Rural, Large Town, and Urban Differences in Optimal Subspecialty Follow-up and Survivorship Care Plan Documentation among Childhood Cancer Survivors

Abstract

Background:

Children with cancer from rural and non-urban areas face unique challenges. Health equity for this population requires attention to geographic disparities in optimal survivorship-focused care.

Methods:

The Oklahoma Childhood Cancer Survivor Cohort was based on all patients reported to the institutional cancer registry and ≤18-years-old at diagnosis between January 1, 2005, and September 24, 2014. Suboptimal follow-up was defined as no completed oncology-related clinic visit five to seven years after their initial diagnosis (survivors were 7–25 years old at end of the follow-up period). The primary predictor of interest was rurality.

Results:

Ninety-four (21%) of the 449 eligible survivors received suboptimal follow-up. There were significant differences (p=0.01) as 36% of survivors from large towns (n=28/78) compared with 21% (n=20/95) and 17% (n=46/276) of survivors from small town/isolated rural and urban areas received suboptimal follow-up, respectively. Forty-five percent of adolescents at diagnosis were not seen in the clinic compared with 17% of non-adolescents (p<0.01). An adjusted risk ratio of 2.2 (95% CI 1.5, 3.2) was observed for suboptimal follow-up among survivors from large towns, compared with survivors from urban areas. Seventy-three percent of survivors (n=271/369) had a documented survivorship care plan with similar trends by rurality.

Conclusions:

Survivors from large towns and those who were adolescents at the time of diagnosis were more likely to receive suboptimal follow-up care compared to survivors from urban areas and those diagnosed younger than thirteen.

Impact:

Observed geographic disparities in survivorship care will inform interventions to promote equitable care for survivors from non-urban areas.

INTRODUCTION

An estimated 500,000 adult survivors of childhood cancer live in the United States and, with therapeutic advances pushing five-year survival rates in recent years to 85%, the number will undoubtedly continue to grow.¹ The recognition of late effects and risk-adapted treatment strategies, notably for leukemia and lymphoma therapy, in recent decades resulted in a decreased burden of late chronic conditions.^2,3 Nevertheless, the cure comes at a cost with severe chronic health conditions occurring in over a quarter of survivors and considerable excess mortality from subsequent malignancies and cardiovascular disease.^4–9 The heterogeneity of pediatric malignancies, associated treatment exposures, and subsequent comorbidities for survivors call for risk-adapted therapy to optimize lifelong health for survivors.¹⁰ Moreover, survivors from non-urban areas may face additional unique challenges and barriers to equitable survivorship care.

Guidelines and survivorship care plans offer comprehensive tools for clinicians and survivors to support optimal care delivery. The Children’s Oncology Group (COG) Long-Term Follow-up Guidelines are regularly updated and efforts such as the International Guideline Harmonization Group (IGHG) help synthesize the growing body of evidence on late effects with the goal to improve care.^11–13 Survivorship care plans, such as Passport for Care (PFC), serve as tools to promote patient education and the diverse care delivery models reflect the creative approaches to meet the needs of this burgeoning population.¹⁴ Nevertheless, nearly a third of survivors receive suboptimal follow-up in the early survivorship period, which worsens dramatically as 70% of adult survivors of childhood cancer report no survivorship-focused care.^15,16 This decline in optimal care coincides with the progressive cumulative incidence of life-threatening late effects, such as heart failure and breast cancer, during the young adult years.^17,18

