Abstract
Objectives:
Common data sources that examine cancer survival provide limited information on health behaviors or social determinants of health. We linked individual-level cancer registry data to county-level data to examine differences in survival among people diagnosed with lung cancer.
Methods:
We linked 2010-2020 National Program of Cancer Registries survival data from 40 registries, covering 85% of the US population, to county-level data on current age-adjusted cigarette smoking prevalence, average daily density of fine particulate matter (PM2.5) in µg/m3 (fine particulate pollution), and overall social vulnerability. We generated Kaplan–Meier survival curves and used multivariable Cox proportional hazards regression to examine overall and cause-specific survival among people aged ≥20 years diagnosed with lung cancer.
Results:
Survival was significantly lower among people who lived in counties with a higher prevalence of cigarette smoking of 14.3% to <17.0% (adjusted hazard ratio [AHR] = 1.06), 17.0% to <20.2% (AHR = 1.08), and 20.2% to 34.8% (AHR = 1.14) compared with 6.7% to <14.3%; fine particulate pollution >12.0 µg/m3 versus ≤12.0 µg/m3 (AHR = 1.04); and social vulnerability scores in the second (AHR = 1.01), third (AHR = 1.02), and fourth (AHR = 1.03) quartiles versus first quartile. Individual-level covariates significantly associated with rates of survival included sex, age at diagnosis, race and ethnicity, histology, stage at diagnosis, receiving surgery during first course of treatment, year of diagnosis, and US Census region.
Conclusions:
Multiple characteristics were associated with lower 5-year lung cancer survival rates. Interventions that address these characteristics (eg, promoting tobacco cessation, reducing exposure to fine particulate pollution) may lead to longer survival after lung cancer diagnosis.
Keywords: lung cancer, survival analysis, air pollution, cigarette smoking, sociological factors
From 2014 to 2020, the 5-year relative survival rate for lung cancer in the United States rose from 28% to 68% for all cancers combined, ranging from 60% at localized stage to 9% at distant stage.
1
Fewer than one-third (27%) of lung cancers are diagnosed at the localized stage, in part due to a lack of symptoms in earlier stages and underuse of lung cancer screening.
2
Once the disease spreads, it can be more challenging to treat,
3
underscoring the importance of prevention and early detection efforts.
Differences in lung cancer survival rates exist by sex, race and ethnicity, rural/urban status, and individual-level and area-level socioeconomic status, which consider factors such as income, education, and occupation.4-7 The social determinants of health (SDOH) can affect outcomes along the cancer continuum.
8
Studies linking ecological data to cancer registry data have found that SDOH such as low neighborhood socioeconomic status, low educational attainment levels, lack of health insurance, and high social vulnerability are associated with high cancer mortality, low survival, and a reduced likelihood of receiving surgery and chemotherapy.9-16 The housing and built environment, which includes SDOH such as air pollution,
17
can also contribute to elevated lung cancer risk.
18
To date, limited information exists on the association between county-level SDOH and lung cancer survival. Of studies cited previously, many are single-state analyses.12,13,15 The primary objective of this study was to examine differences in survival among people diagnosed with lung cancer by county-level characteristics, including 1 health-risk behavior (cigarette smoking prevalence) and 2 SDOH (fine particulate pollution and social vulnerability). We also examined and adjusted for individual-level characteristics to better understand factors associated with survival.
Methods
Measures and Population
Individual-level data
We used the 2010-2020 National Program of Cancer Registries (NPCR) survival analytic file
19
from the November 2023 submission, which provides complete follow-up data through 2020 (data not publicly available). NPCR collects cancer registry data in 46 states, the District of Columbia, and 4 territories.
19
Our main outcomes were overall 5-year survival and lung cancer–specific 5-year survival. Our population included people aged ≥20 years with a diagnosis of lung and bronchus (lung) cancer during 2010-2020. We excluded diagnoses of small cell histology given differences in treatment guidelines and survival rates between small cell and non–small cell lung cancer.20-22 We defined lung cancer by the International Classification for Oncology, Third Edition site codes C34.0-C34.9.
23
We analyzed data from 40 registries covering 85% of the US population (Figure 1).
24
This activity was reviewed by CDC, deemed not research, and conducted consistent with applicable federal law and CDC policy.
Figure 1.
Study inclusion and exclusion flowchart in a study on survival of people with lung cancer, National Program of Cancer Registries, United States, 2010-2020. The National Program of Cancer Registries survival dataset
19
does not include cases identified only by death certificate or autopsy or with benign, in situ, and borderline malignant behavior. Abbreviation: FIPS, Federal Information Processing Standards.
