Abstract
To guide targeted cessation and prevention programming, this study assessed smoking prevalence and described sociodemographic, health, and healthcare use characteristics of adult smokers in public housing. Self-reported data were analyzed from a random sample of 1664 residents aged 35 and older in ten New York City public housing developments in East/Central Harlem. Smoking prevalence was 20.8%. Weighted log-binomial models identified to be having Medicaid, not having a personal doctor, and using health clinics for routine care were positively associated with smoking. Smokers without a personal doctor were less likely to receive provider quit advice. While most smokers in these public housing developments had health insurance, a personal doctor, and received provider cessation advice in the last year (72.4%), persistently high smoking rates suggest that such cessation advice may be insufficient. Efforts to eliminate differences in tobacco use should consider place-based smoking cessation interventions that extend cessation support beyond clinical settings.
Keywords: Smoking, Public housing developments, Chronic diseases, New York City, Urban health services, Primary healthcare
Introduction
Despite substantial declines in smoking prevalence in recent decades [1], cigarette smoking remains the leading preventable cause of disease and death in the USA [2, 3]. Smoking prevalence also remains particularly high among lower-income adults and underserved racial/ethnic groups, likely reflecting the interacting influences of stress [4], discrimination [5], and targeted advertising [6, 7] as well as longstanding economic, physical, and structural barriers to cessation and prevention [8]. As such, current tobacco control approaches are not adequately meeting the health needs of this population [9].
Along with high smoking rates [10–13], public housing developments are increasingly appropriate settings for tobacco policy because of their concentrations of low-income residents, often belonging to underserved racial/ethnic groups, and of individuals, namely elderly residents and adults with chronic conditions, who are especially vulnerable to risks associated with smoking [14, 15]. In addition to the direct risks of smoking, many public housing residents reside in multi-unit buildings, placing them at elevated risk for second-hand smoke (SHS) exposure from neighboring apartments [16, 17]. This confers a greater chronic disease risk among public housing residents [18], further contributing to health inequities.
A recent ruling by the Department of Housing and Urban Development (HUD) requires all public housing authorities to ban indoor smoking by July 2018 [19, 20]. One fifth of the nation’s two million public housing residents reside in New York City (NYC), operated by the New York City Housing Authority (NYCHA) [21]. With consistent evidence that smoking rates are elevated among residents [22, 23], NYCHA faces implementation challenges associated with supporting and enforcing a shift to a smoke-free environment. To ensure the efficacy of implementation efforts and to design a more resident-focused, equitable approach, smoke-free policy efforts must be complemented by multi-faceted, community-specific smoking prevention and cessation support resources [20, 24, 25], informed by local-level data collection and analysis [26, 27].
Thus, we sought to fill a critical local data gap by characterizing pre-ban correlates of smoking, current smokers’ healthcare characteristics, and receipt of clinician-delivered cessation advice to facilitate the effective delivery of smoking cessation interventions. In this study, we analyzed population-based data from urban public housing residents in East/Central Harlem to describe the sociodemographic, health, and healthcare use characteristics of adult smokers.
Methods
Data Source
For this study, we used data from a community needs assessment survey conducted between December 2014 and February 2015 to inform a community health worker initiative, involving 1664 adult residents aged 35 and older residing in ten public housing developments in East/Central Harlem [28]. Identified as official residents by the NYC Housing Authority Tenant Data Files, 7500 adults aged 35 and older were selected at random from the ten developments. Study staff conducted 1600 interviews via telephone in either English or Spanish, while an additional 64 interviews were completed face-to-face. The response rate was 40.5%, and the cooperation rate was 75.7%, using the standard definitions established by the American Association for Public Opinion Research [29]. In all analyses, we statistically weighted survey data by race/ethnicity, gender, and household role to be demographically representative of the selected public housing developments.
Measures
The primary outcome, current smoking, was dichotomized into an indicator for smoking every day or some days using the following question: “Do you now smoke cigarettes every day, some days, or not at all?” Provider advice to quit was assessed with the following question: “During the past 12 months has a doctor, nurse, or other health professional advised you to quit smoking?”
Tobacco smoking correlates examined were selected from previous literature [2]. Sociodemographic characteristics included self-reported age, sex, race/ethnicity, nativity, education, marital status, and employment. Health status variables included weight (underweight, normal, overweight, and obese), and self-reported physician diagnosis of depression and chronic conditions (hypertension, asthma, and diabetes).
For healthcare utilization, participants indicated whether or not (yes/no) they had health insurance, a personal doctor, and a time in the past 12 months when they did not receive needed medical care. Other healthcare use variables included health insurance type (employer-sponsored, Medicaid, Medicare, and other) and sources of regular medical care (private doctor’s office, health clinic, emergency room, and other).
