Recreational drug inhalation and socioenvironmental determinants of pulmonary infection in patients with HIV: a hospital-based case control study

Recreational drug inhalation and socioenvironmental determinants of pulmonary infection in patients with HIV: a hospital-based case control study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Recreational drug inhalation and socioenvironmental determinants of pulmonary infection in patients with HIV: a hospital-based case control study Robert J Blount, Serena Fong, Stephen Stone, Veesta Falahati, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8695194/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Pulmonary infections cause substantial morbidity and mortality among people with HIV. Our objective was to evaluate common urban exposures among those living with HIV which may increase risk of pulmonary infection. Methods We enrolled 75 adult patients infected with HIV and admitted to San Francisco General Hospital into a hospital-based case-control study: 38 cases with pulmonary infection and 37 controls admitted for diagnoses other than pulmonary infection. We administered questionnaires to assess inhaled drug exposures, outdoor and indoor pollution, and occupational exposures. We fit multivariable logistic regression models with pulmonary infection as the dichotomous outcome and each exposure as a primary predictor in separate models, including potential confounders based on biological plausibility and backward selection criteria. Results Participants were middle-aged (median age 48 years) and predominantly male (77%). Poverty was prevalent, with 79% unemployed and 27% unstably housed or homeless. Median CD4 count was lower in cases compared with controls (88 vs 300 cells/µL, p = 0.005). Most cases were admitted with either bacterial pneumonia (76%) or Pneumocystis pneumonia (16%), and the most common admission diagnosis among controls was non-pulmonary infection (43%). Fifty-nine percent of participants had smoked tobacco cigarettes within the month prior to enrollment while 40% had recently smoked marijuana. In adjusted logistic regression models, tobacco smokers had six times higher odds of pulmonary infection (aOR 6.43, 95%CI 1.70–24.4) compared with non or former smokers, while those who had recently smoked marijuana experienced a 79% reduction in odds of pulmonary infection (aOR 0.21, 95%CI 0.06–0.78) compared with no recent marijuana smoking. Unstable housing or homelessness predicted a three-fold increased odds of pulmonary infection (aOR 3.22, 95%CI 1.01–10.3), whereas those sleeping on higher floors were at reduced odds of pulmonary infection, with each floor level above street-level predicting a 44% reduced odds of pulmonary infection (aOR 0.56, 95%CI 0.40–0.80). Finally, opening windows for ventilation was associated with reduced pulmonary infection odds (aOR 0.051, 95%CI 0.004–0.64). Conclusions Pulmonary infection odds were increased with tobacco smoking and homelessness; and decreased among recent marijuana smokers, those sleeping on higher floors and opening windows for ventilation. Clinical trial number: not applicable HIV community acquired pneumonia Pneumocystis pneumonia tuberculosis marijuana illicit drugs social determinants of health Figures Figure 1 Figure 2 Introduction Forty one million children and adults are currently living with HIV worldwide, with 1.3 million new HIV infections and 630,000 deaths attributable to AIDS in 2024 [ 1 ]. In the United States alone, there are over 1.2 million persons who are living with HIV, with more than 32,000 new diagnoses of HIV and 19,000 deaths in persons living with HIV in 2022 [ 2 ]. HIV infection increases susceptibility to pulmonary infections such as bacterial and viral pneumonia, tuberculosis (TB), Pneumocystis pneumonia (PCP), and cryptococcal pneumonia [ 3 ]. Prior studies have shown that persons living with HIV who experience infectious respiratory diseases have increased mortality compared to individuals who are not living with HIV [ 4 ]. Thus, efforts to better understand risk factors for pneumonia are essential to implementing preventative strategies in this population. Recreational drug inhalation is common among persons living with HIV. Of those surveyed in the U.S., 26–30% reported recent marijuana use [ 5 – 7 ] and 42% reported current tobacco cigarette smoking [ 8 ]. In a recent study of self-reported recreational drug use among persons with HIV at seven sites across the U.S., 8% reported use of methamphetamines, including inhalation of crystal methamphetamines [ 7 ], and 7% reported cocaine use, including inhalation of crack cocaine [ 7 ]. Some inhaled toxicants are known to increase the risk of pulmonary infections and can potentiate the effects of HIV on pulmonary infection. This has been most robustly established for active tobacco smoking and secondhand smoke as risk factors for community-acquired pneumonia [ 9 , 10 ] and tuberculosis [ 11 , 12 ]. For instance, in a VA cohort study, it was shown that among persons with HIV, those who currently smoked tobacco had more than double the risk of developing community-acquired pneumonia compared to those who did not smoke [ 13 ]. However, it remains unclear if inhalation of other recreational drugs also increases risk. Though marijuana smoking is associated with chronic lung disease [ 14 ], observational studies evaluating associations between marijuana smoking and pulmonary infection have found conflicting results [ 15 , 16 ]. Recent trends toward the legalization of marijuana make this a particularly salient question, though legalization has not necessarily resulted in an escalation of marijuana use among persons living with HIV [ 17 ]. Several observational studies among predominantly immunocompetent persons have also found associations of socioenvironmental factors such as poverty and air pollution with community-acquired pneumonia [ 18 – 23 ]. However, air pollution associations with tuberculosis [ 24 ], Pneumocystis pneumonia [ 25 ], and cryptococcal pneumonia in vulnerable populations such as persons with HIV are less well characterized. Therefore, we conducted a nested case-control study within the San Francisco site of the International HIV-associated Opportunistic Pneumonias (IHOP) cohort study [ 26 ]. We hypothesized that inhaled exposures to recreational drugs (tobacco cigarettes, marijuana, crack cocaine, and crystal methamphetamine), socioenvironmental factors (homelessness and air pollution), and occupational exposures in patients living with HIV are associated with pulmonary infection (Fig. 1 ). Some of the results of these studies have been previously reported in the form of an abstract [ 27 ]. Methods Study Design and Population We conducted a hospital-based case-control study among adults aged ≥ 18 years with HIV admitted to the San Francisco General Hospital and Trauma Center (SFGH). Cases were defined as patients admitted with pulmonary infection as diagnosed by an attending physician based on history, physical exam, imaging studies, and with or without microbiologic confirmation. Controls were defined as adult patients admitted with a diagnosis other than pulmonary infection. Patients were excluded from this study if: 1) they were unable to complete the study questionnaire; or 2) they were admitted for a psychiatric diagnosis or altered mental status (potential impact on ability to complete the questionnaire). Eligible cases and controls were identified from October 2012 through May 2013 from a daily HIV admissions roster, then recruited during their inpatient stay. Cases and controls giving consent were enrolled consecutively using an incidence-density sampling method. Cases were concurrently enrolled into the International HIV-associated Opportunistic Pneumonias (IHOP) Study, a previously described longitudinal cohort study of HIV-infected adults with clinical and radiographic evidence of pulmonary infection [ 26 ]. Data Collection We developed a standardized questionnaire for this study to collect demographic, socioeconomic, clinical, and exposure-related data from participants [ 28 ]. A trained clinical research coordinator administered this questionnaire to each participant. We accessed clinical data from a standardized chart abstraction performed for the larger IHOP cohort study. Demographic variables included age, sex at birth, race, and ethnicity. Socioeconomic variables included the highest educational level obtained, household income, employment status, and housing status. Participants were categorized as “unstably housed or homeless” if they answered “No” to the following question: “Do you have a stable home address (a place where you have lived for at least the last month)?” For medical history, we collected information regarding recent illness or pulmonary infection, recent antiretroviral therapy, history of chronic obstructive pulmonary disease (COPD), and whether there was recent use of inhaled corticosteroids. We obtained baseline CD4 lymphocyte count and HIV viral load within the six months prior to admission through chart abstraction to best characterize immune status. The standardized questionnaire elicited responses in three exposure categories: 1) recreational drug inhalation, 2) socioenvironmental exposures, and 3) occupational exposures. We queried recreational drug use to ascertain the type, method, quantity, and duration of usage. Regarding inhaled drugs, we inquired about the usage of tobacco, marijuana, crack cocaine, crystal methamphetamine, and vaping devices. Recent use of recreational drugs or tobacco was defined as within 30 days prior to enrollment. For air pollution, we defined traffic density as the number of vehicles per kilometer each hour in 100 to 500-meter buffers around the participant’s sleeping location. We calculated traffic density using previously described methods developed by the California Department of Public Health to link geocoded residential addresses to roadway traffic volumes obtained from the California Department of Transportation Highway Performance Monitoring System [ 29 ]. The bedroom floor level was considered as the vertical distance from street-level outdoor pollution, measured in floors, with street-level floor assigned the value 1. Floor level has been found to be a robust surrogate for residential exposure to traffic pollution, with exposure to street-level pollution decreasing as floor number increases higher from the street [ 30 , 31 ]. We evaluated indoor pollution with questions regarding cooking, heating, and secondhand smoking; and further evaluated indoor air quality via questions about indoor air circulation through use of central air and opening of windows for ventilation. Finally, we assessed occupational exposures to vapor, gases, dust, and fumes (VGDF) [ 32 ] in addition to commuting-related exposures. Statistical Analyses For descriptive analyses we used Student’s t-test for continuous variables with normal distribution, Wilcoxon rank-sum test for nonparametric testing of variables that were not normally distributed, Fisher exact test for categorical variables with any cell n ≤ 5, and chi-square testing for all other categorical variables (Table 1 ). We fit logistic regression models with pulmonary infection as the dichotomous outcome and each exposure as a primary predictor in separate models. Pulmonary infection was defined as a composite dichotomous outcome including any of the following diagnoses: bacterial pneumonia, viral pneumonia, Pneumocystis pneumonia, pulmonary tuberculosis, or cryptococcal pneumonia (Table 2 ). We fit separate logistic regression models for each of the following primary predictors (Table 3 ): Recent smoking within the last 30 days of i) tobacco, ii) marijuana, iii) crack cocaine, or iv) crystal methamphetamine; v) unstable housing or homeless in the last 30 days; vi) traffic density; vii) sleeping (bedroom) floor level; viii) use of open windows for indoor ventilation; ix) cooking with gas; x) secondhand tobacco smoke exposure; xi) commuting on foot or nonmotorized bike vs motorized; and xii) workplace exposure to VGDF. For each model we considered the following potential confounders based on biological plausibility identified in the literature and directed acyclic graphing (DAG): age, sex at birth, smoking tobacco, smoking marijuana, CD4 count, employment status, homelessness, and enrollment season (Fig. 1 ). For each multivariable model we then stepwise deleted potential confounders with a p > 0.2 that a) impacted the primary predictor coefficient magnitude by < 5% or b) did not statistically significantly (p < 0.05) contribute to the model in nested likelihood ratio (LR) testing of models including and excluding the covariate under consideration. The final potential confounders selected for each multivariable logistic regression model i-xii were: i) marijuana, CD4; ii, x) tobacco, CD4; iii, iv, vi) tobacco, CD4, homelessness; v) employment status, CD4; vii) CD4; viii) tobacco, CD4, employment status, season; ix) age, employment status; and xi, xii) age. We considered a general rule of thumb of no more than 1 predictor for every 10 outcomes (4 predictors for 38 positive outcomes), and final models ranged from 2–5 total predictors. Given the small sample size, we further tested each multivariable model for overfitting by evaluating for decrements in the precision of the primary predictor coefficient estimate in adjusted models relative to corresponding unadjusted