Evaluating tests for cluster-randomized trials with few clusters under generalized linear mixed models with covariate adjustment: A simulation study.

Generalized linear mixed models (GLMM) are commonly used to analyze clustered data, but when the number of clusters is small to moderate, standard statistical tests may produce elevated type I error rates. Small-sample corrections have been proposed for continuous or binary outcomes without covariate adjustment. However, appropriate tests to use for count outcomes or under covariate-adjusted models remains unknown. An important setting in which this issue arises is in cluster-randomized trials (CRTs). Because many CRTs have just a few clusters (eg, clinics or health systems), covariate adjustment is particularly critical to address potential chance imbalance and/or low power (eg, adjustment following stratified randomization or for the baseline value of the outcome). We conducted simulations to evaluate GLMM-based tests of the treatment effect that account for the small (10) or moderate (20) number of clusters under a parallel-group CRT setting across scenarios of covariate adjustment (including adjustment for one or more person-level or cluster-level covariates) for both binary and count outcomes. We find that when the intraclass correlation is non-negligible ( ≥ $$ \ge $$ 0.01) and the number of covariates is small ( ≤ $$ \le $$ 2), likelihood ratio tests with a between-within denominator degree of freedom have type I error rates close to the nominal level. When the number of covariates is moderate ( ≥ $$ \ge $$ 5), across our simulation scenarios, the relative performance of the tests varied considerably and no method performed uniformly well. Therefore, we recommend adjusting for no more than a few covariates and using likelihood ratio tests with a between-within denominator degree of freedom.

Active Postlicensure Safety Surveillance for Recombinant Zoster Vaccine Using Electronic Health Record Data.

Recombinant zoster vaccine (RZV) (Shingrix; GlaxoSmithKline, Brentford, United Kingdom) is an adjuvanted glycoprotein vaccine that was licensed in 2017 to prevent herpes zoster (shingles) and its complications in older adults. In this prospective, postlicensure Vaccine Safety Datalink study using electronic health records, we sequentially monitored a real-world population of adults aged ≥50 years who received care in multiple US Vaccine Safety Datalink health systems to identify potentially increased risks of 10 prespecified health outcomes, including stroke, anaphylaxis, and Guillain-Barré syndrome (GBS). Among 647,833 RZV doses administered from January 2018 through December 2019, we did not detect a sustained increased risk of any monitored outcome for RZV recipients relative to either historical (2013-2017) recipients of zoster vaccine live, a live attenuated virus vaccine (Zostavax; Merck & Co., Inc., Kenilworth, New Jersey), or contemporary non-RZV vaccine recipients who had an annual well-person visit during the 2018-2019 study period. We confirmed prelicensure trial findings of increased risks of systemic and local reactions following RZV. Our study provides additional reassurance about the overall safety of RZV. Despite a large sample, uncertainty remains regarding potential associations with GBS due to the limited number of confirmed GBS cases that were observed.

Preoperative Depression Status and 5 Year Metabolic and Bariatric Surgery Outcomes in the PCORnet Bariatric Study Cohort.

OBJECTIVE: To examine whether depression status before metabolic and bariatric surgery (MBS) influenced 5-year weight loss, diabetes, and safety/utilization outcomes in the PCORnet Bariatric Study. SUMMARY OF BACKGROUND DATA: Research on the impact of depression on MBS outcomes is inconsistent with few large, long-term studies. METHODS: Data were extracted from 23 health systems on 36,871 patients who underwent sleeve gastrectomy (SG; n=16,158) or gastric bypass (RYGB; n=20,713) from 2005-2015. Patients with and without a depression diagnosis in the year before MBS were evaluated for % total weight loss (%TWL), diabetes outcomes, and postsurgical safety/utilization (reoperations, revisions, endoscopy, hospitalizations, mortality) at 1, 3, and 5 years after MBS. RESULTS: 27.1% of SG and 33.0% of RYGB patients had preoperative depression, and they had more medical and psychiatric comorbidities than those without depression. At 5 years of follow-up, those with depression, versus those without depression, had slightly less %TWL after RYGB, but not after SG (between group difference = 0.42%TWL, P = 0.04). However, patients with depression had slightly larger HbA1c improvements after RYGB but not after SG (between group difference = - 0.19, P = 0.04). Baseline depression did not moderate diabetes remission or relapse, reoperations, revision, or mortality across operations; however, baseline depression did moderate the risk of endoscopy and repeat hospitalization across RYGB versus SG. CONCLUSIONS: Patients with depression undergoing RYGB and SG had similar weight loss, diabetes, and safety/utilization outcomes to those without depression. The effects of depression were clinically small compared to the choice of operation.

