Abstract
Tobacco and alcohol use are leading causes of premature mortality in the US and concurrent use is associated with even greater health risks. A cross-sectional study of 20,310 patients admitted to a Mid-Atlantic acute health care system between July 1, 2018 and June 30, 2019 were categorized according to smoking and alcohol use disorder (AUD) status. Of the total admissions, 1,464 (7.2%) were current smokers with an AUD. These patients were younger (52.4 vs. 63.9), more likely to be male (64.1% vs. 38.0%) and covered by Medicaid (46.9% vs. 11.6%), and resided in proximity to higher counts of tobacco (10.3 vs. 4.72) and alcohol (2.24 vs. 1.14) retailers than never smokers without an AUD. Clinically, these patients had higher rates of other substance use disorders (60.4% vs. 6.1%), depression (64.6% vs. 34.8%), HIV/AIDS (3.3% vs. 0. 6%), and liver disease (40.7% vs. 13.2%) than never smokers without an AUD. Patients who concurrently smoke and have an AUD face unique and serious health risks. A multimorbidity framework can guide clinical and community-based interventions for individuals with concurrent psychiatric and chronic medical conditions, complex social needs, and adverse environmental exposures.
Keywords: smoking, tobacco use disorder, alcohol use disorder, multimorbidity, built environment
1. Introduction
Tobacco and alcohol use are leading causes of premature mortality in the US (Mokdad et al., 2018). Smoking remains the number one preventable cause of death, claiming more than 400,000 lives each year primarily from heart disease, lung cancer, and other pulmonary disease (U.S. Department of Health and Human Services, 2014). Alcohol-related mortality has doubled in the last 20 years and now exceeds 70,000 deaths per year, with nearly half of those deaths attributable to liver disease and alcohol-involved overdoses (White, Castle, Hingson, & Powell, 2020). Although tobacco and alcohol use are typically treated as independent risk factors, concurrent use is prevalent and mutually reinforcing (Cross, Lotfipour, & Leslie, 2017). More than 46 million US adults co-use tobacco and alcohol and more than 6 million meet criteria for concurrent tobacco use disorder (TUD) and alcohol use disorder (AUD), with these disorders generally defined as an impaired ability to control or discontinue substance use (Falk, Hsiao-Ye, & Hiller-Sturmhöfel, 2006). Adults with an AUD have smoking rates 2 to 3 times higher than adults without a history of AUD (Weinberger, Funk, & Goodwin, 2016) and, conversely, smoking cessation rates for adults with AUD are half that observed for adults who only smoke (Weinberger, Gbedemah, & Goodwin, 2017). Furthermore, the combined exposure to tobacco and alcohol interact to produce a greater than multiplicative increase in risk for certain conditions, such as head and neck cancer (Dal Maso et al., 2016).
Given the poorer clinical response to TUD and AUD interventions observed among individuals with concurrent disorders, (Roche, Ray, Yardley, & King, 2016; Weinberger et al., 2017) new approaches are needed that go beyond simply combining treatments approved separately for each disorder (MacLean, Sofuoglu, & Rosenheck, 2018). That is, like other forms of multimorbidity, treating the co-occurrence of TUD and AUD will require tailored interventions that account for the “full clinical presentation,” which often includes other psychiatric and medical conditions (MacLean et al., 2018, p. 64). For example, a national cohort study of Veterans Health Administration administrative data observed that veterans with co-occurring TUD and AUD had higher rates of homelessness, liver disease, schizophrenia, and other substance use disorders than individuals with either TUD or AUD alone (MacLean et al., 2018).
More recently, it has been argued that the conceptualization of multimorbidity should be expanded to include the larger social and environmental contexts in which patients reside because these factors can directly impact treatment effectiveness and, ultimately, health outcomes (North, Brown, & Pollio, 2016). Prior research has demonstrated a link between poorer treatment outcomes for TUD and AUD among individuals living in communities characterized by adverse socioeconomic conditions and elevated densities of tobacco and alcohol retailers (Mericle, Kaskutas, Polcin, & Karriker-Jaffe, 2018; Nollen et al., 2019; Pulakka et al., 2016). Thus, to improve interventions for co-occurring TUD and AUD, treatment planning should also account for exposures to adverse social and environmental conditions.
To help elucidate the full multimorbid clinical presentation of patients who smoke and have a concurrent AUD, the objective of this study was to describe the sociodemographic characteristics and medical conditions for a hospital-based civilian population of adult patients categorized into groups by smoking and AUD status. In addition, to better understand how environmental conditions relate to the co-occurrence of smoking and AUD, we utilized geographic information system (GIS) methodologies to examine patient group differences in the spatial distribution of their residential addresses and exposures to tobacco and alcohol retailers. Extending the multimorbidity framework to include the environmental context can inform the development of more effective clinical and community-based interventions designed to limit tobacco and alcohol co-use (North et al., 2016; Stahler, Mennis, & Baron, 2013).
2. Material and Methods
2.1. Setting
Patient records came from the Christiana Care Health System electronic health record (EHR). The Christiana Care Health System is headquartered in New Castle County, Delaware and operates two acute care hospitals in the county that together account for 1,227 inpatient beds. These two hospitals provide 88% of non-veteran adult acute care in New Castle County (45,278 hospital discharges/51,262 total discharges; Delaware Health Statistics Center, 2018). In order to maximize the generalizability of findings to the population of local inpatients, and to standardize measures of tobacco and alcohol retailer exposure, only patients who resided in New Castle County were included in this study. This study was reviewed and approved by the Christiana Care Health System Institutional Review Board.
To provide context on the larger geographic setting, New Castle County has a population of 555,133 residents, of whom approximately 65% are White, 25% Black/African American, and the remainder are classified as ‘other’ (Bureau, 2018). Approximately 10% of the population is of Hispanic/Latino ethnicity. Wilmington is the largest city in New Castle County and has a population of 70,904 residents, representing approximately 15% of the county’s population. A City Health Dashboard report on small and medium cities classified Wilmington as a “small industrial-legacy city,” characterized by high poverty rates and large Black populations (NYU Langone Health Department of Population Health, 2020). The racial makeup of Wilmington differs considerably from the surrounding county: approximately 58% of city residents are Black/African American, 35% White, and the remainder classified as ‘other.’ Consistent with national studies demonstrating racial and socioeconomic disparities in tobacco exposure, prior research observed significantly higher rates of tobacco retailer density in Wilmington (Siegel, Brooks, Gbadebo, & Laughery, 2019). Figure 1 presents two choropleth maps that visualize American Community Survey data (Bureau, 2018) for a) the socioeconomic status (SES) domain from the Social Vulnerability Index (Flanagan, Gregory, Hallisey, Heitgerd, & Lewis, 2011) and b) the predominant racial group by census tract for New Castle County. See Appendix Table 1 (A.1) for additional sociodemographic characteristics.
Fig. 1.
Maps of socioeconomic status (SES) vulnerability and predominant racial group by census tract in New Castle County, Delaware. SES vulnerability, a domain of the Centers for Disease Control and Prevention’s Social Vulnerability Index, is based on poverty, unemployment, income, and high school completion. Predominant racial group was determined by the racial group that comprised the greatest percentage of tract population. All SES and race data were obtained from the U.S. Census Bureau’s American Community Survey 2014-2018 5-year estimates.
2.2. Study Population
This study drew on Christiana Care Health System EHR data for 20,310 adult New Castle County residents who were admitted to an inpatient unit between July 1, 2018 and June 30, 2019. We chose to use a one-year time frame for the most recent available data in order to 1) capture any seasonal variation in hospital admissions and 2) maximize the concurrence between patient addresses and tobacco retail location data, which were only available in real-time. Patient residential address, demographic, and clinical data were abstracted from the EHR. Patient addresses were manually cleaned and geocoded using ArcGIS 10.6, yielding a match rate of 98% (20,310/20,706). Of the 396 unmatched patients, 362 had no address information and 34 had addresses that were not locatable.