Oklahoma is a highly rural state with a high burden of chronic health conditions and survivors from non-urban areas face unique challenges for optimal survivorship-focused care. Thirty-four percent of residents live in rural areas in 2019, more than twice the national average of 14%.¹⁹ Rural disparities in the general population represent an important public health challenge.²⁰ For survivors, hypertension and other cardiovascular risk factors potentiate risk for cardiomyopathy and coronary artery disease from anthracyclines and radiation, thus highlighting the critical need to engage high-risk survivors in care to mitigate rural health inequities later in life.²¹ Rural-urban differences in the general population for diabetes, hypertension, heart disease, stroke, and cardiovascular mortality portend even graver consequences for childhood cancer survivors at risk for late effects.^22–24 In the adult literature, rurality has been associated with poor health status and inadequate care among survivors of adult cancers.^25–27 Transportation barriers and distance to tertiary care centers, which contribute to disparities while on active treatment in pediatric oncology, may also adversely impact optimal survivorship care.^28,29 Follow-up care patterns and potential disparities for those from non-urban areas represent key gaps to care for this vulnerable population.

This study aimed to assess the impact of rurality on suboptimal subspecialty follow-up during the early survivorship period through the integration of cancer registry, electronic health records, and geospatial data. We hypothesized that survivors from non-urban areas would be more likely to receive suboptimal subspecialty follow-up care. Secondary outcomes included the proportion of survivors with a survivorship care plan, generated through Passport for Care, by rurality.

MATERIALS AND METHODS

Survivor Cohort Construction

The University of Oklahoma institutional cancer registry was used as the base cohort for this analysis. The Cancer Registries Amendment Act of 1992 established federal support and standards for cancer registries across the United States.³⁰ Over the last several decades, the National Cancer Database (NCDB), in conjunction with the Commission on Cancer and the American Cancer Society, provides rigorous data standards and captures greater than 70% of new diagnoses of cancer annually.³¹ Age at diagnosis, sex, primary diagnosis, the first course of treatment, demographic data, including the address at diagnosis, mortality, and recurrence data are all documented by trained cancer registrars. At Oklahoma Children’s Hospital, the cancer registry reliably includes new diagnoses of pediatric cancer since 2005, thus patients ≤18-years-old diagnosed after January 1, 2005, were considered for the full cohort (n=1421). Patients diagnosed after September 24, 2014, those not seen in the pediatric oncology clinic at the Jimmy Everest Center (JEC) within a year of diagnosis, non-analytic cases (those not diagnosed or receiving the first course of treatment at the institution), and those with documented death within seven years of their initial diagnosis were excluded from the analysis. Survivors with documented relapse were also excluded to focus on those off therapy or the early survivorship period. Subsequent malignant neoplasms were not excluded, as this was not routinely captured by the cancer registry. The age range of survivors at the end of the seven-year follow-up period was 7–25 years old. For the secondary analysis focused on the documentation of a survivorship care plan, we also excluded patients with a history of a primary central nervous system (CNS) tumor as they were not routinely entered into PFC. Documentation of a survivorship care plan was validated through the institutional log-in for the Passport for Care website. This research was submitted to and approved by the University of Oklahoma Health Sciences Institutional Review Board (IRB#: 13687) on September 22, 2021.

Disease Classification and Late Effects Risk Strata

The cancer registry defines the primary malignancy by the International Classification of Diseases Oncology, third edition (ICD-O-3), which facilitates additional categorization based on the International Classification of Childhood Cancer, third edition (ICCC-3).³² Major pediatric oncology disease categories were used to implement late effects risk stratification developed by the British Childhood Cancer Survivor Study.³³ Primary diagnosis and dichotomous treatment exposures, as captured in the cancer registry, were utilized to identify survivors as low, moderate, and high risk for late effects. The full methodology for this was documented previously.¹⁵

