Rural-Urban Disparities in Mortality and Geriatric Assessment among Older Adults with Cancer–The Cancer & Aging Resilience Evaluation (CARE) Registry

Mackenzie E Fowler; Kelly M Kenzik; Mustafa Al-Obaidi; Christian Harmon; Smith Giri; Sankalp Arora; Coryn Stephenson; Moh’d Khushman; Darryl Outlaw; Smita Bhatia; Grant R Williams

doi:10.1016/j.jgo.2023.101505

. Author manuscript; available in PMC: 2024 May 1.

Published in final edited form as: J Geriatr Oncol. 2023 Apr 21;14(4):101505. doi: 10.1016/j.jgo.2023.101505

Rural-Urban Disparities in Mortality and Geriatric Assessment among Older Adults with Cancer–The Cancer & Aging Resilience Evaluation (CARE) Registry

Mackenzie E Fowler ^a, Kelly M Kenzik ^a,^b,¹, Mustafa Al-Obaidi ^b,², Christian Harmon ^b, Smith Giri ^a,^b, Sankalp Arora ^a, Coryn Stephenson ^c, Moh’d Khushman ^a, Darryl Outlaw ^a, Smita Bhatia ^b,^d, Grant R Williams ^a,^b

^aDepartment of Medicine, University of Alabama at Birmingham, 1720 2^nd Avenue South, BDB 860, Birmingham, AL, USA, 35294;

^bInstitute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, 1600 7^th Avenue South, Lowder Building Suite 500, Birmingham, AL, USA, 35233;

^cUniversity of Missouri, 230 Jesse Hall, Columbia, MO 65211,

^dDepartment of Pediatrics, University of Alabama at Birmingham, 1600 7^th Avenue South, Lowder Building, Birmingham, AL, USA, 35233-1771.

Author Contributions

Mackenzie E. Fowler, PhD contributed to the study conception and design, data preparation, analysis and interpretation, writing of the manuscript, and final approval of the article.

Kelly Kenzik, PhD contributed to the study conception and design, data analysis and interpretation, writing of the manuscript, and final approval of the article.

Mustafa Al-Obaidi, MD, MSPH contributed to the material preparation, data collection, writing of the manuscript, and final approval of the article.

Christian Harmon, MD contributed to the material preparation, data collection, writing of the manuscript, and final approval of the article.

Smith Giri, MD, MHS contributed to the study conception and design, writing of the manuscript, and final approval of the article.

Sankalp Arora, MD contributed to the study conception and design, writing of the manuscript, and final approval of the article.

Coryn Stephenson contributed to the study conception and design, writing of the manuscript, and final approval of the article.

Moh’d Khushman, MD contributed to the study conception and design, writing of the manuscript, and final approval of the article.

Darryl Outlaw, MD contributed to the study conception and design, writing of the manuscript, and final approval of the article.

Smita Bhatia, MD, MSPH contributed to the study conception and design, data analysis and interpretation, writing of the manuscript, and final approval of the article.

Grant R. Williams, MD, MSPH contributed to the study conception and design, material preparation, obtaining and collecting data, writing of the manuscript, and final approval of the article.

^1.

K. Kenzik Current Affiliation: Boston University School of Medicine, 85 East Concord Street, Third Floor, Suite 3000, Boston, MA, 02118, USA.

^2.

M. Al-Obaidi Current Affiliation: University of Pennsylvania School of Medicine, 3400 Civic Center Boulevard, Building 421, Philadelphia, PA, 19104, USA.

^✉

Corresponding Author: Mackenzie E. Fowler, PhD, 1720 2^nd Avenue South, BDB 860, Birmingham, AL 35294, 205-975-7967, mefowler@uab.edu, Twitter: @MFowlerPhD

PMCID: PMC10207384 NIHMSID: NIHMS1894445 PMID: 37087962

Abstract

Introduction:

Rural-urban disparities persist in cancer mortality, despite improvement in cancer screening and treatment. Although older adults represent the majority of cancer cases and are over-represented in rural areas, few studies have explored rural-urban disparities in mortality and age-related impairments among older adults with cancer.

Materials and Methods:

We included 962 newly-diagnosed older adults (≥60 years) with cancer who underwent geriatric assessment (GA) at their first pre-chemotherapy visit to an academic medical center in the Southeastern United States. We used Rural-Urban Commuting Area (RUCA) codes to classify residence at time of diagnosis into urban and rural areas. We used one-year survival and pre-treatment frailty as outcomes. We used Cox proportional hazards regression to evaluate the association between residence and one-year mortality, and logistic regression to evaluate the association between residence and pre-treatment frailty. All tests were two-sided.

Results:

Median age at GA was 68.0 (interquartile rage [IQR]: 64.0, 74.0) years; most had colorectal cancer (24.3%) with advanced stage (III/IV 73.2%) disease. Overall, 11.4% resided in rural and 88.6% in urban areas. Rural areas had a higher proportion of White and less educated participants. After adjustment for age, sex, race, education, employment status, and cancer type/stage, rural residence was associated with higher hazard of one-year mortality (hazard ratio [HR]=1.78, 95% confidence interval [CI]=1.23, 2.57) compared to urban residence. Frailty was an effect modifier of this association (HR_Overall=1.83, 95% CI=1.27, 2.57; HR_Frail=2.05, 95% CI=1.23, 3.41; HR_{Not Frail}=1.55, 95% CI=0.90, 2.68).

