

Average SO 2 concentration (ppb) Average NO 2 concentration (ppb) Average PM 2.5 concentration (μg/m 3 ) Average PM 10 concentration (μg/m 3 )

0.45 (0.23–1.12) 7.08 (2.65–8.77)

.0469

<.0001 .0133 a

9.36 (8.08–10.21) 17.59 (16.71–17.94)

<.0001 a

*Cases are defined as individuals who had a lab-confirmed positive PCR or antigen test result for SARS-CoV-2 reported in Indiana who died as a result of COVID-19. Controls are defined as individuals who had a lab-confirmed positive PCR or antigen test for SARS-CoV-2 reported in Indiana who did not die as a result of COVID-19. a Linear regression. b Chi-square. Note. AQI = Air Quality Index; IQR = interquartile range; NO 2 = nitrogen dioxide; PM = particulate matter; SO 2 = sulfur dioxide.

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TABLE 2

FIGURE 1

Odds Ratio ( OR ) and Confidence Interval (CI) From Model 1 for Each 1-Unit Increase in Pollutant Concentration

Reactions That Allow for the Nitrogen Oxide (NO)–Nitrogen Dioxide (NO 2 ) Ratio

Pollutant

95% CI

NO + O 3 → NO 2 + O 2 NO 2 + O 2 → NO + O 3

Average AQI

1.1634

[0.5932, 2.2734]

Average SO 2 concentration (ppb) Average NO 2 concentration (ppb) Average PM 2.5 concentration (μg/m 3 ) Average PM 10 concentration (μg/m 3 )

7.9047 x 10 4

[7.0445 x 10 -4 , 8.8698 x 10 12 ]

0.6427

[0.0752, 5.4971] [1.1195, 18.5664] [0.6989, 1.1411]

Note. O 2 = oxygen; O 3 = ozone. Source: Hagenbjörk et al., 2017.

4.5990*

0.7602

*Statistically significant. Note. AQI = Air Quality Index; NO 2 = nitrogen dioxide; PM = particulate matter; SO 2 = sulfur dioxide.

has been shown to increase when the concen- tration of O 3 is low (Figure 1; Hagenbjörk et al., 2017). This increase is likely because O 3 oxidizes NO to produce NO 2 (Kimbrough et al., 2017). At low levels of O 3 , NO will accumu- late, thereby increasing the NO to NO 2 ratio. Furthermore, inhaled NO in the clini- cal setting has been shown to be protective against SARS-Cov-2, as it can inhibit viral replication of coronaviruses (Rajendran et al., 2022). Currently, NO is used as a vaso- dilator in the treatment of acute respiratory distress syndrome, which is a common result of severe COVID-19 (Alqahtani et al., 2022). It is important to note that these levels of NO would be at much lower concentrations than levels used in clinical settings. Further research is needed to study this hypothesis. Limitations There are four main limitations in our study. First, the exposure data were sampled at the county level. The 90-day average exposures were calculated from this information for each COVID-19 case reported to the Indiana Department of Health with a positive test result during the study period. The use of population-level exposure data for individual patients leaves a broad range of potential vari- ability. Some of the factors that could con- tribute to intra-county variation include the presence of factories, nearby roads, and crop dust. To help account for these factors, we restricted our analysis to MSAs, where there is more likely to be more than one report- ing site per county. When several values for a single day were present in the raw data, these values were averaged before calculat- ing the 90-day average exposure. Our study, however, did not account for travel out of an individual’s county of residence.

TABLE 3

Odds Ratio ( OR ) and Confidence Interval (CI) From Model 2 for Each 1-Unit Increase in Pollutant Concentration

Pollutant

95% CI

Average AQI

0.9578

[0.0426, 2.1549]

Average SO 2 concentration (ppb) Average NO 2 concentration (ppb) Average PM 2.5 concentration (μg/m 3 ) Average PM 10 concentration (μg/m 3 )

1.2016 x 10 4

[1.1302 x 10 -6 , 1.2775 x 10 14 ]

0.1609*

[0.0550, 0.4705] [0.5724, 22.5358] [1.1545, 6.4250]