models. We used pairwise correlation to test for predictor collinearity. We tested logistic linear assumptions by graphing the locally weighted scatterplot smoother (LOWESS) log odds of the outcome against each continuous predictor assessing for linearity compared with fitted regression lines. We tested model adequacy using the specification link test and goodness of fit using the Hosmer-Lemeshow test. In separate models we also tested CD4 count as a possible mediator in the effects of inhalational and socioenvironmental factors on pulmonary infection, though we found mediation unlikely, as our primary exposure variables did not significantly predict CD4 counts in exploratory unadjusted linear regression analyses. Results We enrolled 75 patients living in San Francisco with HIV admitted to SFGH during the 8-month study period: 38 cases with pulmonary infection and 37 controls admitted for diagnoses other than pulmonary infection (Fig. 2 ). Participants were predominantly middle-aged (48 years, IQR 44–54) white (39, 52%) males (58, 77%) (Table 1 ). Poverty was prevalent in the cohort, with 81% of participants reporting annual household income ≤ $ 20,000 USD, 79% reporting unemployment, and 27% reporting unstable housing or homelessness at the time of enrollment. Less than half of the participants (49%) reported education beyond high school. Table 1 Patient demographic, socioeconomic, and clinical characteristics (n = 75) Characteristic* All Subjects Cases Controls p value § n = 75 n = 38 n = 37 Median age (IQR) 48 (44–54) 48 (43–54) 49 (45–54) 0.65 Male sex at birth 58 (77) 30 (79) 28 (76) 0.74 Race White 39 (52) 19 (50) 20 (54) 0.80 Black 27 (36) 15 (39) 12 (32) Other 9 (12) 4 (11) 5 (14) Hispanic ethnicity 9 (12) 3 (8) 6 (16) 0.31 Employment Status Employed 11/72 (15) 5/35 (14) 6/37 (16) 0.40 Student 2/72 (2.8) 0 2/37 (5.4) Retired 2/72 (2.8) 2/35 (5.7) 0 Unemployed 57/72 (79) 28/35 (80) 29/37 (78) Post high school education 31/63 (49) 11/26 (42) 20/37 (54) 0.36 Income † ≤ $ 10,000 29/57 (51) 17/25 (68) 12/32 (38) 0.06 $ 10,000–20,000 17/57 (30) 4/25 (16) 13/32 (41) > $ 20,000 11/57 (19) 4/25 (16) 7/32 (22) Unstably housed or homeless 20 (27) 14 (37) 6 (16) 0.04 Intravenous drug use, ever 40/70 (57) 18/33 (55) 22/37 (59) 0.68 Enrollment during winter ‡ 33 (44) 22 (58) 11 (30) 0.01 Median CD4 count, cells/µL (IQR) 205 (49–425) 88 (39–255) 300 (152–467) 0.005 Detectable viral load > 1000 copies/mL 37 (49) 24 (63) 13 (35) 0.02 Inhaled corticosteroid use in last month 8/71 (11) 3 (8.8) 5 (14) 0.53 *Expressed as n(%) unless otherwise indicated † Annual household income in US dollars (2012–2013 value) ‡ Season of enrollment: Winter vs the preceding Fall or following Spring § Statistical tests used: t-test for continuous variables with normal distribution, Wilcoxon rank-sum test (Mann-Whitney U test) for nonparametric testing of variables that were not normally distributed, Fisher exact test for categorical variables with any cell n ≤ 5, and chi-square test for all other categorical variables. Cases compared with controls were more likely to be unstably housed or homeless (37% vs 16%, p = 0.04); be enrolled in the winter (58% vs 30%, p = 0.01), have a lower median CD4 count (88 vs 300 cells/µL, p = 0.005); and have a detectable HIV viral load (63% vs 35%, p = 0.02). Most cases were diagnosed with either bacterial pneumonia (76%) or PCP (16%), with only one case each of viral pneumonia, pulmonary TB, and cryptococcal pneumonia (Table 2 ). Principal discharge diagnoses among controls were varied, with the plurality of controls admitted for non-pulmonary infection (43%). Table 2 Principal discharge diagnosis in cases and controls (n = 75) Cases, n = 38 Controls, n = 37 Pulmonary infection n (%) Diagnosis category n (%) Bacterial pneumonia 29 (76) Non-pulmonary infection 16 (43) Viral pneumonia 1 (2.6) Cardiovascular/Pulmonary disease* 3 (8.1) Pneumocystis pneumonia 6 (16) Gastrointestinal/Renal disease* 4 (11) Pulmonary tuberculosis 1 (2.6) Cancer, suspected or confirmed 4 (11) Cryptococcal pneumonia 1 (2.6) Musculoskeletal disorder* 3 (8.1) Trauma 5 (14) Alcohol/drug withdrawal 2 (5.4) *Non-infectious Drug inhalation was common in our study: 59% of participants had smoked cigarettes within the month prior to enrollment, smoking a median of 10 cigarettes per day (IQR 2–20), while 40% of participants had recently smoked marijuana, smoking a median of four times per month (IQR 2–27) (Table 3 ). Dual use of tobacco and marijuana was reported by 33% of participants, another 33% reported no recent smoking of either tobacco or marijuana, while the remaining participants smoked either tobacco or marijuana. Table 3 Odds of hospital admission for pulmonary infection among HIV patients, by exposure (n = 75) Exposure All Subjects* Cases* Controls* OR (95%CI) Unadjusted Adjusted † Drug Inhalation ‡ Tobacco 42/71 (59) 24/34 (71) 18/37 (49) 2.53 (0.95–6.75) 6.43 (1.70–24.4) Marijuana 29/72 (40) 11/35 (31) 18/37 (49) 0.48 (0.18–1.27) 0.21 (0.06–0.78) Crack cocaine 12/73 (16) 8/36 (22) 4/37 (11) 2.36 (0.64–8.66) 1.90 (0.47–7.70) Crystal methamphetamine 11/72 (15) 5/35 (14) 6/37 (16) 0.86 (0.24–3.12) 0.28 (0.06–1.35) Socio-environmental Unstable housing or homeless § 20 (27) 14 (37) 6 (16) 3.01 (1.01–9.01) 3.22 (1.01–10.3) Traffic density, || 1000 vehicle·km/hr., mean (SD) 8.36 (5.51) 7.89 (5.64) 8.82 (5.43) 0.97 (0.89–1.06) 0.97 (0.89–1.06) Sleeping floor level, median (IQR) §§ 2 (1–4) 1 (1–3) 3 (2–5) 0.55 (0.39–0.78) 0.56 (0.40–0.80) Windows open for ventilation** 45/52 (87) 15 (71) 30 (97) 0.083 (0.009–0.76) 0.051 (0.004–0.64) Cooking with gas †† 19/42 (45) 5/13 (38) 14/29 (48) 0.67 (0.18–2.54) 0.28 (0.05–1.63) Secondhand smoke 47/63 (75) 20/28 (71) 27/35 (77) 0.74 (0.24–2.31) 0.36 (0.09–1.52) Commute to work on foot/bike ‡‡ 4/10 (40) 2/4 (50) 2/6 (33) 2.0 (0.15–26.7) 5.6 (0.17–186) Occupational §§§ Workplace exposure 7/11 (64) 3/5 (60) 4/6 (67) 0.75 (0.06–8.83) 0.56 (0.03–10.2) *Expressed as n (%) unless otherwise indicated † Potential confounders were selected based on biological plausibility and backward selection criteria. ‡ Active use within the 30 days prior to study enrollment § Participants without a stable residential address in the 30 days prior to enrollment || Traffic density in the 500-meter buffer surrounding the participant’s most recent sleeping address. §§ Vertical level of sleeping location. Street-level = 1, one floor above street-level = 2, etc. **Among stably housed participants, n = 52 †† Those cooking primarily with natural gas compared with those who use primarily electric stove or microwave, among stably housed participants who cook at home, n = 42 ‡‡ Commute by nonmotorized bicycle or walking vs motorized means, n = 10 with one participant working exclusively from home. §§§ Among those currently employed, n = 11 Recent use of other inhaled drugs was less prevalent: 16% recently smoked crack cocaine and 15% recently smoked crystal methamphetamine. Those who recently smoked tobacco had six times higher odds of pulmonary infection compared with non- and former-smoking participants (aOR 6.43, 95%CI 1.70–24.4, p = 0.006). Those who smoked marijuana in the 30 days prior to enrollment, on the other hand, experienced a 79% reduced odds of pulmonary infection compared to participants who had not recently smoked marijuana (aOR 0.21, 95%CI 0.06–0.78, p = 0.02). In a sensitivity analysis, we found no statistically significant differences between those who actively smoked marijuana compared to those who did not for any key demographic, socioeconomic, or clinical variables that might influence the risk for pulmonary infection (Table 4 ). Crack cocaine and crystal methamphetamine were not statistically significantly associated with pulmonary infection, though few participants had recently inhaled these substances. Table 4 Baseline characteristics by marijuana smoking status: a sensitivity analysis (n = 72). Characteristic* Smoked marijuana in last month? p value § All Subjects Yes No n = 72 n = 29 n = 43 Median age (IQR) 48 (44–54) 48 (38–53) 49 (45–55) 0.14 Male sex at birth 56 (78) 22 (76) 34 (79) 0.75 Race White 37 (51) 14 (48) 23 (53) 0.75 Black 26 (36) 12 (41) 14 (33) Other 9 (13) 3 (10) 6 (14) Hispanic ethnicity 9 (13) 3 (10) 6 (14) 0.73 Employment Status Employed 11 (15) 4 (14) 7 (16) 1.0 Student 2 (2.8) 1 (3.5) 1 (2.3) Retired 2 (2.8) 1 (3.5) 1 (2.3) Unemployed 57 (79) 23 (79) 34 (79) Post high school education 31/63 (49) 12/28 (43) 19/35 (54) 0.37 Income † ≤ $ 10,000 29/57 (51) 15/28 (54) 14/29 (48) 0.78 $ 10,000–20,000 17/57 (30) 7/28 (25) 10/29 (34) > $ 20,000 11/57 (19) 6/28 (21) 5/29 (17) Unstably housed or homeless 20 (28) 22 (76) 30 (70) 0.57 Intravenous drug use, ever 40/70 (57) 17/28 (61) 23/42 (55) 0.62 Median CD4 count, cells/µL (IQR) 205 (49–425) 235 (78–417) 154 (53–452) 0.90 Detectable viral load > 1000 copies/mL 35 (49) 13 (45) 22 (51) 0.60 *Expressed as n(%) unless otherwise indicated † Annual household income in US dollars (2012–2013 value) § Statistical tests used: t-test for continuous variables with normal distribution, Wilcoxon rank-sum test (Mann-Whitney U test) for nonparametric testing of variables that were not normally distributed, Fisher exact test for categorical variables with any cell n ≤ 5, and chi-square test for all other categorical variables. Regarding socioenvironmental variables, traffic densities within 100- to 500-meter buffers around current sleeping location were not significantly associated with pulmonary infection. However, the vertical sleeping distance relative to the street was significantly associated with pulmonary infection (Table 3 ). Bedroom floor level was inversely associated with pulmonary infection. The median bedroom floor level was 1st floor (ground, street-level floor) for cases (IQR 1st − 3rd floor), and the 3rd floor for controls (IQR 2nd – 5th floor). Every bedroom floor level above street-level predicted a 44% decrease in odds of pulmonary infection (aOR 0.56, 95%CI 0.40–0.80, p = 0.002). Furthermore, unstably housed or homeless participants had more than a 3-fold increased odds of pulmonary infection compared to stably housed participants (aOR 3.22, 95%CI 1.01–10.3, p = 0.048). Regarding indoor exposures, cooking method and secondhand smoke were not significantly associated with pulmonary infection, whereas opening windows for ventilation was associated with reduced odds of pulmonary infection (aOR 0.051, 95%CI 0.004–0.64, p = 0.02). Finally, we did not find significant associations between commute and workplace exposures, though we were underpowered with only 11 participants employed at the time of study enrollment. Discussion We conducted a small hospital-based case-control study to evaluate socioenvironmental exposures that might increase risk for pulmonary infection, a leading cause of mortality in patients with HIV. We found that cigarette smoking and unstable housing or homelessness were associated with increased odds of hospitalization for pulmonary infection compared to hospitalization for non-pulmonary infection diagnoses among inpatients with HIV. Conversely, marijuana smoking, bedroom floor level, and opening windows for indoor circulation were predictive of decreased pulmonary infection. Cigarette smoking was a strong predictor of pulmonary infection in our study. Smoking is a known risk factor for pulmonary infection in patients with HIV [ 13 , 33 ] as well as in immunocompetent individuals [ 10 ]. This expected finding serves as a positive control and internal validation for other findings in our study. It also indicates that, despite the small sample size of our cohort, we were at least powered adequately to detect large effects. However, confidence intervals were quite wide, especially in the adjusted model. Incremental addition of variables to the model did not lead to the reduced precision, making overfitting less likely, but rather a moderate degree of collinearity with recent marijuana smoking (r = 0.35). On the other hand, marijuana predicted a reduced odds of pulmonary infection, suggesting that this behavior was less “risky” for respiratory infection than smoking tobacco among our participants. Several underlying mechanisms could drive such an association. Firstly, exposure to the products of biomass combustion was roughly 75 times higher with cigarette smoking than with marijuana smoking in this study (median 10 cigarettes per day vs 4 marijuana joints per month, assuming an average joint biomass similar to that of a cigarette) [ 34 ]. Secondly, marijuana smoking could serve as a surrogate measurement for unmeasured protective factors. For instance, perhaps marijuana smokers were healthier or partook in less risky behaviors compared to marijuana nonsmokers. However, our sensitivity analysis suggests that marijuana smokers were similar to non-marijuana smokers in measured demographic, socioeconomic, and clinical variables that could be predictive of pulmonary infection. Thirdly, acute exposure to delta-9-tetrahydrocannabinol (THC) in marijuana has been found to cause bronchodilation in experimental human physiology studies [ 35 , 36 ]. Furthermore, chronic marijuana use does not appear to drop predicted forced expiratory volumes (FEV1) as does cigarette smoking, although findings are not consistent across studies [ 34 , 36 , 37 ]. Bronchodilation can aid in mucus clearance [ 38 , 39 ], an essential pulmonary host defense mechanism against respiratory pathogens [ 40 ]. Thus, it is conceivable that the acute