Abstract
Introduction:
Social determinants of health (SDOH) are known to influence cancer outcomes. It is unknown whether SDOH effects on cancer survival differ by age at diagnosis or whether known differences in survival by race are consistent across age groups. Since the U.S. population is aging and becoming increasingly racially diverse, it is important to understand how SDOH at both neighborhood and individual levels impact survival in cancer survivors across age groups at diagnosis.
Materials and Methods:
This retrospective, population-based study utilized data from the Alabama Statewide Cancer Registry of adults (≥18 years) diagnosed with incident breast, prostate, colorectal (CRC), or lung cancers between 2010 and 2019. We included exposures of social vulnerability index (SVI), rural-urban residency, and insurance status at diagnosis. We evaluated an outcome of overall survival (OS) in months from date of diagnosis to date of death or end of follow-up. We estimated Cox proportional hazards regression models stratified by cancer type adjusting for demographics and clinical characteristics. We conducted additional models stratified by age group at diagnosis (≥65 vs. <65 years) and additionally age-race groups.
Results:
The sample included 26,031 incident breast, 24,567 prostate, 18,821 CRC, and 27,319 lung cancer cases. Across all cancer types, higher SVI was associated with higher risk of death with consistency across age groups. Being insured by Medicare/public insurance was associated with higher risk of death, but effects were higher among those diagnosed <65 and those identifying as White.
Discussion:
Higher levels of SVI and being insured by Medicare or public insurance are associated with worse survival among adults with breast, prostate, CRC, and lung cancer in Alabama with stronger effects observed among those diagnosed under <65. These results indicate that age at diagnosis is important in contextualizing and understanding differences in survival. Future efforts to improve disparities in mortality among cancer survivors should target interventions to those experiencing social vulnerability and younger at diagnosis. However, given that older adults experience differential care needs, social interventions may improve more proximal drivers of mortality in older adults with cancer such as frailty, but this needs further study.
Keywords: social determinants of health, cancer mortality, social vulnerability, rurality, insurance status, age at diagnosis
INTRODUCTION
Most cancer cases occur among older adults age 65 and older. The older adult population is the fastest-growing subset of the United States’ population.1,2 As a result, cancer incidence is expected to increase with 69% of cancer diagnoses expected to occur among older adults by 2040 compared to 60% in 2020.3 In addition, older adults have differential care needs and outcomes compared to younger adults with cancer.4 Moreover, the proportion of individuals identifying as non-White in the United States continues to increase. Non-White populations, particularly individuals identifying as Black, experience poorer cancer outcomes compared to their White counterparts.1,2,5 These ongoing shifts in the sociodemographic distribution of the United States’ population will have differential effects on cancer outcomes and cancer care.3
Social determinants of health (SDOH) are the “conditions in which people are born, grow, learn, work, play, live, and age, and the wider set of structural factors shaping the conditions of daily life.”6 SDOH are inherently neutral concepts, but unfavorable levels of SDOH can have adverse consequences on health outcomes, including cancer survival.6 Non-White individuals are more likely to experience adverse SDOH and often do not experience improvements in cancer survival that are consistent with their White counterparts at more favorable levels of SDOH.7,8
While evidence has examined the relationship between SDOH and cancer survival stratified by race, to our knowledge there is little understanding of how the relationship between SDOH and mortality may differ between older and younger adults with cancer and if racial disparities hold in these associations across age groups. Thus, the goal of this study was to examine the association between various SDOH and survival among cancer survivors stratified by both older versus younger age group at diagnosis and race.
METHODS
Study Population
This is a retrospective, population-based cohort study of data from the Alabama Statewide Cancer Registry (ASCR), which is a statewide, population-based cancer registry collecting data on all incident cases of cancer in Alabama.9 In the current study, we included all adults (≥18 years) diagnosed with incident cancers of the breast, prostate, colon/rectum, or lung between January 1, 2010 and December 31, 2019. We excluded cases that were not the only primary cancer or the first of multiple primary cancers diagnosed in the participant’s lifetime. We did include in situ cases. This study was approved by the Institutional Review Boards of the University of Alabama at Birmingham and the Alabama Department of Public Health. We conducted all procedures in accordance with ethical standards and principles set forth by the Declaration of Helsinki. Data supporting these findings are not publicly available and were used under license for this study, as restrictions apply to their availability, but data are available upon request to the ASCR at the Alabama Department of Public Health.
Exposures
Our primary exposures of interest were census-tract level social vulnerability index (SVI), census tract-level rural-urban status, and individual-level insurance status. We collected census tract at diagnosis from ASCR and linked to the Centers for Disease Control and Prevention’s (CDC) 2010 social vulnerability index. We categorized SVI into quartiles stratified by cancer type. We linked census tract at diagnosis to 2010 Rural-Urban Commuting Area (RUCA) codes to determine rural-urban status of the census tract. We categorized census tracts into metropolitan, micropolitan, small town, or rural utilizing the United States Department of Agriculture Economic Research Service classification.10 We recognize that RUCA codes are not the only possible definition of rural-urban status, but many of the additional federal definitions do not alter observed associations for research conducted in Alabama. For example, our prior paper demonstrated through sensitivity analysis that using the Federal Office of Rural Health Policy definition of rural versus urban does not change results in the same data source used in the current study.11 We also included insurance status based on primary payer at diagnosis from ASCR. We categorized insurance status into categories of private insurance, Medicare, public insurance (non-Medicare), insured, not otherwise specified (NOS), and uninsured.
Outcome
Our primary outcome of interest was overall survival (OS). We calculated survival time in months from the date of diagnosis to the date of death or the date of end of follow-up (if death had not occurred). We set the end of follow-up date to December 31, 2021, as death data are collected via passive surveillance in the ASCR via yearly linkage to the National Death Index and quarterly linkage to the Alabama State Vital Statistics. Since cause of death was unavailable, the outcome was calculated based on death from any cause.
Covariates
We included relevant covariates including age at diagnosis, self-reported sex (for colorectal and lung cancers) and race, Surveillance, Epidemiology, and End Results (SEER) stage at diagnosis, and subtype (for breast cancer). We centered age at diagnosis by subtracting the mean. We also squared the centered age variable to account for violations of the proportional hazards assumption in the Cox proportional regression models (see below). To examine differences by age group at diagnosis (older versus younger), we dichotomized age at diagnosis as ≥65 and <65. We extracted race from ASCR and trichotomized it as White, Black, or other regardless of ethnicity. To examine differences by both age group and race, we categorized participants into age-race groups (≥65 years, White; <65 years, White; ≥65 years, Black; and <65 years, Black). We also included sex abstracted from ASCR. We dichotomized SEER stage as early (in situ or localized) or late (regional or distant involvement). Finally, we defined breast cancer subtype based on both hormone receptors (HR) and human epidermal growth factor-2 receptor (HER2) status. We considered hormone receptor status as positive if either the estrogen receptor or the progesterone receptor was positive. We defined subtypes as HR+/HER2+, HR+/HER2−, HR−/HER2+, or triple-negative breast cancer (TNBC, corresponding to HR−/HER2−). We selected covariates a priori.
Statistical Analysis
We performed a complete case analysis and examined missingness in each sample. In the breast sample, there were 75 participants missing SVI, 1312 missing insurance status, 38 missing rural-urban status, 0 missing the outcome, and 1094 missing covariates (age, race, stage). However, 10,179 were missing the subtype variable primarily due to missing HER2 receptor status. In the prostate sample, 52 participants were missing SVI, 3983 missing insurance status, 31 missing rural-urban status, 0 missing the outcome, and 2301 missing covariates (age, race, stage). In the CRC sample, 34 were missing SVI, 1048 missing insurance status, 17 missing rural-urban status, 0 missing the outcome, and 1285 missing covariates (age, race, sex, stage). Finally, in the lung sample, 53 were missing SVI, 1473 missing insurance status, 36 missing rural-urban status, 0 missing the outcome, and 2260 missing covariates (age, race, sex, stage). We did not consider performing imputation for missing values as availability of the data in ASCR are dependent on what is reported to ADPH from the diagnosing/treating facility. As a result, these data are likely not missing at random because where a participant is diagnosed/treated likely depends on patient-level factors such as where a participant lives, their insurance status, and more.