We used NPCR data to examine sex, age at diagnosis, and race and ethnicity as demographic predictors of survival. We also examined year of diagnosis, histology, stage at diagnosis, and receipt of surgery during the first course of treatment for lung cancer. We coded year of diagnosis as a dichotomous variable (2010-2013 vs 2014-2020); we chose 2013 as a cut point because this was the year that the US Preventive Services Task Force (USPSTF)
25
and American Cancer Society
26
first issued recommendations for lung cancer screening with low-dose computed tomography scans. We examined geographic location, including US Census region and county classification (metropolitan vs nonmetropolitan residence). We defined county classification based on US Department of Agriculture Economic Research Service 2013 Rural–Urban Continuum Codes.
27
County-level data
We used county-level data on 1 health risk behavior (cigarette smoking prevalence) and 2 SDOH (fine particulate pollution and social vulnerability). We used the 2023 Robert Wood Johnson Foundation County Health Rankings database
28
to obtain county-level data on age-adjusted cigarette smoking prevalence and fine particulate pollution. We used this database because it contained data from multiple sources and readily accessible county-level Federal Information Processing Standards (FIPS) codes for data linkage. We used data from the 2020 Behavioral Risk Factor Surveillance System to measure the prevalence of cigarette smoking.
29
We defined smoking as the percentage of adults aged ≥18 years who currently smoke cigarettes, and we categorized smoking prevalence into quartiles: 6.7% to <14.3%, 14.3% to <17.0%, 17.0% to <20.2%, and 20.2% to 34.8%.
We measured fine particulate pollution by the 2019 National Environmental Public Health Tracking Network
30
from data collected by the US Environmental Protection Agency (EPA) Air Quality System.
31
It is defined as the average daily density of fine particulate matter (PM2.5) in µg/m3. The primary annual average standard for PM2.5 set by the EPA in 2019 was 12.0 µg/m3.
32
We coded counties as being at or below the annual standard (≤12.0 µg/m3) or exceeding the annual standard (>12.0 µg/m3).
We measured county-level social vulnerability using the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry’s 2020 Social Vulnerability Index (SVI),
33
calculated from 2016-2020 American Community Survey data.
34
The 2020 SVI provides an overall score of social vulnerability relative to other counties based on 16 social factors grouped into 4 themes: socioeconomic status (<150% federal poverty level, unemployed, housing cost burden, no high school diploma, no health insurance), household characteristics (aged ≥65 years, aged ≤17 years, civilians with a disability, single-parent households, English-language proficiency), racial and ethnic minority status (Hispanic or Latino, of any race; non-Hispanic Black or African American; non-Hispanic Asian; non-Hispanic American Indian or Alaska Native; non-Hispanic Native Hawaiian or Pacific Islander; non-Hispanic ≥2 races; non-Hispanic other race), and housing type and transportation (multiunit structures, mobile home, crowding, no vehicle, group quarters). Consistent with other studies,35,36 we coded counties into 4 quartiles based on the overall level of social vulnerability: <25% (least vulnerable), 25% to <50%, 50% to <75%, and 75% to 100% (most vulnerable).
Data Analysis
We used SEER*Stat version 8.4.1 (National Cancer Institute) to produce a survival database based on our inclusion and exclusion criteria. We exported the database into SAS version 9.4 (SAS Institute Inc) for analysis and linked the survival data to other data sources using county-level FIPS codes. That is, we assigned each individual in the survival database county-level values for current cigarette smoking prevalence, fine particulate pollution, and social vulnerability based on their county address at the time of diagnosis. We excluded from analysis individuals with missing information for county address at diagnosis or without a matching FIPS code (0.02%).
First, we produced descriptive statistics for our population. Then, we used the Kaplan–Meier method to generate unadjusted 5-year survival estimates for overall survival and cause-specific survival. We performed log-rank tests to examine differences in unadjusted survival by sex. People who were followed for <5 years were censored at the end of their follow-up. People who were followed for ≥5 years were censored at 5 years. For all cause-specific analyses, we defined death from lung cancer by cause of death cancer registry codes
37
for “lung and bronchus.” We fitted multivariable Cox regression frailty models of survival using the gamma distribution of the frailty terms. We used the frailty models with county-specific random effects to examine adjusted overall survival and cause-specific survival, estimating hazard ratios (HRs) of death, while accounting for within-county homogeneity in survival.
38
We used the Wald χ2 test to examine differences; P < .05 was considered significant. We conducted all analyses for our entire population and by sex.
We included the following variables in our adjusted regression models: sex (in the overall model), age at diagnosis, race and ethnicity, histology, stage at diagnosis, receipt of surgery during the first course of treatment for lung cancer, year of diagnosis, US Census region, county rural–urban classification, county-level cigarette smoking prevalence, county-level fine particulate pollution, and county-level social vulnerability. We hypothesized that the effect of surgery would vary by stage at diagnosis, so we also included an interaction term for stage at diagnosis and receipt of surgery.