3.5916

2.7235*

*Statistically significant. Note. AQI = Air Quality Index; NO 2 = nitrogen dioxide; PM = particulate matter; SO 2 = sulfur dioxide.

gested several mechanisms that could explain these associations (Karan et al., 2020; Weaver et al., 2022). Additionally, it has been shown that the average 90-day PM 10 concentration is associated with an increased risk of mortal- ity in individuals with chronic lung disease. One possible explanation for this relation- ship is that short-term particulate matter pol- lution has been shown to upregulate ACE-2 and TMPRSS-2, which are key proteins that enable the cellular entry of SARS-CoV-2 (Weaver et al., 2022). The upregulation of these proteins would provide the SARS- CoV-2 virus with more binding sites, which could enable an increased initial viral load. Other researchers have suggested that particulate matter could potentially serve as a carrier, allowing the SARS-CoV-2 virus to travel on its surface (Meo et al., 2021). This function could potentially compound other eects of air pollution, including increased

alveolar permeability, to produce severe dis- ease (Marian et al., 2022). In Italy, particulate matter samples were found to contain SARS- CoV-2 viral particles (Meo et al., 2021). This hypothesis is further supported by the find- ings of numerous studies that demonstrate a positive relationship between particulate matter concentrations and daily COVID-19 case counts (Copat et al., 2020). In our study, Model 2 demonstrated a protec- tive OR for NO 2 . Our hypothesis is that nitrogen oxide (NO), rather than NO 2 , might be driving this protective association. Previous research has identified NO 2 as a risk factor for severe COVID-19 outcomes (Copat et al., 2020; Meo et al., 2021). At low levels of NO 2 and O 3 —as seen in Indiana during the study period—it is hypothesized that the model might instead reflect the protective factor of NO, which exists in a ratio with NO 2 in the atmosphere (Hagen- björk et al., 2017). The ratio of NO and NO 2

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Second, data reported to the Indiana Department of Health are often incomplete, leading to low-quality race and ethnicity data. Despite data cleaning eorts to enhance the quality of case investigation demographic data, 10.9% of cases and 19.5% of controls in our analysis had an unknown race, and 23.8% of cases and 30.6% of controls had an unknown ethnicity. Third, our study was able to assess out- door air pollution exposure only, as indoor air pollution levels were unable to be obtained. Indoor air pollution exposure is expected to vary widely and would greatly aect the actual dose of the measured pol- lutants for the study population (Serafini et al., 2022). Fourth, the data were found to have expo- sure variables with low variability. Within

the study dataset, average O 3 had a range of 0.03 ppm and average CO had a range of 0.28 ppm. These variables had low variability for the sampled counties; as such, these vari- ables were determined to be poor predictors of mortality from COVID-19 and thus were excluded from the models. Conclusion This study identified an increased risk of death from COVID-19 in relation to each 1 μg/m 3 increase in PM 2.5 at the popula- tion level, and in relation to each 1 μg/m 3 increase in PM 10 in individuals with a previ- ously diagnosed chronic lung disease. Addi- tionally, our study found a protective asso- ciation for COVID-19 mortality per every 1 ppb of NO 2 . More research is required to further investigate these associations and to

support policy change eorts to strengthen air pollution regulation.

Acknowledgments: The authors acknowl- edge Thomas Duszynski, MPH, PhD, CPH; Jianjun Zhang, MD, PhD; Hailey Vest, MPH; Darren Frank, MPH; Jennifer Brown, MPH, DVM, DACVPM; Kelly White, MPH, CPH; Ronald Clark; the members of the Indiana Department of Health CDC Reporting Team; and everyone who participated in the 2020 Indiana Department of Health COVID-19 response. Your tireless work and support have made this eort possible.

