Pharmacogenetic testing in primary care could bolster depression treatment: A value proposition.

Pharmacogenetic testing could reduce the time to identify a safe and effective medication for depression; however, it is underutilized in practice. Major depression constitutes the most common mental disorder in the US, and while antidepressant therapy can help, the current trial -and error approach can require patients to endure multiple medication trials before finding one that is effective. Tailoring the fit of pharmacogenetic testing with prescribers' needs across a variety of settings could help to establish a generalizable value proposition to improve likelihood of adoption. We conducted a study to explore the value proposition for health systems using pharmacogenetic testing for mental health medications through prescribers' real-world experiences using implementation science concepts and systematic interviews with prescribers and administrators from four health care systems. To identify a value proposition, we organized the themes according to the Triple Aim framework, a leading framework for health care policy which asserts that high-value care should focus on three key metrics: (1) better health care quality and (2) population-level outcomes with (3) reduced per capita costs. Primary care providers whom we interviewed said that they value pharmacogenetic testing because it would provide more information about medications that they can prescribe, expanding their ability to identify medications that best-fit patients and reducing their reliance on referrals to specialists; they said that this capacity would help meet patients' needs for access to mental health care through primary care. At the same time, prescribers expressed differing views about how pharmacogenetic testing can help with quality of care and whether their views about out-of-pocket cost would prevent them from offering it. Thus, implementation should focus on integrating pharmacogenetic testing into primary care and using strategies to support prescribers' interactions with patients.

Association of reproductive risk factors and breast cancer molecular subtypes: a systematic review and meta-analysis.

BACKGROUND: Associations between reproductive factors and breast cancer (BC) risk vary by molecular subtype (i.e., luminal A, luminal B, HER2, and triple negative/basal-like [TNBC]). In this systematic review and meta-analysis, we summarized the associations between reproductive factors and BC subtypes. METHODS: Studies from 2000 to 2021 were included if BC subtype was examined in relation to one of 11 reproductive risk factors: age at menarche, age at menopause, age at first birth, menopausal status, parity, breastfeeding, oral contraceptive (OC) use, hormone replacement therapy (HRT), pregnancy, years since last birth and abortion. For each reproductive risk factor, BC subtype, and study design (case-control/cohort or case-case), random-effects models were used to estimate pooled relative risks and 95% confidence intervals. RESULTS: A total of 75 studies met the inclusion criteria for systematic review. Among the case-control/cohort studies, later age at menarche and breastfeeding were consistently associated with decreased risk of BC across all subtypes, while later age at menopause, later age of first childbirth, and nulliparity/low parity were associated with increased risk of luminal A, luminal B, and HER2 subtypes. In the case-only analysis, compared to luminal A, postmenopausal status increased the risk of HER2 and TNBC. Associations were less consistent across subtypes for OC and HRT use. CONCLUSION: Identifying common risk factors across BC subtypes can enhance the tailoring of prevention strategies, and risk stratification models can benefit from subtype specificity. Adding breastfeeding status to current BC risk prediction models can enhance predictive ability, given the consistency of the associations across subtypes.

Socioeconomic disparities in immunotherapy use among advanced-stage non-small cell lung cancer patients: analysis of the National Cancer Database.

Socioeconomic and racial disparities exist in access to care among patients with non-small cell lung cancer (NSCLC) in the United States. Immunotherapy is a widely established treatment modality for patients with advanced-stage NSCLC (aNSCLC). We examined associations of area-level socioeconomic status with receipt of immunotherapy for aNSCLC patients by race/ethnicity and cancer facility type (academic and non-academic). We used the National Cancer Database (2015-2016), and included patients aged 40-89 years who were diagnosed with stage III-IV NSCLC. Area-level income was defined as the median household income in the patient's zip code, and area-level education was defined as the proportion of adults aged ≥ 25 years in the patient's zip code without a high school degree. We calculated adjusted odds ratios (aOR) with 95% confidence intervals (95% CI) using multi-level multivariable logistic regression. Among 100,298 aNSCLC patients, lower area-level education and income were associated with lower odds of immunotherapy treatment (education: aOR 0.71; 95% CI 0.65, 0.76 and income: aOR 0.71; 95% CI 0.66, 0.77). These associations persisted for NH-White patients. However, among NH-Black patients, we only observed an association with lower education (aOR 0.74; 95% CI 0.57, 0.97). Across all cancer facility types, lower education and income were associated with lower immunotherapy receipt among NH-White patients. However, among NH-Black patients, this association only persisted with education for patients treated at non-academic facilities (aOR 0.70; 95% CI 0.49, 0.99). In conclusion, aNSCLC patients residing in areas of lower educational and economic wealth were less likely to receive immunotherapy.

