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Abstract

Background

This study examines the geographic representativeness of people who smoke, among newly registered users of a free digital tobacco cessation program (‘EX’).

Methods

User-provided ZIP codes from EX® Program registrants were mapped to Rural Urban Continuum Codes (RUCC). Reach Ratios (ReRas) and 95% confidence intervals were used to determine the extent to which geographic representation in EX was proportionate to their representation in the national population of individuals who smoke, as obtained from the United States National Survey of Drug Use and Health (2018-2020). Under- and overrepresentation was demonstrated by ReRas <1 or >1, respectively. Joinpoint regression analyses were used to determine significant changes in trend for ReRas from 2013 to 2020.

Results

Individuals who reported smoking residing in nonmetro rural areas were significantly overrepresented in 2018 [ReRa = 1.18 (95% CI: 1.15, 1.23)], 2019 [ReRa = 1.10 (95% CI: 1.07, 1.13)], and 2020 [ReRa = 1.10 (95% CI: 1.06, 1.13)]. Individuals who reported smoking from small metro areas were also overrepresented from 2018 to 2020 [ReRas = 1.09 (95% CI: 1.06, 1.11); 1.06 (95% CI: 1.04, 1.08), and 1.05 (95% CI: 1.03, 1.08), respectively] and individuals who reported smoking from large metro areas were underrepresented during the same time frame [ReRas = 0.87 (95% CI: 0.85, 0.89); 0.92 (95% CI: 0.91, 0.94), and 0.93 (95% CI: 0.91, 0.94), respectively]. ReRas for large metro areas decreased from 2013 to 2018 (annual percentage change, APC = −3.26; 95% CI: −6.69, −1.91). ReRas for non-metro areas increased from 2013 to 2017 (APC = 7.35; 95% CI: 4.10, 17.63).

Conclusions

Results suggest that individuals that smoke residing in nonmetro rural areas and small metro areas are proportionally reached by a digital tobacco cessation program. However, there continues to be an underrepresentation of individuals who smoke from large metro areas, which warrants further study.

Keywords

rural health smoking cessation digital health

Introduction

In the United States (US), there is a rural-urban gap in age-adjusted death rates,¹ driven in part by persistent geographic disparities in cigarette smoking.^2-6 States with the highest smoking prevalence are concentrated in “Tobacco Nation” - a set of Midwestern and Southern states, spanning Alabama, Arkansas, Indiana, Kentucky, Louisiana, Michigan, Mississippi, Missouri, Ohio, Oklahoma, Tennessee, and West Virginia,⁷ that also have sizeable rural populations.⁸ Consistent with previous years,⁹ smoking rates in rural areas remained higher than urban areas in 2021 (18.0% vs 10.5%, respectively).⁴ Rural residents are more likely than those in urban areas to start smoking at an earlier age,¹⁰ transition to regular smoking,¹⁰ and smoke more heavily.¹¹ Rural residents are also less likely to quit smoking.^9,12 Given these behaviors, reducing smoking rates among the estimated 60 million people who live in rural communities¹³ is a public health priority.^14-17

Numerous factors contribute to rural-urban tobacco use disparities. Tobacco use is deeply rooted in the social environment in many rural communities.^16,17 Social attitudes and personal beliefs that are favorable towards tobacco products,^3,18,19 limited tobacco-free policies,^19,20 and structural barriers to accessing cessation services,⁹ such as a lack of available healthcare workers,^3,9,20-22 and a greater distance to travel for services²² are factors that contribute to higher smoking rates in rural communities. Other factors that may contribute to smoking-related disparities include underutilization of the United States Food and Drug Administration (FDA)-approved cessation aids¹² and telephone quit lines among individuals residing in rural areas relative to individuals residing in urban areas.²¹