bronchodilation afforded by THC could play a protective role in preventing pulmonary infection. However, bench studies have also found immunomodulatory mechanisms through which marijuana smoke could potentially increase the risk of pulmonary infection. For instance, macrophages in bronchoalveolar lavage after recent marijuana smoking were found to have reduced phagocytic function and impaired cytokine production, key early immune defense mechanisms against inhaled pathogens [ 41 ]. We did not find significant associations between inhaling crack cocaine or crystal methamphetamine on pulmonary infection, though only 15–16% of participants reported recent use such that we were possibly underpowered to detect an effect. Crack cocaine is a known pulmonary toxicant [ 42 ], and there remains concern that it could increase risk for pulmonary infection. Homelessness was a strong predictor of pulmonary infection, possibly operant through multiple social determinants of health mechanisms. Persons experiencing homelessness are at increased risk for exposures to unhealthy environmental conditions such as street-level traffic pollution and extreme cold and heat [ 43 , 44 ], exposures that can increase the risk for pulmonary infection [ 19 , 45 ]. Additionally, noise and nocturnal light exposures are increased in homelessness, resulting in higher stress levels and inadequate sleep [ 46 – 49 ], both of which are associated with community acquired pneumonia [ 50 , 51 ]. In addition to potential environmental exposures, homelessness is also a surrogate for poverty which is a robust independent predictor of pneumonia, TB, and PCP [ 23 , 52 – 54 ]. Poverty is correlated with several additional factors associated with pulmonary infection such as: lack of optimal nutrition [ 23 , 54 – 57 ], increased mental health disorders [ 58 ], and reduced opportunities to access health care and other vital services [ 59 ]. Air pollution concentrations are typically inversely proportional to distance from source, such that exposure diminishes as the distance from the source increases. This is true not only for horizontal distances from traffic pollution [ 60 ], but also vertical distances above street-level pollution [ 30 , 61 ]. Furthermore, exposures to street-level pollutants inside the home decrease as residential floor level increases [ 31 , 62 ]. Therefore, we used the bedroom floor level as a surrogate for street-level pollution exposure, hypothesizing that high floor levels would be associated with reduced odds of pulmonary infections. Indeed, we found that those sleeping on higher floors, more vertically removed from street-level exposures, had reduced odds of pulmonary infection, consistent with other studies [ 12 , 22 ]. Even after controlling for socioeconomic status, the association remained robust. However, additional confounders such as unmeasured wealth metrics and comorbid conditions could also influence what floor levels our participants resided on [ 63 ], thus somewhat limiting the interpretation of this finding. There are additional limitations that should be considered when drawing conclusions from this study. First, due to the small available population of patients with HIV and with pulmonary infection, we utilized a case-control study design. Furthermore, to optimize limited resources, we performed this study in a hospital-based setting, where it can be more challenging to ensure that controls are selected from the same population as cases. We are reassured that, at least geographically, our cases and controls came from the same neighborhoods in San Francisco (Fig. 2 ). We also used incidence density sampling of controls concurrent with enrollment of cases, but inevitably, more cases with pulmonary infection were identified in winter months and more controls without pulmonary infection in the ensuing spring (Table 1 ) introducing potential selection bias. Second, our sample size was small (n = 75), with indoor pollution analyses of cooking practices and indoor circulation further limited to the smaller group of stably housed participants (n = 52), and occupational exposure regression analyses limited by an even smaller group of currently employed participants (n = 11). We addressed this limitation through rigorous multivariable logistic model regression diagnostics to avoid overfitting of models. Third, we were limited by data missingness for two covariates: household income (n = 57) and secondhand smoke exposure (n = 63). To address this limitation, we considered homelessness and employment status in models rather than income for socioeconomic variables. Fourth, the data analyzed is from the early-vaping era and before legalization of marijuana in California. As there were no participants who reported vaping at the time, we were unable to evaluate vaping as an inhalational exposure, and the marijuana usage in this cohort might not reflect current use among persons living with HIV in San Francisco. Finally, as with any observational study, unmeasured confounders could bias associations. Strengths of the study include nesting our study in an ongoing large prospective cohort study of patients living with HIV admitted with pulmonary infection: being able to utilize that existing data collection infrastructure and those standardized instruments. Additionally, we administered an extensive inhaled exposure questionnaire to comprehensively evaluate airborne toxicants that could modify risk for pulmonary infection. Conclusions Cigarette smoking and homelessness were associated with increased odds of hospitalization for pulmonary infection compared to hospitalization for non-pulmonary infection diagnoses among inpatients with HIV; while marijuana smoking, sleeping on higher floors, and opening windows for indoor ventilation were associated with decreased pulmonary infection. Our study suggests stable housing and good air quality living conditions could be important in preventing pulmonary infection among vulnerable populations including those living with HIV. Furthermore, given the recent legalization of marijuana in many states across the U.S., large prospective cohort studies are needed to assess marijuana smoke inhalation risks more adequately. Abbreviations AIDS Acquired Immunodeficiency Syndrome aOR Adjusted Odds Ratio CD4 CD4 T Cell Lymphocytes CI Confidence Interval COPD Chronic Obstructive Pulmonary Disease DAG Directed Acyclic Graph FEV1 Forced Expiratory Volume in 1 second HIV Human Immunodeficiency Virus IHOP International HIV-associated Opportunistic Pneumonias Study IQR Interquartile Range PCP Pneumocystis Pneumonia SD Standard Deviation SFGH San Francisco General Hospital TB Tuberculosis THC Delta-9-tetrahydrocannabinol U.S. United States USD United States Dollars VGDF Vapor, Gas, Dust, and Fumes Declarations Ethics approval and consent to participate This study was reviewed and approved by the University of California San Francisco Institutional Review Board. Voluntary, written informed consent was obtained from each participant prior to study enrollment and procedures in accordance with the Declaration of Helsinki. Availability of data and materials The datasets used and analyzed during the current study are available (in deidentified format) from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research was funded by the following NIH grants : NIEHS F32ES022582 (RB), K24 HL087713 (LH), R01 HL090335 (LH), and R01 HL128156 (LH). Authors’ Contributions R.J.B. wrote the manuscript; R.J.B., L.H., and J.B. conceived the study and designed the experiments; R.J.B., L.H., S.F., S.S., V.F., and E.A. implemented the study and collected the data; L.H. and R.J.B. analyzed the data and interpreted the findings. All authors made substantial contributions to the manuscript, reviewed the manuscript, approved the final version, and agreed to be accountable for all aspects of the manuscript. The authors did not use any Artificial Intelligence (AI) tools or Large Language Models (LLM) to prepare this manuscript. No non-author medical writers were involved in the manuscript preparation. Acknowledgements We would like to thank the participants of this study who have graciously volunteered their time, and the clinical staff at San Francisco General Hospital who assisted with patient selection and enrollment. A special thanks to faculty members of the Advanced Training in Clinical Research Program at University of California San Francisco who provided input for our study design and implementation. References 2025 Global. AIDS Update — AIDS, Crisis and the Power to Transform [ https://www.unaids.org/en/resources/documents/2025/2025-global-aids-update] Fast Facts. HIV in the United States [ https://www.cdc.gov/hiv/data-research/facts-stats/index.html] Blount RJ, Huang L. Clinical Approach to HIV-Associated Pulmonary Disease. Clin Pulmonary Med. 2010;17(5):210–7. Gingo MR, Balasubramani GK, Kingsley L, Rinaldo CR Jr., Alden CB, Detels R, Greenblatt RM, Hessol NA, Holman S, Huang L, et al. The impact of HAART on the respiratory complications of HIV infection: longitudinal trends in the MACS and WIHS cohorts. PLoS ONE. 2013;8(3):e58812. Allshouse AA, MaWhinney S, Jankowski CM, Kohrt WM, Campbell TB, Erlandson KM. The Impact of Marijuana Use on the Successful Aging of HIV-Infected Adults. J Acquir Immune Defic Syndr. 2015;69(2):187–92. Pacek LR, Towe SL, Hobkirk AL, Nash D, Goodwin RD. Frequency of Cannabis Use and Medical Cannabis Use Among Persons Living With HIV in the United States: Findings From a Nationally Representative Sample. AIDS Educ Prev. 2018;30(2):169–81. Crane HM, Nance RM, Whitney BM, Ruderman S, Tsui JI, Chander G, McCaul ME, Lau B, Mayer KH, Batey DS, et al. Drug and alcohol use among people living with HIV in care in the United States by geographic region. AIDS Care. 2021;33(12):1569–76. Mdodo R, Frazier EL, Dube SR, Mattson CL, Sutton MY, Brooks JT, Skarbinski J. Cigarette smoking prevalence among adults with HIV compared with the general adult population in the United States: cross-sectional surveys. Ann Intern Med. 2015;162(5):335–44. Almirall J, Bolibar I, Serra-Prat M, Roig J, Hospital I, Carandell E, Agusti M, Ayuso P, Estela A, Torres A, et al. New evidence of risk factors for community-acquired pneumonia: a population-based study. Eur Respir J. 2008;31(6):1274–84. Baskaran V, Murray RL, Hunter A, Lim WS, McKeever TM. Effect of tobacco smoking on the risk of developing community acquired pneumonia: A systematic review and meta-analysis. PLoS ONE. 2019;14(7):e0220204. Slama K, Chiang CY, Enarson DA, Hassmiller K, Fanning A, Gupta P, Ray C. Tobacco and tuberculosis: a qualitative systematic review and meta-analysis. Int J tuberculosis lung disease: official J Int Union against Tuberculosis Lung Disease. 2007;11(10):1049. Blount RJ, Phan H, Trinh T, Dang H, Merrifield C, Zavala M, Zabner J, Comellas AP, Stapleton EM, Segal MR et al. 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EClinicalMedicine. 2019;7:55–64. Hahn AW, Ruderman SA, Nance RM, Delaney J, Whitney BM, Eltonsy S, Haidar L, Drumright LN, Ma J, Mayer KH, et al. Cannabis use patterns among people with HIV before and after legalization. Drug Alcohol Depend Rep. 2024;13:100291. Global regional et al. and national comparative risk assessment of 84 behaviournvironmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018, 392(10159):1923–1994. Neupane B, Jerrett M, Burnett RT, Marrie T, Arain A, Loeb M. Long-term exposure to ambient air pollution and risk of hospitalization with community-acquired pneumonia in older adults. Am J Respir Crit Care Med. 2010;181(1):47–53. Adaji EE, Ekezie W, Clifford M, Phalkey R. Understanding the effect of indoor air pollution on pneumonia in children under 5 in low- and middle-income countries: a systematic review of evidence. 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Environmental risk factors for Pneumocystis pneumonia hospitalizations in HIV patients. Clin Infect diseases: official publication Infect Dis Soc Am. 2013;56(1):74. Huang L, Cattamanchi A, Davis JL, den Boon S, Kovacs J, Meshnick S, Miller RF, Walzer PD, Worodria W, Masur H et al. HIV-associated Pneumocystis pneumonia. Proceedings of the American Thoracic Society 2011, 8(3):294–300. Blount RJ, Stone SA, Falahati V, Auld E, Balmes J, Huang L. Common Inhaled Toxins Associated With Pulmonary Infection In HIV-Infected Patients Admitted To An Urban Hospital. Am J Respir Crit Care Med 2014:A1665–1665. Blount RJ. Inhaled Exposures Questionnaire. In.; 2013. Blount RJ, Pascopella L, Catanzaro DG, Barry PM, English PB, Segal MR, Flood J, Meltzer D, Jones B, Balmes J, et al. Traffic-Related Air Pollution and All-Cause Mortality during Tuberculosis Treatment in California. Environ Health Perspect. 2017;125(9):097026. Zheng T, Li B, Li X-B, Wang Z, Li S-Y, Peng Z-R. Vertical and horizontal distributions of traffic-related pollutants beside an urban arterial road based on unmanned aerial vehicle observations. Build Environ 2021, 187. Li C, Fu J, Sheng G, Bi X, Hao Y, Wang X, Mai B. Vertical distribution of PAHs in the indoor and outdoor PM2.5 in Guangzhou, China. Build Environ. 