Tobacco and alcohol retail address data were extracted from a public state business license database that was current as of April 17, 2019 (Delaware Division of Revenue, 2019). We included all establishments with a tobacco retail license, excluding entities that do not sell directly to consumers in physical storefronts (e.g., internet retailers). Guided by prior research that has documented a robust link between risky alcohol use and residential exposure to off-premise alcohol retailers (e.g., liquor stores), but not on-premise alcohol retailers (e.g., bars; Gmel et al., 2016) we included only licensed establishments that sold alcoholic beverages for offsite consumption. All retail locations were geocoded using ArcGIS 10.6 with a match rate of 100% (tobacco retaile N=642, alcohol retailer N=160), and used to generate measures of retailer exposure.
2.3. Measures
2.3.1. Demographics
Demographic measures included age, sex, race/ethnicity, and payer status, which were all collected from registrars at admission and recorded in the EHR. Payer status (commercial, Medicaid, Medicare, or self-pay) was used as a proxy for socioeconomic status in lieu of other available data (Casey et al., 2018).
2.3.2. Smoking status
While smoking status is routinely assessed upon admission, it is not standard clinical practice to assess other criteria (e.g., withdrawal) necessary to formally diagnose patients with a TUD. Therefore, we classified patients into current (daily or non-daily), former, or never smoker categories based on a standardized nurse-administered interview conducted at admission and documented in the EHR. For patients with multiple admissions, the last known smoking status was utilized.
2.3.3. Clinical diagnoses
Clinical characteristics were summarized with the Elixhauser Comorbidity Index (Elixhauser, Steiner, Harris, & Coffey, 1998), a method for categorizing comorbidities based on International Classification of Diseases (ICD) diagnosis codes abstracted from the EHR. Dichotomous indicators (present/absent) for each condition, including the presence of an ICD code for AUD, were used to characterize the clinical presentation for patient groups.
2.3.4. Environmental exposures
Environmental exposures to tobacco and offsite alcohol retailers were quantified with proximity and density measures. Proximity was calculated as the Euclidean distance in miles to the nearest tobacco and alcohol retail locations from each patient’s home address. Density was calculated as the number of tobacco and alcohol retail locations within a half-mile buffer of each patient’s home address.
2.4. Statistical Analysis
Descriptive statistics were used to characterize the 3 smoker groups (current, former, and never) X 2 AUD groups (present or not present) for the sociodemographic, clinical, and the tobacco and alcohol retail location exposure variables. Analysis of variance regression models appropriate for the type of outcome (linear for continuous outcomes, logistic for binary outcomes, and multinomial for categorical outcomes with more than two categories) were used to assess for interactions and main effects of smoking and AUD status. Significant interactions were interrogated with post-hoc pairwise comparisons with Bonferroni-adjusted p-values. Elixhauser comorbidities were examined using two logistic regression models. The first model, used to assess main effects and interactions, included covariates for smoking status, AUD status, and their interaction. The second model, used to assess significant differences in comorbidity prevalence by patient subtype, included a single covariate for patient subtype (the six combinations of smoking and AUD status), treating never smokers without AUD as the reference group. Adjusted versions of these models included additional covariates for age, sex, and payer.
The geographic distribution of residential addresses for these patient groups was visualized with spatial intensity maps, a method that displays the continuous spatial distribution of point-level events (e.g., patient addresses) without aggregating them to geographic units (e.g., census tracts). Spatial intensity uses a kernel density approach (with fixed bandwidth used here) to estimate the number of events around any point location on the map, weighting the events by their proximity to that point location (Diggle, 1985). The resulting spatial intensity values represent the expected number of events per unit area at any given location and when mapped depict the spatial concentration of events.
3. Results
3.1. Sociodemographic Characteristics
Table 1 shows the sociodemographic characteristics for the patient groups. In general, the hospital-based sample was representative of the sociodemographic characteristics for the surrounding county with the exception of age (see Table A.1). As would be predicted, older members of the surrounding community were overrepresented in the sample of hospitalized patients. Of the total 20,310 unique adult admissions, current smokers and patients with an AUD accounted for 3,749 (18.5%) and 3,478 (17.1%) of admissions, respectively. Next, classifying patients by both smoking and AUD status, we observed that 1,464 patients (7.2%) were current smokers with an AUD, 2,285 patients (11.3%) were current smokers without an AUD, 1,070 (5.3%) were former smokers with an AUD, 5,298 (26.1%) were former smokers without an AUD, 944 (4.6%) were never smokers with an AUD, and 9,249 (45.5%) were never smokers without an AUD.
Table 1:
Characteristics of New Castle County adult inpatients by smoking history and alcohol use disorder (AUD). Both descriptive summary statistics and ANOVA regression results are presented.
| Current smokers with AUD (N=1464) | Current smokers without AUD (N=2285) | Former smokers with AUD (N=1070) | Former smokers without AUD (N=5298) | Never smokers with AUD (N=944) | Never smokers without AUD (N=9249) | Total (N=20310) | |
|---|---|---|---|---|---|---|---|
| Sociodemographic Characteristics | |||||||
| Age | |||||||
| Mean, SDb,c | 52.4 (13.2)† | 53.6 (16.1) | 64.9 (13.4) | 69.6 (14.6) | 59.3 (16.5) | 63.9 (18.6) | 63.3 (17.6) |
| Median | 54.0 | 55.0 | 65.0 | 71.0 | 61.0 | 66.0 | 65.0 |
| Male, n (%)b,c | 939 (64.1)† | 1066 (46.7) | 714 (66.7) | 2483 (46.9) | 574 (60.8) | 3511 (38.0) | 9287 (45.7) |
| Race, n (%) | |||||||
| White (reference) | 932 (63.7) | 1494 (65.4) | 794 (74.2) | 4053 (76.5) | 638 (67.6) | 6116 (66.1) | 14027 (69.1) |
| Black/African Americana,b | 478 (32.7) | 689 (30.2) | 249 (23.3) | 1065 (20.1) | 258 (27.3) | 2457 (26.6) | 5196 (25.6) |
| Other Racea,b,c | 54 (3.7) | 102 (4.5) | 27 (2.5) | 180 (3.4) | 48 (5.1) | 676 (7.3) | 1087 (5.4) |
| Hispanic/Latino, n (%)b | 54 (3.7) | 118 (5.2) | 34 (3.2) | 164 (3.1) | 47 (5.0) | 547 (5.9) | 964 (4.7) |
| Payer, n (%) | |||||||
| Commercial (reference) | 292 (19.9) | 635 (27.8) | 202 (18.9) | 1129 (21.3) | 267 (28.3) | 2835 (30.7) | 5360 (26.4) |
| Medicaida,c | 687 (46.9) | 787 (34.4) | 195 (18.2) | 390 (7.4) | 225 (23.8) | 1075 (11.6) | 3359 (16.5) |
| Medicareb | 474 (32.4)† | 833 (36.5) | 667 (62.3) | 3763 (71.0) | 444 (47.0) | 5290 (57.2) | 11471 (56.5) |
| Self-paya | 11 (0.8) | 30 (1.3) | 6 (0.6) | 16 (0.3) | 8 (0.8) | 49 (0.5) | 120 (0.6) |
| Tobacco Retail Exposured | |||||||
| Miles to nearest tobacco retailer | |||||||
| Mean, SDa,c | 0.336 (0.413) | 0.384 (0.441) | 0.439 (0.456) | 0.509 (0.520) | 0.425 (0.419) | 0.510 (0.517) | 0.476 (0.499) |
| Median | 0.225 | 0.276 | 0.324 | 0.372 | 0.323 | 0.373 | 0.350 |
| Tobacco retailers within half-mile of home | |||||||
| Mean, SDa,c | 10.3 (13.1) | 8.21 (11.7) | 6.67 (10.5) | 4.59 (7.98) | 6.53 (10.0) | 4.72 (8.26) | 5.67 (9.43) |
| Median | 4.00 | 4.00 | 3.00 | 2.00 | 3.00 | 2.00 | 3.00 |
| Alcohol Retail Exposuree | |||||||
| Miles to nearest alcohol retailer | |||||||
| Mean, SDa,c | 0.433 (0.457) | 0.486 (0.483) | 0.552 (0.499) | 0.644 (0.586) | 0.539 (0.484) | 0.645 (0.579) | 0.602 (0.559) |
| Median | 0.312 | 0.359 | 0.432 | 0.493 | 0.408 | 0.495 | 0.458 |
| Alcohol retailers within half-mile of home | |||||||
| Mean, SDa,c | 2.24 (2.76) | 1.86 (2.60) | 1.63 (2.48) | 1.11 (1.90) | 1.53 (2.27) | 1.14 (1.98) | 1.34 (2.18) |
| Median | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Significant main effect for current smoking status (p<0.05) with no smoking x AUD interaction, reference group = never smokers.