Discussion:

Among older adults with newly diagnosed cancer, rural residence was associated with reduced one-year survival, particularly amongst frail older adults. The rural-urban disparities observed in the current study may be due to frailty in conjunction with disparities in social determinants of health across rural and urban areas. Future studies should focus on understanding and intervening on underlying causes of these disparities.

Keywords: health status disparities, geriatric assessment, neoplasms, rural health, geriatric oncology

INTRODUCTION

Rural residence is associated with higher rates of health-related disparities including low socioeconomic status, constraints in accessing healthcare, and risky health behaviors (e.g., smoking).^1–4 Specifically, rural-urban disparities persist in cancer incidence and mortality despite improvements in cancer screening and treatment.⁵ While cancer incidence is slightly lower in rural versus urban areas, mortality is higher, particularly in smoking-related, screen-detectable, and human papillomavirus-associated cancers.⁵ Although, cancer mortality rates are decreasing overall, a slower decline is observed in rural areas potentially due to higher rates of modifiable risk factors and decreased screening uptake.⁵ Thus, the rural to urban gap in mortality is continuing to increase.⁵

Older adults represent the majority of cancer cases^{6, 7}, and are over-represented in rural areas⁸. This population with cancer may be particularly at risk for rural-urban disparities and the associated disadvantages like risky health behaviors and inferior access to care. Moreover, older adults with cancer are at increased risk of aging-related impairments, particularly frailty.^{9, 10} A geriatric assessment (GA) evaluates aging-related impairments and is recommended in the clinical management of older adults with cancer, in part to identify aging-related impairments predictive of adverse outcomes (e.g., frailty).^11–14 However, few previous studies explore rural-urban disparities in mortality and GA impairments in older patients with cancer.

Our primary objective was to evaluate the association between area of residence (urban or rural) and one-year mortality in a cohort of older adults (≥60y) with cancer. We also aimed to examine the association between area of residence and pre-treatment frailty as a secondary objective. We further investigated whether pre-treatment frailty explains any disparity in mortality across the rural-urban spectrum.

MATERIALS AND METHODS

Study Population

This study included participants from the Cancer & Aging Resilience Evaluation (CARE) Registry at the University of Alabama at Birmingham (UAB) enrolled from September 2017 to May 2022.¹⁵ The CARE study is a single-center study at UAB, an academic medical center in the Southeastern United States (US). The current study includes 962 older adults (≥60y) diagnosed with a malignancy who underwent a GA at UAB within three months prior to or up to six months after date of cancer diagnosis and prior to systemic treatment. We chose 60 years as the enrollment criteria as opposed to the traditional older adult cutoff of 65 in recognition of the uncertainty surrounding the “right” age cutoff and to allow for meaningful age-related sub-analyses.¹⁶ Of the 1,674 eligible patients, we excluded 216 due to missing or refused consent (82 missing; 134 refused) and 20 due to missing date of diagnosis. Additionally, we excluded 282 because GA was not performed within the designated period surrounding diagnosis (three months before or six months after) and 194 because GA was not performed prior to chemotherapy treatment, but rather during or after treatment (Figure 1). Supplemental Table 1 details the differences in baseline characteristics by participation status. A map displaying the geographical distribution of participants in the cohort and their residence status across the Deep South is presented in Figure 2.

Figure 1. — Flow Chart of Participant Inclusion.

Figure 2. — Distribution of Sample across the Region by Rural-Urban Status. Most participants resided in Alabama. A high density of participants resided in urban areas in central Alabama. However, many participants also resided in micropolitan and rural areas throughout the state.

The UAB Institutional Review Board approved this study and all patients provided written consent for study participation. All procedures were conducted according to ethical standards set forth by the Declaration of Helsinki.

Exposure

Area of residence was the primary exposure. The 2010 Rural-Urban Commuting Area (RUCA) codes at the ZIP code level were used to classify the cohort according to residence into urban or rural area. Specific codes and their categorization are displayed in Supplementary Table 2. RUCA codes were developed using data from the 2000 US Census Bureau work commuting information and defined Urbanized Areas and Urban Clusters Data. RUCA codes are based on primary commuting flow to urbanized areas or urbanized clusters with population size 50,000+, 10,000 to 49,999, and 2,500 to 9,999 for urban, micropolitan and rural areas, respectively.¹⁷ We utilized Categorization B based on work from the University of Washington School of Medicine and combined urban and micropolitan groups into one urban category due to similarity in outcomes between the two (see Figure 3; Supplementary Table 3).

Figure 3. — Kaplan-Meier Curves of One-Year Survival by Rural-Urban Status. Rural older adults with cancer had significantly worse one-year survival than metropolitan or micropolitan participants.

Outcomes

Primary Outcome: One-Year Mortality

One-year mortality from diagnosis was the primary outcome. Vital status was updated as of May 15, 2022 by linking the cohort to the Accurint database¹⁸, which utilizes death information from Social Security Administration records, obituaries, and state death records; we supplemented this information with medical records. The last date in the follow-up period was May 15, 2022, corresponding to the last date of survival linkage. Participants were censored at 12 months if no death had occurred as of the last date or if death occurred greater than 12 months after diagnosis. If the last date of follow-up occurred prior to 12 months, censoring occurred at the last date in the follow-up period.

