Racial Differences in Vaginal Fluid Metabolites and Association with Systemic Inflammation Markers among Ovarian Cancer Patients: A Pilot Study.

The vaginal microbiome differs by race and contributes to inflammation by directly producing or consuming metabolites or by indirectly inducing host immune response, but its potential contributions to ovarian cancer (OC) disparities remain unclear. In this exploratory cross-sectional study, we examine whether vaginal fluid metabolites differ by race among patients with OC, if they are associated with systemic inflammation, and if such associations differ by race. Study participants were recruited from the Ovarian Cancer Epidemiology, Healthcare Access, and Disparities Study between March 2021 and September 2022. Our study included 36 study participants with ovarian cancer who provided biospecimens; 20 randomly selected White patients and all 16 eligible Black patients, aged 50-70 years. Acylcarnitines (n = 45 species), sphingomyelins (n = 34), and ceramides (n = 21) were assayed on cervicovaginal fluid, while four cytokines (IL-1ß, IL-10, TNF-α, and IL-6) were assayed on saliva. Seven metabolites showed >2-fold differences, two showed significant differences using the Wilcoxon rank-sum test (p < 0.05; False Discovery Rate > 0.05), and 30 metabolites had coefficients > ±0.1 in a Penalized Discriminant Analysis that achieved two distinct clusters by race. Arachidonoylcarnitine, the carnitine adduct of arachidonic acid, appeared to be consistently different by race. Thirty-eight vaginal fluid metabolites were significantly correlated with systemic inflammation biomarkers, irrespective of race. These findings suggest that vaginal fluid metabolites may differ by race, are linked with systemic inflammation, and hint at a potential role for mitochondrial dysfunction and sphingolipid metabolism in OC disparities. Larger studies are needed to verify these findings and further establish specific biological mechanisms that may link the vaginal microbiome with OC racial disparities.

Early COVID-19 Hospitalizations Among New York State Residents with a History of Invasive Cancer.

Background: Individuals with a history of cancer may be more susceptible to severe COVID-19 due to immunosuppression, comorbidities, or ongoing treatment. We linked inpatient claims data on COVID-19 hospitalizations to cancer diagnoses from the New York State Cancer Registry (NYSCR) to examine associations between prior cancer diagnoses and hospitalizations for COVID-19, and factors associated with death at discharge after COVID-19 hospitalization. Methods: New York State (NYS) residents diagnosed with invasive cancer before July 1, 2021, who were alive on January 1, 2020, were identified from NYSCR data. We obtained claims data for discharge year 2020 and the first half of 2021 from NYS's Statewide Planning and Research Cooperative System (SPARCS), and we linked inpatient records with COVID-19 as the primary diagnosis to cancer data from the NYSCR using deterministic matching methods. We calculated descriptive statistics and conducted multivariable-adjusted logistic regression analyses to examine associations of cancer case characteristics with COVID-19 hospitalization and with vital status at discharge among patients with a history of cancer. All analyses were conducted in SAS 9.4. Results: Our analysis included 1,257,377 individuals with a history of cancer, 10,210 of whom had a subsequent primary COVID-19 hospitalization. Individuals with a history of cancer were 16% more likely to be hospitalized with COVID-19, compared to the general population of NYS, after adjusting for age and sex (95% CI, 14%-19%). Factors independently associated with COVID-19 hospitalization among cancer patients included older age, male sex, non-Hispanic Black race or Hispanic ethnicity, diagnosis with late-stage cancer or with multiple tumors, more recent cancer diagnosis, and New York City (NYC) residency at the time of cancer diagnosis. Factors independently associated with death at discharge among individuals with COVID-19 hospitalization and a prior cancer diagnosis included older age, male sex, non-Hispanic Black or non-Hispanic Asian/Pacific Islander race or Hispanic ethnicity, residence in NYC at the time of COVID-19 hospitalization, and an active cancer diagnosis claim code at the time of COVID-19 hospitalization. Conclusion: This claims-based study identified higher risks of COVID-19 hospitalization and death at discharge among individuals with a history of cancer, and particularly those in certain demographic and diagnostic groups.

The Case of the Missing 2020 Cancers: Using Claims Data to Investigate a Deficit in Incident Cancer Case Reports to the New York State Cancer Registry in 2020.

