Association of Neighborhood Measures of Social Determinants of Health With Breast, Cervical, and Colorectal Cancer Screening Rates in the US Midwest.

Importance: Despite advances in cancer treatment and cancer-related outcomes, disparities in cancer mortality remain. Lower rates of cancer prevention screening and consequent delays in diagnosis may exacerbate these disparities. Better understanding of the association between area-level social determinants of health and cancer screening may be helpful to increase screening rates. Objective: To examine the association between area deprivation, rurality, and screening for breast, cervical, and colorectal cancer in patients from an integrated health care delivery system in 3 US Midwest states (Minnesota, Iowa, and Wisconsin). Design, Setting, and Participants: In this cross-sectional study of adults receiving primary care at 75 primary care practices in Minnesota, Iowa, and Wisconsin, rates of recommended breast, cervical, and colorectal cancer screening completion were ascertained using electronic health records between July 1, 2016, and June 30, 2017. The area deprivation index (ADI) is a composite measure of social determinants of health composed of 17 US Census indicators and was calculated for all census block groups in Minnesota, Iowa, and Wisconsin (11 230 census block groups). Rurality was defined at the zip code level. Using multivariable logistic regression, this study examined the association between the ADI, rurality, and completion of cancer screening after adjusting for age, Charlson Comorbidity Index, race, and sex (for colorectal cancer only). Main Outcomes and Measures: Completion of recommended breast, cervical, and colorectal cancer screening. Results: The study cohorts were composed of 78 302 patients eligible for breast cancer screening (mean [SD] age, 61.8 [7.1] years), 126 731 patients eligible for cervical cancer screening (mean [SD] age, 42.6 [13.2] years), and 145 550 patients eligible for colorectal cancer screening (mean [SD] age, 62.4 [7.0] years; 52.9% [77 048 of 145 550] female). The odds of completing recommended screening were decreased for individuals living in the most deprived (highest ADI) census block group quintile compared with the least deprived (lowest ADI) quintile: the odds ratios were 0.51 (95% CI, 0.46-0.57) for breast cancer, 0.58 (95% CI, 0.54-0.62) for cervical cancer, and 0.57 (95% CI, 0.53-0.61) for colorectal cancer. Individuals living in rural areas compared with urban areas also had lower rates of cancer screening: the odds ratios were 0.76 (95% CI, 0.72-0.79) for breast cancer, 0.81 (95% CI, 0.79-0.83) for cervical cancer, and 0.93 (95% CI, 0.91-0.96) for colorectal cancer. Conclusions and Relevance: Individuals living in areas of greater deprivation and rurality had lower rates of recommended cancer screening, signaling the need for effective intervention strategies that may include improved community partnerships and patient engagement to enhance access to screening in highest-risk populations.

Patient attribution: why the method matters.

OBJECTIVES: To assess the impact of 5 commonly used patient attribution methods on measured healthcare cost, quality, and utilization metrics within an integrated healthcare delivery system. STUDY DESIGN: Cross-sectional analysis of administrative data of all patients attributed (by any of 5 methods) and/or paneled to a primary care provider (PCP) at Mayo Clinic Rochester (MCR) in 2011. METHODS: We retrospectively applied 5 attribution methods to MCR administrative data from January 1, 2010, to December 31, 2011. MCR is an integrated healthcare delivery system serving primary care and referral populations. The referral practice is geographically colocated but otherwise distinct from 6 primary care practice sites that include pediatric, internal medicine, and family medicine groups. Patients attributed by each method were compared on their concordance with PCP empanelment, quality measures, healthcare utilization, and total costs of care. RESULTS: The 5 methods attributed between 61,813 (42%) and 106,152 (72%) of paneled patients to a PCP at MCR, although not necessarily to the paneled PCP. There was marked variation in care utilization and total costs of care, but not quality measures, among patients attributed by the different methods and between those paneled versus not paneled. Patients with more primary care visits were more likely to be attributed by all methods. CONCLUSIONS: Reliable identification of the physician-patient relationship is necessary for accurate evaluation of healthcare processes, efficiencies, and outcomes. Optimization and standardization of attribution methods are therefore essential as health systems, payers, and policy makers seek to evaluate and improve the value of delivered care.

Making the Paradigm Shift from Siloed Population Health Management to an Enterprise-Wide Approach.

Health systems across the United States have started their journeys toward population health management and the future of accountable care. Models of population health management include patient-centered medical homes and private sector accountable care organizations (ACOs). Other models include public sector efforts, such as Physician Group Practice Transition Demonstrations, Medicare Health Care Quality Demonstration Programs, Beacon Communities, Medicare Shared Savings Program, and Pioneer ACOs. As a result, health care organizations often have pockets of population health initiatives that lack an enterprise-wide strategy. The next steps are to build on these efforts, leverage the learnings from these experiences, and incorporate the initiatives into an overarching framework and a road map for the future. This paper describes the current challenge many organizations face to implement an enterprise solution, describes how to transition from existing siloed initiatives, and shares a case study of how Mayo Clinic launched its Mayo Model of Community Care.

The Method for Performance Measurement Matters: Diabetes Care Quality as Measured by Administrative Claims and Institutional Registry.

OBJECTIVES: Performance measurement is used by health care providers, payers, and patients. Historically accomplished using administrative data, registries are used increasingly to track and improve care. We assess how measured diabetes care quality differs when calculated using claims versus registry. DATA SOURCES/STUDY SETTING: Cross-sectional analysis of administrative claims and electronic health records (EHRs) of patients in a multispecialty integrated health system in 2012 (n = 368,883). STUDY DESIGN: We calculated percent of patients attaining glycohemoglobin <8.0 percent, LDL cholesterol <100 mg/dL, blood pressure <140/90 mmHg, and nonsmoking (D4) in cohorts, identified by Medicare Accountable Care Organization/Minnesota Community Measures (ACO-MNCM; claims-based), Healthcare Effectiveness Data and Information Set (HEDIS; claims-based), and registry (EHR-based). DATA COLLECTION/EXTRACTION METHODS: Claims were linked to EHR to create a dataset of performance-eligible patients. PRINCIPAL FINDINGS: ACO-MNCM, HEDIS, and registry identified 6,475, 6,989, and 6,425 measurement-eligible patients. Half were common among the methods; discrepancies were due to attribution, age restriction, and encounter requirements. D4 attainment was lower in ACO-MNCM (36.09 percent) and HEDIS (37.51 percent) compared to registry (43.74 percent) cohorts. CONCLUSIONS: Registry- and claims-based performance measurement methods identify different patients, resulting in different rates of quality metric attainment with implications for innovative population health management.