We conducted a sensitivity analysis excluding subtype in the breast cancer sample. We performed a complete case analysis where participants were excluded if missing any exposure, outcome, or covariates, including any information necessary to define variables (e.g., census tract, date of diagnosis, date of death) We stratified all analyses by cancer type (breast, prostate, colorectal, lung). We examined bivariate descriptive statistics for outcomes and covariates by age group at diagnosis. We used t-tests and χ2 tests for continuous and categorical variables, respectively. In the Supplement, we also present Kaplan-Meier survival curves across each cancer type for each exposure of interest (SVI, insurance status, and rural-urban status) overall and stratified by age group at diagnosis (Figures S1–S8). We completed Cox proportional hazards regression models to evaluate the association between the SDOH of interest and OS. We completed four sets of models: (1) examining the association between SVI and OS adjusted for age, race, sex, cancer stage, and subtype (for breast cancer); (2) examining the association between rural-urban residence status and OS adjusted for the same variables in model 1; (3) examining the association between insurance status and OS adjusted for covariates in model 1; and (4) examining the association between all SDOH of interest (SVI, rural-urban status, and insurance status) and OS adjusted for all variables in model 1. We stratified the final model by age group at diagnosis and further stratified by age-race groups. Supplementary Table S1 provides a table with a detailed summary of these models.
We also conducted a sensitivity analysis with stratification by a 3-level age category (18–50 years, 51–64 years, and ≥65 years). We also conducted a sensitivity analysis adding in the first course treatment (chemotherapy only, radiation only, chemotherapy and radiation, neither chemotherapy nor radiation) and whether participants received surgery (based on if a date of surgery is reported to ASCR). In this sensitivity analysis, we also examined staging as a three-level variable (in situ/localized, regional, and distant). The ASCR only reports first course treatment, which may not reflect the entire treatment course for participants. For all Cox models (except those stratified by age group or age-race groups), we included an interaction term between age at diagnosis and time due to observed violation of the proportional hazards assumption for age at diagnosis. In the Supplement, we also include a table detailing evaluation and results of checking proportional hazards assumptions (Supplementary Table S2). We included cluster-robust standard errors to account for clustering by census tracts. We performed all analyses using SAS Version 9.4 (SAS Institute, Inc., Cary, NC, USA). We set statistical significance at α=0.05.
RESULTS
Description of Study Sample
Figure 1 demonstrates the study flow to obtain the final analytic sample sizes by cancer type. There were a total of 26,031 participants with breast cancer, 24,567 with prostate cancer, 18,821 with colorectal cancer, and 27,319 with lung cancer. Among those with breast cancer, those ≥65 years old at diagnosis were more likely to be White (79.5% vs. 69.3%, p-value: <0.001), have HR+/HER2− breast cancer (74.3% vs. 63.3%, p-value: <0.001), be insured through Medicare (85.8% vs. 10.1%, p-value: <0.001), and to have died during follow up (31.5% vs. 15.8%, p-value: <0.001) [Table 1]. Those older at diagnosis were also less likely to be diagnosed with late-stage disease (31.2% vs. 38.3%, p-value: <0.001). Finally, those who were older at diagnosis had shorter follow-up time (63.4 months vs. 73.7 months, p-value: <0.001) [Table 1].
Figure 1.
Participant flow to arrive at final analytic sample.
Table 1.
Participant Characteristics by Age Group at Diagnosis Stratified by Cancer Type.
| Variable | Breast (n=26,031) | Prostate (n=24,567) | Colorectal (n=18,821) | Lung (n=27,319) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total | ≥65 years | <65 years | Total | ≥65 years | <65 years | Total | ≥65 years | <65 years | Total | ≥65 years | <65 years | |
| Race, n (%) | ||||||||||||
| White | 19168 (73.6) | 8767 (79.5) | 10401 (69.3) | 16345 (66.5) | 9233 (73.0) | 7112 (59.7) | 13908 (73.9) | 7510 (78.0) | 6398 (69.6) | 21883 (80.1) | 13833 (83.4) | 8050 (75.1) |
| Black | 6581 (25.3) | 2194 (19.9) | 4387 (29.3) | 8033 (32.7) | 3317 (26.2) | 4716 (39.6) | 4712 (25.0) | 2049 (21.3) | 2663 (29.0) | 5260 (19.3) | 2664 (16.1) | 2596 (24.2) |
| Other | 282 (1.1) | 71 (0.6) | 211 (1.4) | 189 (0.8) | 94 (0.7) | 95 (0.8) | 201 (1.1) | 73 (0.8) | 128 (1.4) | 176 (0.6) | 98 (0.6) | 78 (0.7) |
| Sex, Male, n (%) | NA | NA | NA | NA | NA | NA | 9951 (52.9) | 4770 (49.5) | 5181 (56.4) | 15567 (57.0) | 9341 (56.3) | 6226 (58.1) |
| Stage, Late, n (%) | 9181 (35.3) | 3444 (31.2) | 5737 (38.3) | 4626 (18.8) | 2383 (18.9) | 2243 (18.8) | 11015 (58.5) | 5361 (55.7) | 5654 (61.5) | 21390 (78.3) | 12592 (75.9) | 8798 (82.0) |
| Subtype, n (%) | ||||||||||||
| HR+/HER2+ | 3164 (12.2) | 1095 (9.9) | 2069 (13.8) | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| HR+/HER2− | 17689 (68.0) | 8200 (74.3) | 9489 (63.3) | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| HR−/HER2+ | 1332 (5.1) | 476 (4.3) | 856 (5.7) | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| TNBC | 3846 (14.8) | 1261 (11.4) | 2585 (17.2) | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| SVI Quartile, n (%) | ||||||||||||