EHR Data Elements

The University of Oklahoma Clinical Research Data Warehouse (CRDW) integrates data from multiple clinical sources across the University of Oklahoma Health Sciences Center. For patients meeting the inclusion criteria, details were extracted using standard query language (SQL) for all clinic visits at the JEC and the Stephenson Cancer Center (SCC) for each patient in the cancer registry as the base cohort. A valid completed visit was defined as a visit that was scheduled, the patient arrived, and the visit was subsequently closed at the end of the encounter. Additionally, we limited to provider visits (e.g. laboratory-only or nurse-only visits were not considered a valid completed visit for the purposes of this analysis). These dates were landmarked to the initial date of diagnosis, as documented in the cancer registry. Optimal follow-up was defined as a completed clinic visit at the JEC or the SCC during a two-year period between five and seven years (60–84 months) after the initial diagnosis. Race/ethnicity was categorized as non-Hispanic White, non-Hispanic Black, Hispanic, American Indian or Other. For each patient, follow-up was assessed with informatics methods on structured database fields; valid completed clinic visits were identified in this first pass. If this approach did not recognize an oncology-related visit between five and seven years, a manual chart review was performed. Of the 152 survivors without a valid clinic visit in the follow-up window, 51 had a documented transfer of care to another institution and three were not recommended to follow-up.

Geocoded Data Elements

The primary association of interest was rurality, defined as urban, large town, small town/isolated rural based on the 2010 Rural-Urban Commuting Area (RUCA) codes, which consider population density and distance to nearest urban centers (Supplemental Table 1).^34,35 The primary urban centers in Oklahoma are the Oklahoma City area, Tulsa, and Lawton. We geocoded patient addresses at the time of diagnosis using the Texas A&M Geocoding Service ESRI Toolbox.³⁶ We joined the addresses to the 2010 US census tract TIGER/Line shapefiles,³⁷ which were then joined to the appropriate census tract-level RUCA codes.³⁵

Additional geographic variables included the Area Deprivation Index (ADI), distance to the medical center, and travel time to the medical center. We obtained the 2019 ADI from the Neighborhood Atlas ^® at the census block group level and used the national rankings to classify patients into quartiles (<25%, 25–49%, 50–74%, and 75–100%).^38,39 To calculate the distance and travel time from the patient’s residence to the medical center, we used the Origin-Destination Cost Matrix Tool in ArcMap, accounting for road networks. All geospatial analyses were conducted in ArcMap 10.8.1 (ESRI, Redlands, CA).

Statistical analyses

The primary outcome of interest was a completed oncology-related clinic visit at either the JEC or SCC between five and seven years after the initial diagnosis, thus categorized as a dichotomous variable. Continuous variables were presented as means or medians, as appropriate based on distribution, and differences between those with and without optimal follow-up were analyzed using the t-test. Categorical variables, such as rurality, race/ethnicity, sex, adolescent at diagnosis, late effects risk strata, primary diagnosis, and PFC, were presented as proportions and differences were assessed using the χ² test.

To calculate risk ratios (RR) and 95% confidence intervals (CI), we used modified Poisson regression due to challenges with convergence in the log-binomial model. In this model, we assumed that the data follow an underlying Poisson distribution, although our outcome was binomial. Standard Poisson regression can lead to overdispersion in binomial data, which can result in the overestimation of the standard error. Modified Poisson regression uses the sandwich estimator to correct for any overestimation of the standard error.^40–42 We used a directed acyclic graph (DAG) to guide the selection of confounders, with race/ethnicity being the only variable necessary to include in the multivariable models to remove confounding.^43,44 (Supplemental Figure 1). Using a DAG allowed us to visualize the causal relationships between variables to determine which variables may confound the relationship between rurality and optimal follow-up without introducing selection bias (i.e., collider stratification bias), over adjustment (i.e., adjusting for a mediator), or unnecessary adjustment (i.e., adjusting for variables that do not introduce bias, but impact precision).^45,46 We considered race/ethnicity, adolescent at diagnosis, ADI, sex, primary diagnosis, late effects risk stratification, and year of diagnosis as potential confounders or mediators on the pathway to suboptimal follow-up, using subject matter knowledge to guide DAG development. This guided statistical modeling for the association between rurality and the risk of suboptimal subspecialty follow-up care among survivors of childhood cancer in Oklahoma. As a secondary outcome and sensitivity analysis, we repeated analyses for the association between rurality and the documentation of a survivorship care plan in Passport for Care for general pediatric oncology survivors (n=369) (Supplemental Figure 2). All statistical analyses were conducted using Stata/SE version 17.0 (Stata Corp LLC, College Station, TX), SAS v. 9.4 (Cary, NC), and R (RStudio, 2022.07.1, Build 554).