Background: As the February 2022 Surveillance, Epidemiology, and End Results (SEER) Call for Data deadlines approached, the New York State Cancer Registry had received reports for approximately 10% fewer consolidated incident cases for 2020 than expected. We used claims data to examine changes in the volume of cancer claim records during the COVID-19 pandemic and possible contributors to the deficit in cancer reports. Methods: The New York State (NYS) Statewide Planning and Research Cooperative System (SPARCS) requires reporting of all patient encounters from licensed ambulatory surgery, emergency department, and hospital inpatient and outpatient providers. Each record includes patient demographics and up to 17 diagnosis codes from the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). For this project, we extracted 6,725,416 SPARCS records with any malignant neoplasm code for 2018 through June 2021 for NYS residents. Using SAS 9.4, we focused on comparing the cancer-related records for 2020 to the records from 2019. Results: Overall, there were 5% more cancer-related records in 2019 than in 2018 (2,009,600 vs 1,914,364), but 8.2% fewer records in 2020 (1,844,054 total) than in 2019. Looking by month and year, the number of claims in the first 2 months of 2020 exceeded the numbers from 2019 by 5%. However, a decrease in the number of claims started in March 2020, with the biggest drop in April 2020, where there was a deficit of 38.8% for cancer-related encounter reports relative to the same month the previous year. Although the numbers rose after April, the number of claims for the last half of 2020 was still 4% lower than the same time frame in 2019. There were substantial decreases in the number of records in 2020 for all encounter types and across levels of each covariate examined, including age, sex, race/ethnicity, and facility region of NYS. In analyses of all reporting facilities, facilities in New York City had a more pronounced and more prolonged drop in reporting in 2020 than facilities in the rest of the state. Conclusion: Although SPARCS data do not provide definitive evidence of decreases in incident cancer diagnoses, these data suggest that there were fewer cancers diagnosed among NYS residents in 2020. Additional analyses are needed to assess the impacts of COVID-19-related delays in cancer diagnosis and treatment on stage at diagnosis and outcomes.

Bias Introduced by Relying on Incomplete Electronic Pathology Reporting for Rapid Case Ascertainment in Patient Contact Studies.

BACKGROUND: Relying on electronic pathology (ePath) reporting to state cancer registries for rapid ascertainment of cases for patient contact research studies may introduce bias if the patient populations differ for reporting facilities with vs without ePath. We examined changes between 2014-2019 in the percent of cases reported to the New York State Cancer Registry by ePath within 3 months of diagnosis and characteristics of the most recent cases by ePath status. Our goal was to assess the potential bias introduced by relying on incomplete ePath reporting for patient recruitment. METHODS: We restricted our analysis to first malignant cancers diagnosed in New York State residents aged 18 years and older. We examined patient characteristics and used χ2 tests to examine differences in the distribution of each characteristic by ePath status for diagnosis years 2017-2019, and used multivariable-adjusted logistic regression to calculate odds ratios and 95% CIs for the association between each patient characteristic and ePath status for all 2017-2019 cancers combined and common cancer sites. All analyses were conducted using SAS 9.4. RESULTS: The percent of cases reported by ePath increased over time from 15.7% in 2014 to 44.8% in 2019. Among 264,607 cancers diagnosed in 2017-2019 and reported through July 2021, there were statistically significant differences in all variables examined by ePath status (all P < .0001). For all cancers combined, cases reported by ePath were more likely to be younger, female, non-Hispanic White, married, live outside of New York City/ Long Island, still be alive, and have received treatment. We observed statistically significant odds ratios for the associations between all variables examined and ePath status for all cancers combined, but the strength and statistical significance of the associations varied by cancer site. CONCLUSIONS: Our results indicate that relying on incomplete ePath reporting for rapid case ascertainment will introduce selection bias in the study sample for patient contact studies. This bias should decrease as additional facilities acquire ePath reporting capability.

Health Care Utilization Prior to Ovarian Cancer Diagnosis in Publicly Insured Individuals in New York State.

BACKGROUND: Women with early-stage ovarian cancer may be asymptomatic or present with nonspecific symptoms. We examined health care utilization prior to ovarian cancer diagnosis to assess whether women with higher utilization differed in their prognosis and outcomes compared to women with low utilization. METHODS: Using Medicaid, Medicare, and New York State Cancer Registry data for ovarian cancer cases diagnosed in 2006-2015, we examined selected health care visits that occurred 1-6 months before ovarian cancer diagnosis. We used multivariable-adjusted logistic regression to estimate odds ratios (ORs) and 95% CIs for associations of sociodemographic factors with number of prediagnostic visits and number of visits with tumor characteristics, and Cox proportional hazards regression to examine differences in survival by number of visits. RESULTS: Women with >5 vs 0 prediagnostic visits were statistically significantly less likely to be diagnosed with distant vs local stage disease (OR, 0.72; 95% CI, 0.54-0.96), and women with 3-5 or >5 vs 0 prediagnostic visits had better overall survival (hazard ratio [HR], 0.88; 95% CI, 0.80-0.96 and HR, 0.90; 95% CI, 0.83-0.98, respectively). In stratified analyses, the association with improved survival was observed only among cases with regional or distant stage disease. CONCLUSIONS: Women with high health care utilization prior to ovarian cancer diagnosis may have better prognosis and survival, possibly because of earlier detection or better access to care throughout treatment. Women and their health care providers should not ignore symptoms potentially indicative of ovarian cancer and should be persistent in following up on symptoms that do not resolve.