Digital tobacco cessation interventions can help to facilitate access to and uptake of cessation services in rural communities. Internet usage is nearly universal across community types (93% among rural adults, 95% among urban adults, 97% among suburban adults), and 80% of adults reported home broadband use in 2023.²³ Most Americans (97%) own a cellphone of some type, with equivalent rates of adoption across community type.²⁴ Interactive and tailored Internet and text message interventions are recommended modalities through which evidence-based tobacco cessation treatment can be delivered on a population-wide basis,²⁵ tailored to the needs of geographic subgroups such as rural residents. Prior research suggests that smokers from rural areas may be more than proportionally reached by digital cessation interventions.^26,27

This study builds on a reach equity analysis of a digital tobacco cessation program (ie, the EX® Program, referred to as “EX”) from 2013 to 2017.²⁷ Changes in population distribution²⁸ and federal initiatives to expand access to high-speed Internet in rural communities²⁹ make it important to monitor the reach of digital modalities to people who smoke. Specifically, the aim of this study is to examine the geographic representativeness of EX registrants from 2018 to 2020.

Methods

Data Sources

Secondary data analyses were conducted using published tables from the National Survey of Drug Use and Health (NSDUH) and data extracted from the EX database. As a result, no sample size calculations were conducted. Included in these analyses are NSDUH participants who provided data between 2018 and 2020 and EX participants who registered between January 2018 to December 2020. EX participant data from 2013 to 2017 were derived from previous published work conducted by Amato and Graham (2018).²⁷

National Survey of Drug Use and Health (NSDUH)

Data were extracted from the 2018-2020 National Survey of Drug Use and Health (NSDUH), a nationally representative, repeated cross-sectional survey of non-institutionalized US civilians, aged 12 years or older. A multi-stage, stratified sampling method is used to recruit NSDUH participants every quarter to assess information on drug use and mental health. In response to the COVID-19 pandemic, NSDUH introduced a multimodal data collection methodology allowing for both in-person and web-based surveys. Given that the mode of data collection has been found to influence estimates, NSDUH advises that estimates collected after 2020 not be compared to prior years. Further details about NSDUH sampling methodology and survey instrumentation are available online.³⁰

EX Program

Data were gathered from registrants of EX® Program (“EX”),³¹ a free multimodal digital tobacco cessation program developed by Truth Initiative, in collaboration with Mayo Clinic Nicotine Dependence Center. EX supports users through multiple, integrated modalities which include a website with interactive tools and tailored content, the longest running online community for tobacco cessation, dynamically tailored text messaging that is fully integrated with the website, and email. EX was launched in 2008 and is available nationally to tobacco users ages 13 and older. It is promoted through a modest search engine marketing spend to reach individuals across the US who search for help quitting tobacco. Website content is search engine optimized and user-generated content from the online community is highly ranked.

To register for EX, individuals are asked to agree to the Terms of Service and Privacy Policy (see: https://www.exprogram.com/terms-of-use/), which grants permission for their data to be used in observational research to advance the science of nicotine dependence treatment, as well as provide an email address and information on age, gender, and zone improvement plan (ZIP) code. No separate informed consent is solicited from registered users, and registered users are not compensated for research participation. Data were extracted for individuals who registered and accepted the Terms of Service between January 2018 and December 2020. Data were de-identified prior to the conduct of any statistical analysis and reported in aggregate. There was no institutional review board (IRB) oversight for this study per 45 CRF 46.104(d) (4ii) exemption.³²

