Affordable Care Act and cancer stage at diagnosis in an underserved population

Survival After Implementation of a Decision Support Tool to Facilitate Evidence-Based Cancer Treatment

PURPOSE

Decision support tools (DSTs) to facilitate evidence-based cancer treatment are increasingly common in care delivery organizations. Implementation of these tools may improve process outcomes, but little is known about effects on patient outcomes such as survival. We aimed to evaluate the effect of implementing a DST for cancer treatment on overall survival (OS) among patients with breast, colorectal, and lung cancer.

METHODS

We used institutional cancer registry data to identify adults treated for first primary breast, colorectal, or lung cancer between December 2013 and December 2017. Our intervention of interest was implementation of a commercial DST for cancer treatment, and outcome of interest was OS. We emulated a single-arm trial with historical comparison and used a flexible parametric model to estimate standardized 3-year restricted mean survival time (RMST) difference and mortality risk ratio (RR) with 95% confidence limits (CLs).

RESULTS

Our study population comprised 1,059 patients with cancer (323 breast, 318 colorectal, and 418 lung). Depending on cancer type, median age was 55-60 years, 45%-67% were racial/ethnic minorities, and 49%-69% were uninsured. DST implementation had little effect on survival at 3 years. The largest effect was observed among patients with lung cancer (RMST difference, 1.7 months; 95% CL, -0.26 to 3.7; mortality RR, 0.95; 95% CL, 0.88 to 1.0). Adherence with tool-based treatment recommendations was >70% before and >90% across cancers.

CONCLUSION

Our results suggest that implementation of a DST for cancer treatment has nominal effect on OS, which may be partially attributable to high adherence with evidence-based treatment recommendations before tool implementation in our setting. Our results raise awareness that improved process outcomes may not translate to improved patient outcomes in some care delivery settings.

Pathways to ovarian cancer diagnosis: a qualitative study

Background

Ovarian cancer is often diagnosed at a late stage, when survival is poor. Qualitative narratives of patients’ pathways to ovarian cancer diagnoses may identify opportunities for earlier cancer detection and, consequently, earlier stage at diagnosis.

Methods

We conducted semi-structured interviews of ovarian cancer patients and survivors (n = 14) and healthcare providers (n = 11) between 10/2019 and 10/2021. Interviews focused on the time leading up to an ovarian cancer diagnosis. Thematic analysis was conducted by two independent reviewers using a two-phase deductive and inductive coding approach. Deductive coding used a priori time intervals from the validated Model of Pathways to Treatment (MPT), including self-appraisal and management of symptoms, medical help-seeking, diagnosis, and pre-treatment. Inductive coding identified common themes within each stage of the MPT across patient and provider interviews.

Results

The median age at ovarian cancer diagnosis was 61.5 years (range, 29–78 years), and the majority of participants (11/14) were diagnosed with advanced-stage disease. The median time from first symptom to initiation of treatment was 2.8 months (range, 19 days to 4.7 years). The appraisal and help-seeking intervals contributed the greatest delays in time-to-diagnosis for ovarian cancer. Nonspecific symptoms, perceptions of health and aging, avoidant coping strategies, symptom embarrassment, and concerns about potential judgment from providers prolonged the appraisal and help-seeking intervals. Patients and providers also emphasized access to care, including financial access, as critical to a timely diagnosis.

Conclusion

Interventions are urgently needed to reduce ovarian cancer morbidity and mortality. Population-level screening remains unlikely to improve ovarian cancer survival, but findings from our study suggest that developing interventions to improve self-appraisal of symptoms and reduce barriers to help-seeking could reduce time-to-diagnosis for ovarian cancer. Affordability of care and insurance may be particularly important for ovarian cancer patients diagnosed in the United States.

Expanding the Secondary Use of Prostate Cancer Real World Data: Automated Classifiers for Clinical and Pathological Stage

Background

Explicit documentation of stage is an endorsed quality metric by the National Quality Forum. Clinical and pathological cancer staging is inconsistently recorded within clinical narratives but can be derived from text in the Electronic Health Record (EHR). To address this need, we developed a Natural Language Processing (NLP) solution for extraction of clinical and pathological TNM stages from the clinical notes in prostate cancer patients.

Methods

Data for patients diagnosed with prostate cancer between 2010 and 2018 were collected from a tertiary care academic healthcare system's EHR records in the United States. This system is linked to the California Cancer Registry, and contains data on diagnosis, histology, cancer stage, treatment and outcomes. A randomly selected sample of patients were manually annotated for stage to establish the ground truth for training and validating the NLP methods. For each patient, a vector representation of clinical text (written in English) was used to train a machine learning model alongside a rule-based model and compared with the ground truth.

Results

A total of 5,461 prostate cancer patients were identified in the clinical data warehouse and over 30% were missing stage information. Thirty-three to thirty-six percent of patients were missing a clinical stage and the models accurately imputed the stage in 21-32% of cases. Twenty-one percent had a missing pathological stage and using NLP 71% of missing T stages and 56% of missing N stages were imputed. For both clinical and pathological T and N stages, the rule-based NLP approach out-performed the ML approach with a minimum F1 score of 0.71 and 0.40, respectively. For clinical M stage the ML approach out-performed the rule-based model with a minimum F1 score of 0.79 and 0.88, respectively.

Conclusions

We developed an NLP pipeline to successfully extract clinical and pathological staging information from clinical narratives. Our results can serve as a proof of concept for using NLP to augment clinical and pathological stage reporting in cancer registries and EHRs to enhance the secondary use of these data.