Acceptability and Adherence to Home, Kiosk, and Clinic Blood Pressure Measurement Compared to 24-H Ambulatory Monitoring.

BACKGROUND: The US Preventive Services Task Force recommends measuring blood pressure (BP) outside of clinic/office settings. While various options are available, including home devices, BP kiosks, and 24-h ambulatory BP monitoring (ABPM), understanding patient acceptability and adherence is a critical factor for implementation. OBJECTIVE: To compare the acceptability and adherence of clinic, home, kiosk, and ABPM measurement. DESIGN: Comparative diagnostic accuracy study which randomized adults to one of three BP measurement arms: clinic, home, and kiosk. ABPM was conducted on all participants. PARTICIPANTS: Adults (18-85 years) receiving care at 12 Kaiser Permanente Washington primary care clinics (Washington State, USA) with a high BP (≥ 138 mmHg systolic or ≥ 88 mmHg diastolic) in the electronic health record with no hypertension diagnosis and on no hypertensive medications and with high BP at a research screening visit. MEASURES: Patient acceptability was measured using a validated survey which was used to calculate an overall acceptability score (range 1-7) at baseline, after completing their assigned BP measurement intervention, and after completing ABPM. Adherence was defined based on the pre-specified number of BP measurements completed. KEY RESULTS: Five hundred ten participants were randomized (mean age 59 years), with mean BP of 150/88. Overall acceptability score was highest (i.e. most acceptable) for Home BP (mean 6.2, SD 0.7) and lowest (least acceptable) for ABPM (mean 5.0, SD 1.0); scores were intermediate for Clinic (5.5, SD 1.1) and Kiosk (5.4, SD 1.0). Adherence was higher for Home (154/170, 90.6%) and Clinic (150/172, 87.2%) than for Kiosk (114/168, 67.9%)). The majority of participants (467/510, 91.6%) were adherent to ABPM. CONCLUSIONS: Participants found home BP measurement most acceptable followed by clinic, BP kiosks, and ABPM. Our findings, coupled with recent evidence regarding the accuracy of home BP measurement, further support the routine use of home-based BP measurement in primary care practice in the US. TRIAL REGISTRATION: ClinicalTrials.gov NCT03130257 https://clinicaltrials.gov/ct2/show/NCT03130257.

Considerations for Subgroup Analyses in Cluster-Randomized Trials Based on Aggregated Individual-Level Predictors.

In research assessing the effect of an intervention or exposure, a key secondary objective often involves assessing differential effects of this intervention or exposure in subgroups of interest; this is often referred to as assessing effect modification or heterogeneity of treatment effects (HTE). Observed HTE can have important implications for policy, including intervention strategies (e.g., will some patients benefit more from intervention than others?) and prioritizing resources (e.g., to reduce observed health disparities). Analysis of HTE is well understood in studies where the independent unit is an individual. In contrast, in studies where the independent unit is a cluster (e.g., a hospital or school) and a cluster-level outcome is used in the analysis, it is less well understood how to proceed if the HTE analysis of interest involves an individual-level characteristic (e.g., self-reported race) that must be aggregated at the cluster level. Through simulations, we show that only individual-level models have power to detect HTE by individual-level variables; if outcomes must be defined at the cluster level, then there is often low power to detect HTE by the corresponding aggregated variables. We illustrate the challenges inherent to this type of analysis in a study assessing the effect of an intervention on increasing COVID-19 booster vaccination rates at long-term care centers.

Differences in Weight Gain Following Residential Relocation in the Moving to Health (M2H) Study.