2005;40(3):329–41. Trupin L, Earnest G, San Pedro M, Balmes JR, Eisner MD, Yelin E, Katz PP, Blanc PD. The occupational burden of chronic obstructive pulmonary disease. Eur Respir J. 2003;22(3):462–9. Brown J, Lipman M. Community-Acquired Pneumonia in HIV-Infected Individuals. Curr Infect Dis Rep. 2014;16(3):397. Ribeiro LI, Ind PW. Effect of cannabis smoking on lung function and respiratory symptoms: a structured literature review. NPJ Prim Care Respir Med. 2016;26:16071. Tashkin DP, Shapiro BJ, Frank IM. Acute pulmonary physiologic effects of smoked marijuana and oral (Delta)9 -tetrahydrocannabinol in healthy young men. N Engl J Med. 1973;289(7):336–41. Tetrault JM, Crothers K, Moore BA, Mehra R, Concato J, Fiellin DA. Effects of marijuana smoking on pulmonary function and respiratory complications: a systematic review. Arch Intern Med. 2007;167(3):221–8. Tashkin DP, Baldwin GC, Sarafian T, Dubinett S, Roth MD. Respiratory and immunologic consequences of marijuana smoking. J Clin Pharmacol. 2002;42(S1):s71–81. Bennett WD. Effect of beta-adrenergic agonists on mucociliary clearance. J Allergy Clin Immunol. 2002;110(6 Suppl):S291–297. Bennett WD, Almond MA, Zeman KL, Johnson JG, Donohue JF. Effect of salmeterol on mucociliary and cough clearance in chronic bronchitis. Pulm Pharmacol Ther. 2006;19(2):96–100. Randell SH, Boucher RC. Effective mucus clearance is essential for respiratory health. Am J Respir Cell Mol Biol. 2006;35(1):20–8. Baldwin GC, Tashkin DP, Buckley DM, Park AN, Dubinett SM, Roth MD. Marijuana and cocaine impair alveolar macrophage function and cytokine production. Am J Respir Crit Care Med. 1997;156(5):1606–13. Forrester JM, Steele AW, Waldron JA, Parsons PE. Crack Lung: An Acute Pulmonary Syndrome with a Spectrum of Clinical and Histopathologic Findings. Am Rev Respir Dis. 1990;142(2):462–7. Van Tol Z, Vanos JK, Middel A, Ferguson KM. Concurrent Heat and Air Pollution Exposures among People Experiencing Homelessness. Environ Health Perspect. 2024;132(1):15003. MacMurdo MG, Mulloy KB, Felix CW, Curtis AJ, Ajayakumar J, Curtis J. Ambient Air Pollution Exposure among Individuals Experiencing Unsheltered Homelessness. Environ Health Perspect. 2022;130(2):27701. Wu J, Wu Y, Wu Y, Yang R, Yu H, Wen B, Wu T, Shang S, Hu Y. The impact of heat waves and cold spells on pneumonia risk: A nationwide study. Environ Res. 2024;245:117958. Goodling E. Intersecting hazards, intersectional identities: A baseline Critical Environmental Justice analysis of US homelessness. Environ Plann E: Nat Space. 2020;3(3):833–56. Petrovich JC, Roark Murphy E, Hardin LK, Koch BR. Creating safe spaces: designing day shelters for people experiencing homelessness. J Social Distress Homeless. 2017;26(1):65–72. Garcia CM, Schrier EF, Carey C, Valle KA, Evans JL, Kushel M. Sleep Quality among Homeless-Experienced Older Adults: Exploratory Results from the HOPE HOME Study. J Gen Intern Med. 2024;39(3):460–9. Barbaresco GQ, Reis AVP, Lopes GDR, Boaventura LP, Castro AF, Vilanova TCF, Da Cunha Júnior EC, Pires KC, Pôrto Filho R, Pereira BB. Effects of environmental noise pollution on perceived stress and cortisol levels in street vendors. J Toxicol Environ health Part A. 2019;82(5):331–7. Patel SR, Malhotra A, Gao X, Hu FB, Neuman MI, Fawzi WW. A prospective study of sleep duration and pneumonia risk in women. Sleep. 2012;35(1):97–101. Recio A, Linares C, Banegas JR, Díaz J. The short-term association of road traffic noise with cardiovascular, respiratory, and diabetes-related mortality. Environ Res. 2016;150:383–90. Cantwell MF, McKenna MT, McCray E, Onorato IM. Tuberculosis and race/ethnicity in the United States: impact of socioeconomic status. Am J Respir Crit Care Med. 1998;157(4 Pt 1):1016–20. Oxlade O, Murray M. Tuberculosis and poverty: why are the poor at greater risk in India? PLoS ONE. 2012;7(11):e47533. Lowe DM, Rangaka MX, Gordon F, James CD, Miller RF. Pneumocystis jirovecii Pneumonia in Tropical and Low and Middle Income Countries: A Systematic Review and Meta-Regression. PLoS ONE. 2013;8(8):e69969. Sinha P, Davis J, Saag L, Wanke C, Salgame P, Mesick J, Horsburgh CR, Hochberg NS. Undernutrition and Tuberculosis: Public Health Implications. J Infect Dis. 2019;219(9):1356–63. Lonnroth K, Jaramillo E, Williams BG, Dye C, Raviglione M. Drivers of tuberculosis epidemics: the role of risk factors and social determinants. Soc Sci Med. 2009;68(12):2240. Jaganath D, Mupere E. Childhood tuberculosis and malnutrition. J Infect Dis. 2012;206(12):1809–15. Davydow DS, Hough CL, Zivin K, Langa KM, Katon WJ. Depression and risk of hospitalization for pneumonia in a cohort study of older Americans. J Psychosom Res. 2014;77(6):528–34. Di Gennaro F, Cotugno S, Guido G, Cavallin F, Pisaturo M, Onorato L, Zimmerhofer F, Pipitò L, De Iaco G, Bruno G, et al. Disparities in tuberculosis diagnostic delays between native and migrant populations in Italy: A multicenter study. Int J Infect diseases: IJID : official publication Int Soc Infect Dis. 2025;150:107279. Zhu Y, Hinds WC, Kim S, Sioutas C. Concentration and Size Distribution of Ultrafine Particles Near a Major Highway. J Air Waste Manag Assoc. 2002;52(9):1032–42. Wu Y, Hao J, Fu L, Wang Z, Tang U. Vertical and horizontal profiles of airborne particulate matter near major roads in Macao, China. Atmos Environ. 2002;36:4907. Jung KH, Bernabe K, Moors K, Yan B, Chillrud SN, Whyatt R, Camann D, Kinney PL, Perera FP, Miller RL. Effects of Floor Level and Building Type on Residential Levels of Outdoor and Indoor Polycyclic Aromatic Hydrocarbons, Black Carbon, and Particulate Matter in New York City. Atmos (Basel). 2011;2(2):96–109. Panczak R, Galobardes B, Spoerri A, Zwahlen M, Egger M. High life in the sky? Mortality by floor of residence in Switzerland. Eur J Epidemiol. 2013;28(6):453–62. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8695194","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595125433,"identity":"d1c0391a-b496-4048-82d5-5cfd14cbcbdc","order_by":0,"name":"Robert J Blount","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYFACHjCZwAemKqBcBjZCWhIYEsBqDpwhWcvBNpgoHi3m7b0HH1f+YMhjY+8xe/xx3mEZfonkBwwfyg7j1CJz5lyy4ZkEhmI2njPmBge3HeaRnJFmwDjjHG4tEhI5ZpINCf8T24AMCZAWg9sJBsy8bXi1mP9sSGCAapkD0pL+gfkvfi1mjAgtDSAtOQbMjPi08JxLlmxIA/nlWJnEmWPpPJLz3xQc7DmXjlsLe+/Bjw02DHn87M3bJCpqrO35eY5vfPCjzBqnFuzgAInqR8EoGAWjYBSgAQD6BVBkdab10QAAAABJRU5ErkJggg==","orcid":"","institution":"University of Iowa","correspondingAuthor":true,"prefix":"","firstName":"Robert","middleName":"J","lastName":"Blount","suffix":""},{"id":595125434,"identity":"41e49b9f-b9e7-414e-aab9-ac3369052443","order_by":1,"name":"Serena Fong","email":"","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Serena","middleName":"","lastName":"Fong","suffix":""},{"id":595125435,"identity":"3d495efd-b790-43c3-aecd-7282878b429c","order_by":2,"name":"Stephen Stone","email":"","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"","lastName":"Stone","suffix":""},{"id":595125436,"identity":"1c4f937a-b22a-46e8-8f4e-c204de7428f2","order_by":3,"name":"Veesta Falahati","email":"","orcid":"","institution":"Kaiser Permanente Oakland Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Veesta","middleName":"","lastName":"Falahati","suffix":""},{"id":595125437,"identity":"470f5bdc-dc1a-4967-9852-db6f67be07d4","order_by":4,"name":"Elizabeth Auld","email":"","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Auld","suffix":""},{"id":595125438,"identity":"13f200e2-6404-49da-8e28-7bfab819d449","order_by":5,"name":"John Balmes","email":"","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Balmes","suffix":""},{"id":595125439,"identity":"4cf76e3f-b7eb-468d-aeda-9f381fcb9928","order_by":6,"name":"Laurence Huang","email":"","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Laurence","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2026-01-25 23:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8695194/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8695194/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103216659,"identity":"44501ef2-a400-48a5-8298-1b8a1951a8ac","added_by":"auto","created_at":"2026-02-23 09:37:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59806,"visible":true,"origin":"","legend":"\u003cp\u003eDirected Acyclic Graph of biologically plausible interrelationships among covariates.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8695194/v1/85f8e9dcd1fe51b70e2b2090.png"},{"id":103216657,"identity":"f4e0d412-403a-454e-850c-26bde0aa3e8f","added_by":"auto","created_at":"2026-02-23 09:37:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":914698,"visible":true,"origin":"","legend":"\u003cp\u003eStudy catchment area, the city of San Francisco. Participant recent sleeping location within the last 30 days depicted with colored circles: blue for controls and red for cases.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8695194/v1/a532898b366334977c230623.png"},{"id":103505404,"identity":"9bfc0149-5495-4232-b229-502346a74b37","added_by":"auto","created_at":"2026-02-26 13:30:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1885846,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8695194/v1/857cf498-8ee5-4dd8-80ae-9b8401eb3933.pdf"},{"id":103216658,"identity":"f042eea8-579b-4725-802e-bf63133c9e63","added_by":"auto","created_at":"2026-02-23 09:37:29","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":23803,"visible":true,"origin":"","legend":"","description":"","filename":"140530.Inhaled.Exposures.Questionnaire.docx","url":"https://assets-eu.researchsquare.com/files/rs-8695194/v1/cd8b6b26b9a325feb1821060.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Recreational drug inhalation and socioenvironmental determinants of pulmonary infection in patients with HIV: a hospital-based case control study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eForty one million children and adults are currently living with HIV worldwide, with 1.3\u0026nbsp;million new HIV infections and 630,000 deaths attributable to AIDS in 2024 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the United States alone, there are over 1.2\u0026nbsp;million persons who are living with HIV, with more than 32,000 new diagnoses of HIV and 19,000 deaths in persons living with HIV in 2022 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. HIV infection increases susceptibility to pulmonary infections such as bacterial and viral pneumonia, tuberculosis (TB), \u003cem\u003ePneumocystis\u003c/em\u003e pneumonia (PCP), and cryptococcal pneumonia [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Prior studies have shown that persons living with HIV who experience infectious respiratory diseases have increased mortality compared to individuals who are not living with HIV [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Thus, efforts to better understand risk factors for pneumonia are essential to implementing preventative strategies in this population.\u003c/p\u003e \u003cp\u003eRecreational drug inhalation is common among persons living with HIV. Of those surveyed in the U.S., 26\u0026ndash;30% reported recent marijuana use [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and 42% reported current tobacco cigarette smoking [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In a recent study of self-reported recreational drug use among persons with HIV at seven sites across the U.S., 8% reported use of methamphetamines, including inhalation of crystal methamphetamines [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and 7% reported cocaine use, including inhalation of crack cocaine [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Some inhaled toxicants are known to increase the risk of pulmonary infections and can potentiate the effects of HIV on pulmonary infection. This has been most robustly established for active tobacco smoking and secondhand smoke as risk factors for community-acquired pneumonia [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and tuberculosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. For instance, in a VA cohort study, it was shown that among persons with HIV, those who currently smoked tobacco had more than double the risk of developing community-acquired pneumonia compared to those who did not smoke [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, it remains unclear if inhalation of other recreational drugs also increases risk. Though marijuana smoking is associated with chronic lung disease [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], observational studies evaluating associations between marijuana smoking and pulmonary infection have found conflicting results [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Recent trends toward the legalization of marijuana make this a particularly salient question, though legalization has not necessarily resulted in an escalation of marijuana use among persons living with HIV [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral observational studies among predominantly immunocompetent persons have also found associations of socioenvironmental factors such as poverty and air pollution with community-acquired pneumonia [\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, air pollution associations with tuberculosis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], \u003cem\u003ePneumocystis\u003c/em\u003e pneumonia [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and cryptococcal pneumonia in vulnerable populations such as persons with HIV are less well characterized.