Significant main effect for former smoking status (p<0.05) with no smoking x AUD interaction, reference group = never smokers.
Significant main effect for AUD (p<0.05) with no smoking x AUD interaction.
Includes all retailers licensed to sell tobacco in physical storefronts.
Includes only alcohol retailers who sell alcohol for offsite consumption (i.e., liquor stores).
Significant interaction of smoking status and AUD (p<0.05), reference group = never smokers without AUD.
Examining Table 1 regression results, significant interactions were present; compared to never smokers without an AUD, current smokers with an AUD were younger, more likely to be male, and less likely to be covered by Medicare. Significant main effects for smoking status showed that current smokers were more likely to be Black/African American, less likely to be ‘other’ race, and more likely to be covered by Medicaid or self-pay than never smokers. Former smokers were significantly older, more likely to be male, less likely to be Black/African American or ‘other’ race, less likely to be Hispanic/Latino, and more likely to be covered by Medicare than never smokers. Additional main effects showed that patients with an AUD were significantly younger, more likely to be male, less likely to be ‘other’ race, and more likely to be covered by Medicaid than patients without an AUD.
3.2. Spatial Data
Table 1 also shows tobacco (N=642) and alcohol (N=160) retailer exposure data. No significant smoking x AUD status interactions were observed for these measures. However, main effects for smoking and AUD status were observed for all four exposure measures. Current smokers lived significantly closer to both their nearest tobacco and alcohol retailers than former and never smokers (post-hoc p-values both <0.05). Current smokers also had a significantly greater number of tobacco and alcohol retailers within a half-mile of their home address than former and never smokers (post-hoc test p-values both <0.05). Similarly, patients with an AUD lived significantly closer to their nearest tobacco and alcohol retailers and had a significantly greater number of tobacco and alcohol retailers within a half-mile of their home address than patients without an AUD (all p-values <0.05). Accounting for both of these main effects, current smokers with an AUD lived in proximity to more than twice the number of tobacco retailers (10.3 vs. 4.72, p<0.05) and nearly twice the number of alcohol retailers (2.24 vs. 1.14, p<0.05) as never smokers without an AUD.
Figure 2 visualizes the spatial distribution of the patient groups within New Castle County. Patients with either a current smoking status or an AUD, or both (Maps A-C, E), were more spatially concentrated in and around Wilmington. Former smokers and never smokers without an AUD (Maps D and F), by contrast, exhibited a more spatially dispersed distribution.
Fig. 2.
Maps of the spatial distribution of each patient subtype population, defined by smoking status (current, former, never) and alcohol use disorder (present, not present) in New Castle County, Delaware.
3.3. Clinical Characteristics
Table 2 presents Elixhauser comorbidity unadjusted prevalence rates for the patient groups. Unadjusted rates were used to better characterize the “real world” clinical presentation of patients who smoke and have an AUD. (For comorbidity odds ratios adjusted for demographics only, smoking x AUD status, and both demographics and smoking x AUD status, see Appendix Tables A.2, A.3, and A.4, respectively.) Examining significant interactions first, relative to never smokers without an AUD, current smokers with an AUD had higher rates of coagulopathy, lower rates of complicated diabetes, higher rates of drug use disorder, and lower rates of obesity (Note: a significant current smoking x AUD interaction was observed for blood loss anemia, though the prevalence of this comorbidity did not significantly differ from that of never smokers without AUD). Former smokers with an AUD had higher rates of blood loss anemia and non-metastatic solid tumors relative to never smokers without an AUD.
Table 2:
Differences in Elixhauser comorbidity prevalence among New Castle County adult inpatients by smoking history and alcohol use disorder (AUD). Both descriptive summary statistics and ANOVA regression results are presented.
| Elixhauser Comorbiditiesa,b | Current smokers with AUD (N=1464) | Current smokers without AUD (N=2285) | Former smokers with AUD (N=1070) | Former smokers without AUD (N=5298) | Never smokers with AUD (N=944) | Never smokers without AUD (N=9249) (reference group) | Total (N=20310) |
|---|---|---|---|---|---|---|---|
| AIDS/HIV, n (%) | 48 (3.3) | 48 (2.1) | 22 (2.1) | 39 (0.7) | 16 (1.7) | 57 (0.6) | 230 (1.1) |
| Deficiency Anemia, n (%) | 284 (19.4) | 302 (13.2) | 295 (27.6) | 1025 (19.3) | 248 (26.3) | 1761 (19.0) | 3915 (19.3) |
| Rheumatoid Arthritis Collagen Vascular Diseases, n (%) | 103 (7.0) | 177 (7.7) | 108 (10.1) | 527 (9.9) | 89 (9.4) | 888 (9.6) | 1892 (9.3) |
| Blood Loss Anemia, n (%) | 89 (6.1)† | 88 (3.9) | 124 (11.6)† | 424 (8.0) | 76 (8.1) | 689 (7.4) | 1490 (7.3) |
| Cardiac Arrythmias, n (%) | 342 (23.4) | 567 (24.8) | 460 (43.0) | 2282 (43.1) | 293 (31.0) | 2730 (29.5) | 6674 (32.9) |
| Congestive Heart Failure, n (%) | 275 (18.8) | 414 (18.1) | 340 (31.8) | 1592 (30.0) | 243 (25.7) | 2105 (22.8) | 4969 (24.5) |
| Chronic Pulmonary Disease, n (%) | 779 (53.2) | 1096 (48.0) | 584 (54.6) | 2531 (47.8) | 366 (38.8) | 2954 (31.9) | 8310 (40.9) |
| Coagulopathy, n (%) | 395 (27.0)† | 252 (11.0) | 302 (28.2) | 882 (16.6) | 261 (27.6) | 1426 (15.4) | 3518 (17.3) |
| Depression, n (%) | 946 (64.6) | 1053 (46.1) | 573 (53.6) | 1951 (36.8) | 516 (54.7) | 3223 (34.8) | 8262 (40.7) |
| Diabetes (uncomplicated), n (%) | 376 (25.7) | 698 (30.5) | 403 (37.7) | 2102 (39.7) | 323 (34.2) | 3271 (35.4) | 7173 (35.3) |
| Diabetes (complicated), n (%) | 250 (17.1)† | 479 (21.0) | 245 (22.9) | 1370 (25.9) | 217 (23.0) | 2051 (22.2) | 4612 (22.7) |
| Drug Use Disorder, n (%) | 884 (60.4)† | 737(32.3) | 329 (30.7) | 469 (8.9) | 255 (27.0) | 568 (6.1) | 3242 (16.0) |
| Hypertension (uncomplicated), n (%) | 1018 (69.5) | 1431 (62.6) | 908 (84.9) | 4418 (83.4) | 713 (75.5) | 6808 (73.6) | 15296 (75.3) |
| Hypertension (complicated), n (%) | 297 (20.3) | 460 (20.1) | 396 (37.0) | 1986 (37.5) | 286 (30.3) | 2726 (29.5) | 6151 (30.3) |
| Hypothyroidism, n (%) | 215 (14.7) | 334 (14.6) | 235 (22.0) | 1413 (26.7) | 194 (20.6) | 2289 (24.7) | 4680 (23.0) |
| Liver Disease, n (%) | 596 (40.7) | 323 (14.1) | 375 (35.0) | 763 (14.4) | 352 (37.3) | 1223 (13.2) | 3632 (17.9) |
| Lymphoma, n (%) | 20 (1.4) | 27 (1.2) | 27 (2.5) | 155 (2.9) | 24 (2.5) | 250 (2.7) | 503 (2.5) |
| Fluid and Electrolyte Disorders, n (%) | 1050 (71.7) | 1200 (52.5) | 808 (75.5) | 3103 (58.6) | 665 (70.4) | 5104 (55.2) | 11930 (58.7) |
| Cancer (metastatic), n (%) | 59 (4.0) | 100 (4.4) | 91 (8.5) | 421 (7.9) | 54 (5.7) | 529 (5.7) | 1254 (6.2) |
| Other Neurological Disorder,c n (%) | 461 (31.5) | 444 (19.4) | 359 (33.6) | 1026 (19.4) | 311 (32.9) | 1771 (19.1) | 4372 (21.5) |
| Obesity, n (%) | 420 (28.7)† | 793 (34.7) | 466 (43.6) | 2371 (44.8) | 414 (43.9) | 4010 (43.4) | 8474 (41.7) |
| Paralysis, n (%) | 103 (7.0) | 156 (6.8) | 101 (9.4) | 394 (7.4) | 81 (8.6) | 736 (8.0) | 1571 (7.7) |
| Peripheral Vascular Disorders, n (%) | 292 (19.9) | 448 (19.6) | 371 (34.7) | 1653 (31.2) | 197 (20.9) | 1850 (20.0) | 4811 (23.7) |
| Psychoses, n (%) | 247 (16.9) | 184 (8.1) | 100 (9.3) | 222 (4.2) | 110 (11.7) | 416 (4.5) | 1279 (6.3) |
