Personalized therapeutics: a potential threat to health equity

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.

Are there socio-economic inequalities in utilization of predictive biomarker tests and biological and precision therapies for cancer? A systematic review and meta-analysis

BACKGROUND

Novel biological and precision therapies and their associated predictive biomarker tests offer opportunities for increased tumor response, reduced adverse effects, and improved survival. This systematic review determined if there are socio-economic inequalities in utilization of predictive biomarker tests and/or biological and precision cancer therapies.

METHODS

MEDLINE, Embase, Scopus, CINAHL, Web of Science, PubMed, and PsycINFO were searched for peer-reviewed studies, published in English between January 1998 and December 2019. Observational studies reporting utilization data for predictive biomarker tests and/or cancer biological and precision therapies by a measure of socio-economic status (SES) were eligible. Data was extracted from eligible studies. A modified ISPOR checklist for retrospective database studies was used to assess study quality. Meta-analyses were undertaken using a random-effects model, with sub-group analyses by cancer site and drug class. Unadjusted odds ratios (ORs) and 95% confidence intervals (CIs) were computed for each study. Pooled utilization ORs for low versus high socio-economic groups were calculated for test and therapy receipt.

RESULTS

Among 10,722 citations screened, 62 papers (58 studies; 8 test utilization studies, 37 therapy utilization studies, 3 studies on testing and therapy, 10 studies without denominator populations or which only reported mean socio-economic status) met the inclusion criteria. Studies reported on 7 cancers, 5 predictive biomarkers tests, and 11 biological and precision therapies. Thirty-eight studies (including 1,036,125 patients) were eligible for inclusion in meta-analyses. Low socio-economic status was associated with modestly lower predictive biomarker test utilization (OR 0.86, 95% CI 0.71-1.05; 10 studies) and significantly lower biological and precision therapy utilization (OR 0.83, 95% CI 0.75-0.91; 30 studies). Associations with therapy utilization were stronger in lung cancer (OR 0.71, 95% CI 0.51-1.00; 6 studies), than breast cancer (OR 0.93, 95% CI 0.78-1.10; 8 studies). The mean study quality score was 6.9/10.

CONCLUSIONS

These novel results indicate that there are socio-economic inequalities in predictive biomarker tests and biological and precision therapy utilization. This requires further investigation to prevent differences in outcomes due to inequalities in treatment with biological and precision therapies.

What Is the Optimal Primary Care Panel Size?: A Systematic Review

BACKGROUND

Primary care for a panel of patients is a central component of population health, but the optimal panel size is unclear.

PURPOSE

To review evidence about the association of primary care panel size with health care outcomes and provider burnout.

DATA SOURCES

English-language searches of multiple databases from inception to October 2019 and Google searches performed in September 2019.

STUDY SELECTION

English-language studies of any design, including simulation models, that assessed the association between primary care panel size and safety, efficacy, patient-centeredness, timeliness, efficiency, equity, or provider burnout.

DATA EXTRACTION

Independent, dual-reviewer extraction; group consensus rating of certainty of evidence.

DATA SYNTHESIS

Sixteen hypothesis-testing studies and 12 simulation modeling studies met inclusion criteria. All but 1 hypothesis-testing study were cross-sectional assessments of association. Three studies each provided low-certainty evidence that increasing panel size was associated with no or modestly adverse effects on patient-centered and effective care. Eight studies provided low-certainty evidence that increasing panel size was associated with variable effects on timely care. No studies assessed the effect of panel size on safety, efficiency, or equity. One study provided very-low-certainty evidence of an association between increased panel size and provider burnout. The 12 simulation studies evaluated 5 models; all used access as the only outcome of care. Five and 2 studies, respectively, provided moderate-certainty evidence that adjusting panel size for case mix and adding clinical conditions to the case mix resulted in better access.

LIMITATION

No studies had concurrent comparison groups, and published and unpublished studies may have been missed.

CONCLUSION

Evidence is insufficient to make evidence-based recommendations about the optimal primary care panel size for achieving beneficial health outcomes.

PRIMARY FUNDING SOURCE

Veterans Affairs Quality Enhancement Research Initiative.

Addressing Health Equity in Public Health Practice: Frameworks, Promising Strategies, and Measurement Considerations

This review describes the context of health equity and options for integrating equity into public health practice. We first discuss how the conceptualization of health equity and how equity considerations in US public health practice have been shaped by multidisciplinary engagements. We then discuss specific ways to address equity in core public health functions, provide examples of relevant frameworks and promising strategies, and discuss conceptual and measurement issues relevant to assessing progress in moving toward health equity. Challenges and opportunities and their implications for future directions are identified.

Translation in Data Mining to Advance Personalized Medicine for Health Equity

Personalized medicine is the development of 'tailored' therapies that reflect traditional medical approaches, with the incorporation of the patient's unique genetic profile and the environmental basis of the disease. These individualized strategies encompass disease prevention, diagnosis, as well as treatment strategies. Today's healthcare workforce is faced with the availability of massive amounts of patient- and disease-related data. When mined effectively, these data will help produce more efficient and effective diagnoses and treatment, leading to better prognoses for patients at both the individual and population level. Designing preventive and therapeutic interventions for those patients who will benefit most while minimizing side effects and controlling healthcare costs, requires bringing diverse data sources together in an analytic paradigm. A resource to clinicians in the development and application of personalized medicine is largely facilitated, perhaps even driven, by the analysis of "big data". For example, the availability of clinical data warehouses is a significant resource for clinicians in practicing personalized medicine. These "big data" repositories can be queried by clinicians, using specific questions, with data used to gain an understanding of challenges in patient care and treatment. Health informaticians are critical partners to data analytics including the use of technological infrastructures and predictive data mining strategies to access data from multiple sources, assisting clinicians' interpretation of data and development of personalized, targeted therapy recommendations. In this paper, we look at the concept of personalized medicine, offering perspectives in four important, influencing topics: 1) the availability of 'big data' and the role of biomedical informatics in personalized medicine, 2) the need for interdisciplinary teams in the development and evaluation of personalized therapeutic approaches, and 3) the impact of electronic medical record systems and clinical data warehouses on the field of personalized medicine. In closing, we present our fourth perspective, an overview to some of the ethical concerns related to personalized medicine and health equity.