| Q1 | 6499 (25.0) | 2686 (24.4) | 3813 (25.4) | 6137 (25.0) | 3124 (24.7) | 3013 (25.3) | 4702 (25.0) | 2437 (25.3) | 2265 (24.7) | 6817 (25.0) | 4433 (26.7) | 2384 (22.2) |
| Q2 | 6516 (25.0) | 2768 (25.1) | 3748 (25.0) | 6141 (25.0) | 3195 (25.3) | 2946 (24.7) | 4697 (25.0) | 2365 (24.6) | 2332 (25.4) | 6830 (25.0) | 4153 (25.0) | 2677 (25.0) |
| Q3 | 6477 (24.9) | 2772 (25.1) | 3705 (24.7) | 6138 (25.0) | 3273 (25.9) | 2865 (24.0) | 4667 (24.8) | 2406 (25.0) | 2261 (24.6) | 6815 (25.0) | 4051 (24.4) | 2764 (25.8) |
| Q4 | 6539 (25.1) | 2806 (25.4) | 3733 (24.9) | 6151 (25.0) | 3052 (24.1) | 3099 (26.0) | 4755 (25.3) | 2424 (25.2) | 2331 (25.4) | 6857 (25.1) | 3958 (23.9) | 2899 (27.0) |
| Rural-Urban Status, n (%) | ||||||||||||
| Metropolitan | 20333 (78.1) | 8553 (77.5) | 11780 (78.5) | 18708 (76.2) | 9393 (74.3) | 9315 (78.1) | 13949 (74.1) | 7059 (73.3) | 6890 (75.0) | 19900 (72.8) | 12117 (73.0) | 7783 (72.6) |
| Micropolitan | 2915 (11.2) | 1263 (11.5) | 1652 (11.0) | 2936 (12.0) | 1641 (13.0) | 1295 (10.9) | 2407 (12.8) | 1262 (13.1) | 1145 (12.5) | 3876 (14.2) | 2349 (14.2) | 1527 (14.2) |
| Small Town | 1731 (6.7) | 772 (7.0) | 959 (6.4) | 1802 (7.3) | 998 (7.9) | 804 (6.7) | 1497 (8.0) | 794 (8.2) | 703 (7.7) | 2220 (8.1) | 1356 (8.2) | 864 (8.1) |
| Rural | 1052 (4.0) | 444 (4.0) | 608 (4.1) | 1121 (4.6) | 612 (4.8) | 509 (4.3) | 968 (5.1) | 517 (5.4) | 451 (4.9) | 1323 (4.8) | 773 (4.7) | 550 (5.1) |
| Insurance Status, n (%) | ||||||||||||
| Private | 9589 (36.8) | 989 (9.0) | 8600 (57.3) | 8348 (34.0) | 1609 (12.7) | 6739 (56.5) | 5277 (28.0) | 768 (8.0) | 4509 (49.1) | 5058 (18.5) | 1293 (7.8) | 3765 (35.1) |
| Medicare | 10969 (42.1) | 9460 (85.8) | 1509 (10.1) | 11638 (47.4) | 9918 (78.4) | 1720 (14.4) | 9685 (51.5) | 8295 (86.1) | 1390 (15.1) | 16940 (62.0) | 14310 (86.2) | 2630 (24.5) |
| Public (non-Medicare) | 2232 (8.6) | 174 (1.6) | 2058 (13.7) | 1922 (7.8) | 556 (4.4) | 1366 (11.5) | 1318 (7.0) | 228 (2.4) | 1090 (11.9) | 2609 (9.6) | 574 (3.5) | 2035 (19.0) |
| Insured, NOS | 2670 (10.3) | 364 (3.3) | 2306 (15.4) | 2135 (8.7) | 498 (3.9) | 1637 (13.7) | 1079 (5.7) | 288 (3.0) | 1174 (12.8) | 1171 (4.3) | 338 (2.0) | 833 (7.8) |
| Uninsured | 571 (2.2) | 45 (0.4) | 526 (3.5) | 524 (2.1) | 63 (0.5) | 461 (3.9) | 1462 (7.8) | 53 (0.6) | 1026 (11.2) | 1541 (5.6) | 80 (0.5) | 1461 (13.6) |
| Died, n (%) | 5836 (22.4) | 3472 (31.5) | 2364 (15.8) | 5205 (21.2) | 3707 (29.3) | 1498 (12.6) | 9108 (48.4) | 5542 (57.5) | 3566 (38.8) | 22812 (83.5) | 14213 (85.7) | 8599 (80.2) |
| Follow-up time, months, mean (SD) | 69.3 (37.0) | 63.4 (36.1) | 73.7 (37.0) | 73.5 (37.6) | 67.7 (37.3) | 79.7 (37.0) | 52.8 (39.6) | 46.6 (38.5) | 59.2 (39.7) | 22.1 (29.0) | 20.5 (27.3) | 24.6 (31.4) |
Statistics estimated via t-tests and χ2 tests for continuous and categorical variables, respectively.
Abbreviations: HER2, human epidermal growth factor-2; HR, hormone receptor; NOS, not otherwise specified; SD, standard deviation; SVI, social vulnerability index.
Among those with prostate cancer, descriptive statistics across age groups at diagnosis were generally similar to those with breast cancer. However, there was no statistically significant difference in stage at diagnosis and those older at diagnosis were more likely to live in census tracts in SVI quartiles 2 or 3 (Q2: 25.3% vs. 24.7%; Q3: 25.9% vs. 24.0%, p-value: <0.001). Additionally, those older at diagnosis were more likely to live in micropolitan or small town census tracts (micropolitan: 13.0% vs. 10.9%; small town: 7.9% vs. 6.7%, p-value: <0.001) [Table 1]. Among those with colorectal cancer, descriptive statistics across age groups at diagnosis were also similar, but there was no statistically significant difference in SVI quartile of residence or rural-urban status. Additionally, those older at diagnosis were less likely to be male (49.5% vs. 56.4%, p-value: <0.001) [Table 1]. Finally, among those with lung cancer, descriptive statistics across age groups at diagnosis were also similar. However, those older at diagnosis were more likely to live in lower SVI quartiles (Q1: 26.7% vs. 22.2%; Q2: 25.0% vs. 25.0%, p-value: <0.001) [Table 1].
Cox Proportional Hazards Regression Models
Among participants with breast cancer in Alabama, results from model 1 indicated that higher SVI quartile (indicating increasing social vulnerability) is associated with higher hazard of death compared to the lowest SVI quartile, particularly for those living in the highest SVI quartile (Q2: 1.08 [95% CI: 1.00, 1.17]; Q3: 1.23 [95% CI: 1.13, 1.33]; Q4: 1.23 [95% CI: 1.14, 1.34]) [Table 2]. In model 2, there was no difference in the hazard of death based on rural-urban residency status compared to metropolitan residence except for among those living in rural areas (micropolitan: 0.98 [95% CI: 0.89, 1.07]; small town: 1.08 [95% CI: 0.97, 1.21]; rural: 1.20 [95% CI: 1.04, 1.37]) [Table 2]. In model 3, being insured by Medicare, other public insurance programs, and being uninsured were associated with higher hazard of death compared to private insurance (Medicare: 1.88 [95% CI: 1.72, 2.06]; other public insurance [non-Medicare]: 1.82 [95% CI: 1.62, 2.05]; uninsured: 2.52 [95% CI: 2.11, 3.02]) [Table 2]. In model 4 adjusting for all SDOH and covariates, results for SVI, rural-urban residency status, and insurance status were consistent with models 1–3 except the rural association lost statistical significance and results for SVI were slightly attenuated [Table 2].
Table 2.
Hazard Ratios (HRs) and 95% Confidence Intervals (95% CIs) of the Association between Social Determinants of Health, Covariates, and Overall Survival Stratified by Cancer Type.