Statistical Analyses
All statistical analyses were conducted using SAS 9.4, adjusting for sampling weights. Descriptive statistics described the distribution of independent variables of the total sample and by smoking status. Chi-squared tests were used to investigate significant differences by smoking status for categorical variables. Log-binomial models were used to examine crude and adjusted associations between smoking status and sociodemographic, health, and healthcare use characteristics. Finally, a series of supplementary log-binomial models were used to examine the adjusted association between the receipt of provider advice to quit among smokers and two healthcare use variables: having a personal doctor and regular source of care. Log-binomial models were used in lieu of logistic regression to avoid exaggerating effect sizes, since the outcome is considered common [30, 31]. All adjusted models included factors previously linked with smoking, specifically age, gender, and race/ethnicity. The significance level for all tests was set at value of p < 0.05.
Results
The majority of residents were female (73.0%), half were Latino (50.3%), 35.7% had less than a high school education, and 43.4% were born outside of the USA. More than half of the residents (53.7%) reported a diagnosis of hypertension, while 27.6% reported diabetes, 28.7% reported current or past asthma, and 25.9% reported having depression (Table 1).
Table 1.
Resident and smoker characteristics, results of chi-square tests, and results of log-binomial models for factors associated with current smokers.
| Total number (n = 1664) | Smokers (n, %) | Non-smokers (n, %) | p value | RR | 95% CI | p value | aRRa | 95% CI | p value | |
|---|---|---|---|---|---|---|---|---|---|---|
| Smoking total | 1657 | 355 (20.8) | 1302 (79.2) | |||||||
| Selected characteristics | ||||||||||
| Demographics | ||||||||||
| Age group | 1664 | 355 | 1302 | <0.001*** | ||||||
| 35–49 | 492 (31.7) | 114 (31.6) | 377 (31.8) | 1.00 | − | − | ||||
| 50–64 | 605 (36.5) | 176 (51.2) | 426 (32.6) | 1.41 | 1.09–1.83 | 0.008** | ||||
| 65+ | 567 (31.8) | 65 (17.2) | 499 (35.6) | 0.55 | 0.38–0.78 | <0.001*** | ||||
| Gender | 1664 | 355 | 1302 | 0.878 | ||||||
| Female | 1289 (73.0) | 271 (73.5) | 1015 (73.0) | 1.02 | 0.83–1.25 | 0.847 | ||||
| Male | 375 (27.0) | 84 (26.5) | 287 (27.0) | 1.00 | − | − | ||||
| Race/ethnicity | 1659 | 353 | 1299 | 0.006** | ||||||
| Non-Latino Black | 755 (40.2) | 197 (50.0) | 553 (37.4) | 1.42 | 1.23–1.65 | <0.001*** | ||||
| Latino | 847 (50.3) | 147 (44.4) | 698 (52.1) | 1.00 | − | − | ||||
| Other | 57 (9.4) | 9 (5.6) | 48 (10.5) | 0.67 | 0.31–1.46 | 0.317 | ||||
| Education | 1630 | 353 | 1271 | 0.835 | ||||||
| < High school | 592 (35.7) | 135 (36.7) | 453 (35.4) | 1.08 | 0.84–1.40 | 0.546 | 1.44 | 1.19–1.73 | <0.001*** | |
| High school graduate | 529 (31.8) | 115 (32.5) | 414 (31.8) | 1.07 | 0.91–1.25 | 0.410 | 1.24 | 1.09–1.40 | <.001*** | |
| Some college/college graduate | 509 (32.5) | 103 (32.8) | 404 (32.8) | 1.00 | − | − | 1.00 | − | − | |
| Nativity | 1664 | 355 | 1302 | <0.001*** | ||||||