Data Availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

RESULTS

The final analytic cohort included 449 childhood cancer survivors (Figure 1), of whom 78.5% (n=358) had a completed clinic visit at the JEC or the SCC between five and seven years after their initial diagnosis (Table 1). Among survivors in Oklahoma (n=440), those from large towns were most likely to receive suboptimal follow-up at 36% (n=28/78) compared to 21% (n=20/95) and 17% (n=46/276) of survivors from small town/isolated rural and urban areas, respectively (p=0.01). The median distance for survivors with suboptimal follow-up was 46.7 miles compared to 24.3 miles for those with optimal follow-up (p<0.01). There was significant heterogeneity in follow-up by primary diagnosis (p<0.01) and survivors at intermediate risk for late effects were more likely to receive suboptimal follow-up at 28% in contrast to 15% of survivors at low and high risk for late effects (p<0.01). Forty-five percent of survivors who were adolescents at the time of diagnosis received suboptimal follow-up compared to 17% of survivors who were younger than thirteen at the time of diagnosis (p<0.01) There were no significant differences in optimal follow-up by sex (p=0.81), race/ethnicity (p=0.28) or ADI (p=0.06).

Figure 1.

Oklahoma Childhood Cancer Survivor Cohort Construction.

Table 1.

Univariate Analysis of Optimal Follow-up 5–7 Years After Initial Diagnosis

	Optimal Follow-up		P-Value
	Yes (n=355)	No (n=94)
Age at Diagnosis, in years (SE; 95%CI)	6.5 (0.26; 6.0–7.0)	10.2(0.64; 8.9–11.5)	<0.01
Age at End of Seven-Year Follow-up, in years (SE; 95%CI)	13.5 (0.26, 13.0–14.0)	17.2 (0.64, 15.9–18.5)	<0.01
Adolescent at Diagnosis			<0.01
Yes	59 (16.6%)	47 (50.0%)
No	296 (83.4%)	47 (50.0%)
Sex			0.81
Male	199 (56.1%)	54 (57.4%)
Female	156 (43.9%)	40 (42.6%)
Race/Ethnicity			0.26
White, Non-Hispanic	226 (63.7%)	65 (69.1%)
Black, Non-Hispanic	29 (8.2%)	12 (12.8%)
Hispanic	61 (17.2%)	11 (11.7%)
American Indian	28 (7.9%)	5 (5.3%)
Other	11 (3.1%)	1 (1.1%)
Area Deprivation Index, percentile (SE; 95%CI)	67.6 (1.3; 65.1–70.2)	72.9 (2.1; 67.8–78.0)	0.06
RUCA			0.01
Urban	230 (64.8%)	46 (48.9%)
Large Town	50 (14.1%)	28 (29.8%)
Small Town/Isolated Rural	75 (21.1%)	20 (21.3%)
Distance to Medical Center, Median, in miles (SE)	24.3 (3.0)	46.7 (7.1)	<0.01
Travel Time to Medical Center, Median, in minutes (SE)	26.4 (3.1)	50.9 (7.7)	<0.01
Primary Diagnosis			<0.01
Leukemia	152 (42.8%)	22 (23.4%)
Non-Hodgkins Lymphoma	19 (5.4%)	9 (9.6%)
Hodgkins Lymphoma	22 (6.2%)	19 (20.2%)
CNS	62 (17.5%)	15 (16.0%)
Retinoblastoma	2 (0.6%)	1 (1.1%)
Bone	14 (3.9%)	5 (5.3%)
Neuroblastoma	17 (4.8%)	1 (1.1%)
Wilms	28 (7.9%)	7 (7.4%)
Sarcoma	10 (2.8%)	1 (1.1%)
Other	29 (8.2%)	14 (14.9%)
Late Effects Risk Stratification			<0.01
Low	153 (43.1%)	28 (29.8%)
Intermediate	146 (41.1%)	56 (59.6%)
High	56 (15.8%)	10 (10.6%)
Passport for Care *			<0.01
Yes	243 (83.5%)	28 (35.9%)
No	48 (16.5%)	50 (64.1%)