| Variable | Breast | Prostate | Colorectal | Lung | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | Model 4 | Model 1 | Model 2 | Model 3 | Model 4 | Model 1 | Model 2 | Model 3 | Model 4 | Model 1 | Model 2 | Model 3 | Model 4 | |
| SVI Quartile | ||||||||||||||||
| Q1 | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | ||||||||
| Q2 | 1.08 (1.00, 1.17) | 1.04 (0.96, 1.13) | 1.14 (1.03, 1.26) | 1.07 (0.97, 1.18) | 1.11 (1.04, 1.19) | 1.10 (1.03, 1.17) | 1.15 (1.10, 1.20) | 1.13 (1.09, 1.18) | ||||||||
| Q3 | 1.23 (1.13, 1.33) | 1.17 (1.08, 1.27) | 1.30 (1.18, 1.44) | 1.19 (1.08, 1.31) | 1.13 (1.06, 1.21) | 1.10 (1.03, 1.18) | 1.15 (1.10, 1.21) | 1.13 (1.08, 1.18) | ||||||||
| Q4 | 1.23 (1.14, 1.34) | 1.16 (1.07, 1.26) | 1.41 (1.27, 1.56) | 1.24 (1.12, 1.38) | 1.19 (1.12, 1.28) | 1.15 (1.08, 1.23) | 1.08 (1.00, 1.16) | 1.05 (0.98, 1.13) | ||||||||
| Rural-Urban Status | ||||||||||||||||
| Metropolitan | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | ||||||||
| Micropolitan | 0.98 (0.89, 1.07) | 0.91 (0.83, 1.00) | 1.31 (1.19, 1.43) | 1.19 (1.09, 1.31) | 1.06 (1.00, 1.14) | 1.03 (0.96, 1.10) | 1.05 (0.99, 1.11) | 1.03 (0.98, 1.09) | ||||||||
| Small Town | 1.08 (0.97, 1.21) | 1.01 (0.91, 1.13) | 1.28 (1.15, 1.42) | 1.17 (1.05, 1.30) | 1.06 (0.99, 1.14) | 1.01 (0.95, 1.09) | 1.01 (0.96, 1.07) | 0.99 (0.93, 1.04) | ||||||||
| Rural | 1.20 (1.04, 1.37) | 1.09 (0.95, 1.25) | 1.11 (0.91, 1.35) | 1.00 (0.83, 1.21) | 1.07 (0.98, 1.18) | 1.01 (0.91, 1.13) | 1.09 (1.06, 1.14) | 1.08 (1.04, 1.12) | ||||||||
| Insurance Status | ||||||||||||||||
| Private | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | ||||||||
| Medicare | 1.88 (1.72, 2.06) | 1.86 (1.70, 2.04) | 2.36 (2.14, 2.61) | 2.30 (2.08, 2.54) | 1.31 (1.22, 1.41) | 1.30 (1.21, 1.40) | 1.11 (1.06, 1.16) | 1.10 (1.05, 1.16) | ||||||||
| Public (non-Medicare) | 1.82 (1.62, 2.05) | 1.79 (1.59, 2.01) | 1.96 (1.73, 2.22) | 1.92 (1.69, 2.18) | 1.62 (1.47, 1.79) | 1.60 (1.45, 1.76) | 1.28 (1.18, 1.40) | 1.27 (1.17, 1.39) | ||||||||
| Insured, NOS | 1.12 (0.99, 1.26) | 1.11 (0.98, 1.26) | 1.05 (0.90, 1.23) | 1.05 (0.90, 1.22) | 1.08 (0.93, 1.24) | 1.08 (0.94, 1.24) | 1.21 (1.07, 1.37) | 1.20 (1.07, 1.35) | ||||||||
| Uninsured | 2.52 (2.11, 3.02) | 2.47 (2.06, 2.95) | 3.28 (2.74, 3.92) | 3.17 (2.65, 3.80) | 1.56 (1.38, 1.77) | 1.54 (1.36, 1.74) | 1.54 (1.41, 1.69) | 1.53 (1.40, 1.68) | ||||||||
| Age at Diagnosis | 1.14 (1.14, 1.15) | 1.14 (1.14, 1.15) | 1.13 (1.13, 1.14) | 1.13 (1.13, 1.14) | 1.20 (1.20, 1.21) | 1.20 (1.20, 1.21) | 1.19 (1.18, 1.20) | 1.19 (1.18, 1.20) | 1.10 (1.10, 1.11) | 1.10 (1.10, 1.11) | 1.10 (1.10, 1.10) | 1.10 (1.10, 1.10) | 1.07 (1.07, 1.07) | 1.07 (1.07, 1.07) | 1.07 (1.07, 1.07) | 1.07 (1.07, 1.07) |
| Race | ||||||||||||||||
| White | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) |
| Black | 1.06 (0.99, 1.13) | 1.11 (1.04, 1.19) | 1.07 (1.00, 1.14) | 1.02 (0.95, 1.09) | 1.08 (1.01, 1.16) | 1.20 (1.12, 1.28) | 1.10 (1.03, 1.18) | 1.05 (0.98, 1.13) | 1.08 (1.03, 1.14) | 1.13 (1.08, 1.19) | 1.09 (1.04, 1.15) | 1.06 (1.00, 1.11) | 0.99 (0.93, 1.06) | 0.99 (0.94, 1.05) | 0.98 (0.92, 1.03) | 0.99 (0.92, 1.05) |
| Other | 0.46 (0.32, 0.67) | 0.46 (0.31, 0.66) | 0.43 (0.29, 0.63) | 0.43 (0.30, 0.64) | 0.97 (0.71, 1.34) | 1.01 (0.73, 1.39) | 0.96 (0.70, 1.30) | 0.97 (0.71, 1.32) | 0.66 (0.53, 0.83) | 0.66 (0.53, 0.83) | 0.64 (0.50, 0.81) | 0.64 (0.50, 0.81) | 0.89 (0.78, 1.00) | 0.88 (0.77, 1.00) | 0.87 (0.77, 0.99) | 0.88 (0.77, 1.00) |
| Sex | ||||||||||||||||
| Male | NA | NA | NA | NA | NA | NA | NA | NA | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) |
| Female | NA | NA | NA | NA | NA | NA | NA | NA | 0.81 (0.77, 0.84) | 0.81 (0.77, 0.84) | 0.80 (0.77, 0.84) | 0.80 (0.77, 0.84) | 0.91 (0.88, 0.94) | 0.91 (0.88, 0.94) | 0.91 (0.88, 0.94) | 0.91 (0.88, 0.94) |
| Stage at Diagnosis | ||||||||||||||||
| Early | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) |
| Late | 1.96 (1.86, 2.06) | 1.97 (1.87, 2.08) | 1.92 (1.82, 2.02) | 1.92 (1.82, 2.02) | 1.33 (1.24, 1.43) | 1.34 (1.24, 1.44) | 1.34 (1.25, 1.44) | 1.34 (1.24, 1.43) | 1.48 (1.41, 1.55) | 1.48 (1.41, 1.55) | 1.46 (1.40, 1.54) | 1.47 (1.40, 1.54) | 1.32 (1.23, 1.42) | 1.33 (1.23, 1.43) | 1.31 (1.22, 1.40) | 1.31 (1.22, 1.40) |
| Subtype | ||||||||||||||||
| HR+/HER2+ | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| HR+/HER2− | 1.14 (1.03, 1.25) | 1.13 (1.03, 1.24) | 1.14 (1.04, 1.25) | 1.14 (1.04, 1.25) | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| HR−/HER2+ | 1.29 (1.13, 1.49) | 1.28 (1.12, 1.47) | 1.32 (1.15, 1.51) | 1.31 (1.14, 1.51) | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| TNBC | 1.50 (1.34, 1.68) | 1.49 (1.33, 1.66) | 1.47 (1.32, 1.64) | 1.47 (1.32, 1.64) | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
Statistical significance set at α=0.05.
Bold indicates statistical significance.
Model 1: social vulnerability index (SVI) adjusted for covariates: age at diagnosis, race, sex (colorectal and lung cancer), stage at diagnosis, subtype (breast cancer)
Model 2: rural-urban status adjusted for covariates
Model 3: insurance status adjusted for covariates
Model 4: SVI, rural-urban status, insurance status adjusted for covariates
Estimated using Cox proportional hazards regression models with robust standard errors to account for census tract clustering
Abbreviations: HER2, human epidermal growth factor-2; HR, hormone receptor; NA, not applicable; NOS, not otherwise specified; TNBC, triple-negative breast cancer.
Among participants with prostate cancer in Alabama, results from model 1 indicated increasing effect sizes for statistically significant higher hazard of death with increasing SVI quartile relative to the lowest SVI quartile (Q2: 1.14 [95% CI: 1.03, 1.26]; Q3: 1.30 [95% CI: 1.18, 1.44]; Q4: 1.41 [95% CI: 1.27, 1.56]) [Table 2]. In model 2, micropolitan residence and small town residence were associated with higher hazard of death compared to metropolitan residence (micropolitan: 1.31 [95% CI: 1.19, 1.43]; small town: 1.28 [95% CI: 1.15, 1.42]) [Table 2]. In model 3, being insured via Medicare, other public insurance, and being uninsured were associated with higher hazard of death compared to private insurance (Medicare: 2.36 [95% CI: 2.14, 2.61]; other public insurance [non-Medicare]: 1.96 [95% CI: 1.73, 2.22]; uninsured: 3.28 [95% CI: 2.74, 3.92]) [Table 2]. In model 4 adjusting for all SDOH and covariates, results for SVI and insurance status were consistent with slight attenuation of SVI effect sizes. Results for micropolitan and rural residence attenuated compared to model 2, but retained statistical significance (Table 2).