| US-born | 983 (56.6) | 282 (77.9) | 697 (51.0) | 1.00 | − | − | 1.00 | − | − | |
| Foreign-born | 681 (43.4) | 73 (22.1) | 605 (49.0) | 0.37 | 0.28–0.49 | <0.001*** | 0.39 | 0.28–0.54 | <.001*** | |
| Marital status | 1628 | 346 | 1275 | <0.001*** | ||||||
| Married/unmarried couple | 362 (26.6) | 58 (17.7) | 303 (28.9) | 1.00 | − | − | 1.00 | − | − | |
| Divorced/separated | 420 (23.9) | 96 (29.1) | 322 (22.5) | 1.83 | 1.34–2.48 | <0.001*** | 1.67 | 1.27–2.21 | <0.001*** | |
| Widowed | 317 (17.9) | 45 (12.9) | 271 (19.2) | 1.08 | 0.66–1.78 | 0.765 | 1.35 | 0.83–2.19 | 0.226 | |
| Never married | 529 (31.7) | 147 (40.3) | 379 (29.4) | 1.91 | 1.29–2.82 | 0.001** | 1.55 | 1.06–2.27 | 0.023* | |
| Employment | 1629 | 351 | 1273 | 0.751 | ||||||
| Employed | 489 (31.9) | 113 (33.5) | 375 (31.4) | 1.10 | 0.81–1.49 | 0.538 | 0.86 | 0.66–1.11 | 0.236 | |
| Retired/unable to work | 818 (48.1) | 163 (46.1) | 652 (48.7) | 1.00 | − | − | 1.00 | − | − | |
| Other | 322 (20.0) | 75 (20.4) | 246 (19.9) | 1.07 | 0.78–1.46 | 0.694 | 0.92 | 0.66–1.29 | 0.638 | |
| Health status | ||||||||||
| Body mass index | 1579 | 338 | 1235 | 0.007** | ||||||
| Underweight | 36 (1.9) | 11 (3.2) | 25 (1.5) | 1.28 | 0.70–2.33 | 0.420 | 1.01 | 0.62–1.65 | 0.960 | |
| Healthy weight | 283 (18.2) | 79 (24.3) | 203 (16.5) | 1.00 | − | − | 1.00 | − | − | |
| Overweight | 555 (36.3) | 106 (32.6) | 445 (37.1) | 0.67 | 0.42–1.08 | 0.103 | 0.62 | 0.42–0.93 | 0.020* | |
| Obese | 705 (43.7) | 142 (39.9) | 562 (44.9) | 0.68 | 0.55–0.84 | <0.001*** | 0.55 | 0.47–0.66 | <0.001*** | |
| Depression | 1650 | 354 | 1289 | <0.001*** | ||||||
| Yes | 439 (25.9) | 123 (34.7) | 314 (23.5) | 1.53 | 1.31–1.78 | <0.001*** | 1.44 | 1.22–1.69 | <0.001*** | |
| No | 1211 (74.1) | 231 (65.3) | 975 (76.5) | 1.00 | − | − | 1.00 | − | − | |
| Diabetes | 1617 | 348 | 1263 | 0.080 | ||||||
| Yes | 482 (27.6) | 91 (23.3) | 389 (28.7) | 0.80 | 0.69–0.92 | 0.002** | 0.81 | 0.70–0.95 | 0.008** | |
| No | 1135 (72.4) | 257 (76.7) | 874 (71.3) | 1.00 | − | − | 1.00 | − | − | |
| Asthma | 355 | 1296 | 0.110 | |||||||
| Yes | 205 (11.9) | 51 (13.9) | 153 (11.3) | 1.26 | 0.96–1.66 | 0.096 | 1.22 | 0.90–1.66 | 0.197 | |
| Yes, no attacks in past year | 274 (16.8) | 73 (20.0) | 200 (15.8) | 1.29 | 1.04–1.61 | 0.023* | 1.27 | 1.03–1.57 | 0.024* | |
| No asthma | 1178 (71.4) | 231 (66.2) | 943 (72.8) | 1.00 | − | − | 1.00 | − | − | |
| Hypertension | 1652 | 353 | 1294 | 0.980 | ||||||
| Yes | 935 (53.7) | 199 (53.6) | 731 (53.5) | 1.00 | 0.89–1.13 | 0.962 | 1.03 | 0.90–1.19 | 0.663 | |
| No | 717 (46.3) | 154 (46.4) | 563 (46.5) | 1.00 | − | − | 1.00 | − | − | |
| Healthcare | ||||||||||
| Have health insurance | 1650 | 353 | 1290 | 0.966 | ||||||
| Yes | 1567 (94.1) | 336 (94.3) | 1226 (94.3) | 1.01 | 0.50–2.06 | 0.973 | 1.13 | 0.54–2.35 | 0.747 | |
| No | 83 (5.9) | 17 (5.7) | 64 (5.7) | 1.00 | − | − | 1.00 | − | − | |
| Health insurance type | 1529 | 329 | 1195 | 0.108 | ||||||
| Employer-sponsored | 285 (19.0) | 64 (17.5) | 221 (19.5) | 1.00 | − | − | 1.00 | − | − | |
| Medicaid | 740 (48.7) | 186 (55.6) | 552 (46.9) | 1.24 | 1.01–1.52 | 0.037* | 1.28 | 1.06–1.54 | 0.012* | |