^*Excluded pediatric neuro-oncology survivors (n=77), as not routinely entered into PFC

A heat map of Oklahoma provides a visualization of optimal subspecialty follow-up by county (Figure 2). We observed a higher percentage of survivors with optimal follow-up in the central part of the state, including the counties surrounding the medical center in Oklahoma City. The adjusted risk ratio for survivors from large towns was 2.17 (95% CI 1.46, 3.23) compared to survivors from urban areas as the referent population (Table 2). For survivors from small town/isolated rural areas, the risk was increased but the 95% CI encompassed one, with survivors from urban areas as the referent population. When checking the interaction between rurality and the distance from the cancer center in the modified Poisson regression model, the algorithm would not converge and therefore distance was not included in the final model. Consequently, we conducted a subanalysis to separate out non-Oklahoma City urban areas compared with Oklahoma City-urban as the referent population (Supplemental Figure 3 and Supplemental Table 2).

Figure 2.

Geographic distribution of optimal subspecialty follow-up 5–7 years after initial diagnosis

Table 2.

Modeling for the association between rurality and the risk of suboptimal subspecialty follow-up care five to seven years after initial diagnosis using Modified Poisson Regression with robust error variance. (n=449)

RUCA	Unadjusted Risk Ratio (95% Confidence Interval)	Adjusted1 RR (95% Confidence Interval)
Large Town vs. Urban	2.04 (1.38, 3.01)	2.17 (1.46, 3.23)
Small Town/Isolated Rural vs. Urban	1.16 (0.72, 1.86)	1.28 (0.79, 2.09)

¹Based on the directed acyclic graph, we adjusted for race/ethnicity

Documentation of a survivorship care plan in PFC was strongly associated with subspecialty follow-up with a risk ratio of 0.20 (95% CI 0.14, 0.28). By rurality, 67% of survivors from large town had a survivorship care plan compared to 79% of survivors from urban areas and 70% of survivors from small town/isolated rural areas (p<0.01). Our secondary analyses yielded similar results to the analysis of rurality and suboptimal subspecialty care follow-up care (Tables 3 and 4, Supplemental Figure 4). The notable exception for this was that late effects risk stratification was not associated with a survivorship care plan (p=0.40).

Table 3.