Estrogen Receptor-ß Expression of Ovarian Tumors and Its Association with Ovarian Cancer Risk Factors.

BACKGROUND: Differential associations between ovarian cancer risk factors and estrogen receptor-α (ERα) ovarian tumor expression have been noted; however, no research has assessed estrogen receptor-ß (ERß) expression. Thus, in exploratory analyses, we assessed the association of several factors with ovarian cancer risk by ERß tumor status. METHODS: We conducted a nested case-control study within the prospective Nurses' Health Study cohorts (NHS/NHSII), with exposures collected through biennial questionnaires. Paraffin-embedded tumor blocks were requested for cases diagnosed from 1976 to 2006 (NHS) and 1989 to 2005 (NHSII) and tissue microarrays were stained for nuclear ERß (ERß-nuc) and cytoplasmic ERß (ERß-cyto), with any staining considered positive (+). We obtained odds ratios (OR) and 95% confidence intervals (CI) using multivariate polytomous logistic regression. RESULTS: We included 245 cases [43% ERß-cyto (+) and 71% ERß-nuc (+)] and 1,050 matched controls. An inverse association was observed between parity and risk of ERß-nuc (+) (OR, parous vs. nulliparous: 0.46; 95% CI, 0.26-0.81), but not ERß-nuc (-) tumors (OR, parous vs. nulliparous: 1.51; 95% CI, 0.45-5.04; P heterogeneity = 0.04). Conversely, parity was inversely associated with ERß-cyto (-) tumors (OR, parous vs. nulliparous: 0.42; 95% CI, 0.23-0.78), but was not associated with ERß-cyto (+) tumors (OR, parous vs. nulliparous: 1.08; 95% CI, 0.45-2.63; P heterogeneity = 0.05). Associations for other exposures, including hormone therapy, did not differ by ERß-nuc or ERß-cyto status. CONCLUSIONS: Our results suggest that parity may influence ovarian cancer risk, in part, through alterations in ERß localization within tumor cells. IMPACT: Alterations in ERß expression and localization appear to be important for ovarian cancer etiology. Future research should confirm our results and assess potential biologic mechanisms for the observed associations.

Impact of preexisting type 2 diabetes mellitus and antidiabetic drugs on all-cause and cause-specific mortality among Medicaid-insured women diagnosed with breast cancer.

BACKGROUND: We investigated the influence preexisting type 2 diabetes mellitus (T2DM) and antidiabetic drugs have on all-cause and cause-specific mortality among Medicaid-insured women diagnosed with breast cancer. METHODS: 9221 women aged <64 years diagnosed with breast cancer and reported to the New York State (NYS) Cancer Registry from 2004 to 2016 were linked with Medicaid claims. Preexisting T2DM was determined by three diagnosis claims for T2DM with at least one claim prior to breast cancer diagnosis and a prescription claim for an antidiabetic drug within three months following breast cancer diagnosis. Estimated menopausal status was determined by age (premenopausal age <50; postmenopausal age ≥50). Hazard ratios (HR) and 95 % confidence intervals (95 %CI) were calculated with Cox proportional hazards regression, adjusting for confounders. RESULTS: Women with preexisting T2DM had greater all-cause (HR = 1.40; 95 %CI 1.21, 1.63), cancer-specific (HR = 1.24; 95 %CI 1.04, 1.47), and cardiovascular-specific (HR = 2.46; 95 %CI 1.54, 3.90) mortality hazard compared to nondiabetic women. In subgroup analyses, the association between T2DM and all-cause mortality was found among non-Hispanic White (HR 1.78 95 %CI 1.38, 2.30) and postmenopausal (HR = 1.47; 95 %CI 1.23, 1.77) women, but not among other race/ethnicity groups or premenopausal women. Additionally, compared to women prescribed metformin, all-cause mortality hazard was elevated among women prescribed sulfonylurea (HR = 1.44; 95 %CI 1.06, 1.94) or insulin (HR = 1.54; 95 %CI 1.12, 2.11). CONCLUSION: Among Medicaid-insured women with breast cancer, those with preexisting T2DM have an increased mortality hazard, especially when prescribed sulfonylurea or insulin. Further research is warranted to determine the role antidiabetic drugs have on survival among women with breast cancer.