| Pulmonary Circulation Disorders, n (%) | 174 (11.9) | 224 (9.8) | 228 (21.3) | 1026 (19.4) | 160 (16.9) | 1420 (15.4) | 3232 (15.9) |
| Renal Failure, n (%) | 191 (13.0) | 294 (12.9) | 278 (26.0) | 1474 (27.8) | 213 (22.6) | 2096 (22.7) | 4546 (22.4) |
| Solid Tumor (no metastasis), n (%) | 178 (12.2) | 296 (13.0) | 292 (27.3)† | 1301 (24.6) | 160 (16.9) | 1751 (18.9) | 3978 (19.6) |
| Peptic Ulcer Disease, n (%) | 117 (8.0) | 119 (5.2) | 148 (13.8) | 346 (6.5) | 95 (10.1) | 514 (5.6) | 1339 (6.6) |
| Valvular Disease, n (%) | 153 (10.5) | 242 (10.6) | 256 (23.9) | 1294 (24.4) | 193 (20.4) | 1844 (19.9) | 3982 (19.6) |
| Weight Loss, n (%) | 371 (25.3) | 346 (15.1) | 287 (26.8) | 854 (16.1) | 200 (21.2) | 1241 (13.4) | 3299 (16.2) |
Logistic regression model includes covariate for patient group with six categories (current/former/never smoking status by AUD/no AUD).
Red and green shading indicate that comorbidity prevalence is significantly higher or lower, respectively, among patient group relative to never smokers without AUD (p<0.05).
Other neurological disorders include Huntington’s disease, hereditary ataxia, spinal muscular atrophy and related syndromes, systemic atrophies primarily affecting central nervous system, Parkinson’s disease, secondary parkinsonism, drug-induced and other chorea, other degenerative diseases of nervous system not elsewhere classified, other degenerative diseases of nervous system in diseases classified elsewhere, multiple sclerosis, other acute disseminated demyelination, other demyelinating diseases of central nervous system, epilepsy and recurrent seizures, other disorders of brain, dysphasia, aphasia, and convulsions not elsewhere classified.
Significant interaction of smoking status and AUD (p<0.05), reference group = never smokers without AUD. Interactions assessed using a separate logistic regression model with covariates for smoking status (current/former/never), AUD (yes/no), and the smoking status x AUD interaction.
There were numerous main effects for both current and former (vs. never) smokers and for patients with (vs. without) an AUD (see Table 2 for full details). Highlighting the joint impact of these main effects for current smokers with an AUD, relative to never smokers without an AUD, we observed higher rates of HIV/AIDS, chronic pulmonary disease, depression, liver disease, fluid and electrolyte disorders, neurological disorders, psychoses, peptic ulcer disease, and weight loss. In contrast, we observed lower rates of rheumatoid arthritis, cardiac arrythmias, congestive heart failure, uncomplicated diabetes, complicated and uncomplicated hypertension, hypothyroidism, lymphoma, metastatic and non-metastatic cancer, pulmonary circulation disorders, renal failure, and valvular disease. In the adjusted analyses (see Tables A.2–A.4), the ostensibly protective effects associated with current smoking and an AUD, relative to the patients who never smoked and did not have an AUD, were no longer significant for certain clinical conditions (e.g., metastatic cancer) but remained for others (e.g., diabetes).
4. Discussion
This hospital-based population study, using novel GIS analytic methods, contributes to a more comprehensive characterization of the full multimorbid clinical presentation for patients who smoke and have a concurrent AUD. While prior research has established that co-use is prevalent and mutually reinforcing (Cross et al., 2017), the results of this study help to place co-use in a larger clinical, social, and environmental context. Representing approximately 1 in 14 adult admissions, patients who currently smoked and had a concurrent AUD were younger, more likely to be male, and were more likely covered by Medicaid (or self-pay), reflecting their lower SES. In addition, these patients had notably higher prevalence rates for other psychiatric conditions and a distinct pattern of physical conditions, even after adjusting for sociodemographic characteristics, and were more likely to reside in adverse environments.
Regarding psychiatric multimorbidity, more than 60% of patients who were current smokers and had an AUD were diagnosed with another drug use disorder. By comparison, the prevalence of a drug use disorder for all admitted patients and never smokers without an AUD were approximately 16% and 6%, respectively. In the general US adult population, the twelve-month and lifetime prevalence rates for drug use disorder are approximately 4% and 10%, respectively (Grant et al., 2016). Similarly elevated prevalence rates were observed for psychoses and depression, consistent with prior research conducted in a veteran population (MacLean et al., 2018).
The clinical presentation regarding physical conditions for current smokers with an AUD was more nuanced. Prevalence rates were elevated for conditions where smoking, AUD, and other substance use disorders are risk factors, including HIV/AIDS and liver disease (White et al., 2020). However, prevalence rates were unexpectedly lower for hypertension, diabetes, cancer, and renal failure. Several of these lower rates could attributed to patient group differences in age, sex, and payer, including hypertension and cancer. However, the “protective” effects observed for the current smoker/AUD patient group remained for diabetes and renal failure after adjusting for covariates. These results closely replicate the findings reported by MacLean and colleagues (MacLean et al., 2018), who called for additional research to investigate this unexpected effect.
Extending the multimorbidity framework to the environmental context, we found that current smokers with an AUD were more likely to reside in socioeconomically vulnerable and racially segregated areas relative to other patient groups. In addition, this patient group was exposed to approximately twice the density of tobacco and alcohol retailers relative to other patient groups. These findings are consistent with prior research that has documented robust associations between smoking and risky alcohol use and residing in areas characterized by socioeconomic disadvantage and elevated tobacco and alcohol retailer densities (Brenner, Borrell, Barrientos-Gutierrez, & Diez Roux, 2015; Pulakka et al., 2016).
Several limitations should be noted. First, smoking status was based on selfreport and could have contributed to an underestimate of prevalence. Relatedly, clinician differences in the documentation of AUDs may have also contributed to an underestimate of prevalence (Smothers, Yahr, & Ruhl, 2004). If our measures of smoking status and AUD were in fact more specific than sensitive, the results regarding co-use should be interpreted to reflect patients at the more disordered end of the use spectrum. Second, the data for this study come from a single health system and the results do not necessarily generalize to inpatients from other inpatient settings or adults without access to health care, though they are generally consistent with results from prior research conducted in different clinical settings (MacLean et al., 2018). Third, this was a cross-sectional study, which precludes drawing causal inferences. However, it was not the objective of this study to reestablish tobacco or alcohol use as risk factors for other conditions, nor was it to demonstrate that environmental conditions directly contribute to co-use, as different methods would be necessary to achieve that aim. Rather, these results can help inform treatment planning and future clinical research designed to address co-use.