Secondary Outcome: Frailty

Participants completed a patient-reported GA known as the Cancer & Aging Resilience Evaluation ‘CARE tool’ on first visit to the medical oncology clinic at UAB.¹⁵ The CARE tool is based on the GA developed by the Cancer and Aging Research Group and is comprised of patient-reported assessments of domains including cognition, function, physical performance, nutrition, falls, anxiety and depression, comorbidities, social activities, health-related quality of life (HRQoL), and socioeconomic/demographic characteristics.^{11, 15, 19} All GA outcomes in this study were assessed prior to systemic treatment.

We assessed frailty using a 44-item frailty index based on the principles of deficit accumulation.^{20, 21} Each item queried the presence of a health deficit across GA domains with scoring: 0 (absence of deficit), 0.5 (intermediate response [e.g. sometimes/maybe]), or 1 (presence of deficit). Individual item scores were totaled and divided by total number of items to obtain a frailty score (0–1; 0=no deficits, 1=44/44 deficits). Scores were categorized using standard threshold scoring: robust (<0.2), pre-frail (0.2–0.35), frail (>0.35) (construction of frailty index detailed in Supplement, Appendix A).^22–24 Participants received a CARE-Frailty Index score if at least 30 items were not missing. For the current analysis, we combined robust and pre-frail groups. Construction of the CARE-Frailty Index are detailed elsewhere and in Appendix A.²⁵ Specific measures for GA domains are described in Supplemental Table 4.

Covariates

Demographic characteristics (age at study enrollment, race/ethnicity, sex, and marital status), socioeconomic status (education and employment status), and clinical variables (cancer type and stage) were included as covariates. Race/ethnicity, sex, education, and employment status were self-reported by patients, while age, cancer type, and cancer stage were abstracted from the electronic medical record.

Statistical Analysis

Descriptive statistics for demographics, socioeconomic status, and clinical variables by area of residence were examined using chi-square tests and t-tests for categorical and continuous variables, respectively. Fisher’s exact test was used for categorical variables with insufficient expected cell size.

Cox proportional hazards regression was performed to evaluate the association between area of residence and one-year mortality. Sequential adjustment was used to elucidate the contribution of different groups of predictors on this association. The first model was adjusted for demographics: age, race, and sex. The second model was further adjusted for socioeconomic and clinical characteristics: education, employment status, cancer type, and cancer stage. Covariates in Cox models were selected a priori and on basis of bivariate analysis at baseline.

Sequentially adjusted logistic regression models were conducted to evaluate the association between area of residence and frailty. The initial model was adjusted for age, race, and sex and the final model was further adjusted for education, employment status, marital status, cancer type, and cancer stage. Covariates in adjusted models were again selected a priori and on basis of bivariate analysis at baseline.

Moderation and/or mediation of the association between area of residence and one-year mortality was evaluated by frailty status. Finally, a sensitivity analysis was performed among participants with gastrointestinal (GI) cancers given high prevalence of these cancers in the sample. All tests were two-sided with significance set at α=0.05. All analyses were conducted using SAS Version 9.4 (SAS Institute, Cary, NC). The data underlying this article cannot be shared publicly to protect privacy of participants. The data will be shared on reasonable request to the corresponding author.

RESULTS

Overall, the median age at study participation was 68.0y (interquartile range [IQR]: 64.0, 74.0); 60.4% were male and 75.6% were White. Most participants had colorectal cancer (24.5%) or pancreatic cancer (21.8%) and a majority had stage III or IV disease at baseline (73.1%). The majority of participants lived in urban areas (853; 88.7%) with the rest living in rural areas (109; 11.3%). Urban areas had a higher proportion of Black participants (urban: 23.5%, rural: 13.9%, p=0.017). Urban areas had a higher proportion of highly educated participants (Some graduate school/advanced degree: urban: 15.4%, rural: 9.7%, p=0.027). Additionally, urban areas had a lower proportion of disabled participants (urban: 11.5%; rural: 21.6%, p=0.017) [Table 1]. The mean follow-up time from diagnosis to end of follow-up was 10.1 months (standard deviation [SD]=3.2) with rural areas having shorter mean follow-up (urban: 10.2±3.1, rural: 9.3±3.8 months; p=0.022). Rural areas had a higher proportion of participants with one-year mortality, reporting pre-treatment impaired Eastern Cooperative Oncology Group Performance Status (ECOG PS) [≥2], reporting pre-treatment impaired ability to walk one block, pre-treatment polypharmacy (reporting taking nine or more daily medications), and reporting pre-treatment impaired physical health-related quality of life (HRQoL) (one-year mortality: urban 22.0%, rural 34.9%, p=0.003; ECOG ≥2: urban 29.4%, rural 40.2%, p=0.026; impaired ability to walk one block: urban 49.8%, rural 60.4%, p=0.045; polypharmacy: urban 19.8%, rural 31.7%, p=0.006; impaired physical HRQoL: urban 40.2%, rural 51.5%, p=0.032) (Table 2). No significant bivariate differences existed between other demographics and clinical variables (Table 1, Table 2).

Table 1.