Corresponding Author: Anna E. Heilers, Indi- ana Department of Health, 2 North Meridian

Street, Indianapolis, IN 46204. Email: aeheilers@gmail.com

References

Alqahtani, J.S., Aldhahir, A.M., Al Ghamdi, S.S., AlBahrani, S., AlD- raiwiesh, I.A., Alqarni, A.A., Latief, K., Raya, R.P., & Oyelade, T. (2022). Inhaled nitric oxide for clinical management of COVID- 19: A systematic review and meta-analysis. International Journal of Environmental Research and Public Health , 19 (19), Article 12803. https://doi.org/10.3390/ijerph191912803 Centers for Disease Control and Prevention. (2024). National Center for Health Statistics: Indiana . https://www.cdc.gov/nchs/pressroom/ states/indiana/in.htm Copat, C., Cristaldi, A., Fiore, M., Grasso, A.D., Zuccarello, P., Signorelli, S.S., Conti, G.O., & Ferrante, M. (2020). The role of air pollution (PM and NO 2 ) in COVID-19 spread and lethality: A systematic review. Environmental Research , 191 , Article 110129. https://doi.org/10.1016/j.envres.2020.110129 Hagenbjörk, A., Malmqvist, E., Mattisson, K., Sommar, N.J., & Modig, L. (2017). The spatial variation of O 3 , NO, NO 2 and NO x and the relation between them in two Swedish cities. Environmen- tal Monitoring and Assessment , 189 (4), Article 161. https://doi. org/10.1007/s10661-017-5872-z Karan, A., Ali, K., Teelucksingh, S., & Sakhamuri, S. (2020). The impact of air pollution on the incidence and mortality of COVID- 19. Global Health Research and Policy , 5 (1), Article 39. https://doi. org/10.1186/s41256-020-00167-y Kimbrough, S., Owen, R.C., Snyder, M., & Richmond-Bryant, J. (2017). NO to NO 2 conversion rate analysis and implications for dispersion model chemistry methods using Las Vegas, Nevada, near-road field measurements. Atmospheric Environment , 165 , 23–34. https://doi.org/10.1016/j.atmosenv.2017.06.027 Marian, B., Yan, Y., Chen, Z., Lurmann, F., Li, K., Gilliland, F., Eckel, S.P., & Garcia, E. (2022). Independent associations of short- and long-term air pollution exposure with COVID-19 mortality

among Californians. Environmental Advances , 9 , Article 100280. https://doi.org/10.1016/j.envadv.2022.100280 Meo, S.A., Abukhalaf, A.A., Alessa, O.M., Alarifi, A.S., Sami, W., & Klono, D.C. (2021). Eect of environmental pollutants PM 2.5 , CO, NO 2 , and O 3 on the incidence and mortality of SARS-CoV-2 infection in five regions of the USA. International Journal of Envi- ronmental Research and Public Health , 18 (15), Article 7810. https:// doi.org/10.3390/ijerph18157810 Rajendran, R., Chathambath, A., Al-Sehemi, A.G., Pannipara, M., Unnikrishnan, M.K., Aleya, L., Raghavan, R.P., & Mathew, B. (2022). Critical role of nitric oxide in impeding COVID-19 trans- mission and prevention: A promising possibility. Environmental Science and Pollution Research , 29 (26), 38657–38672. https://doi. org/10.1007/s11356-022-19148-4 Serafini, M.M., Maddalon, A., Iulini, M., & Galbiati, V. (2022). Air pollution: Possible interaction between the immune and ner- vous system? International Journal of Environmental Research and Public Health , 19 (23), Article 16037. https://doi.org/10.3390/ ijerph192316037 Weaver, A.K., Head, J.R., Gould, C.F., Carlton, E.J., & Remais, J.V. (2022). Environmental factors influencing COVID-19 incidence and severity. Annual Review of Public Health , 43 (1), 271–291. https://doi.org/10.1146/annurev-publhealth-052120-101420 Xu, L., Taylor, J.E., & Kaiser, J. (2022). Short-term air pollution expo- sure and COVID-19 infection in the United States. Environmental Pollution , 292 (Pt. B), Article 118369. https://doi.org/10.1016/j. envpol.2021.118369 Zang, S.-T., Luan, J., Li, L., Yu, H.-X., Wu, Q.-J., Chang, Q., & Zhao, Y.-H. (2022). Ambient air pollution and COVID-19 risk: Evidence from 35 observational studies. Environmental Research , 204 (Pt. B), Article 112065. https://doi.org/10.1016/j.envres.2021.112065