We tested the proportional hazards assumption by conducting z tests for proportions with homogeneity to examine the association between weighted Schoenfeld residuals and survival time, with P < .05 considered significant. We found that most predictors violated the assumption. However, the large population size for our analysis (>1 million events) led to extremely high power to detect violations. Even slight deviations from the proportional hazards assumption were likely to be significant. To examine these violations further, we visually examined residual plots and unadjusted 5-year survival curves from the Kaplan–Meier analysis. In looking at these plots, most predictors showed little indication of a violation. As such, we did not include additional variables, time-dependent interactions, or stratifications. Our estimates are still valid regardless of potential violations
39
; the HRs presented represent the average effect during 5 years of follow-up.
Results
Of 1 560 284 lung cancer diagnoses during 2010-2020 in our analytic dataset, 742 186 (47.6%) were among adult females and 818 098 (52.4%) were among adult males (Table 1). Most people were aged 65 to 84 years at diagnosis (n = 963 353; 61.7%) and non-Hispanic White (n = 1 249 592; 80.3%). More than half (n = 844 684; 54.1%) of people were diagnosed with adenocarcinoma and 46.2% (n = 721 249) at distant stage. One-quarter of people lived in counties with a cigarette smoking prevalence in the highest quartile of 20.2% to 34.8% (n = 387 221; 24.8%), and 5.7% (n = 89 033) lived in counties with fine particulate pollution >12.0 µg/m3. One-third of people (n = 514 342; 33.0%) lived in the most socially vulnerable counties.
Table 1.
Characteristics of people with lung cancer, National Program of Cancer Registries, 40 cancer registries, United States, 2010-2020
a
Characteristic
Descriptive statistics, no. (%)
Total
Female
Male
Total
1 560 284 (100.0)
742 186 (47.6)
818 098 (52.4)
Age at diagnosis, y
<45
20 891 (1.3)
11 118 (1.5)
9773 (1.2)
45-64
474 679 (30.4)
226 423 (30.5)
248 256 (30.3)
65-84
963 353 (61.7)
454 549 (61.2)
508 804 (62.2)
≥85
101 361 (6.5)
50 096 (6.7)
51 265 (6.3)
Race and ethnicity
Hispanic or Latino, all races
77 071 (5.0)
36 066 (4.9)
41 005 (5.0)
Non-Hispanic American Indian or Alaska Native
9125 (0.6)
4506 (0.6)
4619 (0.6)
Non-Hispanic Asian or Pacific Islander
46 920 (3.0)
22 252 (3.0)
24 668 (3.0)
Non-Hispanic Black or African American
173 925 (11.2)
78 303 (10.6)
95 622 (11.7)
Non-Hispanic White
1 249 592 (80.3)
599 431 (80.9)
650 161 (79.7)
Histology
b
Adenocarcinoma
844 684 (54.1)
443 641 (59.8)
401 043 (49.0)
Squamous cell carcinoma
435 884 (27.9)
166 361 (22.4)
269 523 (32.9)
Other histology
279 716 (17.9)
132 184 (17.8)
147 532 (18.0)
Stage at diagnosis
Localized
408 133 (26.2)
217 787 (29.3)
190 346 (23.3)
Regional
376 123 (24.1)
175 009 (23.6)
201 114 (24.6)
Distant
721 249 (46.2)
324 275 (43.7)
396 974 (48.5)
Unknown
54 779 (3.5)
25 115 (3.4)
29 664 (3.6)
Receipt of surgery as a first course of treatment
Received surgery
420 696 (27.2)
223 622 (30.4)
197 074 (24.3)
Did not receive surgery
1 123 768 (72.8)
511 394 (69.6)
612 374 (75.7)
US Census region
Northeast
337 151 (21.6)
169 307 (22.8)
167 844 (20.5)
Midwest
243 029 (15.6)
115 445 (15.6)
127 584 (15.6)
South
683 836 (43.8)
310 083 (41.8)
373 753 (45.7)
West
296 268 (19.0)
147 351 (19.9)
148 917 (18.2)
County classification
c
Metropolitan
1 283 483 (82.3)
620 827 (83.6)
662 656 (81.0)
Nonmetropolitan
276 801 (17.7)
121 359 (16.4)
155 442 (19.0)
County-level cigarette smoking prevalence
d
6.7% to <14.3%
386 192 (24.8)
196 304 (26.4)
189 888 (23.2)
14.3% to <17.0%
391 080 (25.1)
190 188 (25.6)
200 892 (24.6)
17.0% to <20.2%
395 791 (25.4)
185 411 (25.0)
210 380 (25.7)
20.2% to 34.8%
387 221 (24.8)
170 283 (22.9)
216 938 (26.5)
County-level fine particulate pollution
e
≤12.0 µg/m3
1 470 364 (94.3)
697 959 (94.1)