Baseline Participant Characteristics by Rural-Urban Residence

Variable	Total	Urban 853 (88.7)	Rural 109 (11.3)	p-value
*Demographics*
Age Group, n(%)				0.248
60–64	277 (29.0)	241 (28.4)	36 (33.6)
65–74	447 (46.8)	405 (47.8)	42 (39.3)
75+	231 (24.2)	202 (23.8)	29 (27.1)
Sex, male n(%)	581 (60.4)	518 (60.7)	63 (57.8)	0.556
Ethnicity, non-Hispanic n(%)	931 (97.5)	824 (97.3)	107 (99.1)	0.508
Race, n(%)				0.017
White	723 (75.6)	630 (74.3)	93 (86.1)
Black	214 (22.4)	199 (23.5)	15 (13.9)
Other	19 (2.0)	19 (2.2)	0 (0)
Education, n(%)				0.027
< High School	133 (15.1)	110 (14.1)	23 (22.3)
High School	241 (27.3)	207 (26.5)	34 (33.0)
Some college/Associate’s/ Bachelor’s	379 (42.9)	343 (44.0)	36 (35.0)
Some graduate school/Advanced degree	130 (14.7)	120 (15.4)	10 (9.7)
Marital Status, n(%)				0.061
Single/Widowed/Divorced/Separated	348 (39.6)	316 (40.7)	32 (31.1)
Married	532 (60.5)	461 (59.3)	71 (68.9)
Employment Status, n(%)				0.017
Retired	524 (59.9)	474 (61.3)	50 (49.0)
Disabled	111 (12.7)	89 (11.5)	22 (21.6)
Working	151 (17.3)	134 (17.3)	17 (16.7)
Other	89 (10.2)	76 (9.8)	13 (12.8)
Living Arrangement, n(%)				0.293
Spouse	501 (61.5)	436 (60.6)	65 (68.4)
Alone	203 (24.9)	185 (25.7)	18 (19.0)
Other	111 (13.6)	99 (13.8)	12 (12.6)
*Cancer Variables*
Cancer Type, n(%)				0.830
Colorectal	235 (24.5)	206 (24.2)	29 (26.6)
Pancreatic	209 (21.8)	188 (22.1)	21 (19.3)
Hepatobiliary	117 (12.2)	101 (11.9)	16 (14.7)
Esophageal-Gastric	67 (7.0)	61 (7.2)	6 (5.5)
Head/Neck	79 (8.2)	72 (8.5)	7 (6.4)
Other	254 (26.4)	224 (26.3)	30 (27.5)
Cancer Stage, n(%)				0.947
I/II	255 (26.9)	226 (26.9)	29 (26.6)
III/IV	694 (73.1)	614 (73.1)	80 (73.4)

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^aSome participants may be missing demographics

Table 2.

Baseline Outcome and Geriatric Assessment (GA) Domain Characteristics by Rural-Urban Residence

Variable	Total	Urban 853 (88.7)	Rural 109 (11.3)	p-value
Outcomes
Survival Time, mean (SD)	10.1 (3.2)	10.2 (3.1)	9.3 (3.8)	0.022
Death, One-Year, n (%)	226 (23.5)	188 (22.0)	38 (34.9)	0.003
Frailty, n (%)	286 (31.1)	248 (30.4)	38 (36.9)	0.177
GA Impairments
Report 1+ falls, n(%)	171 (19.8)	149 (19.4)	22 (22.2)	0.511
ECOG PS ≥2, n(%) ^a	276 (30.6)	235 (29.4)	41 (40.2)	0.026
Report limitations walking one block, n(%)	463 (51.0)	402 (49.8)	61 (60.4)	0.045
IADL dependence, n(%) ^a	504 (54.6)	440 (53.7)	64 (62.1)	0.103
ADL dependence, n(%) ^a	179 (19.2)	160 (19.3)	19 (18.5)	0.831
≥3 comorbidities, n(%)	441 (50.2)	384 (49.3)	57 (57.6)	0.121
Polypharmacy, n(%) ^a	188 (21.2)	156 (19.8)	32 (31.7)	0.006
Malnutrition, n(%)	399 (48.4)	355 (48.4)	44 (48.4)	0.989
Report social activity limitation, n(%)	400 (45.1)	352 (44.8)	48 (47.1)	0.672
Cognitive Impairment, n(%)	62 (6.9)	54 (6.8)	8 (7.8)	0.704
Anxiety, n(%)	180 (20.4)	159 (20.3)	21 (21.4)	0.790
Depression, n(%)	112 (12.5)	96 (12.1)	16 (15.7)	0.306
Fatigue, n(%)	512 (55.4)	449 (54.8)	63 (60.6)	0.261
Visual Deficit, n(%)	237 (26.7)	211 (26.8)	26 (25.5)	0.777
Hearing Deficit, n(%)	264 (29.7)	228 (29.0)	36 (35.0)	0.218
Impaired Physical HRQoL, n(%) ^a	363 (41.5)	312 (40.2)	51 (51.5)	0.032
Impaired Mental HRQoL, n(%) ^a	369 (40.2)	323 (39.6)	46 (45.1)	0.289
Report financial distress, n(%)	221 (25.2)	198 (25.6)	23 (22.6)	0.512

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These variables represent domains assessed in the GA and comprised within the Cancer and Aging Resilience Evaluation (CARE)-Frailty Index. Abbreviations: ECOG PS=Eastern Cooperative Oncology Group Performance Status; IADL=instrumental activities of daily living; ADL=activities of daily living; polypharmacy= self-report of taking 9+ daily medications; HRQoL=health-related quality of life

b Some participants may be missing GA outcomes

The proportional hazards assumption for Cox regression held. Figure 3 shows the Kaplan-Meier survival curves by urban-micropolitan-rural status. After adjustment for age, race, sex, education, employment status, and cancer type/stage, rural versus urban residence was associated with 1.78 times higher hazard of one-year mortality (95% CI: 1.23, 2.57) (Supplementary Table 5).