Geographic Classification of Registered Users from EX

ZIP codes obtained from user registration data were used to classify the geographic location of new registered users on EX. ZIP codes were converted into federal information processing system (FIPS) county codes, using information from the United States (US) Department of Housing and Urban Development (HUD) Office of Policy Development and Research’s HUD – United States Postal Service (USPS) Crosswalk files.³³ FIPS county codes were then mapped to the United States Department of Agriculture Rural-Urban Continuum Codes (RUCC). The RUCC is a 9-point classification system for distinguishing metropolitan (metro) counties based on the population size of the metro area,³⁴ and nonmetropolitan (nonmetro) counties by their degree of urbanization and adjacency to a metro area, originally developed in 1974 and updated every decade.³⁴ Data were processed using the 2013 RUCC to allow for comparability between EX registrant data and detailed tables that were published by the US National Survey of Drug Use and Health (NSDUH). Although RUCC uses a 9-point classification system, geographic area types were combined into the following categories to ensure that samples in all groups were of sufficient size to support meaningful inferences based on confidence intervals and to be comparable with prior work, conducted by Amato and Graham (2018)²⁷: large metro (RUCC 1), small metro (RUCC 2 or 3), and nonmetro (rural, RUCC 4-9) areas. Large metro areas had populations of 1 million or more people, while small metro areas had populations of fewer than 1 million people, and nonmetro areas included counties outside of defined metropolitan areas.²⁷ Similar categorizations for geographic area types have been reported in other studies.^35,36

Reach Ratio Calculations

Reach ratios (ReRas) for each geographic category were calculated within each year, following the same methodology as Amato & Graham (2018).²⁷ ReRas were calculated using the proportion of registered EX users as the numerator and the proportion of US smokers (aged 12 years or older, defined according to 30-day point prevalence) obtained from published NSDUH tables (2.24 and 2.25 A for 2017 and 2018³⁷; 2.35 and 2.35 A for 2019 and 2020³⁸) by geographic category as the denominator. Then, 95% confidence intervals were calculated using the Wald interval method.^39,40 ReRas and 95% confidence intervals that included or crossed 1.0 signaled that the proportion of EX registrants in a geographic category was similar to that found in NSDUH (representing the general smoking population). ReRas and 95% confidence intervals that were less than 1.0 indicated that the proportion of registered EX users in a geographic region was less than that found in NSDUH (“underrepresented”), and when greater than 1.0, the proportion of registered EX users in a geographic region was greater than that found in NSDUH (“overrepresented”).

Joinpoint Regression

To analyze trends in the reach ratios (ReRas) from 2013 to 2020, joinpoint regression analyses were conducted (Supplemental Figure 1 and Supplemental Table 1). Joinpoint analyses are useful in identifying inflection points which are indicative of statistically significant changes in trend for each year (ie, to determine if the change in trend was statistically significant from zero, or “no trend”). Analyses were conducted using Joinpoint Regression Software (Surveillance Research Program, National Cancer Institute; Rockville, MD).⁴¹

Results

Characteristics of EX Registrants

Between 2018 and 2020, a total of 86 822 new users registered on EX. Among those users, 84 212 (97.0%) provided valid ZIP codes and were assigned RUCC values. Characteristics of registered EX users were consistent across 2018 to 2020. As shown in Table 1, roughly two thirds were female (range: 65.7% to 68.0%). In terms of age, more than one third were between the ages of 25-30 years (range: 34.1% to 34.4%) and nearly a third were 31-44 years (range: 27.7% to 33.5%).

Table 1.

Sample Characteristics of Registered EX® Program Users, by Year (2018-2020)

Sample characteristic, n (column %)	2018	2019	2020
Total registered EX users	17 573 (100.0)	39 937 (100.0)	26 702 (100.0)
Gender
Male	6047 (31.9)	13 371 (32.7)	8914 (33.0)
Female	12 907 (68.0)	27 345 (66.9)	17 733 (65.7)
Another identity	14 (0.1)	144 (0.4)	336 (1.3)
Age
Under 18 years	1841 (9.7)	5598 (13.7)	2779 (10.3)
18-24 years	3183 (16.8)	7596 (18.6)	5783 (21.4)
25-30 years	6528 (34.4)	13 928 (34.1)	9291 (34.4)
31-44 years	6358 (33.5)	11 337 (27.7)	7605 (28.2)
45-64 years	875 (4.6)	1313 (3.2)	946 (3.5)
Over 65 years	183 (1.0)	1099 (2.7)	579 (2.2)
Geographic classification (RUCC)
Large metro (1)	7366 (41.9)	17 494 (43.8)	12 009 (45.0)
Small metro (2,3)	6423 (36.6)	14 189 (35.5)	9342 (35.0)
Nonmetro (4-9)	3784 (21.5)	8254 (20.7)	5351 (20.0)

Note. Nonmetro values reflect the sum of urbanized, less urbanized, and completely rural values. RUCC = Rural Urban Continuum Codes.