Among participants with CRC in Alabama, models 1, 2, and 3 results were generally consistent with results among participants with breast cancer. In model 4, adjusting for all SDOH and covariates, results were consistent with models 1–3. Increasing SVI quartiles were associated with higher hazard of death compared to SVI quartile 1 (Q2: 1.10 [95% CI: 1.03, 1.17]; Q3: 1.10 [95% CI: 1.03, 1.18]; Q4: 1.15 [95% CI: 1.08, 1.23]) [Table 2]. There was no difference in hazard of death for any residency status compared to metropolitan (Table 2). Being insured by Medicare, other public insurance, and being uninsured were associated with higher hazard of death compared to private insurance (Medicare: 1.30 [95% CI: 1.21, 1.41]; other public insurance [non-Medicare]: 1.60 [1.45, 1.76]; uninsured: 1.54 [1.36, 1.74]) [Table 2].
Finally, among participants with lung cancer in Alabama, models 1 results were consistent with those for breast and CRC (SVI Q2: 1.15 [95% CI: 1.10, 1.20]; SVI Q3: 1.15 [95% CI: 1.10, 1.21]; SVI Q4: 1.08 [95% CI: 1.00, 1.16]) [Table 2]. In model 2, rural residence was associated with 9% higher hazard of death compared to metropolitan residence (1.09 [95% CI: 1.06, 1.14]) [Table 2]. In model 3, being insured by Medicare, other public insurance, insured via a not otherwise specified plan, and being uninsured were associated with higher hazard of death compared to private insurance (Medicare: 1.11 [95% CI: 1.06, 1.16]; other public insurance [non-Medicare]: 1.28 [95% CI: 1.18, 1.40]; insured, not otherwise specified: 1.21 [95% CI: 1.07, 1.37]; uninsured: 1.54 [95% CI: 1.41, 1.69]) [Table 2]. In model 4 adjusting for all SDOH and covariates, results were consistent with models 1–3 (Table 2). On sensitivity analysis adding in treatment, receipt of surgery, and three-level cancer staging, results did not change (Supplementary Table S3). Additionally, on sensitivity analysis excluding subtype due to missingness, results did not change (Supplementary Table S4).
Stratified Cox Proportional Hazards Regression Models
Some differences were observed upon stratification by age group at diagnosis. Across all cancer types, effect sizes for the association between SVI quartile and increasing hazard of death were generally similar across age group strata. The 95% CIs overlapped across all SVI quartiles and age groups for each cancer type (Table 3). Effects for rural-urban residency status were inconsistent across cancer types. For example, the effect for living in a small town was lower in magnitude among those ≥65 than those <65 with breast cancer, but all other effects were similar. The effect for living in a micropolitan area was higher in magnitude among those <65 with prostate cancer, but all other effects were similar. Regardless, 95% CIs overlapped heavily for all residency effects across age groups and cancer types. similar across all residency categories and age groups for each cancer type (Table 3). Finally, effects of higher hazard of death among those insured by Medicare, other public insurance, or uninsured were higher among those <65 at diagnosis except for those insured by plans not otherwise specified. However, 95% CIs largely overlapped across insurance status, but not between age groups, suggesting that those younger than 65 at diagnosis may experience greater effects of having public insurance or no insurance compared to private insurance than those 65 and older at diagnosis (Table 3). On sensitivity analysis by three-level age categories, the pattern remained the same (Supplementary Table S4).
Table 3.
Hazard Ratios (HRs) and 95% Confidence Intervals (95% CIs) of the Association between Social Determinants of Health and Covariates on Overall Survival across Cancer Types Stratified by Age Group at Diagnosis.
| Variable | Breast | Prostate | Colorectal | Lung | ||||
|---|---|---|---|---|---|---|---|---|
| ≥65 Years | <65 Years | ≥65 Years | <65 Years | ≥65 Years | <65 Years | ≥65 Years | <65 Years | |
| SVI Quartile | ||||||||
| Q1 | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) |
| Q2 | 1.13 (1.02, 1.26) | 1.06 (0.93, 1.22) | 1.29 (1.16, 1.44) | 1.18 (0.97, 1.43) | 1.03 (0.95, 1.11) | 1.01 (0.91, 1.11) | 1.01 (0.97, 1.06) | 1.07 (1.00, 1.15) |
| Q3 | 1.18 (1.06, 1.31) | 1.24 (1.09, 1.41) | 1.33 (1.20, 1.49) | 1.47 (1.21, 1.79) | 1.12 (1.04, 1.21) | 1.14 (1.04, 1.26) | 1.10 (1.05, 1.15) | 1.08 (1.01, 1.15) |
| Q4 | 1.30 (1.16, 1.45) | 1.20 (1.05, 1.37) | 1.48 (1.32, 1.65) | 1.50 (1.23, 1.84) | 1.15 (1.06, 1.25) | 1.13 (1.02, 1.26) | 1.12 (1.07, 1.18) | 1.10 (1.03, 1.18) |
| Rural-Urban Status | ||||||||
| Metropolitan | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) |
| Micropolitan | 0.91 (0.81, 1.03) | 0.90 (0.79, 1.03) | 1.01 (0.92, 1.12) | 1.22 (1.04, 1.44) | 0.88 (0.80, 0.96) | 1.03 (0.93, 1.14) | 0.99 (0.94, 1.04) | 1.04 (0.98, 1.10) |
| Small Town | 0.88 (0.78, 1.00) | 1.03 (0.87, 1.22) | 1.03 (0.91, 1.17) | 1.15 (0.93, 1.42) | 0.96 (0.87, 1.07) | 1.03 (0.90, 1.18) | 0.96 (0.89, 1.03) | 1.02 (0.94, 1.10) |
| Rural | 0.91 (0.75, 1.09) | 1.02 (0.84, 1.24) | 0.96 (0.81, 1.13) | 1.15 (0.92, 1.42) | 0.89 (0.78, 1.03) | 1.00 (0.85, 1.17) | 0.98 (0.91, 1.05) | 1.00 (0.92, 1.09) |
| Insurance Status | ||||||||
| Private | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) |
| Medicare | 1.49 (1.30, 1.71) | 2.50 (2.21, 2.82) | 1.53 (1.37, 1.72) | 2.29 (2.01, 2.62) | 1.40 (1.26, 1.56) | 1.75 (1.59, 1.93) | 1.11 (1.04, 1.18) | 1.34 (1.27, 1.42) |
| Public (non-Medicare) | 1.28 (0.91, 1.76) | 2.06 (1.83, 2.32) | 1.44 (1.19, 1.74) | 2.26 (1.93, 2.66) | 1.34 (1.10, 1.64) | 1.74 (1.57, 1.93) | 1.03 (0.93, 1.14) | 1.36 (1.28, 1.44) |
| Insured, NOS | 0.95 (0.73, 1.24) | 1.13 (0.99, 1.30) | 1.33 (1.08, 1.63) | 0.89 (0.73, 1.08) | 1.15 (0.94, 1.40) | 1.05 (0.94, 1.17) | 1.22 (1.07, 1.40) | 1.09 (1.00, 1.20) |
| Uninsured | 2.00 (1.24, 3.24) | 2.62 (2.18, 3.14) | 1.71 (1.07, 2.74) | 3.50 (2.85, 4.31) | 1.18 (0.77, 1.81) | 1.67 (1.51, 1.85) | 1.44 (1.14, 1.82) | 1.46 (1.36, 1.57) |
| Race | ||||||||
| White | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) |
| Black | 1.07 (0.98, 1.17) | 1.13 (1.02, 1.24) | 1.09 (1.00, 1.18) | 1.18 (1.04, 1.33) | 1.04 (0.98, 1.12) | 1.04 (0.96, 1.12) | 0.99 (0.95, 1.04) | 0.93 (0.88, 0.98) |
| Other | 0.68 (0.42, 1.10) | 0.22 (0.11, 0.45) | 0.98 (0.67, 1.45) | 0.41 (0.17, 0.98) | 0.63 (0.46, 0.88) | 0.59 (0.41, 0.84) | 0.82 (0.63, 1.06) | 0.70 (0.55, 0.89) |
| Sex | ||||||||
| Male | NA | NA | NA | NA | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) |
| Female | NA | NA | NA | NA | 0.92 (0.87, 0.97) | 0.81 (0.75, 0.86) | 0.83 (0.81, 0.86) | 0.82 (0.78, 0.85) |
| Stage at Diagnosis | ||||||||
| Early | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) |
| Late | 2.39 (2.23, 2.56) | 3.77 (3.46, 4.10) | 2.33 (2.15, 2.51) | 3.10 (2.79, 3.46) | 2.03 (1.93, 2.15) | 3.94 (3.62, 4.30) | 2.36 (2.26, 2.46) | 2.75 (2.58, 2.94) |
| Subtype | ||||||||
| HR+/HER2+ | 1.0 (Ref) | 1.0 (Ref) | NA | NA | NA | NA | NA | NA |
| HR+/HER2− | 0.91 (0.82, 1.02) | 1.13 (0.99, 1.28) | NA | NA | NA | NA | NA | NA |
| HR−/HER2+ | 1.18 (0.98, 1.42) | 1.40 (1.15, 1.69) | NA | NA | NA | NA | NA | NA |
| TNBC | 1.42 (1.25, 1.63) | 2.04 (1.77, 2.36) | NA | NA | NA | NA | NA | NA |
Statistical significance set at α=0.05.