BACKGROUND: Neighborhoods may play an important role in shaping long-term weight trajectory and obesity risk. Studying the impact of moving to another neighborhood may be the most efficient way to determine the impact of the built environment on health. We explored whether residential moves were associated with changes in body weight. METHODS: Kaiser Permanente Washington electronic health records were used to identify 21,502 members aged 18-64 who moved within King County, WA between 2005 and 2017. We linked body weight measures to environment measures, including population, residential, and street intersection densities (800 m and 1,600 m Euclidian buffers) and access to supermarkets and fast foods (1,600 m and 5,000 m network distances). We used linear mixed models to estimate associations between postmove changes in environment and changes in body weight. RESULTS: In general, moving from high-density to moderate- or low-density neighborhoods was associated with greater weight gain postmove. For example, those moving from high to low residential density neighborhoods (within 1,600 m) gained an average of 4.5 (95% confidence interval [CI] = 3.0, 5.9) lbs 3 years after moving, whereas those moving from low to high-density neighborhoods gained an average of 1.3 (95% CI = -0.2, 2.9) lbs. Also, those moving from neighborhoods without fast-food access (within 1600m) to other neighborhoods without fast-food access gained less weight (average 1.6 lbs [95% CI = 0.9, 2.4]) than those moving from and to neighborhoods with fast-food access (average 2.8 lbs [95% CI = 2.5, 3.2]). CONCLUSIONS: Moving to higher-density neighborhoods may be associated with reductions in adult weight gain.

A Centralized Program with Stepped Support Increases Adherence to Colorectal Cancer Screening Over 9 Years: a Randomized Trial.

BACKGROUND: Screening over many years is required to optimize colorectal cancer (CRC) outcomes. OBJECTIVE: To evaluate the effect of a CRC screening intervention on adherence to CRC screening over 9 years. DESIGN: Randomized trial. SETTING: Integrated health care system in Washington state. PARTICIPANTS: Between August 2008 and November 2009, 4653 adults in a Washington state integrated health care system aged 50-74 due for CRC screening were randomized to usual care (UC; N =1163) or UC plus study interventions (interventions: N = 3490). INTERVENTIONS: Years 1 and 2: (arm 1) UC or this plus study interventions; (arm 2) mailed fecal tests or information on scheduling colonoscopy; (arm 3) mailings plus brief telephone assistance; or (arm 4) mailings and assistance plus nurse navigation. In year 3, stepped-intensity participants (arms 2, 3, and 4 combined) still eligible for screening were randomized to either stopped or continued interventions in years 3 and 5-9. MAIN MEASURES: Time in adherence to CRC testing over 9 years (covered time, primary outcome), and percent with no CRC testing in participants assigned to any intervention compared to UC only. Poisson regression models estimated incidence rate ratios for covered time, adjusting for patient characteristics and accounting for variable follow-up time. KEY RESULTS: Compared to UC, intervention participants had 21% more covered time over 9 years (57.5% vs. 69.1%; adjusted incidence rate ratio 1.21, 95% confidence interval 1.16-1.25, P<0.001). Fecal testing accounted for almost all additional covered time among intervention patients. Compared to UC, intervention participants were also more likely to have completed at least one CRC screening test over 9 years or until censorship (88.6% vs. 80.6%, P<0.001). CONCLUSIONS: An outreach program that included mailed fecal tests and phone follow-up led to increased adherence to CRC testing and fewer age-eligible individuals without any CRC testing over 9 years. TRIAL REGISTRATION: Systems of Support (SOS) to Increase Colon Cancer Screening and Follow-up (SOS), NCT00697047, clinicaltrials.gov/ct2/show/NCT00697047.

Clinic, Home, and Kiosk Blood Pressure Measurements for Diagnosing Hypertension: a Randomized Diagnostic Study.