Geographic Representation of EX

As shown in Table 2, the proportion of individuals who smoke in the US national population from NSDUH and among registered EX users remained relatively stable from 2018 to 2020. To provide additional context, originally published data by Amato & Graham (2018) from 2013 to 2017²⁷ are also included in Table 2. The proportion of individuals who smoke in the US national population that resided in large metro areas ranged between 47.2% and 50.3%, while between 31.0% and 33.7% resided in small metro areas, and 18.2% to 20.2% resided in nonmetro areas from 2013 to 2020, according to published NSDUH tables.

Table 2.

Proportions of Smokers in the United States From the National Survey of Drug Use and Health (NSDUH, Aged 12+ Years) and Registered EX® Program Users, by Geographic Distribution and Year (2013-2020)

Population and geographically classified area (RUCC)	2013	2014	2015	2016	2017	2018	2019	2020
United States smokers (12+ years) from published NSDUH tables
Large metro (1)	48.1%	50.1%	50.3%	49.2%	48.2%	47.5%	48.5%	47.2%
Small metro (2,3)	31.6%	31.0%	32.9%	32.0%	33.7%	33.6%	33.2%	33.7%
Nonmetro (4-9)	20.2%	18.9%	16.8%	18.8%	18.2%	18.9%	18.3%	19.0%
Registered EX® program users
Large metro (1)	48.9%	50.6%	48.9%	48.9%	42.7%	41.9%	43.8%	45.0%
Small metro (2,3)	33.2%	32.5%	33.1%	36.7%	36.2%	36.6%	35.5%	35.0%
Nonmetro (4-9)	18.0%	16.8%	18.1%	19.5%	21.1%	21.5%	20.7%	20.0%

Note. The current study utilizes the same methodological approach and data sources as Amato & Graham (2018). The data presented from 2013 to 2017 are derived from Amato & Graham (2018) to provide readers with a baseline comparison to the data presented from this research conducted using updated data from 2018 to 2020. RUCC = Rural Urban Continuum Codes.

Reach Equity of EX

As previously reported,²⁷ when compared to national smoking prevalence data from NSDUH, registered EX users from rural areas were significantly underrepresented in 2013 and 2014, proportionately represented in 2015 and 2016, and overrepresented in 2017. Reach equity analyses (Table 3) showed that individuals who smoke residing in rural (nonmetro) areas continued to be significantly overrepresented in 2018 [ReRa = 1.18 (95% CI: 1.15, 1.23)], 2019 [ReRa = 1.10 (95% CI: 1.07, 1.13)], and 2020 [ReRa = 1.10 (95% CI: 1.06, 1.13)]. Individuals who smoke residing in small metro areas were overrepresented in 2018 [ReRa = 1.09 (95% CI: 1.06, 1.11), 2019 [ReRa = 1.06 (95% CI: 1.04, 1.08), and 2020 [ReRa = 1.05 (95% CI: 1.03, 1.08). Finally, individuals who smoke residing in large metro areas were underrepresented during the same time frame [ReRas = 0.87 (95% CI: 0.85, 0.89); 0.92 (95% CI: 0.91, 0.94), and 0.93 (95% CI: 0.91, 0.94), respectively].

Table 3.