Bold indicates statistical significance.
Estimated using Cox proportional hazards regression models with robust standard errors to account for census tract clustering.
Abbreviations: HER2, human epidermal growth factor-2; HR, hormone receptor; NA, not applicable; NOS, not otherwise specified; SVI, social vulnerability index; TNBC, triple-negative breast cancer.
Upon stratification by age-race groups (≥65 years, White; <65 years, White; ≥65 years, Black; and <65 years, Black), among those with breast cancer, effects of higher SVI quartile on higher hazard of death remained among White individuals. Similar statistically significant associations were not observed among Black individuals. However, 95% CIs overlapped. Effects of SVI on hazard of death were generally similar across age-race groups among those with colorectal or lung cancer with overlap of 95% CIs. However, among White men with prostate cancer, increasing SVI quartile was significantly associated with higher hazard of death, particularly among those <65 years old at diagnosis – though, there was some overlap between 95% CIs across age group at diagnosis among White men with prostate cancer. Effects of SVI were not statistically significant among Black men with prostate cancer except at the highest SVI quartile (Q4) and age differences were only evident at the highest SVI quartile among Black men. Hazard ratios were also higher in magnitude among White compared to Black men with prostate cancer with some overlap of 95% CIs (Table 4). Effects of rural-urban residency status were generally consistent across age-race groups and cancer types (Table 4). Being insured via Medicare, public insurance, or being uninsured resulted in higher hazard of death across all cancer types, but effect sizes were generally higher among White individuals and those diagnosed at 65 years or younger across all cancer types (Table 4).
Table 4.
Hazard Ratios (HRs) and 95% Confidence Intervals (95% CIs) of the Association between Social Determinants of Health and Covariates on Overall Survival across Cancer Types Stratified by Age-Race Group at Diagnosis.
| Variable | Breast | Prostate | Colorectal | Lung | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ≥65 Years, White | <65 Years, White | ≥65 Years, Black | <65 Years, Black | ≥65 Years, White | <65 Years, White | ≥65 Years, Black | <65 Years, Black | ≥65 Years, White | <65 Years, White | ≥65 Years, Black | <65 Years, Black | ≥65 Years, White | <65 Years, White | ≥65 Years, Black | <65 Years, Black | |
| SVI Quartile | ||||||||||||||||
| Q1 | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) |
| Q2 | 1.11 (1.00, 1.25) | 1.13 (0.97, 1.31) | 1.27 (0.91, 1.78) | 0.87 (0.65, 1.16) | 1.32 (1.18, 1.48) | 1.31 (1.04, 1.64) | 1.16 (0.88, 1.53) | 0.89 (0.62, 1.28) | 1.03 (0.95, 1.12) | 1.00 (0.89, 1.12) | 1.02 (0.82, 1.27) | 1.03 (0.83, 1.28) | 1.03 (0.98, 1.08) | 1.06 (0.99, 1.14) | 0.90 (0.76, 1.07) | 1.16 (0.95, 1.42) |
| Q3 | 1.18 (1.06, 1.32) | 1.26 (1.08, 1.48) | 1.22 (0.87, 1.71) | 1.11 (0.85, 1.44) | 1.34 (1.19, 1.50) | 1.64 (1.30, 2.08) | 1.25 (0.99, 1.58) | 1.14 (0.82, 1.58) | 1.12 (1.04, 1.22) | 1.14 (1.02, 1.27) | 1.11 (0.91, 1.36) | 1.14 (0.94, 1.39) | 1.10 (1.05, 1.16) | 1.08 (1.00, 1.16) | 1.02 (0.88, 1.19) | 1.08 (0.90, 1.29) |
| Q4 | 1.33 (1.17, 1.50) | 1.23 (1.03, 1.46) | 1.28 (0.94, 1.74) | 1.09 (0.85, 1.38) | 1.47 (1. 29, 1.68) | 1.56 (1.19, 2.04) | 1.41 (1.14, 1.76) | 1.26 (0.92, 1.73) | 1.19 (1.09, 1.31) | 1.11 (0.97, 1.27) | 1.08 (0.90, 1.29) | 1.17 (0.97, 1.41) | 1.14 (1.08, 1.20) | 1.11 (1.02, 1.19) | 1.03 (0.90, 1.18) | 1.12 (0.95, 1.33) |
| Rural-Urban Status Status | ||||||||||||||||
| Metropolitan | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) |
| Micropolitan | 0.94 (0.82, 1.07) | 0.91 (0.77, 1.08) | 0.75 (0.56, 1.01) | 0.86 (0.68, 1.08) | 1.00 (0.86, 1.16) | 1.32 (1.09, 1.60) | 1.17 (0.96, 1.42) | 1.04 (0.79, 1.38) | 0.85 (0.77, 0.95) | 1.04 (0.93, 1.17) | 0.93 (0.78, 1.11) | 0.97 (0.80, 1.18) | 0.99 (0.94, 1.05) | 1.00 (0.94, 1.07) | 0.95 (0.82, 1.10) | 1.24 (1.06, 1.45) |
| Small Town | 0.84 (0.73, 0.97) | 0.98 (0.78, 1.24) | 1.00 (0.79, 1.26) | 1.11 (0.88, 1.40) | 0.92 (0.76, 1.10) | 1.15 (0.87, 1.51) | 1.10 (0.88, 1.39) | 1.16 (0.86, 1.58) | 0.94 (0.84, 1.04) | 1.02 (0.88, 1.19) | 1.02 (0.81, 1.27) | 1.06 (0.84, 1.34) | 0.96 (0.89, 1.04) | 1.00 (0.91, 1.10) | 0.93 (0.81, 1.08) | 1.05 (0.89, 1.24) |
| Rural | 0.90 (0.73, 1.11) | 1.17 (0.92, 1.47) | 0.91 (0.62, 1.33) | 0.86 (0.60, 1.22) | 0.92 (0.76, 1.10) | 1.37 (1.01, 1.88) | 1.11 (0.83, 1.49) | 0.95 (0.72, 1.27) | 0.92 (0.80, 1.07) | 0.98 (0.82, 1.18) | 0.76 (0.58, 0.99) | 1.01 (0.73, 1.38) | 0.96 (0.89, 1.05) | 0.98 (0.87, 1.10) | 1.07 (0.91, 1.28) | 1.05 (0.89, 1.24) |
| Insurance Status | ||||||||||||||||
| Private | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) |
| Medicare | 1.55 (1.32, 1.82) | 3.02 (2.58, 3.54) | 1.36 (1.01, 1.82) | 1.92 (1.59, 2.32) | 1.55 (1.35, 1.76) | 2.38 (1.98, 2.86) | 1.44 (1.15, 1.80) | 2.19 (1.80, 2.66) | 1.45 (1.28, 1.64) | 1.81 (1.61, 2.03) | 1.25 (1.00, 1.56) | 1.61 (1.36, 1.90) | 1.12 (1.05, 1.20) | 1.35 (1.27, 1.43) | 1.02 (0.87, 1.20) | 1.32 (1.18, 1.49) |
| Public (non-Medicare) | 1.59 (1.11, 2.29) | 2.24 (1.92, 2.62) | 0.62 (0.28, 1.37) | 1.78 (1.50, 2.12) | 1.44 (1.11, 1.86) | 2.41 (1.91, 3.04) | 1.39 (1.03, 1.89) | 2.13 (1.71, 2.64) | 1.39 (1.10, 1.75) | 1.77 (1.54, 2.03) | 1.19 (0.80, 1.78) | 1.63 (1.37, 1.92) | 1.04 (0.92, 1.17) | 1.37 (1.27, 1.47) | 0.95 (0.74, 1.22) | 1.32 (1.17, 1.49) |
| Insured, NOS | 1.05 (0.78, 1.42) | 1.05 (0.89, 1.24) | 0.78 (0.45, 1.36) | 1.29 (1.03, 1.62) | 1.27 (0.98, 1.65) | 0.78 (0.60, 1.01) | 1.41 (1.00, 1.98) | 1.02 (0.78, 1.35) | 1.15 (0.91, 1.46) | 1.03 (0.91, 1.18) | 1.14 (0.76, 1.69) | 1.00 (0.81, 1.24) | 1.24 (1.07, 1.44) | 1.10 (1.00, 1.22) | 1.08 (0.77, 1.51) | 1.06 (0.87, 1.30) |