772 405 (94.5)
>12.0 µg/m3
89 033 (5.7)
43 827 (5.9)
45 206 (5.5)
County-level SVI quartile
f
<25%
182 067 (11.7)
88 563 (11.9)
93 504 (11.4)
25% to <50%
384 734 (24.7)
186 931 (25.2)
197 803 (24.2)
50% to <75%
479 141 (30.7)
228 442 (30.8)
250 699 (30.6)
75% to 100%
514 342 (33.0)
238 250 (32.1)
276 092 (33.7)
Kaplan–Meier Analysis
The unadjusted overall 5-year survival rate was 25.0% (Table 2); the rate was significantly higher among adult females (30.4%) than among adult males (20.2%) (P < .001) (Table 2 and Figure 2). By race and ethnicity, the survival rate was highest among people reported as non-Hispanic Asian/Pacific Islander (33.5%) and lowest among people reported as non-Hispanic Black/African American (21.5%). The largest differences in survival rates were by first-course surgery: 59.2% of people who received surgery versus 11.9% of people who did not receive surgery. By county-level characteristics, survival rates were lower in counties at higher quartiles of age-adjusted cigarette smoking prevalence and overall social vulnerability. While cause-specific survival rates were higher than for overall survival, our pattern of results by subgroup characteristics was similar (eTable 1 and eFigure 1 in the Supplement).
Table 2.
Overall 5-year survival rate of people with lung cancer, by sex and demographic characteristics, Kaplan–Meier analysis, National Program of Cancer Registries, 40 cancer registries, United States, 2010–2020
a
Characteristic
Overall 5-year survival, %
Total
Female
Male
Total
25.0
30.4
20.2
Age at diagnosis, y
<45
41.3
46.6
35.2
45-64
29.2
35.8
23.2
65-84
24.2
29.3
19.6
≥85
9.4
11.8
7.1
Race and ethnicity
Hispanic or Latino, all races
27.5
34.5
27.7
Non-Hispanic American Indian or Alaska Native
21.9
26.6
17.4
Non-Hispanic Asian or Pacific Islander
33.5
39.2
28.3
Non-Hispanic Black or African American
21.5
26.8
17.2
Non-Hispanic White
25.0
30.3
20.2
Histology
b
Adenocarcinoma
28.4
33.7
22.7
Squamous cell carcinoma
20.5
23.5
18.7
Other histology
21.7
28.1
15.9
Stage at diagnosis
Localized
51.7
58.2
44.3
Regional
30.9
36.4
26.3
Distant
7.7
9.7
6.2
Unknown
17.1
20.4
14.3
Receipt of surgery as a first course of treatment
Received surgery
59.2
66.0
51.6
Did not receive surgery
11.9
14.5
9.8
US Census region
Northeast
28.9
34.6
23.2
Midwest
24.2
28.9
20.0
South
23.0
28.4
18.6
West
25.7
30.8
20.6
County classification
c
Metropolitan
25.7
31.0
20.7
Nonmetropolitan
21.7
27.1
17.6
County-level cigarette smoking prevalence
d
6.7% to <14.3%
29.0
34.3
23.4
14.3% to <17.0%
25.7
31.0
20.7
17.0% to <20.2%
24.1
29.1
19.6
20.2% to 34.8%
21.3
26.5
17.3
County-level fine particulate pollution
e
≤12.0 µg/m3
25.0
30.4
20.2
>12.0 µg/m3
24.6
29.8
19.7
County-level SVI quartile
f
<25%
26.5
31.5
21.8
25% to <50%
26.6
32.2
21.4
50% to <75%
24.6
30.0
19.8
75% to 100%
23.6
29.0
19.0
Figure 2.
Overall survival of people with lung cancer, Kaplan–Meier survival curves, National Program of Cancer Registries, 41 cancer registries, United States, 2010–2020. Data source: Centers for Disease Control and Prevention.
19
Number of people at risk, by sex
Sex
Survival time, y
0
1
2
3
4
5
Female
742 186
412 390
281 561
202 756
148 359
108 703
Male
818 098
378 856
236 774
161 561
114 278
81 210
Multivariable Cox Proportional Hazards Regression
In the adjusted Cox proportional hazards regression model for overall 5-year survival (Table 3), adult males versus adult females (adjusted HR [AHR] = 1.25; 95% CI, 1.24-1.25) and people aged ≥45 years versus <45 years (AHRs range: 1.21-2.23) had a higher risk of death from all causes (ie, lower overall survival). Non-Hispanic American Indian/Alaska Native people had lower survival than non-Hispanic White people (AHR = 1.03; 95% CI, 1.00-1.06). Non-Hispanic Asian/Pacific Islander (AHR = 0.70; 95% CI, 0.69-0.71) and Hispanic (AHR = 0.91; 95% CI, 0.90-0.92) people had better survival than non-Hispanic White people.