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 SPECIAL REPORT

Navigating Community- Engaged Research to Understand How Drought Affects Water Quality

Nicholas Stoll, MPH Department of Environmental & Occupational Health, Colorado School of Public Health Francesca Macaluso, MPH Department of Environmental & Occupational Health, Colorado School of Public Health Christine Canaly San Luis Valley Ecosystem Council Katherine A. James, PhD Department of Environmental & Occupational Health, Colorado School of Public Health

communication between research teams and community members cultivates distrust of both scientists and their parent institutions, resulting in additional barriers to improving community health in already higher-risk and historically marginalized communities (Key et al., 2019). CEnR methods represent an alter- native to historical scientific practices and have been highlighted by the environmental justice movement, among others, as a more sustain- able mode of public health practice (Brenner & Manice, 2011; Vohland et al., 2021). CEnR methods have been branded dier- ently in dierent contexts. The term “citizen science” has been used to describe research led by formally trained researchers with sup- port from community volunteers or partici- pants, whereas “community science” has been used to describe the same work in a way that is more inclusive of undocumented persons, or to dierentiate work that is primarily driven by the needs or desires of the community (Key et al., 2019; Silvertown, 2009). Community- engaged methods, however, are increasingly being conceptualized as a spectrum of com- munity involvement in science rather than as entirely distinct research models (Key et al., 2019). Projects can range from a) commu- nity-informed, community-consultation, or community-participation in data collection to b) community-driven or lead, where commu- nity members formulate their own research questions and research teams provide techni- cal support as needed. By nature, the kinds of activities within community-led projects that involve public collaboration and participa-

(8tra)t Community-engaged research (CEnR) is becoming more prevalent in environmental health. With a spectrum of community involvement characterizing this research, it has the potential to foster a diverse set of collaborative dynamics. Our research team has facilitated a regional CEnR eort focusing on the eects of drought driven by climate change. Specifically, the CEnR is focusing on regional water quality by analyzing samples from privately owned groundwater wells and examining the samples for heavy metal contaminants. This special report outlines lessons learned and project management strategies used to facilitate large- scale community-engaged environmental science. Recruitment methods evolved in response to obstacles and included direct mailings, listservs and social media promotion, direct recruitment by local leaders, and local newspaper coverage. Participant onboarding evolved as new recruitment methods were implemented but was standardized to achieve process eciency. External data sources were used to align contextual information of sampled wells to the submitted samples. Open-source tools were used to streamline reporting of results to participants. Scalability, project management, bidirectional involvement with community members and organizations, and accountability are important themes to consider when facilitating environmental CEnR. Keywords: community-engaged research, climate change, drought, water quality, environmental monitoring

I ntroduction Community-engaged research (CEnR) is becoming more prevalent in environ- mental health due to increasing recognition of the problematic legacy of “colonial science,” wherein outside research teams use funding

to investigate their own scientific concerns without consideration for community needs and perspectives (Flatow, 2021; Odeny & Bosurgi, 2022; Watson, 2021). Particularly in the field of public health, lack of community involvement in research projects and poor

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tion, such as data collection, environmental sampling, or e orts to increase public aware- ness of issues at the local level, can vary and be largely dependent on the research topic, study area, or other factors (Lin Hunter et al., 2023; Silvertown, 2009; Vohland et al., 2021; Wilderman, 2007). Despite the growing popularity of CEnR methods, more widespread adoption and uti- lization of community-generated data have been limited by the perception that data col- lected through participatory methods is of lower quality. Comparisons of community- generated and professionally generated data for environmental monitoring tell a di erent story—when projects are designed appropri- ately and provide adequate participant sup- port, the data generated are comparable with data generated by professional researchers (Danielsen et al., 2014; Kosmala et al., 2016). Researchers have identified structured research training, performance feedback, and results communication as key factors for participant follow-through and confidence in unsupervised participation (Silvertown, 2009; Stanifer et al., 2022). Additionally, data cleaning and analysis methods for the thorough validation of community-generated data and outlier identification are increas- ingly being developed and refined (Li et al., 2020). These additional steps and design considerations for community-engaged envi- ronmental monitoring projects—including large-scale recruitment, participant train- ing and onboarding, continual participant management and support, and communica- tion with community members—require a major allocation of time and resources for research teams (Silvertown, 2009; Vohland et al., 2021). Without embracing community involvement, however, research projects that strive to collect field data over a large geo- graphical area will be challenged to do so. Our research team has facilitated several e orts of community-engaged environmental research in the San Luis Valley of Colorado. This rural region covers >8,000 mi 2 of alpine desert valley with some of the lowest median household incomes in the state (San Luis Val- ley Development Resources Group & Council of Governments, 2021). As a region with high environmental levels of heavy metals due to both natural geological features and human mining activity, exposure to these metals is an ongoing community concern bolstered by