| Uninsured | 2.52 (1.45, 4.37) | 2.68 (2.11, 3.41) | 1.54 (0.63, 3.73) | 2.44 (1.87, 3.18) | 1.55 (0.78, 3.06) | 4.49 (3.41, 5.92) | 2.06 (1.07, 3.97) | 2.91 (2.20, 3.85) | 1.36 (0.82, 2.25) | 1.94 (1.71, 2.19) | 1.52 (0.76, 3.05) | 1.31 (1.09, 1.58) | 1.63 (1.28, 2.06) | 1.48 (1.37, 1.60) | 1.08 (0.64, 1.84) | 1.40 (1.20, 1.64) |
| Sex | ||||||||||||||||
| Male | NA | NA | NA | NA | NA | NA | NA | NA | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) |
| Female | NA | NA | NA | NA | NA | NA | NA | NA | 0.93 (0.87, 0.98) | 0.80 (0.74, 0.87) | 0.92 (0.82, 1.03) | 0.81 (0.72, 0.91) | 0.84 (0.81, 0.87) | 0.81 (0.77, 0.86) | 0.80 (0.73, 0.86) | 0.84 (0.77, 0.91) |
| Stage at Diagnosis | ||||||||||||||||
| Early | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) |
| Late | 2.34 (2.16, 2.53) | 3.78 (3.41, 4.20) | 2.60 (2.25, 3.01) | 3.78 (3.27, 4.36) | 2.18 (1.99, 2.39) | 3.03 (2.61, 3.52) | 2.70 (2.34, 3.12) | 3.19 (2.72, 3.75) | 1.97 (1.86, 2.10) | 3.97 (3.57, 4.43) | 2.26 (2.00, 2.55) | 3.91 (3.38, 4.53) | 2.35 (2.25, 2.46) | 2.61 (2.43, 2.81) | 2.37 (2.13, 2.64) | 3.24 (2.83, 3.72) |
| Subtype | ||||||||||||||||
| HR+/HER2+ | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | 1.0 (Ref) | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| HR+/HER2− | 0.86 (0.76, 0.97) | 1.13 (0.96, 1.33) | 1.11 (0.87, 1.40) | 1.15 (0.92, 1.43) | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| HR−/HER2+ | 1.22 (1.00, 1.49) | 1.37 (1.07, 1.77) | 1.06 (0.71, 1.57) | 1.43 (1.05, 1.93) | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| TNBC | 1.33 (1.14, 1.55) | 2.23 (1.84, 2.70) | 1.74 (1.32, 2.29) | 1.87 (1.49, 2.35) | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
Statistical significance set at α=0.05.
Bold indicates statistical significance.
Estimated using Cox proportional hazards regression models with robust standard errors to account for census tract clustering.
Abbreviations: HER2, human epidermal growth factor-2; HR, hormone receptor; NA, not applicable; NOS, not otherwise specified; SVI, social vulnerability index; TNBC, triple-negative breast cancer.
DISCUSSION
Results from this study indicate that adverse social determinants of health including greater neighborhood social vulnerability and being insured via public mechanisms or being uninsured result in higher hazard of death among adults with breast, prostate, colorectal, and lung cancers in Alabama. However, the effects of these social determinants of health differ by cancer type, age at diagnosis, and race.
Consistent with prior literature, higher neighborhood-level SVI was associated with higher hazard of death among adults with cancer.12–14 The effect of SVI Q3 on survival was higher among those <65, but the effect of SVI Q4 on survival was higher among those ≥65 with breast and prostate cancer. Among those with CRC and lung cancer, results did not differ across age groups at diagnosis. Upon further stratification by race, effects for White individuals were generally higher in magnitude than effects for Black individuals with breast or prostate cancer across age groups. However, no major differences by race or age group were observed in CRC or lung cancer. Given the overlap in 95% CIs, these results should be interpreted with caution.
Prior evidence suggests that increasing neighborhood-level socioeconomic status has a protective effect on cancer (breast, prostate, and CRC) survival among White individuals but not among Black individuals which is consistent with the “diminishing returns” hypothesis.7 Our study results do not suggest a similar pattern. However, the primary consideration in our study is age group at diagnosis and all-cause mortality rather than cancer-specific mortality. Thus, it’s possible that diminishing returns among Black cancer survivors are not experienced equally across age groups at diagnosis. Differences across age groups at diagnosis could be a result of the “age as a leveler” hypothesis suggesting that adults who age into older adulthood have either overcome potential effects of adverse SDOH to reach older adulthood or have died prior to reaching an age or stage of life where cancer would develop.15,16
Additionally, differences by age group at diagnosis may be explained by the “illness as a leveler” hypothesis where individuals with poorer health do not experience the same effects of adverse SDOH.15,16 Since older adults often have greater multimorbidity and poor health status than younger adults, the magnitude of this association may be explained by the health status of the population.17,18 Moreover, discrepancies in our results relative to prior studies may reflect that all-cause and cancer-specific mortality are affected differently by race. Future studies should examine similar associations with cancer-specific survival.
Some statistically significant associations were observed between rural-urban residency status and hazard of death with rural residence associated with higher hazard of death among those with lung cancer, but micropolitan and small town residence associated with higher hazard of death among those with prostate cancer and no significant associations in fully adjusted models for breast and CRC. Results were generally consistent across age group strata and age-race group strata. This is contrary to prior literature suggesting that living in less developed areas (e.g., small town, rural) contributes to poorer cancer survival.19,20 It is possible that the effect of rurality is being diluted by the association with SVI and/or insurance status. However, other studies have shown that rurality is a consistent independent predictor of cancer mortality.19–21 Given that these results are only generalizable to the state of Alabama, the context of Alabama should be considered.