Reach Ratios (ReRas) and 95% Confidence Intervals for the Geographic Distribution of EX® Program, by Year (2013-2020)

Type of area (RUCC)	Year, ReRas [95% CI]
Type of area (RUCC)	2013	2014	2015	2016	2017	2018	2019	2020
Large metro (1)	1.01 [1.00, 1.03]	1.01 [0.99, 1.03]	0.97 [0.94, 1.00]	0.89 [0.85, 0.94]	0.89 [0.86, 0.91]	0.87 [0.85, 0.89]	0.92 [0.91, 0.94]	0.93 [0.91, 0.94]
Small metro (2,3)	1.05 [1.03, 1.07]	1.05 [1.02, 1.08]	1.00 [0.96, 1.04]	1.15 [1.08, 1.22]	1.08 [1.05, 1.11]	1.09 [1.06, 1.11]	1.06 [1.04, 1.08]	1.05 [1.03, 1.08]
Nonmetro (4-9)	0.89 [0.87, 0.91]	0.89 [0.86, 0.92]	1.08 [1.02, 1.14]	1.03 [0.94, 1.14]	1.16 [1.12, 1.21]	1.18 [1.15, 1.23]	1.10 [1.07, 1.13]	1.10 [1.06, 1.13]

Note. Bold text indicates values that are statistically significant from 1, assuming P-value <0.05. EX® program is a digital tobacco cessation intervention program available in the United States.

Changes in Reach Equity of EX

ReRas for large metro areas decreased from 2013 to 2018 (significant annual percentage change, APC = −3.26; 95% CI: −6.69, −1.91). ReRas for non-metro areas increased from 2013 to 2017 (significant APC = 7.35; 95% CI: 4.10, 17.63). No significant inflection points were found for small metro areas (Supplemental Table 1 and Supplemental Figure 1).

Discussion

This study updates prior research²⁷ regarding the reach equity of EX – a free, nationally available digital tobacco cessation program. Across the 8-year observation period spanning from 2013 to 2020, individuals who smoke residing in rural (nonmetro) areas were proportionally – and more than proportionally in 7 of the 8 years – represented among registered EX users. The most recent reach ratios showed that individuals who smoke and reside in rural (nonmetro) areas are overrepresented (by 10% to 18%). Individuals who smoke residing in small metro areas were also more than proportionally represented in 7 of the 8 years, though reach ratios were smaller in magnitude (5% to 9% overrepresented) than individuals who smoke residing in rural (nonmetro) areas. A different pattern emerged for large metro areas. Across the same 8-year period, individuals who smoke and reside in large metro areas were proportionally represented from 2013 to 2015, but were underrepresented by 7% to 13% from 2016 to 2020.

Several factors may explain the observed shifts in geographic representation patterns, particularly the consistent underrepresentation of large metro area smokers from 2016 onward and the sustained overrepresentation of rural smokers. Federal initiatives to expand high-speed Internet access in rural communities^42,43 may have enhanced rural residents' ability to access digital cessation resources. The substantial increase in rural Internet adoption (from 78% in 2018 to 93% in 2023)²³ suggests improved digital infrastructure that could facilitate greater participation in online cessation programs. An evolving urban cessation landscape may explain the underrepresentation of urban smokers. Large metropolitan areas may offer increasingly diverse cessation resources, including specialized clinics, workplace wellness programs, and community-based interventions. Urban smokers may have more face-to-face treatment options, potentially reducing reliance on digital-only programs. Additionally, the higher prevalence of nondaily and social smoking patterns in urban areas^44,45 may influence help-seeking behavior, as these smokers may be less likely to perceive themselves as needing formal cessation assistance.^44-46 Lastly, the search engine optimization and modest marketing spend of EX may naturally reach rural populations more effectively, as these communities may have fewer competing cessation resources appearing in search results. Rural smokers searching for cessation help online may encounter fewer alternatives, leading to higher conversion rates to programs like EX. Future research should explore the intersections of smoking behavior, rural/urban residence, and access to cessation services.