scientific evidence. Several studies have noted elevated levels of arsenic in environmental and biological samples from the San Luis Val- ley (Hamman et al., 1989; James et al., 2013, 2014; Thiros et al., 2010). More recently, there is evidence suggesting that the prolonged and significant drought in the region likely is con- tributing to even greater levels of heavy metals in the region’s groundwater, due to less dilu- tion from aquifer recharge. These changes are further exacerbating existing water quality concerns and posing new challenges for long- term water management. Residents of the San Luis Valley have an established legacy of environmental steward- ship and activism, working to protect water quality and quantity through their many community-led environmental nonprofit organizations and historical partnerships with university, state, and federal government scientists. Community collaboration in envi- ronmental health work is central to the spirit and identity of this vast and geographically isolated region of Colorado, making a partici- patory science research model both a moral and logistical imperative. Our research team led one such commu- nity-engaged project in the San Luis Valley with the goal of expanding previous water quality data sources on heavy metal con- taminants. Only 23% of environmental CEnR focuses on the abiotic elements (e.g., water quality, air pollution) and there is limited published research on the application of envi- ronmental CEnR in rural settings (Locke et al., 2019; Pocock et al., 2017). In this special report, we outline the many lessons learned throughout this endeavor and the project management tools and strategies we have used, which can provide significant benefit to the greater scientific community, especially for anyone embarking on large-scale commu- nity-engaged environmental science projects.

tion, and water resources shared recruitment information via their own private listservs and social media accounts to approximately 130 individuals. On partnering with local community leaders, we were advised to promote our e orts in local newspapers. Newspaper advertisements and coverage drastically increased recruitment to approxi- mately 500 participants, while our three local community leaders individually recruited a smaller subset of approximately 60 partici- pants. At the time this article was printed, 916 individuals have registered to participate and provide water samples from their homes. In total, 745 participants have successfully returned water samples. Participant Onboarding Several di erent modes of entry were avail- able for individuals to participate in our community water sampling e ort (Figure 1). Recruitment materials provided a monitored phone number with voicemail, emails to proj- ect coordinators, and a QR code and short URL for direct registration. All entry points were funneled through a Qualtrics survey to gather participant contact information and as much information about their private well as possible. Individual phone calls proved time- intensive but necessary to reach our target population. Contact via email was stream- lined by using templated responses and redi- rection to the Qualtrics survey. Qualtrics was invaluable as a standardized tool for informa- tion gathering but required several renditions in survey design to achieve e§ciency. Sampling Logistics Information from onboarding (e.g., address of the property where the well is located, current property owners, well permit information) was used to build a dataset with sample infor- mation. Each sample was associated with a well permit maintained by the Colorado Divi- sion of Water Resources, which allowed us to access information on well casting, depth, and latitude and longitude coordinates (Colorado Department of Natural Resources, n.d.). Participants were provided a sampling con- tainer labeled with their name, unique iden- tifier for their well, and a one-page informa- tion sheet on the sampling protocol. While standardized sample IDs are more practical for research purposes, ensuring that these identi- fiers were meaningful to participants was key

Methods

Recruitment Di erent methods of recruitment were used throughout this project. Initial recruitment e orts involved direct mailing of recruitment materials to approximately 120 individu- als who had previously participated in local water quality research. When responses from these mailings tapered o , community part- ner organizations in agriculture, conserva-

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