After adjustment for age, race, sex, education, employment status, marital status, and cancer type/stage, rural versus urban residence was associated with 1.18 times higher odds of pre-treatment frailty (95% confidence interval [CI]: 0.72, 1.91) (data not shown). Given lack of a statistically significant association between rural-urban residency and frailty status, a formal mediation analysis was not completed. There was also no evidence of interaction between area of residence and pre-treatment frailty in the hazard of death (p=0.300). However, models were stratified to assess for effect modification. Overall, rural versus urban residence was associated with 1.83 times higher hazard of one-year mortality (95% CI: 1.27, 2.64) after adjustment for all prior variables plus frailty status; frailty was also an independent predictor of one-year mortality (hazard ratio [HR]: 1.88, 95% CI: 1.39, 2.54) [Supplementary Table 6]. After stratification, rural versus urban residence was associated with 2.05 times higher hazard of one-year mortality (95% CI: 1.23, 3.41) among frail patients and 1.55 times higher hazard (95% CI: 0.90, 2.68) among non-frail patients. These results indicate that frailty status is an effect modifier of the association between rural-urban residence and risk of one-year mortality with a percent change in the hazard ratios of 24.4% by frailty status. Further examination reveals that 65.8% of rural, frail older adults died within one year of diagnosis compared to 40.7% of urban, frail older adults (p=0.004) whereas, no difference was detected among those without frailty (rural: 36.9%; urban: 30.2%, p=0.269). Moreover, rural, frail older adults had significantly shorter time to death than urban, frail older adults (rural: 7.8±4.4 months; urban: 9.4±3.7 months, p=0.016), but no difference was observed among those without frailty (rural: 10.1±3.2 months; urban: 10.6±2.6 months; p=0.257). Results of the sensitivity analysis among participants with GI cancers only remained consistent with overall results (data not shown).

DISCUSSION

In this examination of the association between area of residence and one-year mortality as well as between area of residence and pre-treatment frailty among older adults with cancer in the Southeastern US, our results suggest that rural residence is associated with higher risk of one-year mortality. Pre-treatment frailty is an effect modifier of this association, with results showing rural, frail older adults had about 24% higher risk of one-year mortality than rural, non-frail older adults. Rural residence confers unfavorable risk to one-year mortality among older adults with cancer, particularly GI cancers, in the Southeastern US. Frailty may, in part, drive this disparity.

Overall, we observed greater risk of one-year mortality for rural versus urban older adults in this study. There may be a greater gap in mortality for rural versus urban older adults with cancer not captured in this sample. All patients in the current sample received recommended GA and had at least one cancer care visit at an academic medical center, yet there remained a mortality disparity between rural and urban residency groups. Thus, other important factors such as social determinants of health (SDoH) may explain this disparity. SDoH are defined as the “conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks.”²⁶ SDoH are categorized into five groups: economic stability, education access and quality, health care access and quality, neighborhood and built environment, and social and community context.²⁶ Access to care, further described below, is an important SDoH which may contribute to the observed mortality disparity. However, other SDoH such as lower education, poverty, lack of transportation, greater distance to healthcare, and others are more prevalent in rural versus urban areas and may further explain observed mortality differences.²⁷ The Southeastern US has greater rates of SDoH impairments, particularly in rural areas, which may be reflected in the current sample.^{1, 28} In addition, in the context of cancer, rural patients with cancer experience challenges traveling to areas with optimal cancer care.²⁹ However, we were unable to assess travel time and distance in the current study. Rural patients are also more likely to experience financial hardship and gaps in insurance coverage leading to poorer outcomes.²⁹ Older adults may be particularly susceptible to poor outcomes associated with the interplay between poor SDoH (e.g., distance to care) and rurality given their over-representation in rural areas.⁸ Again, there may be a larger disparity with greater SDoH issues (i.e., poverty, poor education, distance to care) among patients not captured in this sample and there may be differential associations in other areas of the US. Additional studies are needed with a community-based sample, a more geographically generalizable sample, and the ability to measure and evaluate SDoHs and their roles in these disparities.

Frailty confers approximately 24% higher risk of one-year mortality among rural versus urban older adults with cancer and may account for some of the observed rural-urban mortality disparity. Frailty has been consistently associated with mortality, which we replicate in this study, but seems to present additional risk specifically among rural patients.^{10, 22} These results suggest that intervening on frailty in rural older adults with cancer prior to treatment could help alleviate poorer outcomes in this subgroup. While rural-urban residency was not significantly associated with frailty in the current study, prior evidence suggests that frailty is more prevalent among rural older adults.^{10, 22, 30} However, much of the evidence for rural versus urban disparities in frailty prevalence is outside the US, so additional studies are needed in the geographic, social, and healthcare context of the US.³⁰ Additionally, frailty is likely only one contributor to rural-urban mortality disparities and/or is reflective of other disadvantages of rural residence. For example, frailty has also been associated with SDoH disadvantage including low income, occupation status, and living in neighborhoods with high prevalence of underrepresented individuals or high prevalence of disrepair in the physical environment.^31–33 Given the association between rurality and SDoH disadvantage as described above, the observed effect modification by frailty status may be further explained by SDoH. Future studies should examine the relationship between SDoH, frailty, and rurality on mortality.