High-quality evidence indicates that hospitalization represents an opportune time to intervene on tobacco and alcohol use (Mcqueen, Howe, Allan, Mains, & Hardy, 2011; Rigotti, Clair, Monafo, & Stead, 2012). However, substance use disorders often go undetected in hospital settings (Smothers et al., 2004). It is therefore critical that screening programs are systematically implemented in tandem with accessible treatment programs, which can be facilitated by establishing an inpatient addiction consult service (Weinstein, Wakeman, & Nolan, 2018). Treatment planning for patients who smoke and have a concurrent AUD should account for their full multimorbid clinical presentation. That is, smoking cessation and AUD treatments will likely be more effective when addressing patients’ co-existing psychiatric and other medical conditions and complex social needs. Fortunately, there are evidence-based interventions available for high-risk patients that can augment tobacco and alcohol use treatments and be readily implemented in acute care settings (e.g., intensive case management; Raven, Kushel, Ko, Penko, & Bindman, 2016).
Going further, treatment planning may eventually be able to account for the environmental context. Residing in socioeconomically vulnerable communities with high rates of exposure to tobacco and alcohol retailers is predictive of poorer TUD and AUD treatment outcomes (Nollen et al., 2019). Recent feasibility research suggests that facilitated extinction training, a tailored pharmacological and behavioral intervention, can help patients desensitize to environmental cues that could otherwise trigger cravings for tobacco and other substances (Brandon et al., 2018). As future research refines this approach, it can be incorporated into treatment planning for patients who have concurrent TUD and AUD and reside in adverse environments. In addition, novel community-based interventions, such as policies that impose limits on the numbers of tobacco and alcohol retailer licenses in socioeconomically vulnerable communities, can further promote the effectiveness of TUD and AUD treatments (Lawman et al., 2020).
5. Conclusions
This study helps to characterize the full multimorbid clinical presentation for hospitalized patients who concurrently smoke and have an AUD. Furthermore, our findings provide additional support for calls to employ a multimorbidity framework to guide future research, clinical care, and policy interventions for individuals with concurrent substance use and other psychiatric disorders, chronic medical conditions, complex social needs, and residence in adverse environments (MacLean et al., 2018; North et al., 2016).
Highlights.
Tobacco and alcohol use are leading causes of premature mortality
The co-use of tobacco and alcohol is prevalent and mutually reinforcing
Co-use is associated with other clinical conditions and risk factors
A multimorbidity framework can guide clinical and community-based interventions
Acknowledgements:
We thank Bayo M. Gbadebo and Chenesia Brown for their technical assistance in creating the dataset.
Role of the Funder/Sponsor:
This project was supported in part by the Delaware INBRE program, with a grant from the National Institute of General Medical Sciences – NIGMS (P20 GM103446) from the National Institutes of Health and the State of Delaware. The funding source had no role in the design and conduct of the study design; collection, management, analysis, and interpretation of the data; and decision to submit the manuscript for publication.
Appendix 1:
Characteristics of the New Castle County, DE general population according to residence in the City of Wilmington
| Wilmington (N=70904) | Non-Wilmington (N=484229) | New Castle County (Total) (N=555133) | |
|---|---|---|---|
| Sociodemographic Characteristicsa | |||
| Age groups, n (%)* | |||
| Under 18 | 16424 (23.2) | 104760 (21.6) | 121184 (21.8) |
| 18-34 | 18291 (25.8) | 115326 (23.8) | 133617 (24.1) |
| 35-54 | 18148 (25.6) | 127623 (26.4) | 145771 (26.3) |
| 55-74 | 14399 (20.3) | 106103 (21.9) | 120502 (21.7) |
| 75 and older | 3642 (5.1) | 30417 (6.3) | 34059 (6.1) |
| Male, n (%)* | 33365 (47.1) | 235505 (48.6) | 268870 (48.4) |
| Race, n (%)* | |||
| White | 24902 (35.1) | 333281 (68.8) | 358183 (64.5) |
| Black/African American | 41359 (58.3) | 97249 (20.1) | 138608 (25.0) |
| Other Race | 4643 (6.5) | 53699 (11.1) | 58342 (10.5) |
| Hispanic/Latino/a, n (%)* | 7257 (10.2) | 46814 (9.7) | 54071 (9.7) |
| Living below poverty level, n (%)b* | 17138 (25.1) | 44392 (9.4) | 61530 (11.4) |
| Tobacco Retail Exposured | |||
| Tobacco retailers, n | 167 | 475 | 642 |
| Tobacco retailers per 1000 people* | 2.36 | 0.98 | 1.16 |
| Alcohol Retail Exposuree | |||
| Alcohol retailers, n | 44 | 116 | 160 |
| Alcohol retailers per 1000 people* | 0.62 | 0.24 | 0.28 |
Sociodemographic data from U.S. Census Bureau, American Community Survey 2014-2018 estimates.
Counts and percentages reflect poverty status for the civilian non-institutionalized population, not the total population.
Includes all retailers licensed to sell tobacco in physical storefronts.