\u003c/p\u003e \u003cp\u003eTherefore, we conducted a nested case-control study within the San Francisco site of the International HIV-associated Opportunistic Pneumonias (IHOP) cohort study [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. We hypothesized that inhaled exposures to recreational drugs (tobacco cigarettes, marijuana, crack cocaine, and crystal methamphetamine), socioenvironmental factors (homelessness and air pollution), and occupational exposures in patients living with HIV are associated with pulmonary infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Some of the results of these studies have been previously reported in the form of an abstract [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eWe conducted a hospital-based case-control study among adults aged\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;18 years with HIV admitted to the San Francisco General Hospital and Trauma Center (SFGH). Cases were defined as patients admitted with pulmonary infection as diagnosed by an attending physician based on history, physical exam, imaging studies, and with or without microbiologic confirmation. Controls were defined as adult patients admitted with a diagnosis other than pulmonary infection. Patients were excluded from this study if: 1) they were unable to complete the study questionnaire; or 2) they were admitted for a psychiatric diagnosis or altered mental status (potential impact on ability to complete the questionnaire). Eligible cases and controls were identified from October 2012 through May 2013 from a daily HIV admissions roster, then recruited during their inpatient stay. Cases and controls giving consent were enrolled consecutively using an incidence-density sampling method. Cases were concurrently enrolled into the International HIV-associated Opportunistic Pneumonias (IHOP) Study, a previously described longitudinal cohort study of HIV-infected adults with clinical and radiographic evidence of pulmonary infection [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eWe developed a standardized questionnaire for this study to collect demographic, socioeconomic, clinical, and exposure-related data from participants [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. A trained clinical research coordinator administered this questionnaire to each participant. We accessed clinical data from a standardized chart abstraction performed for the larger IHOP cohort study. Demographic variables included age, sex at birth, race, and ethnicity. Socioeconomic variables included the highest educational level obtained, household income, employment status, and housing status. Participants were categorized as \u0026ldquo;unstably housed or homeless\u0026rdquo; if they answered \u0026ldquo;No\u0026rdquo; to the following question: \u0026ldquo;Do you have a stable home address (a place where you have lived for at least the last month)?\u0026rdquo; For medical history, we collected information regarding recent illness or pulmonary infection, recent antiretroviral therapy, history of chronic obstructive pulmonary disease (COPD), and whether there was recent use of inhaled corticosteroids. We obtained baseline CD4 lymphocyte count and HIV viral load within the six months prior to admission through chart abstraction to best characterize immune status.\u003c/p\u003e \u003cp\u003eThe standardized questionnaire elicited responses in three exposure categories: 1) recreational drug inhalation, 2) socioenvironmental exposures, and 3) occupational exposures. We queried recreational drug use to ascertain the type, method, quantity, and duration of usage. Regarding inhaled drugs, we inquired about the usage of tobacco, marijuana, crack cocaine, crystal methamphetamine, and vaping devices. Recent use of recreational drugs or tobacco was defined as within 30 days prior to enrollment. For air pollution, we defined traffic density as the number of vehicles per kilometer each hour in 100 to 500-meter buffers around the participant\u0026rsquo;s sleeping location. We calculated traffic density using previously described methods developed by the California Department of Public Health to link geocoded residential addresses to roadway traffic volumes obtained from the California Department of Transportation Highway Performance Monitoring System [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The bedroom floor level was considered as the vertical distance from street-level outdoor pollution, measured in floors, with street-level floor assigned the value 1. Floor level has been found to be a robust surrogate for residential exposure to traffic pollution, with exposure to street-level pollution decreasing as floor number increases higher from the street [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. We evaluated indoor pollution with questions regarding cooking, heating, and secondhand smoking; and further evaluated indoor air quality via questions about indoor air circulation through use of central air and opening of windows for ventilation. Finally, we assessed occupational exposures to vapor, gases, dust, and fumes (VGDF) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] in addition to commuting-related exposures.\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eFor descriptive analyses we used Student\u0026rsquo;s t-test for continuous variables with normal distribution, Wilcoxon rank-sum test for nonparametric testing of variables that were not normally distributed, Fisher exact test for categorical variables with any cell n\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;5, and chi-square testing for all other categorical variables (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We fit logistic regression models with pulmonary infection as the dichotomous outcome and each exposure as a primary predictor in separate models. Pulmonary infection was defined as a composite dichotomous outcome including any of the following diagnoses: bacterial pneumonia, viral pneumonia, \u003cem\u003ePneumocystis\u003c/em\u003e pneumonia, pulmonary tuberculosis, or cryptococcal pneumonia (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We fit separate logistic regression models for each of the following primary predictors (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e): Recent smoking within the last 30 days of i) tobacco, ii) marijuana, iii) crack cocaine, or iv) crystal methamphetamine; v) unstable housing or homeless in the last 30 days; vi) traffic density; vii) sleeping (bedroom) floor level; viii) use of open windows for indoor ventilation; ix) cooking with gas; x) secondhand tobacco smoke exposure; xi) commuting on foot or nonmotorized bike vs motorized; and xii) workplace exposure to VGDF. For each model we considered the following potential confounders based on biological plausibility identified in the literature and directed acyclic graphing (DAG): age, sex at birth, smoking tobacco, smoking marijuana, CD4 count, employment status, homelessness, and enrollment season (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For each multivariable model we then stepwise deleted potential confounders with a p\u0026thinsp;\u0026gt;\u0026thinsp;0.2 that a) impacted the primary predictor coefficient magnitude by \u0026lt;\u0026thinsp;5% or b) did not statistically significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) contribute to the model in nested likelihood ratio (LR) testing of models including and excluding the covariate under consideration. The final potential confounders selected for each multivariable logistic regression model i-xii were: i) marijuana, CD4; ii, x) tobacco, CD4; iii, iv, vi) tobacco, CD4, homelessness; v) employment status, CD4; vii) CD4; viii) tobacco, CD4, employment status, season; ix) age, employment status; and xi, xii) age. We considered a general rule of thumb of no more than 1 predictor for every 10 outcomes (4 predictors for 38 positive outcomes), and final models ranged from 2\u0026ndash;5 total predictors. Given the small sample size, we further tested each multivariable model for overfitting by evaluating for decrements in the precision of the primary predictor coefficient estimate in adjusted models relative to corresponding unadjusted models. We used pairwise correlation to test for predictor collinearity. We tested logistic linear assumptions by graphing the locally weighted scatterplot smoother (LOWESS) log odds of the outcome against each continuous predictor assessing for linearity compared with fitted regression lines. We tested model adequacy using the specification link test and goodness of fit using the Hosmer-Lemeshow test. In separate models we also tested CD4 count as a possible mediator in the effects of inhalational and socioenvironmental factors on pulmonary infection, though we found mediation unlikely, as our primary exposure variables did not significantly predict CD4 counts in exploratory unadjusted linear regression analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe enrolled 75 patients living in San Francisco with HIV admitted to SFGH during the 8-month study period: 38 cases with pulmonary infection and 37 controls admitted for diagnoses other than pulmonary infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Participants were predominantly middle-aged (48 years, IQR 44\u0026ndash;54) white (39, 52%) males (58, 77%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Poverty was prevalent in the cohort, with 81% of participants reporting annual household income \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u003cspan\u003e$\u003c/span\u003e20,000 USD, 79% reporting unemployment, and 27% reporting unstable housing or homelessness at the time of enrollment. Less than half of the participants (49%) reported education beyond high school.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient demographic, socioeconomic, and clinical characteristics (n\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCharacteristic*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAll Subjects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep value\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMedian age (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (44\u0026ndash;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48 (43\u0026ndash;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49 (45\u0026ndash;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMale sex at birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHispanic ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c2\" namest=\"c1\" rowspan=\"4\"\u003e \u003cp\u003eEmployment\u003c/p\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11/72 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5/35 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6/37 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/72 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2/37 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/72 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2/35 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57/72 (79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28/35 (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29/37 (78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePost high school education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31/63 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11/26 (42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20/37 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIncome\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e \u003cspan\u003e$\u003c/span\u003e10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29/57 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17/25 (68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12/32 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e10,000\u0026ndash;20,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17/57 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4/25 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13/32 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026gt; \u003cspan\u003e$\u003c/span\u003e20,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11/57 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4/25 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7/32 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eUnstably housed or homeless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eIntravenous drug use, ever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40/70 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18/33 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22/37 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eEnrollment during winter\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMedian CD4 count, cells/\u0026micro;L (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205 (49\u0026ndash;425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88 (39\u0026ndash;255)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e300 (152\u0026ndash;467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDetectable viral load\u0026thinsp;\u0026gt;\u0026thinsp;1000 copies/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eInhaled corticosteroid use in last month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8/71 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Expressed as n(%) unless otherwise indicated\u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026dagger;\u003c/sup\u003eAnnual household income in US dollars (2012\u0026ndash;2013 value)\u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026Dagger;\u003c/sup\u003eSeason of enrollment: Winter vs the preceding Fall or following Spring\u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026sect;\u003c/sup\u003eStatistical tests used: t-test for continuous variables with normal distribution, Wilcoxon rank-sum test (Mann-Whitney U test) for nonparametric testing of variables that were not normally distributed, Fisher exact test for categorical variables with any cell n\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;5, and chi-square test for all other categorical variables.