Maria DeYoreo

Vertically integrated health systems are expanding across the U.S., but there is no evidence that system-affiliated providers are better able to reduce socioeconomic disparities than unaffiliated providers. This paper develops and interprets statistical models for estimating the longitudinal effects of an exposure, health system affiliation, on within- and between-group disparities. Mixed effect difference-in-differences regression models were used to estimate the effect of physician organization (PO) health system affiliation on socioeconomic disparities in diabetes medication adherence for fee-for-service Medicare beneficiaries from 2013 to 2019, decomposing the effect of affiliation on disparities into within- and between-PO socioeconomic disparities. We find that after becoming affiliated, adherence for dually-eligible beneficiaries worsened relative to non dually-eligible beneficiaries within POs, but between-PO disparities were reduced. Simulation studies were used to evaluate the methods, and showed the importance of centering dual-eligibility status and its interaction with affiliation status at the PO-year level and including the PO-year level means as predictors to decompose effects of affiliation into within and between-PO effects.

Objective: To describe the effect of geographically limited disasters on health plan (ie, contract) quality performance scores using a broad set of clinical quality and patient experience measures. Study Design: Retrospective analyses to assess the impact of disasters on Medicare Advantage contracts’ quality-of-care performance scores in 2017 and 2018 for 11 Part C clinical quality and patient experience measures used in the Medicare Advantage Star Ratings. Methods: We calculated each Medicare Advantage contract’s disaster exposure using the percentage of the contract’s beneficiaries residing in a Federal Emergency Management Agency–designated disaster area during the measurement period. Using linear mixed models, we estimated the association between contract-level disaster exposures and performance scores during the performance period measured, with random effects for contract and fixed effects for year, contract characteristics, and the disaster exposure, using repeated cross-sectional data on contracts from 2016 to 2018. Results: We found no evidence that geographically limited disasters meaningfully affected contract quality performance scores. The disasters studied were associated with statistically significant but small changes in performance scores for 1 of 11 measures in both years. Conclusions: The lack of evidence that being in a disaster-affected area had a meaningful negative impact on quality measure performance suggests that performance measurement programs are robust to the impact of short-term localized disasters and continue to function as intended.

Colorectal cancer (CRC) incidence rates have decreased among adults aged 50 years or older while increasing in adults under age 50 years. Understanding these trends is challenging because of the multiple related time scales of age, diagnosis period, and birth cohort. We analyzed incidence rates of rectal, distal colon, and proximal colon cancer for individuals aged 20 years or more from the Surveillance, Epidemiology, and End Results Program for diagnosis years 1978–2017. We used a 2-stage generalized linear model to determine age, period, and cohort effects for CRC incidence. We first estimated birth cohort effects among people under age 45 years. We used these results to specify prior distributions for cohort effects in a Bayesian model to estimate period effects among people aged 45 years or more. There was no evidence of period effects for people under age 45 years. Risks of rectal and distal colon cancer increased for later birth cohorts. Compared with the 1943–1952 birth cohort, the 1983–1992 birth cohort had 2.2 times the risk of rectal cancer, 1.9 times the risk of distal colon cancer, and 1.3 times the risk of proximal colon cancer. For people aged ≥45 years, period effects showed declines in CRC risk that were attributable to screening.

Background: To compare serious illness programs (SIPs) using recently developed patient experience measures, adjustment must be made for patient characteristics not under control of the programs. Objectives: To develop a case-mix adjustment model to enable fair comparison of patient experience between SIPs by investigating the roles of patient characteristics, proxy response, and mode of survey administration (mail-only vs. mail with telephone follow-up) in survey responses. Methods: Using survey data from 2263 patients from 32 home-based SIPs across the United States, we fit regression models to assess the association between patient-level variables and scores for seven quality measures (Communication, Care Coordination, Help for Symptoms, Planning for Care, Support for Family and Friends, and two global assessments of care). Characteristics that are not consequences of the care the program delivered were considered as adjustors. Results: Final recommended case-mix adjustors are age, education, primary diagnosis, self-reported functional status, self-rated physical health, self-rated mental health, proxy respondent use, and response percentile (a measure of how soon a person responded compared with others in the same program and mode). Age, primary diagnosis, self-rated mental health, and proxy respondent use had the most impact on program-level scores. We also recommend adjusting for mode of survey administration. We find that up to 12 percent of pairs of programs would have their rankings reversed by adjustment. Conclusions: To ensure fair comparison of programs, scores should be case-mix adjusted for variables that influence patients' reports about care quality, but are not under the control of the program administering care.