Table 3.
Overall 4-year survival of people with lung cancer, by sex and demographic characteristics, multivariable Cox proportional hazards regression, National Program of Cancer Registries, 40 cancer registries, United States, 2010-2020
a
Characteristic
Adjusted hazard ratio (95% CI)
b
Total
Female
Male
Sex
Female
1 [Reference]
1 [Reference]
1 [Reference]
Male
1.25 (1.24-1.25)
c
Age at diagnosis, y
<45
1 [Reference]
1 [Reference]
1 [Reference]
45-64
1.21 (1.19-1.23)
c
1.08 (1.06-1.11)
c
1.19 (1.16-1.22)
c
65-84
1.53 (1.50-1.55)
c
1.38 (1.35-1.42)
c
1.47 (1.44-1.51)
c
≥85
2.23 (2.19-2.28)
c
2.05 (2.00-2.11)
c
2.12 (2.06-2.17)
c
Race and ethnicity
Hispanic or Latino, all races
0.91 (0.90-0.92)
c
0.87 (0.86-0.89)
c
0.93 (0.92-0.94)
c
Non-Hispanic American Indian or Alaska Native
1.03 (1.00-1.06)
c
1.03 (0.99-1.07)
1.02 (0.99-1.06)
Non-Hispanic Asian or Pacific Islander
0.70 (0.69-0.71)
c
0.66 (0.65-0.68)
c
0.72 (0.71-0.73)
c
Non-Hispanic Black or African American
1.00 (0.99-1.01)
1.00 (0.99-1.01)
1.00 (0.99-1.00)
Non-Hispanic White
1 [Reference]
1 [Reference]
1 [Reference]
Histology
d
Adenocarcimoma
1 [Reference]
1 [Reference]
1 [Reference]
Squamous cell carcinoma
1.19 (1.18-1.19)
c
1.29 (1.28-1.30)
c
1.13 (1.12-1.13)
c
Other histology
1.28 (1.27-1.29)
c
1.27 (1.26-1.28)
c
1.29 (1.28-1.29)
c
Stage at diagnosis within each surgery status
Distant
1 [Reference]
1 [Reference]
1 [Reference]
Among cases without surgery
Localized
0.31 (0.31-0.31)
c
0.29 (0.29-0.30)
c
0.33 (0.32-0.33)
c
Regional
0.50 (0.50-0.51)
c
0.51 (0.50-0.51)
c
0.51 (0.50-0.51)
c
Among cases with surgery
Localized
0.25 (0.24-0.25)
c
0.23 (0.22-0.23)
c
0.26 (0.26-0.27)
c
Regional
0.46 (0.45-0.47)
c
0.44 (0.43-0.45)
c
0.45 (0.44-0.46)
c
First-course surgery within each stage
Did not receive surgery
1 [Reference]
1 [Reference]
1 [Reference]
Cases diagnosed at localized stage
0.32 (0.31-0.32)
c
0.28 (0.28-0.29)
c
0.35 (0.35-0.36)
c
Cases diagnosed at regional stage
0.36 (0.36-0.36)
c
0.32 (0.32-0.33)
c
0.39 (0.39-0.40)
c
Cases diagnosed at distant stage
0.40 (0.39-0.40)
c
0.37 (0.36-0.38)
c
0.44 (0.43-0.45)
c
Year of diagnosis
2010-2013
1 [Reference]
1 [Reference]
1 [Reference]
2014-2020
0.86 (0.86-0.86)
c
0.85 (0.84-0.85)
c
0.87 (0.86-0.87)
c
US Census region
Northeast
1 [Reference]
1 [Reference]
1 [Reference]
Midwest
0.99 (0.99-1.00)
1.00 (0.98-1.01)
0.99 (0.98-1.00)
South
1.03 (1.02-1.04)
c
1.03 (1.02-1.04)
c
1.03 (1.02-1.04)
c
West
1.03 (1.02-1.04)
c
1.04 (1.02-1.05)
c
1.03 (1.02-1.04)
c
County classification
e
Metropolitan
1 [Reference]
1 [Reference]
1 [Reference]
Nonmetropolitan
1.01 (1.00-1.01)
c
1.00 (0.99-1.01)
1.01 (1.00-1.02)
c
County-level cigarette smoking prevalence
f
6.7% to <14.3
1 [Reference]
1 [Reference]
1 [Reference]
14.3% to <17.0%
1.06 (1.05-1.06)
c
1.06 (1.05-1.07)
c
1.05 (1.04-1.06)
c
17.0% to <20.2%
1.08 (1.07-1.09)
c
1.09 (1.08-1.11)
c
1.07 (1.06-1.08)
c
20.2% to 34.8%
1.14 (1.13-1.15)
c
1.16 (1.14-1.18)
c
1.13 (1.11-1.14)
c
County-level fine particulate pollution
g
≤12.0 µg/m3
1 [Reference]
1 [Reference]
1 [Reference]
>12.0 µg/m3
1.04 (1.02-1.06)
c
1.04 (1.01-1.06)
c
1.04 (1.02-1.07)
c
County-level SVI quartile
h
<25%
1 [Reference]
1 [Reference]
1 [Reference]
25% to <50%
1.01 (1.00-1.02)
c
1.00 (0.99-1.01)
1.02 (1.01-1.03)
c
50% to <75%
1.02 (1.01-1.03)
c
1.00 (0.99-1.02)
1.03 (1.02-1.04)
c
75% to 100%
1.03 (1.02-1.04)
c
1.02 (1.00-1.03)
c
1.04 (1.03-1.06)
c
People diagnosed with squamous cell carcinoma (AHR = 1.19; 95% CI, 1.18-1.19) and other histology (AHR = 1.28; 95% CI, 1.27-1.29) had lower survival than people with adenocarcinoma. We found a significant interaction between stage at diagnosis and receipt of surgery as a first course of treatment (P < .001). Survival was higher at localized and regional versus distant stage disease, and the association between stage at diagnosis and higher survival was stronger among people who received surgery than among those who did not receive surgery. We observed better survival among people diagnosed during 2014–2020 versus 2010–2013 (AHR = 0.86; 95% CI, 0.86-0.86). Survival was lower among people residing in the South (AHR = 1.03; 95% CI, 1.02-1.04) and West (AHR = 1.03; 95% CI, 1.02-1.04) than in the Northeast.