Includes only alcohol retailers who sell alcohol for offsite consumption (i.e., liquor stores).
p-value <0.001.
Appendix 2:
Adjusted odds ratios for Elixhauser comorbidities among New Castle County adult inpatients by demographic characteristics
| Age | Sex | Payer | |||||
|---|---|---|---|---|---|---|---|
| Elixhauser Comorbiditiesa,b,c | Age in Years AOR (95% CI)d | Male AOR (95% CI) | Female (reference) | Medicaid AOR (95% CI) | Medicare AOR (95% CI) | Self-Pay AOR (95% CI) | Commercial (reference) |
| AIDS/HIV | 0.9 (0.8, 0.9) | 1.6 (1.2, 2.1) | -- | 4.3 (2.9, 6.6) | 2.9 (1.8, 4.7) | 3.9 (0.9, 11.1) | -- |
| Deficiency Anemia | 1.0 (1.0, 1.0) | 0.6 (0.6, 0.7) | -- | 1.5 (1.4, 1.7) | 2.1 (1.8, 2.3) | 0.9 (0.5, 1.5) | -- |
| Rheumatoid Arthritis Collagen Vascular Diseases | 1.0 (1.0, 1.0) | 0.4 (0.4, 0.5) | -- | 1.0 (0.8, 1.2) | 1.8 (1.5, 2.1) | 0.4 (0.1, 1.1) | -- |
| Blood Loss Anemia | 1.1 (1.1, 1.1) | 0.7 (0.6, 0.8) | -- | 1.3 (1.1, 1.6) | 1.5 (1.3, 1.8) | 1.0 (0.3, 2.2) | -- |
| Cardiac Arrythmias | 1.2 (1.2, 1.2) | 2.0 (1.8, 2.1) | -- | 1.2 (1.0, 1.3) | 1.9 (1.7, 2.1) | 0.7 (0.4, 1.2) | -- |
| Congestive Heart Failure | 1.2 (1.1, 1.2) | 1.4 (1.3, 1.5) | -- | 1.6 (1.4, 1.8) | 2.1 (1.9, 2.4) | 1.3 (0.7, 2.1) | -- |
| Chronic Pulmonary Disease | 1.0 (1.0, 1.0) | 0.7 (0.7, 0.8) | -- | 1.7 (1.5, 1.9) | 2.1 (1.9, 2.3) | 0.8 (0.5, 1.3) | -- |
| Coagulopathy | 1.0 (1.0, 1.1) | 1.4 (1.3, 1.5) | -- | 1.8 (1.6, 2.0) | 1.8 (1.6, 2.0) | 1.8 (1.1, 2.9) | -- |
| Depression | 0.9 (0.9, 0.9) | 0.5 (0.5, 0.5) | -- | 1.8 (1.7, 2.0) | 2.1 (1.9, 2.3) | 0.4 (0.3, 0.7) | -- |
| Diabetes (uncomplicated) | 1.1 (1.0, 1.1) | 1.2 (1.2, 1.3) | -- | 1.1 (1.0, 1.2) | 1.7 (1.5, 1.8) | 1.3 (0.9, 1.9) | -- |
| Diabetes (complicated) | 1.0 (1.0, 1.0) | 1.4 (1.3, 1.5) | -- | 1.3 (1.2, 1.5) | 1.9 (1.7, 2.1) | 1.8 (1.2, 2.8) | -- |
| Drug Use Disorder | 0.8 (0.8, 0.8) | 1.3 (1.2, 1.5) | -- | 5.7 (5.1, 6.5) | 3.3 (2.9, 3.7) | 1.4 (0.8, 2.4) | -- |
| Hypertension (uncomplicated) | 1.4 (1.3, 1.4) | 1.4 (1.3, 1.5) | -- | 1.2 (1.1, 1.3) | 1.6 (1.4, 1.7) | 0.7 (0.5, 1.1) | -- |
| Hypertension (complicated) | 1.2 (1.2, 1.2) | 1.5 (1.4, 1.6) | -- | 1.5 (1.3, 1.7) | 2.4 (2.1, 2.6) | 1.6 (1.0, 2.5) | -- |
| Hypothyroidism | 1.1 (1.1., 1.1) | 0.4 (0.3, 0.4) | -- | 0.8 (0.7, 0.9) | 1.3 (1.2, 1.4) | 0.4 (0.2, 0.8) | -- |
| Liver Disease | 1.0 (1.0, 1.0) | 1.2 (1.1, 1.3) | -- | 1.8 (1.6, 2.0) | 1.5 (1.3, 1.6) | 1.2 (0.8, 2.0) | -- |
| Lymphoma | 1.1 (1.0, 1.1) | 1.2 (1.0, 1.4) | -- | 0.8 (0.5, 1.2) | 1.6 (1.2, 2.1) | 1.0 (0.2, 3.2) | -- |
| Fluid and Electrolyte Disorders | 1.1 (1.0, 1.1) | 1.0 (0.9, 1.0) | -- | 2.2 (2.0, 2.4) | 2.3 (2.1, 2.5) | 2.0 (1.4, 2.9) | -- |
| Cancer (metastatic) | 1.1 (1.1, 1.1) | 0.8 (0.7, 0.9) | -- | 0.8 (0.6, 1.0) | 1.0 (0.8, 1.2) | 0.3 (0.1, 1.0) | -- |
| Other Neurological Disordere | 1.0 (1.0, 1.0) | 1.2 (1.1, 1.2) | -- | 1.9 (1.7, 2.2) | 2.6 (2.3, 2.9) | 1.0 (0.5, 1.6) | -- |
| Obesity | 0.9 (0.9, 1.0) | 0.7 (0.7, 0.7) | -- | 0.6 (0.6, 0.7) | 1.3 (1.2, 1.5) | 0.5 (0.4, 0.8) | -- |
| Paralysis | 1.0 (1.0, 1.0) | 1.2 (1.1, 1.4) | -- | 1.3 (1.0, 1.5) | 2.2 (1.8, 2.5) | 1.3 (0.6, 2.5) | -- |
| Peripheral Vascular Disorders | 1.1 (1.1, 1.2) | 1.4 (1.3, 1.5) | -- | 1.2 (1.0, 1.4) | 2.2 (2.0, 2.5) | 0.7 (0.4, 1.3) | -- |
| Psychoses | 0.9 (0.9, 0.9) | 1.0 (0.9, 1.1) | -- | 3.7 (3.0, 4.6) | 5.1 (4.1, 6.3) | 0.9 (0.2, 2.5) | -- |
| Pulmonary Circulation Disorders | 1.1 (1.1, 1.1) | 0.9 (0.9, 1.0) | -- | 1.5 (1.3, 1.8) | 2.1 (1.9, 2.4) | 1.2 (0.6, 2.1) | -- |
| Renal Failure | 1.2 (1.1, 1.2) | 1.6 (1.4, 1.7) | -- | 1.3 (1.1, 1.5 | 2.4 (2.1, 2.7) | 1.1 (0.6, 1.9) | -- |
| Solid Tumor (no metastasis) | 1.2 (1.2, 1.2) | 1.1 (1.0, 1.2) | -- | 0.7 (0.6, 0.8) | 1.0 (0.9, 1.1) | 0.2 (0.1, 0.5) | -- |
| Peptic Ulcer Disease | 1.0 (1.0, 1.1) | 0.9 (0.8, 1.0) | -- | 1.7 (1.4, 2.1) | 2.0 (1.7, 2.4) | 1.7 (0.7, 3.4) | -- |
| Valvular Disease | 1.2 (1.2, 1.2) | 1.0 (0.9, 1.1) | -- | 1.0 (0.8, 1.2) | 1.7 (1.5, 1.9) | 0.8 (0.4, 1.5) | -- |
| Weight Loss | 1.0 (1.0, 1.1) | 0.9 (0.9, 1.0 | -- | 1.8 (1.6, 2.1) | 1.9 (1.7, 2.2) | 1.9 (1.1, 3.0) | -- |
Logistic regression model includes covariates for age, sex, and payer.
Red and green shading indicate that comorbidity odds are significantly higher or lower, respectively, relative to reference group (p<0.05).
Due to rounding and the precision of the results presented some significant odds ratios may be listed as 1.0 and/or corresponding confidence bounds may include 1.0.
AORs correspond to increasing 5-year age increments.
Other neurological disorders include Huntington’s disease, hereditary ataxia, spinal muscular atrophy and related syndromes, systemic atrophies primarily affecting central nervous system, Parkinson’s disease, secondary parkinsonism, drug-induced and other chorea, other degenerative diseases of nervous system not elsewhere classified, other degenerative diseases of nervous system in diseases classified elsewhere, multiple sclerosis, other acute disseminated demyelination, other demyelinating diseases of central nervous system, epilepsy and recurrent seizures, other disorders of brain, dysphasia, aphasia, and convulsions not elsewhere classified.