Interrupted Time-Series Analysis of Stereotactic Radiosurgery for Brain Metastases Before and After the Affordable Care Act

The 2010 Patient Protection and Affordable Care Act was aimed at reducing healthcare costs, improving healthcare quality, and expanding health insurance coverage among uninsured individuals in the United States. We examined trends in the utilization of radiation therapies and stereotactic radiosurgery before and after its implementation among U.S. adults hospitalized with brain metastasis. Interrupted time-series analyses of data on 383,934 Nationwide Inpatient Sample hospitalizations (2005-2010 and 2011-2013) were performed, whereby yearly and quarterly cross-sectional data were evaluated and Affordable Care Act implementation was considered the main exposure variable, stratifying by patient and hospital characteristics. Overall, we observed a declining trend in radiation therapy over time, with an upward shift post-Affordable Care Act. A downward shift in radiation therapy post-Affordable Care Act was observed among Northeastern and rural hospitals, whereas an upward shift was noted among specific patient (females, 18-39 or ≥ 65 years of age, Charlson Comorbidity Index (CCI) ≥10, non-elective admissions, Medicare, self-pay, no pay or other insurance) and hospital (Midwestern, Western, non-teaching urban) subgroups. Stereotactic radiosurgery utilization among recipients of radiation therapy increased over time among Hispanics, elective admissions, and rural hospitals, whereas post-Affordable Care Act was associated with increased stereotactic radiosurgery among African-Americans and non-elective admissions and decreased stereotactic radiosurgery among elective admissions, and rural hospitals. Whereas hospitalized adults in the United States utilized less radiation therapy over the nine-year period, utilization of radiation therapy, in general, and stereotactic radiosurgery, in particular, were not consistent among distinct subgroups defined by patient and hospital characteristics, with some traditionally underserved populations more likely to receive healthcare services post-Affordable Care Act. The Affordable Care Act may be helpful at closing the gap in access to technological advances such as stereotactic radiosurgery for treating brain metastases.

Patterns and association of vaccination among adults with a history of cancer in the USA: a population-based study

Aim: To assess the association of vaccination status among adults with history of cancer in a population-based cohort in the USA. Materials & methods: National Health Interview Survey datasets (2008-2018) have been accessed and information about the patterns and associations of the following vaccinations were collected (influenza vaccination, pneumococcal vaccination, hepatitis B vaccination, hepatitis A vaccination and shingles vaccination). Association of different sociodemographic variables with each of the above types of vaccination was studied through multivariable logistic regression analysis. Results: Private health insurance (vs no private insurance) was associated with higher percentages of recommended vaccination (influenza vaccination: 65 vs 59.7%; pneumococcal vaccination: 74.9 vs 68.8%; hepatitis B vaccination: 22.9 vs 19.3%; hepatitis A vaccination: 10.1 vs 8.6%; shingles vaccination: 33.8 vs 26.7%; p < 0.001 for all comparisons). Within multivariable logistic regression analyses, African American race, lower education and lower income were associated with less probability of adherence to recommended vaccination (for influenza vaccination; odds ratio (OR) for black race vs white race: 0.785; 95% CI: 0.717-0.859; OR for ≤high school vs >high school education: 0.763; 95% CI: 0.723-0.805; OR for income ≤US$45,000 vs >US$45,000: 0.701; 95% CI: 0.643-0.764). Conclusion: There is evidence of socio-economic disparities in adherence to recommended vaccination among this cohort of cancer survivors in the USA. More efforts need to be done to ensure that recommended vaccination is being delivered to all cancer survivors in need (including enhancing coverage and awareness to under-represented groups of the society).

Changes in Disparities in Stage of Breast Cancer Diagnosis in Pennsylvania After the Affordable Care Act

Background: This study sought to determine if increased access to health insurance following the Affordable Care Act (ACA) resulted in an increased proportion of early-stage breast cancer diagnosis among women in Pennsylvania, particularly minorities, rural residents, and those of lower socioeconomic status. Materials and Methods: Data on 35,735 breast cancer cases among women 50-64 and 68-74 years of age in Pennsylvania between 2010 and 2016 were extracted from the Pennsylvania Cancer Registry and analyzed in 2019. Women 50-64 years of age were subdivided by race/ethnicity, area of residence, and socioeconomic status as measured by area deprivation index (ADI). We compared the proportions of early-stage breast cancer diagnosis pre-ACA (2010-2013) and post-ACA (2014-2016) for all women 50-64 years of age to all women 68-74 years of age. This comparison was also made between paired sociodemographic subgroups for women 50-64 years of age. Multivariable logistic regression models were constructed to assess how race, area of residence, ADI, and primary care physician (PCP) density interacted to impact breast cancer diagnosis post-ACA. Results: The proportion of early-stage breast cancer diagnosis increased by 1.71% post-ACA among women 50-64 years of age (p < 0.01), whereas women 68-74 years of age saw no change. Multivariable logistic regression analysis demonstrated that minority women had lower odds of early-stage breast cancer diagnosis pre-ACA, but not post-ACA, when controlling for ADI. Meanwhile, increased area-level socioeconomic advantage was associated with higher odds of being diagnosed with early-stage breast cancer pre- and post-ACA irrespective of controlling for race, area of residence, or PCP density. Conclusions: Enhanced access to health insurance under the ACA was associated with an increased proportion of early-stage breast cancer diagnosis in Pennsylvanian women 50-64 years of age and may have reduced racial, but not socioeconomic, disparities in breast cancer diagnosis.