Background: Microsimulation models are mathematical models that simulate event histories for individual members of a population. They are useful for policy decisions because they simulate a large number of individuals from an idealized population, with features that change over time, and the resulting event histories can be summarized to describe key population-level outcomes. Model calibration is the process of incorporating evidence into the model. Calibrated models can be used to make predictions about population trends in disease outcomes and effectiveness of interventions, but calibration can be challenging and computationally expensive. Methods: This paper develops a technique for sequentially updating models to take full advantage of earlier calibration results, to ultimately speed up the calibration process. A Bayesian approach to calibration is used because it combines different sources of evidence and enables uncertainty quantification which is appealing for decision-making. We develop this method in order to re-calibrate a microsimulation model for the natural history of colorectal cancer to include new targets that better inform the time from initiation of preclinical cancer to presentation with clinical cancer (sojourn time), because model exploration and validation revealed that more information was needed on sojourn time, and that the predicted percentage of patients with cancers detected via colonoscopy screening was too low. Results: The sequential approach to calibration was more efficient than recalibrating the model from scratch. Incorporating new information on the percentage of patients with cancers detected upon screening changed the estimated sojourn time parameters significantly, increasing the estimated mean sojourn time for cancers in the colon and rectum, providing results with more validity. Conclusions: A sequential approach to recalibration can be used to efficiently recalibrate a microsimulation model when new information becomes available that requires the original targets to be supplemented with additional targets.

Background: Breastfeeding offers many medical and neurodevelopmental advantages for birthing parents and infants; however, the majority of parents stop breastfeeding before it is recommended. Professional lactation support by the International Board Certified Lactation Consultants (IBCLCs) increases breastfeeding rates; however, many communities lack access to IBCLCs. Black and Latinx parents have lower breastfeeding rates, and limited access to professional lactation support may contribute to this disparity. Virtual “telelactation” consults that use two-way video have the potential to increase access to IBCLCs among disadvantaged populations. We present a protocol for the digital Tele-MILC trial, which uses mixed methods to evaluate the impact of telelactation services on breastfeeding outcomes. The objective of this pragmatic, parallel design randomized controlled trial is to assess the impact of telelactation on breastfeeding duration and exclusivity and explore how acceptability of and experiences with telelactation vary across Latinx, Black, and non-Black and non-Latinx parents to guide future improvement of these services. Methods: 2400 primiparous, pregnant individuals age > 18 who intend to breastfeed and live in the USA underserved by IBCLCs will be recruited. Recruitment will occur via Ovia, a pregnancy tracker mobile phone application (app) used by over one million pregnant individuals in the USA annually. Participants will be randomized to (1) on-demand telelactation video calls on personal devices or (2) ebook on infant care/usual care. Breastfeeding outcomes will be captured via surveys and interviews and compared across racial and ethnic groups. This study will track participants for 8 months (including 6 months postpartum). Primary outcomes include breastfeeding duration and breastfeeding exclusivity. We will quantify differences in these outcomes across racial and ethnic groups. Both intention-to-treat and as-treated (using instrumental variable methods) analyses will be performed. This study will also generate qualitative data on the experiences of different subgroups of parents with the telelactation intervention, including barriers to use, satisfaction, and strengths and limitations of this delivery model. Discussion: This is the first randomized study evaluating the impact of telelactation on breastfeeding outcomes. It will inform the design and implementation of future digital trials among pregnant and postpartum people, including Black and Latinx populations which are historically underrepresented in clinical trials. Trial registration: ClinicalTrials.gov NCT04856163. Registered on April 23, 2021