| Medicare | 382 (24.6) | 56 (19.7) | 324 (25.8) | 0.88 | 0.61–1.26 | 0.471 | 1.10 | 0.84–1.44 | 0.482 | |
| Other | 122 (7.7) | 23 (7.2) | 98 (7.8) | 1.02 | 0.69–1.49 | 0.937 | 1.13 | 0.82–1.56 | 0.457 | |
| Sources of medical care | 1630 | 346 | 1277 | 0.009** | ||||||
| Doctor’s office | 547 (32.6) | 106 (25.1) | 438 (34.6) | 1.00 | − | − | 1.00 | − | − | |
| Health clinic | 694 (42.6) | 169 (51.9) | 522 (40.1) | 1.58 | 1.16–2.17 | 0.004** | 1.54 | 1.18–2.02 | 0.002** | |
| Emergency room | 292 (18.3) | 54 (17.3) | 238 (18.7) | 1.22 | 0.80–1.86 | 0.349 | 1.27 | 0.85–1.90 | 0.240 | |
| Other | 97 (6.5) | 17 (5.7) | 79 (6.6) | 1.16 | 0.74–1.80 | 0.520 | 1.32 | 0.84–2.08 | 0.229 | |
| Have personal doctor | 1618 | 350 | 1262 | 0.138 | ||||||
| Yes | 1379 (83.8) | 297 (80.3) | 1076 (84.6) | 1.00 | − | − | 1.00 | − | − | |
| No | 239 (16.2) | 53 (19.7) | 186 (15.4) | 1.26 | 0.96–1.66 | 0.098 | 1.41 | 1.08–1.84 | 0.012* | |
| Did not receive medical care | 1637 | 351 | 1279 | 0.658 | ||||||
| Yes | 211 (12.9) | 45 (11.9) | 163 (13.0) | 0.91 | 0.67–1.25 | 0.564 | 0.92 | 0.69–1.21 | 0.547 | |
| No | 1426 (87.1) | 306 (88.1) | 1116 (87.0) | 1.00 | − | − | 1.00 | − | − | |
CI confidence interval, RR relative risk
*P < 0.05; **P < 0.01; ***P < 0.001.
aRelative risks adjusted for age, gender, and race/ethnicity
The prevalence of current smoking among adults aged 35 and older was 20.8%. Smokers were more likely to be between the ages of 50 and 64 (p < 0.001), non-Latino black (p = 0.006), US-born (p < 0.001), never married (p < 0.001), a healthy weight (p = 0.007), and diagnosed with depression (p < 0.001). The majority of smokers had health insurance (94.3%), a personal doctor (80.3%), received medical care as needed (88.1%), and used the health clinic for regular care (51.9%).
After adjusting for age, gender, and race/ethnicity, current smoking was positively associated with being divorced/separated (aRR = 1.67; 95% CI = 1.27–2.21) and never married (aRR = 1.55; 95% CI = 1.06–2.27) versus being married/unmarried couple, having less than a high school education (aRR = 1.44; 95% CI = 1.19–1.73) or high school graduate (aRR = 1.24; 95% CI = 1.09–1.40) versus being a college graduate, having depression (aRR = 1.44; 95% CI = 1.22–1.69), and having past asthma (aRR = 1.27; 95% CI = 1.03–1.57). Smoking was inversely associated with being foreign-born (aRR = 0.39, 95% CI = 0.28–0.54) versus US born, overweight (aRR = 0.62; 95% CI = 0.42–0.93) and obese (aRR = 0.55; 95% CI = 0.47–0.66) versus normal weight, and having diabetes (aRR = 0.81; 95% CI = 0.70–0.95).
Having Medicaid (aRR = 1.28; 95% CI = 1.06–1.54) versus employer-sponsored health insurance, using a health clinic (aRR = 1.54; 95% CI = 1.18–2.02) versus a private doctor’s office, and not having a personal doctor (aRR = 1.41; 95% CI = 1.08–1.84) were also associated with current smoking. Last, supplementary analyses showed that 84.0% of current smokers reported having received provider smoking cessation advice over the past year (Table 2). Receiving smoking cessation advice was significantly less likely among smokers without a personal doctor (aRR = 0.68; 95% CI = 0.51–0.91).
Table 2.