Univariate analysis for Passport for Care documentation

	Passport for Care		P-Value
	Yes (n=271)	No (n=98)
Age at Diagnosis, in years (SE; 95%CI)	6.6 (0.31; 6.0–7.2)	9.1 (0.60; 8.0–10.3)	<0.01
Age at End of Seven-Year Follow-up, in years (SE; 95%CI)	13.6 (0.31; 13.0–14.2)	16.1 (0.60; 15.0–17.3)	<0.01
Adolescent at Diagnosis			<0.01
Yes	54 (20.0%)	38 (38.8%)
No	217 (80.0%)	60 (61.2%)
Sex			0.72
Male	152 (56.1%)	59 (60.2%)
Female	119 (43.9%)	39 (39.8%)
Race			0.76
White, Non-Hispanic	180 (66.4%)	63 (64.3%)
Black, Non-Hispanic	21 (7.7%)	10 (10.2%)
Hispanic	46 (17.0%)	15 (15.3%)
American Indian	18 (6.6%)	9 (9.2%)
Other 1	6 (2.2%)	1 (1.0%)
Area Deprivation Index, percentile (SE; 95%CI)	67.4 (1.5; 64.5–70.3)	73.0 (2.6; 67.9–78.2)	0.05
RUCA			<0.01
Urban	182 (67.1%)	47 (48.0%)
Large Town	36 (13.3%)	28 (28.6%)
Small Town/Isolated Rural	53 (19.6%)	23 (23.5%)
Distance to Medical Center, Median, in miles (SE)	20.6 (3.4)	38.9 (7.1)	<0.01
Travel Time to Medical Center, Median, in minutes (SE)	24.9 (3.5)	41.3 (7.6)	<0.01
Primary Diagnosis			0.08
Leukemia	130 (48.0%)	44 (44.9%)
Non-Hodgkin Lymphoma	18 (6.6%)	10 (10.2%)
Hodgkin Lymphoma	29 (10.7%)	12 (12.2%)
Bone	14 (5.2%)	5 (5.1%)
Neuroblastoma	14 (5.2%)	4 (4.1%)
Wilms	31 (11.4%)	4 (4.1%)
Sarcoma	10 (3.7%)	1 (1.0%)
Other	25 (9.2%)	18 (18.4%)
Late Effects Risk Stratification			0.43
Low	126 (46.5%)	53 (54.1%)
Intermediate	115 (42.4%)	36 (36.7%)
High	30 (11.1%)	9 (9.2%)

^*Excluded pediatric neuro-oncology survivors (n=77), as not routinely entered into PFC

Table 4.

Modeling for the association between rurality and PFC using Modified Poisson Regression with robust error variance. (n=449)

RUCA	Unadjusted Risk Ratio (95% Confidence Interval)	Adjusted1 RR (95% Confidence Interval)
Large Town vs. Urban	1.64 (1.23, 2.17)	1.70 (1.28, 2.27)
Small Town/Isolated Rural vs. Urban	1.41 (1.05, 1.89)	1.49 (1.10, 2.01)

¹Based on the directed acyclic graph, we adjusted for race/ethnicity

DISCUSSION

Survivors of childhood cancer from non-urban areas face complex challenges in optimal oncology-related follow-up care. Those from large towns were least likely to see an oncologist in the early survivorship period. This observed disparity persisted despite adjustment for race/ethnicity as a potential confounder. Furthermore, a strong association between optimal follow-up and a documented survivorship care plan with similar differences by rurality reinforces the importance to integrate late effects education into routine cancer care. The geographic distribution of suboptimal follow-up informs potential strategies to re-engage survivors at greatest risk for late effects, particularly during the transition to adult care when the burden of serious adverse events increases.

Surprisingly, survivors from small town/rural areas had a similar likelihood as those from urban areas of receiving optimal subspecialty follow-up. Among survivors from large towns, no clear pattern emerged in the geographical distribution of survivors with suboptimal care, though the small population size was a challenge and is an area for future work. One limitation of this study was the lack of data on primary care visits in the community and, if care was delivered, whether this constituted survivorship-focused care. The shortage of primary care providers across the United States is well-documented and evidence suggests worsening inequities in the workforce, especially in rural areas.^47,48 Survivors from rural areas may have a differential level of access to primary care or prefer to return to their primary oncologist for their survivorship care needs whereas those from large towns may prefer exclusive care from their primary care provider. Nevertheless, a lack of clarity in provider roles portends suboptimal survivorship-focused care as primary care providers often report unmet information needs on the late effects of cancer treatment.⁴⁹ Indeed, the transition to primary care requires clear, ongoing communication and additional training of community providers concerning specific treatment-related late effects.⁵⁰