By county-level characteristics, survival was lower among people in counties with fine particulate pollution >12.0 µg/m3 versus ≤12.0 µg/m3 (AHR = 1.04; 95% CI, 1.02-1.06). Survival was also lower among people in counties at higher quartiles of age-adjusted cigarette smoking prevalence (AHR range: 1.06-1.14) and social vulnerability (AHR range: 1.01-1.03) than among people in counties in the first quartile. Our model results for overall survival stratified by sex, as well as our model results for cause-specific survival (eTable 2 in the Supplement), were generally comparable with our models for overall survival.
Discussion
We linked individual-level cancer registry data and county-level data from other sources to examine differences in survival among people diagnosed with lung cancer. In our multivariable regression analysis, we observed lower survival among people living in counties with higher age-adjusted cigarette smoking prevalence. Continued smoking after a cancer diagnosis can lead to an increased risk of a second primary cancer, increased risk of postoperative treatment complications, decreased efficacy of radiation and chemotherapy, and lower survival.
40
Thus, it is important to address tobacco use across the cancer continuum. The USPSTF recommends that all people who receive lung cancer screening who currently smoke also receive smoking cessation counseling.
41
Comprehensive tobacco control programs—which include components such as tobacco-free policies, mass outreach communication campaigns, cessation interventions such as quitlines, and decreased marketing of tobacco products—can be implemented to effectively prevent and reduce tobacco use.
42
Survival was lower among people living in counties with higher versus lower fine particulate pollution. Exposure to fine particulate pollution, even at or below recommended levels, has multiple health consequences, including an increased risk of hospitalization for cardiovascular and respiratory disease43-45 and increased all-cause, cardiopulmonary, and lung cancer mortality.
46
Limiting industrial air pollution in communities with disproportionate exposure
17
could reduce its negative health effect and potentially reduce differences in lung cancer survival.
We also observed lower survival rates among people in more socially vulnerable counties. Our results align with previous studies showing an effect of higher area-level social vulnerability on lung cancer outcomes, including lower receipt of surgery and late-stage diagnosis.
47
These findings demonstrate the importance of collecting information on social vulnerability to help inform cancer care.
37
Interventions that address social vulnerability have involved patient navigation, improving the neighborhood and built environment and improving economic stability.
48
Aspects of the built environment that facilitate tobacco use, such as higher tobacco retailer density,
49
may be especially important to consider within the context of lung cancer survival.
The strongest individual-level predictors of lower survival rates were older age at diagnosis and distant-stage disease. We observed better survival rates among people diagnosed with lung cancer during 2014–2020 versus 2010–2013. This finding may be due to improvements in lung cancer treatment over time. The first immunotherapy treatment for lung cancer was approved by the US Food and Drug Administration in 2015.