Appendix 3:
Unadjusted odds ratios for Elixhauser comorbidity prevalence among New Castle County adult inpatients by smoking history and alcohol use disorder (AUD)
| Elixhauser Comorbiditiesa,b,c | Current smokers with AUD (N=1464); OR, (95% CI) | Current smokers without AUD (N=2285); OR, (95% CI) | Former smokers with AUD (N=1070); OR (95% CI) | Former smokers without AUD (N=5298); OR (95% CI) | Never smokers with AUD (N=944); OR (95% CI) | Never smokers without AUD (N=9249); reference group |
|---|---|---|---|---|---|---|
| AIDS/HIV | 5.5 (3.7, 8.1) | 3.5 (2.3, 5.1) | 3.4 (2.0, 5.5) | 1.2 (0.8, 1.8) | 2.8 (1.5, 4.7) | -- |
| Deficiency Anemia | 1.0 (0.9, 1.2) | 0.6 (0.6, 0.7) | 1.6 (1.4, 1.9) | 1.0 (0.9, 1.1) | 1.5 (1.3, 1.8) | -- |
| Rheumatoid Arthritis Collagen Vascular Diseases | 0.7 (0.6, 0.9) | 0.8 (0.7, 0.9) | 1.1 (0.9, 1.3) | 1.0 (0.9, 1.2) | 1.0 (0.8, 1.2) | -- |
| Blood Loss Anemia | 0.8 (0.6, 1.0)† | 0.5 (0.4, 0.6) | 1.6 (1.3, 2.0)† | 1.1 (1.0, 1.2) | 1.1 (0.8, 1.4) | -- |
| Cardiac Arrythmias | 0.7 (0.6, 0.8) | 0.8 (0.7, 0.9) | 1.8 (1.6, 2.0) | 1.8 (1.7, 1.9) | 1.1 (0.9, 1.2) | -- |
| Congestive Heart Failure | 0.8 (0.7, 0.9) | 0.8 (0.7, 0.8) | 1.6 (1.4, 1.8) | 1.5 (1.4, 1.6) | 1.2 (1.0, 1.4) | -- |
| Chronic Pulmonary Disease | 2.4 (2.2, 2.7) | 2.0 (1.8, 2.2) | 2.6 (2.3, 2.9) | 1.9 (1.8, 2.1) | 1.3 (1.2, 1.5) | -- |
| Coagulopathy | 2.0 (1.8, 2.3)† | 0.7 (0.6, 0.8) | 2.2 (1.9, 2.5) | 1.1 (1.0, 1.2) | 2.1 (1.8, 2.4) | -- |
| Depression | 3.6 (3.2, 4.0) | 1.6 (1.5, 1.8) | 2.2 (1.9, 2.5) | 1.1 (1.0, 1.2) | 2.3 (2.0, 2.7) | -- |
| Diabetes (uncomplicated) | 0.6 (0.6, 0.7) | 0.8 (0.7, 0.9) | 1.1 (1.0, 1.3) | 1.2 (1.1, 1.3) | 1.0 (0.8, 1.1) | -- |
| Diabetes (complicated) | 0.7 (0.6, 0.8)† | 0.9 (0.8, 1.0) | 1.0 (0.9, 1.2) | 1.2 (1.1, 1.3) | 1.0 (0.9, 1.2) | -- |
| Drug Use Disorder | 23.3 (20.4, 26.7)† | 7.3 (6.4, 8.2) | 6.8 (5.8, 7.9) | 1.5 (1.3, 1.7) | 5.7 (4.8, 6.7) | -- |
| Hypertension (uncomplicated) | 0.8 (0.7, 0.9) | 0.6 (0.5, 0.7) | 2.0 (1.7, 2.4) | 1.8 (1.7, 2.0) | 1.1 (0.9, 1.3) | -- |
| Hypertension (complicated) | 0.6 (0.5, 0.7) | 0.6 (0.5, 0.7) | 1.4 (1.2, 1.6) | 1.4 (1.3, 1.5) | 1.0 (0.9, 1.2) | -- |
| Hypothyroidism | 0.5 (0.4, 0.6) | 0.5 (0.5, 0.6) | 0.9 (0.7, 1.0) | 1.1 (1.0, 1.2) | 0.8 (0.7, 0.9) | -- |
| Liver Disease | 4.5 (4.0, 5.1) | 1.1 (0.9, 1.2) | 3.5 (3.1, 4.1) | 1.1 (1.0, 1.2) | 3.9 (3.4, 4.5) | -- |
| Lymphoma | 0.5 (0.3, 0.8) | 0.4 (0.3, 0.6) | 0.9 (0.6, 1.4) | 1.1 (0.9, 1.3) | 0.9 (0.6, 1.4) | -- |
| Fluid and Electrolyte Disorders | 2.1 (1.8, 2.3) | 0.9 (0.8, 1.0) | 2.5 (2.2, 2.9) | 1.1 (1.1, 1.2) | 1.9 (1.7, 2.2) | -- |
| Cancer (metastatic) | 0.7 (0.5, 0.9) | 0.8 (0.6, 0.9) | 1.5 (1.2, 1.9) | 1.4 (1.2, 1.6) | 1.0 (0.7, 1.3) | -- |
| Other Neurological Disorderd | 1.9 (1.7, 2.2) | 1.0 (0.9, 1.1) | 2.1 (1.9, 2.4) | 1.0 (0.9, 1.1) | 2.1 (1.8, 2.4) | -- |
| Obesity | 0.5 (0.5, 0.6)† | 0.7 (0.6, 0.8) | 1.0 (0.9, 1.1) | 1.1 (1.0, 1.1) | 1.0 (0.9, 1.2) | -- |
| Paralysis | 0.9 (0.7, 1.1) | 0.8 (0.7, 1.0) | 1.2 (1.0, 1.5) | 0.9 (0.8, 1.1) | 1.1 (0.8, 1.4) | -- |
| Peripheral Vascular Disorders | 1.0 (0.9, 1.1) | 1.0 (0.9, 1.1) | 2.1 (1.9, 2.4) | 1.8 (1.7, 2.0) | 1.1 (0.9, 1.2) | -- |
| Psychoses | 4.3 (3.6, 5.1) | 1.9 (1.6, 2.2) | 2.2 (1.7, 2.7) | 0.9 (0.8, 1.1) | 2.8 (2.2, 3.5) | -- |
| Pulmonary Circulation Disorders | 0.7 (0.6, 0.9) | 0.6 (0.5, 0.7) | 1.5 (1.3, 1.7) | 1.3 (1.2, 1.4) | 1.1 (0.9, 1.3) | -- |
| Renal Failure | 0.5 (0.4, 0.6) | 0.5 (0.4, 0.6) | 1.2 (1.0, 1.4) | 1.3 (1.2, 1.4) | 1.0 (0.8, 1.2) | -- |
| Solid Tumor (no metastasis) | 0.6 (0.5, 0.7) | 0.6 (0.6, 0.7) | 1.6 (1.4, 1.9)† | 1.4 (1.3, 1.5) | 0.9 (0.7, 1.0) | -- |
| Peptic Ulcer Disease | 1.5 (1.2, 1.8) | 0.9 (0.8, 1.1) | 2.7 (2.2, 3.3) | 1.2 (1.0, 1.4) | 1.9 (1.5, 2.4) | -- |
| Valvular Disease | 0.5 (0.4, 0.6) | 0.5 (0.4, 0.5) | 1.3 (1.1, 1.5) | 1.3 (1.2, 1.4) | 1.0 (0.9, 1.2) | -- |
| Weight Loss | 2.2 (1.9, 2.5) | 1.2 (1.0, 1.3) | 2.4 (2.0, 2.7) | 1.2 (1.1, 1.4) | 1.7 (1.5, 2.0) | -- |
Logistic regression model includes covariate for patient group with six categories (current/former/never smoking status by AUD/no AUD).
Red and green shading indicate that comorbidity odds are significantly higher or lower, respectively, among patient group relative to never smokers without AUD (p<0.05).
Due to rounding and the precision of the results presented some significant odds ratios may be listed as 1.0 and/or corresponding confidence bounds may include 1.0.
Other neurological disorders include Huntington’s disease, hereditary ataxia, spinal muscular atrophy and related syndromes, systemic atrophies primarily affecting central nervous system, Parkinson’s disease, secondary parkinsonism, drug-induced and other chorea, other degenerative diseases of nervous system not elsewhere classified, other degenerative diseases of nervous system in diseases classified elsewhere, multiple sclerosis, other acute disseminated demyelination, other demyelinating diseases of central nervous system, epilepsy and recurrent seizures, other disorders of brain, dysphasia, aphasia, and convulsions not elsewhere classified.
Significant interaction of smoking status and AUD (p<0.05), reference group = never smokers without AUD. Interactions assessed using a separate logistic regression model with covariates for smoking status (current/former/never), AUD (yes/no), and the smoking status x AUD interaction.