A team-based approach to survivorship-focused care integrates oncology, primary care, subspecialists, patients, and caregivers with the goal to optimize long-term health and outcomes for survivors.⁵¹ Shared care with oncology and primary care offers improved follow-up for survivors.⁵² Survivorship care plans, particularly those available digitally such as PFC, are an initial step to bridge gaps in guideline-adherent care, whether by subspecialists or primary care. Seventy-three percent of pediatric oncology survivors had a survivorship care plan in the Oklahoma Childhood Cancer Survivor Cohort, in contrast to other reports in the literature of only a minority receiving a survivorship care plan, which could facilitate care coordination.⁵³ While an important starting point, optimal care is not guaranteed since primary care providers report additional barriers to the implementation of survivorship care plans.^54,55 Therefore, evidence-based interventions to promote team-based care in partnership with primary care as standard practice for survivors are essential to ensure the long-term quality of life for this vulnerable and unique population.

The COVID-19 pandemic spurred the explosion of telehealth applications across the medical field. Within pediatric oncology, the early disruption in long-term follow-up care of survivors and the high acceptability of telehealth during the pandemic illustrate the need for adaptable health systems.^56,57 Digital technologies offer innovative, evidence-based strategies to combat cancer care inequities, such as those observed among survivors from large towns in Oklahoma.⁵⁸ The success of telementoring models, such as Extension for Community Healthcare Outcomes (ECHO), to increase local capacity for subspecialty care in limited resource settings serve as a template for survivorship care delivery among survivors far from tertiary care centers.⁵⁹ Distance was strongly associated with optimal follow-up care in this study. Consequently, states with a large geographic area and limited subspecialty locations, such as Oklahoma, would benefit from approaches such as ECHO. Other digital interventions, such as mobile health, text messaging, and web-based platforms, challenge traditional models to engage survivors in their care.⁶⁰

Our study has many strengths, including the availability of an institutional cancer registry at a Commission on Cancer institution. This allowed us to use high-quality cancer data, coupled with EHR data, to evaluate factors related to childhood cancer survivorship. However, limitations include only having a single address at cancer diagnosis, which may not reflect residence during survivorship care, resulting in potential misclassification of rurality status. The use of the five to seven-year window after initial diagnosis as the early survivorship period may overlap with ongoing surveillance for disease recurrence by the primary oncologist and not represent survivorship-focused care. While 73% of general pediatric oncology survivors had a documented survivorship plan (n=271/369), survivors from CNS tumors may have different recommendations for follow-up care and this merits further investigation. Moreover, because there are two children’s hospitals providing treatment and survivorship care in the state, those residing in the northeastern part of the state may seek survivorship care at another institution. A manual chart review of all survivors without a completed clinic visit within the follow-up window excluded survivors with a documented transfer of care, including survivorship care, which helps ameliorate this bias. Additionally, these observations in follow-up care may not be generalizable to rural states with earlier Medicaid expansion, particularly for the identified disparities among adolescents at diagnosis who transitioned to young adulthood during the follow-up window, as insurance status is critical and has been shown to improve access to care for young adults.⁶¹ Oklahoma was a late adopter of Medicaid expansion with implementation in July 2021, which may have exacerbated these observed disparities.⁶² Fragmented healthcare systems across the United States highlight the need for access to services, data interoperability, and health information exchange to improve care, particularly for survivors from non-urban areas.⁶³

Approximately one-fifth of survivors were not seen in an oncology-related subspecialty clinic during the early survivorship period five to seven years after their initial diagnosis. Survivors from large towns, adolescents at diagnosis, and those further from the cancer center were significantly less likely to receive optimal follow-up. Primary diagnosis and late effects risk strata were also associated with optimal follow-up, which raises questions for further research to target at-risk groups. This analysis focused primarily on rurality. There is a clear need for continued, longitudinal surveillance of survivor cohorts from non-urban communities to assess inequities and develop health systems-based interventions tailored to the unique needs of this at-risk community. Clinical research informatics methods, such as those presented here, to integrate cancer registry, EHR, geospatial, and other data sources provide a framework for population health management of survivors at high risk for late effects.