50
Earlier detection of lung cancer may have also led to better survival rates during 2014-2020 versus 2010-2013. In 2013, the USPSTF
25
first issued recommendations for lung cancer screening with low-dose computed tomography scans based on evidence of its effectiveness in reducing lung cancer mortality.
51
In 2021, the USPSTF updated its recommendations to broaden eligibility for screening.
41
However, lung cancer screening remains underused; population-based estimates of screening have ranged from 4.4%
52
to 16.4%.
53
Opportunities to improve lung cancer screening include increasing promotion of screening, increasing health care provider awareness and knowledge of screening guidelines, and implementing evidence-based interventions such as patient navigation.54-56
Survival was lower among people who did not receive surgery than among those who did receive surgery during the first course of treatment. While we adjusted for variables that affect treatment receipt and recommendations, including histology and stage at diagnosis,21,22 other unobserved factors may have influenced our findings. Some people may not have received guideline-concordant staging or treatment, both of which have been found to be positively associated with survival in people with non–small cell lung cancer.
57
Other studies have found that people with comorbid conditions, which can increase the risk of death, are less likely than those without such conditions to receive surgery.
58
However, even after adjusting for factors such as comorbidity, lower odds of surgery persist in certain groups, including by race and ethnicity and location of residence.
59
Additional studies that consider these and other factors, including cancer care access, could further our understanding of differences in survival rates.
Our results for overall survival and cause-specific survival were similar, possibly due to the large percentage of people whose reported death was from lung cancer. Among people who died during the study period and who had a reported cause of death, 80% died of lung cancer. This statistic highlights the seriousness of lung cancer and the importance of tobacco cessation and early detection to improve survival. People with lung cancer also have more comorbid conditions than those without lung cancer.60,61 These comorbidities increase the potential of dying of other diseases, in turn narrowing the gap between overall and cause-specific survival. For example, 1 study reported a 43% prevalence of any cardiovascular disease among people with lung cancer.
62
Limitations
This study had at least 4 limitations. First, we excluded cancer registries in states that did not meet data quality standards or conduct linkage with the National Death Index. While the selected registries in our survival analysis still covered 85% of the population, our findings may not be generalizable to the entire United States. Second, county-level social vulnerability may mediate the association between race and ethnicity and survival. Because we adjusted for both variables in our analysis, and because the SVI includes racial and ethnic minority status as a subtheme, the effect of race on survival may have been underestimated.
Third, we examined overall social vulnerability rather than individual social factors included in the SVI. While this approach provides a holistic understanding of how multiple social factors may contribute to survival, some factors may be more relevant to survival than others. Finally, our county-level results cannot be used to draw conclusions about individuals and may not reflect individual experiences accurately. Some studies have found a poor correlation between individual-level and census tract–level measures of SDOH and social disadvantage,
63
highlighting limitations to using county-level data as a means of understanding individual needs.
Conclusions
This study observed lower survival among people living in counties with higher age-adjusted cigarette smoking prevalence, higher fine particulate pollution, and higher social vulnerability. Opportunities for intervention may include implementing comprehensive tobacco control programs, limiting industrial air pollution in communities, and using patient navigation services to improve access to lung cancer screening and treatment. Our study highlights the benefits of linking county-level data to cancer registry data to better understand risk factors associated with cancer survival. The collection of cancer registry data on health risk behaviors and SDOH at the individual level could further our understanding of how the environment may affect survival. Individual measures may be better predictors of survival, while county measures provide information about the broader environment that can affect a person’s behavioral and socioeconomic conditions. Having data at both levels could also facilitate analysis to examine contextual effects (eg, effect of county-level social vulnerability on survival after adjustment for individual-level social vulnerability) and compositional effects (eg, effect of individual-level social vulnerability on the association between county-level social vulnerability and survival). The results from this study can be used to further inform multilevel prevention, screening, and treatment interventions among populations at greatest risk of dying of lung cancer.