\u003c/p\u003e \u003cp\u003eCases compared with controls were more likely to be unstably housed or homeless (37% vs 16%, p\u0026thinsp;=\u0026thinsp;0.04); be enrolled in the winter (58% vs 30%, p\u0026thinsp;=\u0026thinsp;0.01), have a lower median CD4 count (88 vs 300 cells/\u0026micro;L, p\u0026thinsp;=\u0026thinsp;0.005); and have a detectable HIV viral load (63% vs 35%, p\u0026thinsp;=\u0026thinsp;0.02). Most cases were diagnosed with either bacterial pneumonia (76%) or PCP (16%), with only one case each of viral pneumonia, pulmonary TB, and cryptococcal pneumonia (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Principal discharge diagnoses among controls were varied, with the plurality of controls admitted for non-pulmonary infection (43%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrincipal discharge diagnosis in cases and controls (n\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCases, n\u0026thinsp;=\u0026thinsp;38\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eControls, n\u0026thinsp;=\u0026thinsp;37\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePulmonary infection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDiagnosis category\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacterial pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-pulmonary infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViral pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCardiovascular/Pulmonary disease*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (8.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePneumocystis\u003c/em\u003e pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGastrointestinal/Renal disease*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary tuberculosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCancer, suspected or confirmed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCryptococcal pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMusculoskeletal disorder*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (8.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrauma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlcohol/drug withdrawal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (5.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Non-infectious\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDrug inhalation was common in our study: 59% of participants had smoked cigarettes within the month prior to enrollment, smoking a median of 10 cigarettes per day (IQR 2\u0026ndash;20), while 40% of participants had recently smoked marijuana, smoking a median of four times per month (IQR 2\u0026ndash;27) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Dual use of tobacco and marijuana was reported by 33% of participants, another 33% reported no recent smoking of either tobacco or marijuana, while the remaining participants smoked either tobacco or marijuana.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOdds of hospital admission for pulmonary infection among HIV patients, by exposure (n\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAll Subjects*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCases*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eControls*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug Inhalation\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTobacco\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42/71\u003c/p\u003e \u003cp\u003e(59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24/34 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18/37\u003c/p\u003e \u003cp\u003e(49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003cp\u003e(0.95\u0026ndash;6.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6.43\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(1.70\u0026ndash;24.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarijuana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29/72\u003c/p\u003e \u003cp\u003e(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11/35 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18/37\u003c/p\u003e \u003cp\u003e(49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003cp\u003e(0.18\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.21\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.06\u0026ndash;0.78)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrack cocaine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12/73\u003c/p\u003e \u003cp\u003e(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8/36\u003c/p\u003e \u003cp\u003e(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/37\u003c/p\u003e \u003cp\u003e(11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003cp\u003e(0.64\u0026ndash;8.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003cp\u003e(0.47\u0026ndash;7.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrystal methamphetamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11/72\u003c/p\u003e \u003cp\u003e(15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5/35\u003c/p\u003e \u003cp\u003e(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6/37\u003c/p\u003e \u003cp\u003e(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003cp\u003e(0.24\u0026ndash;3.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003cp\u003e(0.06\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-environmental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnstable housing or homeless\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003cp\u003e(27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e(37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.01\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(1.01\u0026ndash;9.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3.22\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(1.01\u0026ndash;10.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraffic density,\u003csup\u003e||\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e1000 vehicle\u0026middot;km/hr., mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.36 (5.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.89 (5.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.82\u003c/p\u003e \u003cp\u003e(5.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e(0.89\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e(0.89\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleeping floor level, median (IQR)\u003csup\u003e\u0026sect;\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(1\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(2\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.39\u0026ndash;0.78)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.40\u0026ndash;0.80)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWindows open for ventilation**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45/52 (87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.083\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.009\u0026ndash;0.76)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.051\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.004\u0026ndash;0.64)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCooking with gas\u003csup\u003e\u0026dagger;\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19/42 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5/13 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14/29 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003cp\u003e(0.18\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003cp\u003e(0.05\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondhand smoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47/63 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20/28 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27/35 (77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003cp\u003e(0.24\u0026ndash;2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003cp\u003e(0.09\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommute to work on foot/bike\u003csup\u003e\u0026Dagger;\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4/10 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/4 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/6 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003cp\u003e(0.15\u0026ndash;26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003cp\u003e(0.17\u0026ndash;186)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupational\u003csup\u003e\u0026sect;\u0026sect;\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorkplace exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/11 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3/5 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/6 (67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003cp\u003e(0.06\u0026ndash;8.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003cp\u003e(0.03\u0026ndash;10.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Expressed as n (%) unless otherwise indicated\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026dagger;\u003c/sup\u003ePotential confounders were selected based on biological plausibility and backward selection criteria.\u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026Dagger;\u003c/sup\u003eActive use within the 30 days prior to study enrollment\u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026sect;\u003c/sup\u003eParticipants without a stable residential address in the 30 days prior to enrollment\u003c/p\u003e \u003cp\u003e \u003csup\u003e||\u003c/sup\u003eTraffic density in the 500-meter buffer surrounding the participant\u0026rsquo;s most recent sleeping address.\u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026sect;\u0026sect;\u003c/sup\u003eVertical level of sleeping location. Street-level\u0026thinsp;=\u0026thinsp;1, one floor above street-level\u0026thinsp;=\u0026thinsp;2, etc.\u003c/p\u003e \u003cp\u003e**Among stably housed participants, n\u0026thinsp;=\u0026thinsp;52\u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026dagger;\u0026dagger;\u003c/sup\u003eThose cooking primarily with natural gas compared with those who use primarily electric stove or microwave, among stably housed participants who cook at home, n\u0026thinsp;=\u0026thinsp;42\u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026Dagger;\u0026Dagger;\u003c/sup\u003eCommute by nonmotorized bicycle or walking vs motorized means, n\u0026thinsp;=\u0026thinsp;10 with one participant working exclusively from home.\u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026sect;\u0026sect;\u0026sect;\u003c/sup\u003eAmong those currently employed, n\u0026thinsp;=\u0026thinsp;11\u003c/p\u003e \u003cp\u003eRecent use of other inhaled drugs was less prevalent: 16% recently smoked crack cocaine and 15% recently smoked crystal methamphetamine. Those who recently smoked tobacco had six times higher odds of pulmonary infection compared with non- and former-smoking participants (aOR 6.43, 95%CI 1.70\u0026ndash;24.4, p\u0026thinsp;=\u0026thinsp;0.006). Those who smoked marijuana in the 30 days prior to enrollment, on the other hand, experienced a 79% reduced odds of pulmonary infection compared to participants who had not recently smoked marijuana (aOR 0.21, 95%CI 0.06\u0026ndash;0.78, p\u0026thinsp;=\u0026thinsp;0.02). In a sensitivity analysis, we found no statistically significant differences between those who actively smoked marijuana compared to those who did not for any key demographic, socioeconomic, or clinical variables that might influence the risk for pulmonary infection (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Crack cocaine and crystal methamphetamine were not statistically significantly associated with pulmonary infection, though few participants had recently inhaled these substances.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics by marijuana smoking status: a sensitivity analysis (n\u0026thinsp;=\u0026thinsp;72).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c3\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eSmoked marijuana in last month?