Association of area-level socioeconomic status and non-small cell lung cancer stage by race/ethnicity and health care-level factors: Analysis of the National Cancer Database.

BACKGROUND: This study examined whether the association of socioeconomic status (SES) and non-small cell lung cancer (NSCLC) stage varied by race/ethnicity and health care access measures. METHODS: This study used data from the 2004-2016 National Cancer Database for patients aged 18-89 years who had been diagnosed with Stage 0-IV NSCLC. Adjusted odds ratios (aORs) with 95% confidence intervals (CIs) were calculated for the associations of area-level SES with an advanced stage at diagnosis via multilevel, multivariable logistic regression. The stage at diagnosis was dichotomized into early (0-II) and advanced (III-IV) stages, and area-level SES was categorized on the basis of the patient's zip code level: (1) the proportion of adults aged ≥25 years without a high school degree and (2) the median household income. The models were stratified by race/ethnicity (non-Hispanic [NH] White, NH Black, Hispanic, Asian, American Indian/Alaskan Native, and Native Hawaiian/Pacific Islander), insurance status (none, government, and private), and health care facility type (community, comprehensive community, academic/research, and integrated network). RESULTS: The study population included 1,329,972 patients. Although only 17% of the NH White patients were in the lowest income quartile, 50% of the NH Black patients were in this group. Lower area-level education and income were associated with higher odds of an advanced-stage diagnosis (aOR for education, 1.12; 95% CI, 1.10-1.13; aOR for income, 1.13; 95% CI, 1.11-1.14). These associations persisted among NH White, NH Black, Hispanic, and Asian patients; among those with government and private insurance (but not the uninsured); and among those treated at each facility type. CONCLUSIONS: Area-level income and education are strongly associated with an advanced NSCLC diagnosis regardless of the facility type and among those with government and private insurance.

Ovarian Cancer Epidemiology, Healthcare Access and Disparities (ORCHiD): methodology for a population-based study of black, Hispanic and white patients with ovarian cancer.

INTRODUCTION: Less than 40% of patients with ovarian cancer (OC) in the USA receive stage-appropriate guideline-adherent surgery and chemotherapy. Black patients with cancer report greater depression, pain and fatigue than white patients. Lack of access to healthcare likely contributes to low treatment rates and racial differences in outcomes. The Ovarian Cancer Epidemiology, Healthcare Access and Disparities study aims to characterise healthcare access (HCA) across five specific dimensions-Availability, Affordability, Accessibility, Accommodation and Acceptability-among black, Hispanic and white patients with OC, evaluate the impact of HCA on quality of treatment, supportive care and survival, and explore biological mechanisms that may contribute to OC disparities. METHODS AND ANALYSIS: We will use the Surveillance Epidemiology and Ends Results dataset linked with Medicare claims data from 9744 patients with OC ages 65 years and older. We will recruit 1641 patients with OC (413 black, 299 Hispanic and 929 white) from cancer registries in nine US states. We will examine HCA dimensions in relation to three main outcomes: (1) receipt of quality, guideline adherent initial treatment and supportive care, (2) quality of life based on patient-reported outcomes and (3) survival. We will obtain saliva and vaginal microbiome samples to examine prognostic biomarkers. We will use hierarchical regression models to estimate the impact of HCA dimensions across patient, neighbourhood, provider and hospital levels, with random effects to account for clustering. Multilevel structural equation models will estimate the total, direct and indirect effects of race on treatment mediated through HCA dimensions. ETHICS AND DISSEMINATION: Result dissemination will occur through presentations at national meetings and in collaboration with collaborators, community partners and colleagues across othercancer centres. We will disclose findings to key stakeholders, including scientists, providers and community members. This study has been approved by the Duke Institutional Review Board (Pro00101872). Safety considerations include protection of patient privacy. All disseminated data will be deidentified and summarised.