BACKGROUND: The US Preventive Services Task Force recommends blood pressure (BP) measurements using 24-h ambulatory monitoring (ABPM) or home BP monitoring before making a new hypertension diagnosis. OBJECTIVE: Compare clinic-, home-, and kiosk-based BP measurement to ABPM for diagnosing hypertension. DESIGN, SETTING, AND PARTICIPANTS: Diagnostic study in 12 Washington State primary care centers, with participants aged 18-85 years without diagnosed hypertension or prescribed antihypertensive medications, with elevated BP in clinic. INTERVENTIONS: Randomization into one of three diagnostic regimens: (1) clinic (usual care follow-up BPs); (2) home (duplicate BPs twice daily for 5 days); or (3) kiosk (triplicate BPs on 3 days). All participants completed ABPM at 3 weeks. MAIN MEASURES: Primary outcome was difference between ABPM daytime and clinic, home, and kiosk mean systolic BP. Differences in diastolic BP, sensitivity, and specificity were secondary outcomes. KEY RESULTS: Five hundred ten participants (mean age 58.7 years, 80.2% white) with 434 (85.1%) included in primary analyses. Compared to daytime ABPM, adjusted mean differences in systolic BP were clinic (-4.7mmHg [95% confidence interval -7.3, -2.2]; P<.001); home (-0.1mmHg [-1.6, 1.5];P=.92); and kiosk (9.5mmHg [7.5, 11.6];P<.001). Differences for diastolic BP were clinic (-7.2mmHg [-8.8, -5.5]; P<.001); home (-0.4mmHg [-1.4, 0.7];P=.52); and kiosk (5.0mmHg [3.8, 6.2]; P<.001). Sensitivities for clinic, home, and kiosk compared to ABPM were 31.1% (95% confidence interval, 22.9, 40.6), 82.2% (73.8, 88.4), and 96.0% (90.0, 98.5), and specificities 79.5% (64.0, 89.4), 53.3% (38.9, 67.2), and 28.2% (16.4, 44.1), respectively. LIMITATIONS: Single health care organization and limited race/ethnicity representation. CONCLUSIONS: Compared to ABPM, mean BP was significantly lower for clinic, significantly higher for kiosk, and without significant differences for home. Clinic BP measurements had low sensitivity for detecting hypertension. Findings support utility of home BP monitoring for making a new diagnosis of hypertension. TRIAL REGISTRATION: ClinicalTrials.gov NCT03130257 https://clinicaltrials.gov/ct2/show/NCT03130257.

Differential associations of the built environment on weight gain by sex and race/ethnicity but not age.

OBJECTIVE: To explore the built environment (BE) and weight change relationship by age, sex, and racial/ethnic subgroups in adults. METHODS: Weight trajectories were estimated using electronic health records for 115,260 insured Kaiser Permanente Washington members age 18-64 years. Member home addresses were geocoded using ArcGIS. Population, residential, and road intersection densities and counts of area supermarkets and fast food restaurants were measured with SmartMaps (800 and 5000-meter buffers) and categorized into tertiles. Linear mixed-effect models tested whether associations between BE features and weight gain at 1, 3, and 5 years differed by age, sex, and race/ethnicity, adjusting for demographics, baseline weight, and residential property values. RESULTS: Denser urban form and greater availability of supermarkets and fast food restaurants were associated with differential weight change across sex and race/ethnicity. At 5 years, the mean difference in weight change comparing the 3rd versus 1st tertile of residential density was significantly different between males (-0.49 kg, 95% CI: -0.68, -0.30) and females (-0.17 kg, 95% CI: -0.33, -0.01) (P-value for interaction = 0.011). Across race/ethnicity, the mean difference in weight change at 5 years for residential density was significantly different among non-Hispanic (NH) Whites (-0.47 kg, 95% CI: -0.61, -0.32), NH Blacks (-0.86 kg, 95% CI: -1.37, -0.36), Hispanics (0.10 kg, 95% CI: -0.46, 0.65), and NH Asians (0.44 kg, 95% CI: 0.10, 0.78) (P-value for interaction <0.001). These findings were consistent for other BE measures. CONCLUSION: The relationship between the built environment and weight change differs across demographic groups. Careful consideration of demographic differences in associations of BE and weight trajectories is warranted for investigating etiological mechanisms and guiding intervention development.

Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain.