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep value\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAll Subjects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMedian age (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (44\u0026ndash;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48 (38\u0026ndash;53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49 (45\u0026ndash;55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMale sex at birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34 (79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23 (53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHispanic ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c2\" namest=\"c1\" rowspan=\"4\"\u003e \u003cp\u003eEmployment\u003c/p\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34 (79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePost high school education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31/63 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12/28 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19/35 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIncome\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e \u003cspan\u003e$\u003c/span\u003e10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29/57 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15/28 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14/29 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e10,000\u0026ndash;20,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17/57 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7/28 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10/29 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026gt; \u003cspan\u003e$\u003c/span\u003e20,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11/57 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6/28 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5/29 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eUnstably housed or homeless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 (70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eIntravenous drug use, ever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40/70 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17/28 (61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23/42 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMedian CD4 count, cells/\u0026micro;L (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205 (49\u0026ndash;425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e235 (78\u0026ndash;417)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e154 (53\u0026ndash;452)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDetectable viral load\u0026thinsp;\u0026gt;\u0026thinsp;1000 copies/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Expressed as n(%) unless otherwise indicated\u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026dagger;\u003c/sup\u003eAnnual household income in US dollars (2012\u0026ndash;2013 value)\u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026sect;\u003c/sup\u003eStatistical tests used: t-test for continuous variables with normal distribution, Wilcoxon rank-sum test (Mann-Whitney U test) for nonparametric testing of variables that were not normally distributed, Fisher exact test for categorical variables with any cell n\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;5, and chi-square test for all other categorical variables.\u003c/p\u003e \u003cp\u003eRegarding socioenvironmental variables, traffic densities within 100- to 500-meter buffers around current sleeping location were not significantly associated with pulmonary infection. However, the vertical sleeping distance relative to the street was significantly associated with pulmonary infection (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Bedroom floor level was inversely associated with pulmonary infection. The median bedroom floor level was 1st floor (ground, street-level floor) for cases (IQR 1st \u0026minus;\u0026thinsp;3rd floor), and the 3rd floor for controls (IQR 2nd \u0026ndash; 5th floor). Every bedroom floor level above street-level predicted a 44% decrease in odds of pulmonary infection (aOR 0.56, 95%CI 0.40\u0026ndash;0.80, p\u0026thinsp;=\u0026thinsp;0.002). Furthermore, unstably housed or homeless participants had more than a 3-fold increased odds of pulmonary infection compared to stably housed participants (aOR 3.22, 95%CI 1.01\u0026ndash;10.3, p\u0026thinsp;=\u0026thinsp;0.048). Regarding indoor exposures, cooking method and secondhand smoke were not significantly associated with pulmonary infection, whereas opening windows for ventilation was associated with reduced odds of pulmonary infection (aOR 0.051, 95%CI 0.004\u0026ndash;0.64, p\u0026thinsp;=\u0026thinsp;0.02). Finally, we did not find significant associations between commute and workplace exposures, though we were underpowered with only 11 participants employed at the time of study enrollment.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe conducted a small hospital-based case-control study to evaluate socioenvironmental exposures that might increase risk for pulmonary infection, a leading cause of mortality in patients with HIV. We found that cigarette smoking and unstable housing or homelessness were associated with increased odds of hospitalization for pulmonary infection compared to hospitalization for non-pulmonary infection diagnoses among inpatients with HIV. Conversely, marijuana smoking, bedroom floor level, and opening windows for indoor circulation were predictive of decreased pulmonary infection.\u003c/p\u003e \u003cp\u003eCigarette smoking was a strong predictor of pulmonary infection in our study. Smoking is a known risk factor for pulmonary infection in patients with HIV [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] as well as in immunocompetent individuals [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This expected finding serves as a positive control and internal validation for other findings in our study. It also indicates that, despite the small sample size of our cohort, we were at least powered adequately to detect large effects. However, confidence intervals were quite wide, especially in the adjusted model. Incremental addition of variables to the model did not lead to the reduced precision, making overfitting less likely, but rather a moderate degree of collinearity with recent marijuana smoking (r\u0026thinsp;=\u0026thinsp;0.35).\u003c/p\u003e \u003cp\u003eOn the other hand, marijuana predicted a reduced odds of pulmonary infection, suggesting that this behavior was less \u0026ldquo;risky\u0026rdquo; for respiratory infection than smoking tobacco among our participants. Several underlying mechanisms could drive such an association. Firstly, exposure to the products of biomass combustion was roughly 75 times higher with cigarette smoking than with marijuana smoking in this study (median 10 cigarettes per day vs 4 marijuana joints per month, assuming an average joint biomass similar to that of a cigarette) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Secondly, marijuana smoking could serve as a surrogate measurement for unmeasured protective factors. For instance, perhaps marijuana smokers were healthier or partook in less risky behaviors compared to marijuana nonsmokers. However, our sensitivity analysis suggests that marijuana smokers were similar to non-marijuana smokers in measured demographic, socioeconomic, and clinical variables that could be predictive of pulmonary infection. Thirdly, acute exposure to delta-9-tetrahydrocannabinol (THC) in marijuana has been found to cause bronchodilation in experimental human physiology studies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, chronic marijuana use does not appear to drop predicted forced expiratory volumes (FEV1) as does cigarette smoking, although findings are not consistent across studies [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Bronchodilation can aid in mucus clearance [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], an essential pulmonary host defense mechanism against respiratory pathogens [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Thus, it is conceivable that the acute bronchodilation afforded by THC could play a protective role in preventing pulmonary infection. However, bench studies have also found immunomodulatory mechanisms through which marijuana smoke could potentially increase the risk of pulmonary infection. For instance, macrophages in bronchoalveolar lavage after recent marijuana smoking were found to have reduced phagocytic function and impaired cytokine production, key early immune defense mechanisms against inhaled pathogens [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. We did not find significant associations between inhaling crack cocaine or crystal methamphetamine on pulmonary infection, though only 15\u0026ndash;16% of participants reported recent use such that we were possibly underpowered to detect an effect. Crack cocaine is a known pulmonary toxicant [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and there remains concern that it could increase risk for pulmonary infection.\u003c/p\u003e \u003cp\u003eHomelessness was a strong predictor of pulmonary infection, possibly operant through multiple social determinants of health mechanisms. Persons experiencing homelessness are at increased risk for exposures to unhealthy environmental conditions such as street-level traffic pollution and extreme cold and heat [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], exposures that can increase the risk for pulmonary infection [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Additionally, noise and nocturnal light exposures are increased in homelessness, resulting in higher stress levels and inadequate sleep [\u003cspan additionalcitationids=\"CR47 CR48\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], both of which are associated with community acquired pneumonia [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In addition to potential environmental exposures, homelessness is also a surrogate for poverty which is a robust independent predictor of pneumonia, TB, and PCP [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Poverty is correlated with several additional factors associated with pulmonary infection such as: lack of optimal nutrition [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR55 CR56\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], increased mental health disorders [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], and reduced opportunities to access health care and other vital services [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAir pollution concentrations are typically inversely proportional to distance from source, such that exposure diminishes as the distance from the source increases. This is true not only for horizontal distances from traffic pollution [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], but also vertical distances above street-level pollution [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Furthermore, exposures to street-level pollutants inside the home decrease as residential floor level increases [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Therefore, we used the bedroom floor level as a surrogate for street-level pollution exposure, hypothesizing that high floor levels would be associated with reduced odds of pulmonary infections. Indeed, we found that those sleeping on higher floors, more vertically removed from street-level exposures, had reduced odds of pulmonary infection, consistent with other studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Even after controlling for socioeconomic status, the association remained robust. However, additional confounders such as unmeasured wealth metrics and comorbid conditions could also influence what floor levels our participants resided on [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], thus somewhat limiting the interpretation of this finding.\u003c/p\u003e \u003cp\u003eThere are additional limitations that should be considered when drawing conclusions from this study. First, due to the small available population of patients with HIV and with pulmonary infection, we utilized a case-control study design. Furthermore, to optimize limited resources, we performed this study in a hospital-based setting, where it can be more challenging to ensure that controls are selected from the same population as cases. We are reassured that, at least geographically, our cases and controls came from the same neighborhoods in San Francisco (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We also used incidence density sampling of controls concurrent with enrollment of cases, but inevitably, more cases with pulmonary infection were identified in winter months and more controls without pulmonary infection in the ensuing spring (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) introducing potential selection bias. Second, our sample size was small (n\u0026thinsp;=\u0026thinsp;75), with indoor pollution analyses of cooking practices and indoor circulation further limited to the smaller group of stably housed participants (n\u0026thinsp;=\u0026thinsp;52), and occupational exposure regression analyses limited by an even smaller group of currently employed participants (n\u0026thinsp;=\u0026thinsp;11). We addressed this limitation through rigorous multivariable logistic model regression diagnostics to avoid overfitting of models. Third, we were limited by data missingness for two covariates: household income (n\u0026thinsp;=\u0026thinsp;57) and secondhand smoke exposure (n\u0026thinsp;=\u0026thinsp;63). To address this limitation, we considered homelessness and employment status in models rather than income for socioeconomic variables. Fourth, the data analyzed is from the early-vaping era and before legalization of marijuana in California. As there were no participants who reported vaping at the time, we were unable to evaluate vaping as an inhalational exposure, and the marijuana usage in this cohort might not reflect current use among persons living with HIV in San Francisco. Finally, as with any observational study, unmeasured confounders could bias associations.