While intervening on frailty may be a potential strategy for improving health equity between rural and urban older adults with cancer, frailty must first be identified. Although the International Society of Geriatric Oncology (SIOG), American Society of Clinical Oncology (ASCO), and National Comprehensive Cancer Network (NCCN) recommend GA as part of routine clinical care of older adults with cancer for identification of aging-related impairments, rural patients are less likely to receive guideline-concordant care in general which negatively affects outcomes and continues to widen rural-urban disparities.^{12, 13, 34–37} Despite greater likelihood of frailty among rural individuals and among older adults with cancer, recommended GA may also be missed in rural communities.^{10, 22, 30} Furthermore, the ASCO Guideline for Geriatric Oncology details specific recommendations for guiding clinical care and management based on GA results, such as predicting chemotherapy toxicity, modifying chemotherapy dosing, and intervening on GA-identified impairments (e.g., physical/occupational therapy for impaired instrumental activities of daily living, involving primary care physicians in management of comorbidities).¹³ Lack of GA can thus limit identification of frailty, quality of care, and management of older adults with cancer, particularly those in rural areas. Integrating SDoH measures into GA may demonstrate utility in identifying potentially intervenable SDoH disadvantages and, in combination with frailty interventions, further reduce differences in mortality between rural and urban groups. Additional research is needed to determine the utility of SDoH measures within GA, the most valid, reliable measures of SDoH ascertainment, relevant interventions for SDoH issues, and the variation in rural-urban mortality explained by frailty plus SDoH.

Finally, access to care may be a key SDoH factor in driving rural versus urban mortality disparities. Rural areas lack specialty oncology care including providers, treatment centers, and resources.²⁹ In addition, rural areas -- particularly community oncology clinics -- lack access to geriatrics specialists specifically trained to identify impairments such as frailty in the clinic setting.^{38, 39} Therefore, in addition to lack of optimal oncology care, rural patients with cancer may also lack access to trained providers to identify important GA impairments like frailty who can subsequently communicate with the oncologist. In fact, the point-of-care for most rural patients are primary care providers (PCPs), however there is a shortage of PCPs in rural areas, leading to high patient-provider ratio, particularly in the Southeast.^40–42 Rural PCPs may lack clinic time to identify GA impairments. Rural patients also have higher comorbidity burden, so PCPs may place greater focus on managing other chronic conditions and their acute sequelae.⁴³ Future studies should delve into the complex interplay between rurality, access to care, and GA-identifiable impairments.

This study has several strengths. This is one of the first studies examining the effects of residence on one-year mortality and pre-treatment frailty specifically among older adults with cancer. This is a real-world, clinic-based sample with greater diversity than clinical trials or observational studies (~24% non-White and ~42% lower educated [high school or less] participants).⁴⁴ Additionally, given most participants in this sample had GI cancers at later stages, one-year mortality may also be reflective of cancer-specific mortality. However, predominance of GI and later stage cancers does limit generalizability, and to determine true cancer-specific mortality a link to national death index data will be necessary. Additional limitations exist. First, the cross-sectional nature of the GA makes causal inference between residence and frailty challenging. There is also no non-cancer control group for comparison. Additionally, all GA domains were self-reported and may be subject to information bias. The rural subset in the current study was limited by those who could attend an academic medical center, sometimes at great distance, bolstering the idea that the observed differences may be even greater in a more community-based cohort. In addition, cancer stage and type may be important mediators of the association between rurality and outcomes. However, the current sample is not significantly different by rural-urban residence in terms of cancer stage and cancer type. Future studies with cohorts differing significantly by cancer stage and type should examine these factors as potential mediators. Racial differences within residence areas may also exist. However, limited sample size of non-White people within rural areas of this sample made assessment of racial disparities difficult. Future studies should seek to evaluate intersectionality of racial disparities with rural-urban disparities. Additionally, most participants reside in Alabama and adjacent states and validation of these findings in a more nationally representative sample is necessary. Furthermore, cancer treatment and adherence may be important factors in the association between rural-urban residence and mortality and between rural-urban residence and frailty. However, we are unable to meaningfully assess cancer treatment and adherence in the current study. Finally, a follow-up assessment of frailty may be more predictive of mortality than a baseline assessment. To date, our follow-up assessments are limited and a subgroup analysis would limit statistical power. Prior evidence suggests that frailty states rarely transition backwards.⁴⁵ This suggests that utilizing a follow-up assessment of frailty may not dramatically alter our current results. Future analyses are planned to examine the contribution of cancer treatment, adherence, and follow-up GA on rural-urban mortality disparities.

CONCLUSION

In conclusion, among older adults with cancer, rural residence is associated with greater risk of one-year mortality prior to systemic therapy compared with urban areas. Frailty is an effect modifier of this association, conferring enhanced risk of one-year mortality among rural patients. Detailed assessment of the role of social determinants of health on frailty and rural-urban mortality disparities is necessary. Important next steps will include implementation of appropriate strategies to address drivers of this disparity and improve health equity between rural and urban older adults with cancer.