Strengths of this study include available ZIP code data on 97% of registered EX users, a large sample of users across each year, and consistency of self-reported data across years. There are also some limitations in this study, related to data collection methodologies. First, the research described provides results from a secondary data analysis and no sample size calculations were conducted. Further, reliance on published tables provided by NSDUH for these analyses did not allow for the comparison of regional differences across demographic factors, such as age, gender, and socioeconomic status, between participants from NSDUH and registered EX users. This is an area to be explored in future research. Second, although NSDUH data for 2021 and 2022 are available, data collection methodologies varied during these years due to the COVID-19 pandemic which precluded inclusion of these data in this study. Research should continue to explore rural access to digital cessation treatment given impacts of the COVID-19 pandemic on Internet usage⁴⁷ and the rapid rate of Internet adoption in rural communities (78% in 2018 to 93% in 2023).²³ Third, study estimates may be biased if some households participated in more than one NSDUH survey administration over the study period. Finally, results from this study generalize only to the specific digital cessation program in this research. Given these considerations, future studies are needed for the continued monitoring of geographic representativeness of digital tobacco cessation programs over time. This is especially true for large metro areas where underrepresentation was found.

Conclusions

This study extends a previously published reach equity analysis of EX – a widely used, free digital tobacco cessation program²⁷ – by examining additional data spanning from 2018 to 2020. Results suggest that individuals who smoke and reside in rural (nonmetro) areas and small metro areas are proportionally or more than proportionately reached through a digital treatment modality, while those who reside in large metro areas are underrepresented across this 8-year period from 2013 to 2020. Continued monitoring of geographic representativeness of digital cessation programs over time is needed.

Supplemental Material

Supplemental Material - Geographic Representativeness of a Digital Tobacco Cessation Intervention: An Exploration of Changes in Rural Reach From 2013-2020

Supplemental Material for Geographic Representativeness of a Digital Tobacco Cessation Intervention: An Exploration of Changes in Rural Reach From 2013-2020 by Elizabeth K. Do, Sarah Cha, Kristiann Koris, Diana Davidson, Tatum L. McKay, Elizabeth C. Hair, Amanda L. Graham in Tobacco Use Insights

Supplemental Material

Supplemental Material - Geographic Representativeness of a Digital Tobacco Cessation Intervention: An Exploration of Changes in Rural Reach From 2013-2020

Footnotes

Acknowledgements

The authors would like to thank and acknowledge the participants of this study.

Elizabeth K. Do

Amanda L. Graham

Ethical Considerations

Study participants are all registered users on the tobacco cessation program (“EX”), who had accepted the Terms of Service (TOS, see: https://www.exprogram.com/terms-of-use/) on this free, nationally available program in order to use the tobacco cessation program. There was no institutional review board (IRB) oversight for this study, as research falls under 45 CRF 46.104(d) (4ii) (see: ).

Authors Contributions

Elizabeth K. Do: methodology, formal analysis, investigation, data curation, project administration, writing-original draft, writing-review & editing, supervision.

Sarah Cha: methodology, formal analysis, data curation, project administration, writing-original draft, writing-review & editing.

Kristiann Koris: writing-original draft, writing-review & editing.

Diana Davidson: writing-original draft, writing-review & editing.

Tatum L. McKay: formal analysis, writing-original draft, writing-review & editing.

Elizabeth C. Hair: supervision, project administration, writing-original draft, writing-review & editing.

Amanda L. Graham: supervision, project administration, methodology, data curation, writing-original draft, writing-review & editing.

Funding

This study was internally funded by Truth Initiative.

Declaration of Conflicting Interests

The authors are employees of Truth Initiative, a nonprofit public health foundation that sells digital tobacco cessation programs to support its mission-driven work.

Data Availability Statement

A data sharing agreement is required for the use of all data. Our research team does not share data with tobacco industry representatives or affiliated researchers. Investigators seeking access to the data used in the study should make a written request to the corresponding author and submit a detailed research plan including the purpose of the proposed research, required variables, duration of the analysis phase, institutional review board approval, Federal Wide Assurance (FWA) information, and documentation of investigator training in human participants.*

Supplemental Material

Supplemental material for this article is available online.

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