\u003c/p\u003e \u003cp\u003eStrengths of the study include nesting our study in an ongoing large prospective cohort study of patients living with HIV admitted with pulmonary infection: being able to utilize that existing data collection infrastructure and those standardized instruments. Additionally, we administered an extensive inhaled exposure questionnaire to comprehensively evaluate airborne toxicants that could modify risk for pulmonary infection.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCigarette smoking and homelessness were associated with increased odds of hospitalization for pulmonary infection compared to hospitalization for non-pulmonary infection diagnoses among inpatients with HIV; while marijuana smoking, sleeping on higher floors, and opening windows for indoor ventilation were associated with decreased pulmonary infection. Our study suggests stable housing and good air quality living conditions could be important in preventing pulmonary infection among vulnerable populations including those living with HIV. Furthermore, given the recent legalization of marijuana in many states across the U.S., large prospective cohort studies are needed to assess marijuana smoke inhalation risks more adequately.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIDS\u0026nbsp; \u0026nbsp;Acquired Immunodeficiency Syndrome\u003c/p\u003e\n\u003cp\u003eaOR\u0026nbsp; \u0026nbsp; \u0026nbsp;Adjusted Odds Ratio\u003c/p\u003e\n\u003cp\u003eCD4\u0026nbsp; \u0026nbsp; \u0026nbsp;CD4 T Cell Lymphocytes\u003c/p\u003e\n\u003cp\u003eCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Confidence Interval\u003c/p\u003e\n\u003cp\u003eCOPD\u0026nbsp;Chronic Obstructive Pulmonary Disease\u003c/p\u003e\n\u003cp\u003eDAG\u0026nbsp; \u0026nbsp;\u0026nbsp;Directed Acyclic Graph\u003c/p\u003e\n\u003cp\u003eFEV1\u0026nbsp; \u0026nbsp;Forced Expiratory Volume in 1 second\u003c/p\u003e\n\u003cp\u003eHIV\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Human Immunodeficiency Virus\u003c/p\u003e\n\u003cp\u003eIHOP\u0026nbsp; \u0026nbsp;International HIV-associated Opportunistic Pneumonias Study\u003c/p\u003e\n\u003cp\u003eIQR\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Interquartile Range\u003c/p\u003e\n\u003cp\u003ePCP\u0026nbsp; \u0026nbsp;\u0026nbsp;Pneumocystis Pneumonia\u003c/p\u003e\n\u003cp\u003eSD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Standard Deviation\u003c/p\u003e\n\u003cp\u003eSFGH\u0026nbsp;\u0026nbsp;San Francisco General Hospital\u003c/p\u003e\n\u003cp\u003eTB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tuberculosis\u003c/p\u003e\n\u003cp\u003eTHC\u0026nbsp; \u0026nbsp; \u0026nbsp;Delta-9-tetrahydrocannabinol\u003c/p\u003e\n\u003cp\u003eU.S.\u0026nbsp; \u0026nbsp; \u0026nbsp;United States\u003c/p\u003e\n\u003cp\u003eUSD\u0026nbsp; \u0026nbsp;\u0026nbsp;United States Dollars\u003c/p\u003e\n\u003cp\u003eVGDF\u0026nbsp;\u0026nbsp;Vapor, Gas, Dust, and Fumes\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the University of California San Francisco Institutional Review Board. Voluntary, written informed consent was obtained from each participant prior to study enrollment and procedures in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available (in deidentified format) from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the following NIH grants\u003cstrong\u003e:\u003c/strong\u003e NIEHS F32ES022582 (RB), K24 HL087713 (LH), R01 HL090335 (LH), and R01 HL128156 (LH).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR.J.B. wrote the manuscript; R.J.B., L.H., and J.B. conceived the study and designed the experiments; R.J.B., L.H., S.F., S.S., V.F., and E.A. implemented the study and collected the data; L.H. and R.J.B. analyzed the data and interpreted the findings. All authors made substantial contributions to the manuscript, reviewed the manuscript, approved the final version, and agreed to be accountable for all aspects of the manuscript. The authors did not use any Artificial Intelligence (AI) tools or Large Language Models (LLM) to prepare this manuscript. No non-author medical writers were involved in the manuscript preparation.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the participants of this study who have graciously volunteered their time, and the clinical staff at San Francisco General Hospital who assisted with patient selection and enrollment. A special thanks to faculty members of the Advanced Training in Clinical Research Program at University of California San Francisco who provided input for our study design and implementation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e2025 Global. 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Effect of beta-adrenergic agonists on mucociliary clearance. J Allergy Clin Immunol. 2002;110(6 Suppl):S291\u0026ndash;297.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBennett WD, Almond MA, Zeman KL, Johnson JG, Donohue JF. Effect of salmeterol on mucociliary and cough clearance in chronic bronchitis. Pulm Pharmacol Ther. 2006;19(2):96\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRandell SH, Boucher RC. Effective mucus clearance is essential for respiratory health. Am J Respir Cell Mol Biol. 2006;35(1):20\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaldwin GC, Tashkin DP, Buckley DM, Park AN, Dubinett SM, Roth MD. Marijuana and cocaine impair alveolar macrophage function and cytokine production. Am J Respir Crit Care Med. 1997;156(5):1606\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForrester JM, Steele AW, Waldron JA, Parsons PE. Crack Lung: An Acute Pulmonary Syndrome with a Spectrum of Clinical and Histopathologic Findings. Am Rev Respir Dis. 1990;142(2):462\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Tol Z, Vanos JK, Middel A, Ferguson KM. Concurrent Heat and Air Pollution Exposures among People Experiencing Homelessness. Environ Health Perspect. 2024;132(1):15003.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacMurdo MG, Mulloy KB, Felix CW, Curtis AJ, Ajayakumar J, Curtis J. Ambient Air Pollution Exposure among Individuals Experiencing Unsheltered Homelessness. Environ Health Perspect. 2022;130(2):27701.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu J, Wu Y, Wu Y, Yang R, Yu H, Wen B, Wu T, Shang S, Hu Y. The impact of heat waves and cold spells on pneumonia risk: A nationwide study. Environ Res. 2024;245:117958.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoodling E. 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Effects of environmental noise pollution on perceived stress and cortisol levels in street vendors. J Toxicol Environ health Part A. 2019;82(5):331\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel SR, Malhotra A, Gao X, Hu FB, Neuman MI, Fawzi WW. A prospective study of sleep duration and pneumonia risk in women. Sleep. 2012;35(1):97\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRecio A, Linares C, Banegas JR, D\u0026iacute;az J. The short-term association of road traffic noise with cardiovascular, respiratory, and diabetes-related mortality. Environ Res. 2016;150:383\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCantwell MF, McKenna MT, McCray E, Onorato IM. Tuberculosis and race/ethnicity in the United States: impact of socioeconomic status. Am J Respir Crit Care Med. 1998;157(4 Pt 1):1016\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOxlade O, Murray M. 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J Infect Dis. 2012;206(12):1809\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavydow DS, Hough CL, Zivin K, Langa KM, Katon WJ. Depression and risk of hospitalization for pneumonia in a cohort study of older Americans. J Psychosom Res. 2014;77(6):528\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Gennaro F, Cotugno S, Guido G, Cavallin F, Pisaturo M, Onorato L, Zimmerhofer F, Pipit\u0026ograve; L, De Iaco G, Bruno G, et al. Disparities in tuberculosis diagnostic delays between native and migrant populations in Italy: A multicenter study. Int J Infect diseases: IJID : official publication Int Soc Infect Dis. 2025;150:107279.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Y, Hinds WC, Kim S, Sioutas C. Concentration and Size Distribution of Ultrafine Particles Near a Major Highway. J Air Waste Manag Assoc. 2002;52(9):1032\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Y, Hao J, Fu L, Wang Z, Tang U. Vertical and horizontal profiles of airborne particulate matter near major roads in Macao, China. Atmos Environ. 2002;36:4907.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung KH, Bernabe K, Moors K, Yan B, Chillrud SN, Whyatt R, Camann D, Kinney PL, Perera FP, Miller RL. Effects of Floor Level and Building Type on Residential Levels of Outdoor and Indoor Polycyclic Aromatic Hydrocarbons, Black Carbon, and Particulate Matter in New York City. Atmos (Basel). 2011;2(2):96\u0026ndash;109.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanczak R, Galobardes B, Spoerri A, Zwahlen M, Egger M. High life in the sky? Mortality by floor of residence in Switzerland. Eur J Epidemiol. 2013;28(6):453\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV, community acquired pneumonia, Pneumocystis pneumonia, tuberculosis, marijuana, illicit drugs, social determinants of health","lastPublishedDoi":"10.21203/rs.3.rs-8695194/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8695194/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePulmonary infections cause substantial morbidity and mortality among people with HIV. Our objective was to evaluate common urban exposures among those living with HIV which may increase risk of pulmonary infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe enrolled 75 adult patients infected with HIV and admitted to San Francisco General Hospital into a hospital-based case-control study: 38 cases with pulmonary infection and 37 controls admitted for diagnoses other than pulmonary infection. We administered questionnaires to assess inhaled drug exposures, outdoor and indoor pollution, and occupational exposures. We fit multivariable logistic regression models with pulmonary infection as the dichotomous outcome and each exposure as a primary predictor in separate models, including potential confounders based on biological plausibility and backward selection criteria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were middle-aged (median age 48 years) and predominantly male (77%). Poverty was prevalent, with 79% unemployed and 27% unstably housed or homeless. Median CD4 count was lower in cases compared with controls (88 vs 300 cells/µL, p = 0.005). Most cases were admitted with either bacterial pneumonia (76%) or \u003cem\u003ePneumocystis\u003c/em\u003e pneumonia (16%), and the most common admission diagnosis among controls was non-pulmonary infection (43%). Fifty-nine percent of participants had smoked tobacco cigarettes within the month prior to enrollment while 40% had recently smoked marijuana. In adjusted logistic regression models, tobacco smokers had six times higher odds of pulmonary infection (aOR 6.43, 95%CI 1.70–24.4) compared with non or former smokers, while those who had recently smoked marijuana experienced a 79% reduction in odds of pulmonary infection (aOR 0.21, 95%CI 0.06–0.78) compared with no recent marijuana smoking. Unstable housing or homelessness predicted a three-fold increased odds of pulmonary infection (aOR 3.22, 95%CI 1.01–10.3), whereas those sleeping on higher floors were at reduced odds of pulmonary infection, with each floor level above street-level predicting a 44% reduced odds of pulmonary infection (aOR 0.56, 95%CI 0.40–0.80). Finally, opening windows for ventilation was associated with reduced pulmonary infection odds (aOR 0.051, 95%CI 0.004–0.64).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePulmonary infection odds were increased with tobacco smoking and homelessness; and decreased among recent marijuana smokers, those sleeping on higher floors and opening windows for ventilation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable\u003c/p\u003e","manuscriptTitle":"Recreational drug inhalation and socioenvironmental determinants of pulmonary infection in patients with HIV: a hospital-based case control study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 09:37:23","doi":"10.21203/rs.3.rs-8695194/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-24T08:15:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T05:28:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198287684195725044136067415059429963831","date":"2026-03-09T13:28:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329218227843541807130568385286993368331","date":"2026-03-09T04:54:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140674095746555609425033852482552237508","date":"2026-03-08T00:31:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-04T13:16:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17935741780825129896303461510354408970","date":"2026-02-23T13:24:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-19T04:43:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-19T04:34:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-02T06:53:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-30T22:18:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-01-30T22:11:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8f6574b3-feb3-4694-ba71-c1432bb514f6","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T00:08:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 09:37:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8695194","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8695194","identity":"rs-8695194","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}