Supplementary Material

NIHMS1894445-supplement-1.docx^{(49.7KB, docx)}

ACKNOWLEDGMENTS

Declaration of Competing Interests

Moh’d Khushman reports receiving speaker fees from CARIS Life Science and Pfizer. Smith Giri has received honoraria from Carevive, Sanofi, and Select Medical; his institution has received grants or contracts from Carevive, PackHealth, and Janssen.

Funding

This work was supported by the National Institutes of Health (K08CA234225, G. Williams, PI). Author M.E.F. receives research support from the Agency for Healthcare Research and Quality (5T32HS013852, M. Mugavero, PI).

The Doris Duke Charitable Foundation Caregiving Affected Research Early-career Scientists (CARES) Retention Program at UAB.

The funder did not have a role in the conception or design of the study, data collection, analyses, or interpretation, writing of the report, or the decision to submit the article for publication.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Prior Presentation: Fowler ME, Kenzik KM, Al-Obaidi M, Harmon C, Giri S, Arora S, Khushman M, Outlaw D, Bhatia S, Williams GR. Rural-urban disparities in geriatric assessment impairment and mortality among older adults with cancer – Results from the Cancer and Aging Resilience Evaluation (CARE) Registry. American Society of Clinical Oncology Annual Scientific Meeting. June 3–7, 2022. Chicago, IL and Online. Part of clinical symposium: Moving Past Geriatric Assessment to Implementation

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

NIHMS1894445-supplement-1.docx^{(49.7KB, docx)}

[R1] 1.Long AS, Hanlon AL, Pellegrin KL. Socioeconomic variables explain rural disparities in US mortality rates: implications for rural health research and policy. SSM Popul Health. 2018;6:72–74. doi: 10.1016/j.ssmph.2018.08.009 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R2] 2.Douthit N, Kiv S, Dwolatzky T, Biswas S. Exposing some important barriers to health care access in the rural USA. Public Health. 2015;129(6):611–620. doi: 10.1016/j.puhe.2015.04.001 [DOI] [PubMed] [Google Scholar]

[R3] 3.Martin SL, Kirkner GJ, Mayo K, Matthews CE, Durstine JL, Hebert JR. Urban, rural, and regional variations in physical activity. J Rural Health. 2005;21(3):239–244. doi: 10.1111/j.1748-0361.2005.tb00089.x [DOI] [PubMed] [Google Scholar]

[R4] 4.Doescher MP, Jackson JE, Jerant A, Hart LG. Prevalence and trends in smoking: a national rural study. J Rural Health. 2006;22(2):112–118. doi: 10.1111/j.1748-0361.2006.00018.x [DOI] [PubMed] [Google Scholar]

[R5] 5.Henley SJ, Anderson RN, Thomas CC, Massetti GM, Peaker B, Richardson LC. Invasive cancer incidence, 2004–2013, and deaths, 2006–2015, in nonmetropolitan and metropolitan counties - United States. MMWR Surveill Summ. 2017;66(No. SS-14):1–13. doi: 10.15585/mmwr.ss6614a1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R6] 6.Smith BD, Smith GL, Hurria A, Hortobagyi GN, Buchholz TA. Future of cancer incidence in the United States: burdens upon an aging, changing nation. J Clin Oncol. 2009;27(17):2758–2765. doi: 10.1200/JCO.2008.20.8983 [DOI] [PubMed] [Google Scholar]

[R7] 7.American Cancer Society. Cancer Facts & Figures 2022. 2022.

[R8] 8.Smith AS, Trevelyan E. The older population in rural America: 2012–2016. Vol. ACS-41. 2018. American Community Survey Reports. https://www.census.gov/content/dam/Census/library/publications/2019/acs/acs-41.pdf [Google Scholar]

[R9] 9.Brunello A, Sandri R, Extermann M. Multidimensional geriatric evaluation for older cancer patients as a clinical and research tool. Cancer Treat Rev. 2009;35(6):487–492. doi: 10.1016/j.ctrv.2009.04.005 [DOI] [PubMed] [Google Scholar]

[R10] 10.Handforth C, Clegg A, Young C, et al. The prevalence and outcomes of frailty in older cancer patients: a systematic review. Ann Oncol. 2015;26(6):1091–1101. doi: 10.1093/annonc/mdu540 [DOI] [PubMed] [Google Scholar]

[R11] 11.Hurria A, Gupta S, Zauderer M, et al. Developing a cancer-specific geriatric assessment: a feasiblity study. Cancer. 2005;104(9):1998–2005. doi: 10.1002/cncr.21422 [DOI] [PubMed] [Google Scholar]

[R12] 12.Wildiers H, Heeren P, Puts M, et al. International Society of Geriatric Oncology consensus on geriatric assessment in older patients with cancer. J Clin Oncol. 2014;32(24):2595–2603. doi: 10.1200/JCO.2013.54.8347 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R13] 13.Mohile SG, Dale W, Somerfield MR, et al. Practical assessment and management of vulnerabilities in older patients receiving chemotherapy: ASCO Guideline for Geriatric Oncology. J Clin Oncol. 2018;36(22):2326–2347. doi: 10.1200/JCO.2018.78.8687 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R14] 14.Dotan E, Walter LC, Browner IS, et al. NCCN Guidelines(R) Insights: Older Adult Oncology, Version 1.2021. Journal of the National Comprehensive Cancer Network : JNCCN. Sep 20 2021;19(9):1006–1019. doi: 10.6004/jnccn.2021.0043 [DOI] [PubMed] [Google Scholar]