The sample comprises about 25% non-metropolitan participants. However, about 42% of Alabama’s total population lives in rural areas.22 As Alabama is a highly rural state with suboptimal healthcare infrastructure across much of the state,22 it may be that rural versus urban status is less of a concern in Alabama, whereas, other social factors such as SVI may contribute more heavily to high cancer-related mortality rates in Alabama. Additionally, differing definitions are available to define “rural”, “urban”, or other categories of residency status (e.g., RUCA codes, Rural-Urban Continuum Codes, Federal Office of Rural Health Policy definitions). However, literature has demonstrated that these federally-derived definitions of rurality are not concordant with individuals’ perceptions of their own rural-urban environment.23 Since rural-urban status is often used as a proxy for access to care, health behaviors, environmental factors, and other factors that may have adverse consequences for cancer outcomes, the individual perception of one’s own rural-urban status may be more heavily related to mechanistic causes of disparate cancer-related outcomes than federally-derived definitions created for policy implementation, budget management, and other governmental programming.23 Again, discrepancies in our results with those of prior literature may still reflect the use of a survival outcome based on death from any cause rather than from cancer specifically. However, rural-urban differences have been also observed across all causes of mortality in prior studies.
The current results suggest that insurance status has a major impact on survival among cancer survivors independent of other SDOH and covariates with publicly funded insurance programs (e.g., Medicare) and being uninsured associated with significantly higher hazard of death across cancer types compared to private insurance status. These results are particularly salient among those with breast or prostate cancer and/or among those less than 65 years old at cancer diagnosis. These results may be explained by the importance of screening for early detection and diagnosis of breast and prostate cancer. Prior evidence suggests that those insured via Medicaid and those who are uninsured are less able to and less likely to access screening for these cancers compared to those privately insured24 thus resulting in higher likelihood of later stage at diagnosis and poorer survival. However, these results remain independent of adjustment for stage at diagnosis. It is possible that being insured via public programs (e.g., Medicare/Medicaid) and/or being uninsured also negatively impacts the quality of treatment and ability to access treatment further contributing to poor outcomes.25,26 The effects for insurance were lower in magnitude among those with colorectal or lung cancers. However, significant effects remained suggesting a potential effect of screening for these cancers; though, screening uptake for prostate and lung cancer are generally lower.27
CRC and lung cancer are more likely to be more aggressive in nature, emphasizing the need for effective, high-quality treatment which may be limited based on insurance status.28 Moreover, these higher effects among those less than 65 years old at diagnosis could be a result of the “age as a leveler” hypothesis or the “illness as a leveler” hypothesis discussed above15,16
Finally, it is possible that the difference across age groups is a proxy for income. Older adults are more likely to be on a Medicare plan, but younger adults on public insurance programs are more likely to be on a Medicaid plan reflecting low income of this population.29,30 Age-race stratification results did not indicate major differences by race and age group at diagnosis beyond the effects of stratifying by age group alone indicating that the effects of age group at diagnosis are a key consideration in understanding the relationship between insurance status and survival among older adults with cancer in Alabama.
Relevance to Geriatric Oncology
Results suggest that the observed associations are important for both older and younger adults, and may be stronger for younger adults (particularly those with breast and prostate cancer). However, SVI and insurance status both remain important predictors for both younger and older cancer survivors. Particularly, insurance status is significant for both younger and older adults’ survival in this study, but insurance status affects each age group differently. However, even controlling for insurance status, higher SVI remains independently statistically significantly associated with higher risk of death, indicating that neighborhood vulnerability may still impact outcomes among older adults, despite accessibility to health insurance afforded to older adults due to Medicare eligibility.
Importantly, older adults with cancer face differential care needs.4 For example, older adults with cancer are at increased risk of frailty, which increases the risk of premature mortality.31,32 SDOH have also been associated with the development of frailty.20,33,34 The current results are unable to evaluate the effects of SDOH on the complexity of the clinical course of older adults with cancer relative to younger adults. Future studies should examine the effect of SDOH on more proximal clinical outcomes among older adults with cancer. Moreover, older adults are more likely to require chemotherapy dose reductions and/or more likely to experience chemotherapy toxicity.35–38 Thus, the impact of SDOH on clinical factors driving the decision to dose-reduce and/or the impact of SDOH on chemotherapy toxicity as intermediate clinical outcomes affecting survival.
This study is not without limitations. First, cause of death information was not available. Thus, these results are only reflective of all-cause mortality which may indicate differences in causes other than the cancer of interest, particularly among those with breast and prostate cancer. Second, as the data source is a population-based registry in Alabama, results are only generalizable to the state of Alabama. Third, we only examined four cancer types. While the cancer types examined are the most incident cancers in the United States and in Alabama, it is conceivable that differences in these results may be observed for cancers of other sites. Additionally, we were unable to control for health behaviors or treatment in the modeling. However, aside from seeking cancer screenings for early detection, health behaviors such as smoking or physical inactivity are more likely to influence the incidence of cancer rather than mortality from cancer. First course treatment is available in ASCR data, but is not reflective of all treatments received in the first line of therapy and may be inconsistently reported to the Registry. Additionally, ASCR does not routinely collect comorbidity data. Thus, we were unable to control for comorbidity burden which may differ by race and/or age group. Finally, we were limited in the SDOH we were able to examine and we were only able to examine SDOH and covariates at diagnosis. It is possible that other SDOH variables may demonstrate different results and may be related to age group at diagnosis differently than those examined in the current study. In future studies, it is important to examine SDOH that may result in intervenable targets. For example, insurance status may be intervened on via expanding Medicaid in non-expansion states; however, other measures such as historical industrial pollution or lack of public transportation may be much more difficult to change especially in a short period of time. Moreover, SDOH and covariates could change over time, which would affect mortality outcomes. However, area-level SDOH are less likely to dramatically differ over the course of a few years.
Despite its limitations, this study has several strengths. First, to our knowledge, this is one of the first studies to examine differences in the effects of SDOH across age groups at cancer diagnosis, which provides insights into differences in intervention development which may be necessary based on population subgroup. Secondly, this was a population-based study so while generalizability was limited to Alabama, external validity is high for the population of interest. Finally, this study produces targets and hypotheses for future intervention development and future studies aimed at improving health equity in population-based cancer outcomes.
In conclusion, taken together these results suggest that higher neighborhood social vulnerability and being insured via public mechanisms or being uninsured result in higher hazard of death among adults, but these effects are generally higher among those <65 years old at diagnosis. Implications for these results include designing interventions to decrease neighborhood vulnerability, particularly in areas where cancer incidence is higher among younger adults, via increasing educational opportunities, improving healthcare access (e.g., mobile screening units), and improving insurance coverage via policy change such as Medicaid expansion in non-expansion states (e.g., Alabama). Overall, when evaluating SDOH on survival in cancer, age at diagnosis should be considered and future work should seek to determine underlying mechanisms and qualitative context for these findings.
Supplementary Material
Funding
This work was supported by the National Cancer Institute at the National Institutes of Health (grant number R01CA239120 to [RA]), U54CA280779); and the Breast Cancer Research Foundation of Alabama. Funders had no role in the design, data collection, data analysis, or interpretation of study findings.
Footnotes
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Conflict of Interest Statement
The authors declare no potential conflicts of interest.
Ethics Approval Statement
This study was approved by the Institutional Review Boards of the University of Alabama at Birmingham and the Alabama Department of Public Health (ADPH). All procedures were conducted in accordance with ethical standards and principles of the Declaration of Helsinki.
Data Availability Statement
The data that support the findings of this study are available from the Alabama Statewide Cancer Registry at the Alabama Department of Public Health. Restrictions apply to the availability of these data, which were used under license for this study.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from the Alabama Statewide Cancer Registry at the Alabama Department of Public Health. Restrictions apply to the availability of these data, which were used under license for this study.