OBJECTIVE: To determine whether selected features of the built environment can predict weight gain in a large longitudinal cohort of adults. METHODS: Weight trajectories over a 5-year period were obtained from electronic health records for 115,260 insured patients aged 18-64 years in the Kaiser Permanente Washington health care system. Home addresses were geocoded using ArcGIS. Built environment variables were population, residential unit, and road intersection densities captured using Euclidean-based SmartMaps at 800-m buffers. Counts of area supermarkets and fast food restaurants were obtained using network-based SmartMaps at 1600, and 5000-m buffers. Property values were a measure of socioeconomic status. Linear mixed effects models tested whether built environment variables at baseline were associated with long-term weight gain, adjusting for sex, age, race/ethnicity, Medicaid insurance, body weight, and residential property values. RESULTS: Built environment variables at baseline were associated with differences in baseline obesity prevalence and body mass index but had limited impact on weight trajectories. Mean weight gain for the full cohort was 0.06 kg at 1 year (95% CI: 0.03, 0.10); 0.64 kg at 3 years (95% CI: 0.59, 0.68), and 0.95 kg at 5 years (95% CI: 0.90, 1.00). In adjusted regression models, the top tertile of density metrics and frequency counts were associated with lower weight gain at 5-years follow-up compared to the bottom tertiles, though the mean differences in weight change for each follow-up year (1, 3, and 5) did not exceed 0.5 kg. CONCLUSIONS: Built environment variables that were associated with higher obesity prevalence at baseline had limited independent obesogenic power with respect to weight gain over time. Residential unit density had the strongest negative association with weight gain. Future work on the influence of built environment variables on health should also examine social context, including residential segregation and residential mobility.

Characterising percentage energy from ultra-processed foods by participant demographics, diet quality and diet cost: findings from the Seattle Obesity Study (SOS) III.

Higher consumption of 'ultra-processed' (UP) foods has been linked to adverse health outcomes. The present paper aims to characterise percentage energy from UP foods by participant socio-economic status (SES), diet quality, self-reported food expenditure and energy-adjusted diet cost. Participants in the population-based Seattle Obesity Study III (n 755) conducted in WA in 2016-2017 completed socio-demographic and food expenditure surveys and the FFQ. Education and residential property values were measures of SES. Retail prices of FFQ component foods (n 378) were used to estimate individual-level diet cost. Healthy Eating Index (HEI-2015) and Nutrient Rich Food Index 9.3 (NRF9.3) were measures of diet quality. UP foods were identified following NOVA classification. Multivariable linear regressions were used to test associations between UP foods energy, socio-demographics, two estimates of food spending and diet quality measures. Higher percentage energy from UP foods was associated with higher energy density, lower HEI-2015 and NRF9.3 scores. The bottom decile of diet cost ($216·4/month) was associated with 67·5 % energy from UP foods; the top decile ($369·9/month) was associated with only 48·7 % energy from UP foods. Percentage energy from UP foods was inversely linked to lower food expenditures and diet cost. In multivariate analysis, percentage energy from UP foods was predicted by lower food expenditures, diet cost and education, adjusting for covariates. Percentage energy from UP foods was linked to lower food spending and lower SES. Efforts to reduce UP foods consumption, an increasingly common policy measure, need to take affordability, food expenditures and diet costs into account.

Safety surveillance and the estimation of risk in select populations: Flexible methods to control for confounding while targeting marginal comparisons via standardization.

We consider the critical problem of pharmacosurveillance for adverse events once a drug or medical product is incorporated into routine clinical care. When making inference on comparative safety using large-scale electronic health records, we often encounter an extremely rare binary adverse outcome with a large number of potential confounders. In this context, it is challenging to offer flexible methods to adjust for high-dimensional confounders, whereas use of the propensity score (PS) can help address this challenge by providing both confounding control and dimension reduction. Among PS methods, regression adjustment using the PS as a covariate in an outcome model has been incompletely studied and potentially misused. Previous studies have suggested that simple linear adjustment may not provide sufficient control of confounding. Moreover, no formal representation of the statistical procedure and associated inference has been detailed. In this paper, we characterize a three-step procedure, which performs flexible regression adjustment of the estimated PS followed by standardization to estimate the causal effect in a select population. We also propose a simple variance estimation method for performing inference. Through a realistic simulation mimicking data from the Food and Drugs Administration's Sentinel Initiative comparing the effect of angiotensin-converting enzyme inhibitors and beta blockers on incidence of angioedema, we show that flexible regression on the PS resulted in less bias without loss of efficiency, and can outperform other methods when the PS model is correctly specified. In addition, the direct variance estimation method is a computationally fast and reliable approach for inference.

Challenges and Opportunities for Using Big Health Care Data to Advance Medical Science and Public Health.