Appendix 4:
Adjusted odds ratios for Elixhauser comorbidity prevalence among New Castle County adult inpatients by smoking history and alcohol use disorder (AUD)
| Elixhauser Comorbiditiesa,b,c | Current smokers with AUD; AOR, (95% CI) | Current smokers without AUD; AOR, (95% CI) | Former smokers with AUD; AOR (95% CI) | Former smokers without AUD; AOR (95% CI) | Never smokers with AUD; AOR (95% CI) | Never smokers without AUD; reference group |
|---|---|---|---|---|---|---|
| AIDS/HIV | 3.2 (2.1, 4.7) | 2.2 (1.5, 3.3) | 3.0 (1.8, 4.9) | 1.4 (0.9, 2.1) | 2.2 (1.2, 3.8) | -- |
| Deficiency Anemia | 1.2 (1.0, 1.4) | 0.7 (0.6, 0.8) | 1.8 (1.6, 2.1) | 1.0 (0.9, 1.1) | 1.8 (1.5, 2.1) | -- |
| Rheumatoid Arthritis Collagen Vascular Diseases | 1.0 (0.8, 1.3) | 1.0 (0.8, 1.2) | 1.3 (1.1, 1.6) | 1.0 (0.9, 1.2) | 1.3 (1.0, 1.6) | -- |
| Blood Loss Anemia | 1.1 (0.9, 1.4) | 0.6 (0.5, 0.8) | 1.8 (1.5, 2.2) | 1.0 (0.9, 1.2) | 1.3 (1.0, 1.7) | -- |
| Cardiac Arrythmias | 1.2 (1.0, 1.4)† | 1.3 (1.2, 1.5) | 1.6 (1.4, 1.9) | 1.4 (1.3, 1.5) | 1.2 (1.1, 1.5) | -- |
| Congestive Heart Failure | 1.2 (1.0, 1.4)† | 1.1 (1.0, 1.3) | 1.5 (1.3, 1.7) | 1.2 (1.1, 1.3) | 1.4 (1.2, 1.6) | -- |
| Chronic Pulmonary Disease | 3.1 (2.7, 3.5) | 2.3 (2.1, 2.5) | 2.9 (2.5, 3.3) | 1.9 (1.8, 2.1) | 1.6 (1.4, 1.8) | -- |
| Coagulopathy | 2.2 (1.9, 2.5)† | 0.8 (0.7, 0.9) | 1.9 (1.7, 2.2) | 1.0 (0.9, 1.1) | 2.1 (1.8, 2.5) | -- |
| Depression | 4.1 (3.6, 4.7) | 1.6 (1.4, 1.8) | 2.8 (2.5, 3.3) | 1.2 (1.2, 1.3) | 2.8 (2.5, 3.3) | -- |
| Diabetes (uncomplicated) | 0.7 (0.6, 0.8)† | 0.9 (0.8, 1.0) | 1.0 (0.9, 1.1) | 1.1 (1.0, 1.1) | 1.0 (0.8, 1.1) | -- |
| Diabetes (complicated) | 0.7 (0.6, 0.9)† | 1.0 (0.9, 1.1) | 0.9 (0.8, 1.0) | 1.1 (1.0, 1.2) | 1.0 (0.9, 1.2) | -- |
| Drug Use Disorder | 17.2 (14.8, 19.9)† | 5.2 (4.6, 6.0) | 8.2 (6.9, 9.7)† | 2.1 (1.8, 2.4) | 5.4 (4.5, 6.5) | -- |
| Hypertension (uncomplicated) | 1.4 (1.2, 1.6) | 1.0 (0.9, 1.1) | 1.7 (1.4, 2.0) | 1.2 (1.1, 1.3) | 1.4 (1.2, 1.7) | -- |
| Hypertension (complicated) | 0.9 (0.8, 1.1) | 0.9 (0.8, 1.0) | 1.3 (1.1, 1.4) | 1.1 (1.0, 1.2) | 1.2 (1.0, 1.4) | -- |
| Hypothyroidism | 1.0 (0.8, 1.1) | 0.7 (0.7, 0.8) | 1.1 (1.0, 1.3) | 1.1 (1.0, 1.2) | 1.1 (1.0, 1.4) | -- |
| Liver Disease | 4.4 (3.8, 5.0) | 1.1 (0.9, 1.2) | 3.5 (3.0, 4.0) | 1.1 (1.0, 1.2) | 3.9 (3.4, 4.5) | -- |
| Lymphoma | 0.6 (0.4, 1.0) | 0.5 (0.3, 0.8) | 0.9 (0.6, 1.3) | 1.0 (0.8, 1.2) | 1.0 (0.6, 1.5) | -- |
| Fluid and Electrolyte Disorders | 2.5 (2.2, 2.8) | 1.0 (1.0, 1.2) | 2.4 (2.1, 2.8) | 1.0 (0.9, 1.1) | 2.2 (1.9, 2.6) | -- |
| Cancer (metastatic) | 1.0 (0.7, 1.3) | 1.0 (0.8, 1.2) | 1.7 (1.3, 2.1) | 1.3 (1.2, 1.5) | 1.2 (0.9, 1.6) | -- |
| Other Neurological Disorderd | 2.0 (1.8, 2.3) | 1.1 (1.0, 1.2) | 2.0 (1.7, 2.3) | 0.9 (0.8, 1.0) | 2.1 (1.8, 2.5) | -- |
| Obesity | 0.6 (0.5, 0.7)† | 0.7 (0.6, 0.8) | 1.1 (1.0, 1.3) | 1.1 (1.0, 1.2) | 1.1 (1.0, 1.3) | -- |
| Paralysis | 0.9 (0.7, 1.1) | 0.9 (0.7, 1.1) | 1.1 (0.9, 1.3) | 0.8 (0.7, 1.0) | 1.1 (0.8, 1.3) | -- |
| Peripheral Vascular Disorders | 1.6 (1.4, 1.9) | 1.5 (1.3, 1.7) | 2.1 (1.8, 2.4) | 1.5 (1.4, 1.6) | 1.2 (1.0, 1.5) | -- |
| Psychoses | 3.7 (3.1, 4.4) | 1.6 (1.4, 2.0) | 2.1 (1.6, 2.6) | 0.9 (0.8, 1.1) | 2.8 (2.2, 3.4) | -- |
| Pulmonary Circulation Disorders | 1.0 (0.8, 1.2) | 0.8 (0.7, 0.9) | 1.5 (1.3, 1.8) | 1.2 (1.1, 1.3) | 1.3 (1.1, 1.6) | -- |
| Renal Failure | 0.7 (0.6, 0.8) | 0.7 (0.6, 0.8) | 1.0 (0.9, 1.2) | 1.0 (1.0, 1.1) | 1.1 (0.9, 1.3) | -- |
| Solid Tumor (no metastasis) | 1.0 (0.8, 1.2) | 0.9 (0.8, 1.1) | 1.7 (1.4, 1.9)† | 1.2 (1.1, 1.3) | 1.0 (0.9, 1.3) | -- |
| Peptic Ulcer Disease | 1.8 (1.5, 2.3) | 1.1 (0.9, 1.4) | 2.8 (2.3, 3.4) | 1.1 (0.9, 1.3) | 2.2 (1.7, 2.7) | -- |
| Valvular Disease | 0.9 (0.7, 1.0) | 0.8 (0.7, 0.9) | 1.3 (1.1, 1.5) | 1.1 (1.0, 1.2) | 1.3 (1.1, 1.6) | -- |
| Weight Loss | 2.7 (2.3, 3.1) | 1.4 (1.2, 1.6) | 2.4 (2.1, 2.8) | 1.1 (1.0, 1.3) | 2.0 (1.7, 2.3) | -- |
Logistic regression model includes covariate for patient group with six categories (current/former/never smoking status by AUD/no AUD), adjusted for age, sex, and payer.
Red and green shading indicate that adjusted comorbidity odds are significantly higher or lower, respectively, among patient group relative to never smokers without AUD (p<0.05).
Due to rounding and the precision of the results presented some significant odds ratios may be listed as 1.0 and/or corresponding confidence bounds may include 1.0.
dOther neurological disorders include Huntington’s disease, hereditary ataxia, spinal muscular atrophy and related syndromes, systemic atrophies primarily affecting central nervous system, Parkinson’s disease, secondary parkinsonism, drug-induced and other chorea, other degenerative diseases of nervous system not elsewhere classified, other degenerative diseases of nervous system in diseases classified elsewhere, multiple sclerosis, other acute disseminated demyelination, other demyelinating diseases of central nervous system, epilepsy and recurrent seizures, other disorders of brain, dysphasia, aphasia, and convulsions not elsewhere classified.
Significant interaction of smoking status and AUD (p<0.05), reference group = never smokers without AUD. Interactions assessed using a separate logistic regression model with covariates for smoking status (current/former/never), AUD (yes/no), and the smoking status x AUD interaction, adjusted for age, sex, and payer.
Footnotes
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Declaration of Interest: Dr. Siegel declares that he has no conflicts of interest. Ms. Brooks declares that she has no conflicts of interest. Dr. Ragozine-Bush declares that she has no conflicts of interest. Dr. Schnoll declares that he has no conflicts of interest. Dr. Curriero declares that he has no conflicts of interest.
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