[R15] 15.Williams GR, Kenzik KM, Parman M, et al. Integrating geriatric assessment into routine gastrointestinal (GI) consultation: the cancer and aging resilience evaluation (CARE). J Geriatr Oncol. 2019;11(2):270–273. doi: 10.1016/j.jgo.2019.04.008 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R16] 16.Aleixo GFP, Choi SK, Tan AJ, et al. Is “Geriatric” Assessment Just for Older Patients? Oncologist. Apr 2020;25(4):355–358. doi: 10.1634/theoncologist.2019-0373 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R17] 17.University of Washington School of Medicine. RUCA Data: General Methods. Accessed March 21, 2022, https://depts.washington.edu/uwruca/ruca-methodology.php

[R18] 18.Accurint®. LexisNexis®. Accessed October 30, 2019. http://www.accurint.com.

[R19] 19.Giri S, Al-Obaidi M, Weaver A, et al. Association between chronologic age and geriatric assessment-identified impairments: findings from the CARE Registry. J Natl Compr Canc Netw. 2021;19(8):922–927. doi: 10.6004/jnccn.2020.7679 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Rockwood K, Mitnitski A. Frailty in relation to the accumulation of deficits. J Gerontol A Biol Sci Med Sci. 2007;62(7):722–727. doi: 10.1093/gerona/62.7.722 [DOI] [PubMed] [Google Scholar]

[R21] 21.Giri S, Mir N, Al-Obaidi M, et al. Use of Single-Item Self-Rated Health Measure to Identify Frailty and Geriatric Assessment-Identified Impairments Among Older Adults with Cancer. The oncologist. Feb 3 2022;27(1):e45–e52. doi: 10.1093/oncolo/oyab020 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R22] 22.Guerard EJ, Deal AM, Chang Y, et al. Frailty index developed from a cancer-specific geriatric assessment and the association with mortality among older adults with cancer. J Natl Compr Canc Netw. 2017;15(7):894–902. doi: 10.6004/jnccn.2017.0122 [DOI] [PubMed] [Google Scholar]

[R23] 23.Giri S, Al-Obaidi M, Harmon C, et al. Impact of geriatric assessment (GA) based frailty index (CARE-Frailty Index) on mortality and treatment-related toxicity among older adults with gastrointestinal (GI) malignancies. J Clin Oncol. 2021;39(15 Suppl)doi: 10.1200/JCO.2021.39.15_suppl.12046 [DOI] [Google Scholar]

[R24] 24.Williams GR, Deal AM, Sanoff HK, et al. Frailty and health-related quality of life in older women with breast cancer. Support Care Cancer. 2019;27(7):2693–2698. doi: 10.1007/s00520-018-4558-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R25] 25.Giri S, Al-Obaidi M, Harmon C, et al. Patient-reported geriatric assessment-based frailty index among older adults with gastrointestinal malignancies. J Am Geriatr Soc. 2022;Online ahead of printdoi: 10.1111/jgs.18054 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R26] 26.U.S. Department of Health and Human Services Office of Disease Prevention and Health Promotion. Healthy People 2030. Accessed June 22, 2022. https://health.gov/healthypeople/objectives-and-data/social-determinants-health [PubMed]

[R27] 27.Centers for Disease Control and Prevention. Rural Health: Preventing Chronic Diseases and Promoting Health in Rural Communities. 2019. Accessed June 22, 2022. https://www.cdc.gov/chronicdisease/pdf/factsheets/Rural-Health-Overview-H.pdf

[R28] 28.Singh GK, Daus GP, Allender M, et al. Social determinants of health in the United States: addressing major health inequality trends for the nation, 1935–2016. Int J MCH AIDS. 2017;6(2):139–164. doi: 10.21106/ijma.236 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R29] 29.Levit LA, Byatt L, Lyss AP, et al. Closing the rural cancer care gap: three institutional approaches. JCO Oncol Pract. 2020;16(7):422–430. doi: 10.1200/OP.20.00174 [DOI] [PubMed] [Google Scholar]

[R30] 30.Xu R, Li Q, Guo F, Zhao M, Zhang L. Prevalence and risk factors of frailty among people in rural areas: a systematic review and meta-analysis. BMJ Open. Apr 27 2021;11(4):e043494. doi: 10.1136/bmjopen-2020-043494 [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Rural-Urban Disparities in Mortality and Geriatric Assessment among Older Adults with Cancer–The Cancer & Aging Resilience Evaluation (CARE) Registry

Mackenzie E Fowler, PhD

Kelly M Kenzik, PhD

Mustafa Al-Obaidi, MD, MSPH

Christian Harmon, MD

Smith Giri, MD, MHS

Sankalp Arora, MD

Coryn Stephenson

Moh’d Khushman, MD

Darryl Outlaw, MD

Smita Bhatia, MD, MPH

Grant R Williams, MD, MSPH

Abstract

Introduction:

Materials and Methods:

Results:

Discussion:

INTRODUCTION

MATERIALS AND METHODS

Study Population

Figure 1.

Figure 2.

Exposure

Figure 3.

Outcomes

Primary Outcome: One-Year Mortality

Secondary Outcome: Frailty

Covariates

Statistical Analysis

RESULTS

Table 1.

Table 2.

DISCUSSION

CONCLUSION

Supplementary Material

ACKNOWLEDGMENTS

Declaration of Competing Interests

Funding

Footnotes

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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