Supplemental Material
sj-docx-1-phr-10.1177_00333549251410521 – Supplemental material for Differences in Lung Cancer Survival by Demographic Characteristics and Social Determinants of Health, United States, 2010-2020
Supplemental material, sj-docx-1-phr-10.1177_00333549251410521 for Differences in Lung Cancer Survival by Demographic Characteristics and Social Determinants of Health, United States, 2010-2020 by Christine M. Kava, Shifan Dai, David A. Siegel, Susan A. Sabatino, Jin Qin, Florence K.L. Tangka and S. Jane Henley in Public Health Reports®
sj-docx-2-phr-10.1177_00333549251410521 – Supplemental material for Differences in Lung Cancer Survival by Demographic Characteristics and Social Determinants of Health, United States, 2010-2020
Supplemental material, sj-docx-2-phr-10.1177_00333549251410521 for Differences in Lung Cancer Survival by Demographic Characteristics and Social Determinants of Health, United States, 2010-2020 by Christine M. Kava, Shifan Dai, David A. Siegel, Susan A. Sabatino, Jin Qin, Florence K.L. Tangka and S. Jane Henley in Public Health Reports®
sj-docx-3-phr-10.1177_00333549251410521 – Supplemental material for Differences in Lung Cancer Survival by Demographic Characteristics and Social Determinants of Health, United States, 2010-2020
Supplemental material, sj-docx-3-phr-10.1177_00333549251410521 for Differences in Lung Cancer Survival by Demographic Characteristics and Social Determinants of Health, United States, 2010-2020 by Christine M. Kava, Shifan Dai, David A. Siegel, Susan A. Sabatino, Jin Qin, Florence K.L. Tangka and S. Jane Henley in Public Health Reports®
sj-xlsx-4-phr-10.1177_00333549251410521 – Supplemental material for Differences in Lung Cancer Survival by Demographic Characteristics and Social Determinants of Health, United States, 2010-2020
Supplemental material, sj-xlsx-4-phr-10.1177_00333549251410521 for Differences in Lung Cancer Survival by Demographic Characteristics and Social Determinants of Health, United States, 2010-2020 by Christine M. Kava, Shifan Dai, David A. Siegel, Susan A. Sabatino, Jin Qin, Florence K.L. Tangka and S. Jane Henley in Public Health Reports®
Acknowledgments
The authors thank Trevor Thompson, BS (formerly with the CDC Division of Cancer Prevention and Control), for his consultation on the analysis, members of CDC’s Division of Cancer Prevention and Control Internal Data User Group for their critical feedback at the beginning phases of this project, and the central cancer registries that support data collection for NPCR.
Footnotes
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article.
Data Availability: The National Program of Cancer Registries dataset used for this analysis is not publicly available because of privacy and legal restrictions. Information about accessing public-use data from the US Cancer Statistics can be found at https://www.cdc.gov/cancer/uscs/.
Disclaimer: The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of CDC.
Supplemental Material: Supplemental material for this article is available online. The authors have provided these supplemental materials to give readers additional information about their work. These materials have not been edited or formatted by Public Health Reports’s scientific editors and, thus, may not conform to the guidelines of the AMA Manual of Style, 11th Edition.
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Associated Data
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Supplementary Materials
sj-docx-1-phr-10.1177_00333549251410521 – Supplemental material for Differences in Lung Cancer Survival by Demographic Characteristics and Social Determinants of Health, United States, 2010-2020
Supplemental material, sj-docx-1-phr-10.1177_00333549251410521 for Differences in Lung Cancer Survival by Demographic Characteristics and Social Determinants of Health, United States, 2010-2020 by Christine M. Kava, Shifan Dai, David A. Siegel, Susan A. Sabatino, Jin Qin, Florence K.L. Tangka and S. Jane Henley in Public Health Reports®
sj-docx-2-phr-10.1177_00333549251410521 – Supplemental material for Differences in Lung Cancer Survival by Demographic Characteristics and Social Determinants of Health, United States, 2010-2020
Supplemental material, sj-docx-2-phr-10.1177_00333549251410521 for Differences in Lung Cancer Survival by Demographic Characteristics and Social Determinants of Health, United States, 2010-2020 by Christine M. Kava, Shifan Dai, David A. Siegel, Susan A. Sabatino, Jin Qin, Florence K.L. Tangka and S. Jane Henley in Public Health Reports®
sj-docx-3-phr-10.1177_00333549251410521 – Supplemental material for Differences in Lung Cancer Survival by Demographic Characteristics and Social Determinants of Health, United States, 2010-2020
Supplemental material, sj-docx-3-phr-10.1177_00333549251410521 for Differences in Lung Cancer Survival by Demographic Characteristics and Social Determinants of Health, United States, 2010-2020 by Christine M. Kava, Shifan Dai, David A. Siegel, Susan A. Sabatino, Jin Qin, Florence K.L. Tangka and S. Jane Henley in Public Health Reports®
sj-xlsx-4-phr-10.1177_00333549251410521 – Supplemental material for Differences in Lung Cancer Survival by Demographic Characteristics and Social Determinants of Health, United States, 2010-2020
Supplemental material, sj-xlsx-4-phr-10.1177_00333549251410521 for Differences in Lung Cancer Survival by Demographic Characteristics and Social Determinants of Health, United States, 2010-2020 by Christine M. Kava, Shifan Dai, David A. Siegel, Susan A. Sabatino, Jin Qin, Florence K.L. Tangka and S. Jane Henley in Public Health Reports®
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