Results of log-binomial models for factors associated with current smokers who reported provider advice to quit.
| Smokers reporting provider advice to quit (n, %) (n = 355) | aRRa | 95 CI% | p value | |
|---|---|---|---|---|
| Total | 305 (84.0) | |||
| Sources of medical care | 298 | |||
| Doctor’s office | 93 (26.5) | 1.00 | – | – |
| Health clinic | 152 (54.1) | 1.01 | 0.94–1.09 | 0.770 |
| Emergency room | 40 (14.2) | 0.80 | 0.60–1.08 | 0.144 |
| Other | 13 (5.1) | 0.90 | 0.69–1.17 | 0.439 |
| Have personal doctor | 300 | |||
| Yes | 266 (85.4) | 1.00 | – | – |
| No | 34 (14.6) | 0.68 | 0.51–0.91 | 0.010* |
CI confidence interval, RR relative risk
*P < 0.05; **P < 0.01; ***P < 0.001.
aRelative risks adjusted for age, gender, and race/ethnicity
Discussion
Among Central/East Harlem public housing residents, 20.8% were current smokers, which is notably higher than the citywide average for adults (13.9%) [32]. Consistent with prior literature, current smoking was associated with reporting being non-Latino black [1], being never married [33], having lower levels of education [34–36], and having depression [37, 38]. Foreign-born adults [39, 40], adults with higher BMIs [41], and adults with diabetes [38] were less likely to be current smokers.
The significant associations between healthcare characteristics and smoking in our study were similar to those reported by other researchers. Similar to Jorm et al., who found that current smokers were less likely to use primary care services overall [42], we found that not having a personal physician was associated with current smoking. Current smokers without a regular doctor were also less likely to report receiving provider advice to quit in the past year, mirroring a previous study finding that current smokers with a regular physician for healthcare were more likely to receive provider advice to quit and to plan to quit within the next 30 days than individuals without a regular source of care [43]. Smoking was more likely among Medicaid enrollees than among persons covered by employer-sponsored health insurance, similar to national findings [1].
In this study, the majority of public housing residents who smoke (72.4%) had health insurance, a personal doctor, and received provider advice to quit in the last year, which indicates that doctors generally identify smoking behavior in this population. However, persistently high smoking rates suggest that counseling may not translate into treatment and/or address persistent structural, social, and economic triggers to smoke. Previous studies have found that low-income smokers are less likely to use evidence-based cessation aids during a quit attempt compared to high-income smokers, despite their effectiveness [44–46]. In addition, providers may be less likely to engage vulnerable populations in tobacco use treatment [47]. Disparities in delivery of tobacco cessation treatment should be investigated further in the context of low-income and public housing communities [48].
Though these findings indicate that regular primary care providers may increase the likelihood of smoking cessation, increasing access to treatment may also require additional place-based approaches that consider the community context. One such approach would be to link patients to community health workers (CHWs), who have been shown to improve patient and clinical coordination [49], ensure linkages to social services [50], and build neighborhood capacity as it relates to social cohesion [51]. The CHW model can also fill gaps in services by facilitating behavior change and extending cessation support beyond clinical settings [52, 53]. Engaging community members in an intervention that embeds CHWs into public housing has been effective in increasing cessation rates [53, 54].
Limitations
This study has several limitations. First, observed associations should be interpreted with caution given that this study used cross-sectional data and lacks the temporal order necessary to make causal inferences. Second, self-reported diagnoses of chronic conditions could lead to misclassification [55], though agreement between self-reported chronic conditions and medical records has been found to be high for diabetes [56–58] and hypertension [56, 58], and intermediate for asthma [59] and depression [60]. Further, in this population with high prevalence of chronic diseases, residents may be hesitant to admit their smoking status [61]. Finally, generalizability of this study may be impacted by a 40.5% response rate and is limited to public housing developments with similar demographic profiles. However, most non-response was due to inability to contact someone during the rapid data collection period rather than refusal (13.0%), and the participating sample was representative of the target population in its demographic profile.
Conclusion
Awareness of local smoking patterns can help identify potentially effective targeted strategies for supporting residents to quit smoking and for preventing smoking initiation. Findings from this study suggest that smoking cessation interventions can take advantage of the high insurance coverage and healthcare-seeking practices, but efforts should be made to increase linkages to primary care and use of evidence-based treatments. Community-based cessation efforts implemented in other settings should incorporate the social context and target treatments to specific groups to achieve more successful cessation efforts. Multi-level, targeted cessation interventions will be fundamental to the effectiveness of the forthcoming smoking ban, and results from this study can inform smoking cessation efforts to support the policy [25, 27].
Acknowledgments
Research supported by Harlem Health Advocacy Partners, a project funded and administered by the New York City Department of Health and Mental Hygiene, Grant 15DP023501R0X00. The efforts are supported in part by the Centers for Disease Control and Prevention (CDC) Grant U48DP001904.
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