Ovarian cancer in California: Guideline adherence, survival, and the impact of geographic location, 1996-2014

Role of neighborhood context in ovarian cancer survival disparities: current research and future directions

Ovarian cancer is the fifth leading cause of cancer-associated mortality among US women with survival disparities seen across race, ethnicity, and socioeconomic status, even after accounting for histology, stage, treatment, and other clinical factors. Neighborhood context can play an important role in ovarian cancer survival, and, to the extent to which minority racial and ethnic groups and populations of lower socioeconomic status are more likely to be segregated into neighborhoods with lower quality social, built, and physical environment, these contextual factors may be a critical component of ovarian cancer survival disparities. Understanding factors associated with ovarian cancer outcome disparities will allow clinicians to identify patients at risk for worse outcomes and point to measures, such as social support programs or transportation aid, that can help to ameliorate such disparities. However, research on the impact of neighborhood contextual factors in ovarian cancer survival and in disparities in ovarian cancer survival is limited. This commentary focuses on the following neighborhood contextual domains: structural and institutional context, social context, physical context represented by environmental exposures, built environment, rurality, and healthcare access. The research conducted to date is presented and clinical implications and recommendations for future interventions and studies to address disparities in ovarian cancer outcomes are proposed.

Use of Period Analysis to Timely Assess Five-Year Relative Survival for Patients with Ovarian Cancer from Taizhou, Eastern China

OBJECTIVE

Ovarian cancer is a deadly gynecologic malignancy with a poor prognosis. It is essential to evaluate the early detection and screening programs of ovarian cancer via timely assessment of long-time survival, particularly in China where those data are incredibly limited. Here, we aimed to provide timely and accurately assessment of long-term survival estimate of ovarian cancer patients from eastern China.

METHODS

Data of 770 ovarian cancer patients diagnosed between 2004-2018 were obtained from four cancer registries in Taizhou, eastern China, were included. We used period analysis to calculate five-year relative survival (RS) of aforementioned ovarian cancer patients for overall and the stratification by age at diagnosis and region.

RESULTS

Our findings demonstrated that the overall five-year RS for ovarian cancer patients in Taizhou between 2014 and 2018 was 69.2%, while urban areas were higher compared to rural areas (77.6% vs. 64.9%). We also observed a significant age gradient with the five-year RS decreasing from 79.6% for age group < 55 years to 66.9% for age group > 74 years. Furthermore, we identified a clear upward trend of five-year RS over the study period, both overall and stratified by region and age at diagnosis.

CONCLUSION

This is the first study in China using period analysis to provide the most up-to-date five-year RS for ovarian cancer patients from Taizhou, eastern China, which reaches 69.2% during 2014-2018. Our results provide valuable information for timely assessment of early detection and screening programs for ovarian cancer in eastern China.

Impact of the Area of Residence of Ovarian Cancer Patients on Overall Survival

Survival disparities persist in ovarian cancer and may be linked to the environments in which patients live. The main objective of this study was to analyze the global impact of the area of residence of ovarian cancer patients on overall survival. The data were obtained from the Surveillance, Epidemiology and End Results (SEER) database. We included all the patients with epithelial ovarian cancers diagnosed between 2010 and 2016. The areas of residence were analyzed by the hierarchical clustering of the principal components to group similar counties. A multivariable Cox proportional hazards model was then fitted to evaluate the independent effect of each predictor on overall survival. We included a total of 16,806 patients. The clustering algorithm assigned the 607 counties to four clusters, with cluster 1 being the most disadvantaged and cluster 4 having the highest socioeconomic status and best access to care. The area of residence cluster remained a statistically significant independent predictor of overall survival in the multivariable analysis. The patients living in cluster 1 had a risk of death more than 25% higher than that of the patients living in cluster 4. This study highlights the importance of considering the sociodemographic factors within the patient's area of residence when developing a care plan and follow-up.

Geographic differences in community oncology provider and practice location characteristics in the central United States

PURPOSE

How care delivery influences urban-rural disparities in cancer outcomes is unclear. We sought to understand community oncologists' practice settings to inform cancer care delivery interventions.

METHODS

We conducted secondary analysis of a national dataset of providers billing Medicare from June 1, 2019 to May 31, 2020 in 13 states in the central United States. We used Kruskal-Wallis rank and Fisher's exact tests to compare physician characteristics and practice settings among rural and urban community oncologists.

FINDINGS

We identified 1,963 oncologists practicing in 1,492 community locations; 67.5% practiced in exclusively urban locations, 11.3% in exclusively rural locations, and 21.1% in both rural and urban locations. Rural-only, urban-only, and urban-rural spanning oncologists practice in an average of 1.6, 2.4, and 5.1 different locations, respectively. A higher proportion of rural community sites were solo practices (11.7% vs 4.0%, P<.001) or single specialty practices (16.4% vs 9.4%, P<.001); and had less diversity in training environments (86.5% vs 67.8% with <2 medical schools represented, P<.001) than urban community sites. Rural multispecialty group sites were less likely to include other cancer specialists.

CONCLUSIONS

We identified 2 potentially distinct styles of care delivery in rural communities, which may require distinct interventions: (1) innovation-isolated rural oncologists, who are more likely to be solo providers, provide care at few locations, and practice with doctors with similar training experiences; and (2) urban-rural spanning oncologists who provide care at a high number of locations and have potential to spread innovation, but may face high complexity and limited opportunity for care standardization.