Methodological advancements in epidemiology, biostatistics, and data science have strengthened the research world's ability to use data captured from electronic health records (EHRs) to address pressing medical questions, but gaps remain. We describe methods investments that are needed to curate EHR data toward research quality and to integrate complementary data sources when EHR data alone are insufficient for research goals. We highlight new methods and directions for improving the integrity of medical evidence generated from pragmatic trials, observational studies, and predictive modeling. We also discuss needed methods contributions to further ease data sharing across multisite EHR data networks. Throughout, we identify opportunities for training and for bolstering collaboration among subject matter experts, methodologists, practicing clinicians, and health system leaders to help ensure that methods problems are identified and resulting advances are translated into mainstream research practice more quickly.

A Randomized Trial of External Practice Support to Improve Cardiovascular Risk Factors in Primary Care.

PURPOSE: We conducted a randomized controlled trial to compare the effectiveness of adding various forms of enhanced external support to practice facilitation on primary care practices' clinical quality measure (CQM) performance. METHODS: Primary care practices across Washington, Oregon, and Idaho were eligible if they had fewer than 10 full-time clinicians. Practices were randomized to practice facilitation only, practice facilitation and shared learning, practice facilitation and educational outreach visits, or practice facilitation and both shared learning and educational outreach visits. All practices received up to 15 months of support. The primary outcome was the CQM for blood pressure control. Secondary outcomes were CQMs for appropriate aspirin therapy and smoking screening and cessation. Analyses followed an intention-to-treat approach. RESULTS: Of 259 practices recruited, 209 agreed to be randomized. Only 42% of those offered educational outreach visits and 27% offered shared learning participated in these enhanced supports. CQM performance improved within each study arm for all 3 cardiovascular disease CQMs. After adjusting for differences between study arms, CQM improvements in the 3 enhanced practice support arms of the study did not differ significantly from those seen in practices that received practice facilitation alone (omnibus P = .40 for blood pressure CQM). Practices randomized to receive both educational outreach visits and shared learning, however, were more likely to achieve a blood pressure performance goal in 70% of patients compared with those randomized to practice facilitation alone (relative risk = 2.09; 95% CI, 1.16-3.76). CONCLUSIONS: Although we found no significant differences in CQM performance across study arms, the ability of a practice to reach a target level of performance may be enhanced by adding both educational outreach visits and shared learning to practice facilitation.

Improving pragmatic clinical trial design using real-world data.

BACKGROUND: Pragmatic clinical trials often use automated data sources such as electronic health records, claims, or registries to identify eligible individuals and collect outcome information. A specific advantage that this automated data collection often yields is having data on potential participants when design decisions are being made. We outline how this data can be used to inform trial design. METHODS: Our work is motivated by a pragmatic clinical trial evaluating the impact of suicide-prevention outreach interventions on fatal and non-fatal suicide attempts in the 18 months after randomization. We illustrate our recommended approaches for designing pragmatic clinical trials using historical data from the health systems participating in this study. Specifically, we illustrate how electronic health record data can be used to inform the selection of trial eligibility requirements, to estimate the distribution of participant characteristics over the course of the trial, and to conduct power and sample size calculations. RESULTS: Data from 122,873 people with patient health questionnaire (PHQ) responses, recorded in their electronic health records between 1 July 2010 and 31 March 2012, were used to show that the suicide attempt rate in the 18 months following completion of the questionnaire varies by response to item nine of the PHQ. We estimated that the proportion of individuals with a prior recorded elevated PHQ (i.e. history of suicidal ideation) would decrease from approximately 50% at the beginning of a trial to about 5%, 50 weeks later. Using electronic health record data, we conducted simulations to estimate the power to detect a 25% reduction in suicide attempts. Simulation-based power calculations estimated that randomizing 8000 participants per randomization arm would allow 90% power to detect a 25% reduction in the suicide attempt rate in the intervention arm compared to usual care at an alpha rate of 0.05. CONCLUSIONS: Historical data can be used to inform the design of pragmatic clinical trials, a strength of trials that use automated data collection for randomizing participants and assessing outcomes. In particular, realistic sample size calculations can be conducted using real-world data from the health systems in which the trial will be conducted. Data-informed trial design should yield more realistic estimates of statistical power and maximize efficiency of trial recruitment.

Comparative Effectiveness and Safety of Bariatric Procedures for Weight Loss: A PCORnet Cohort Study.

Background: There has been a dramatic shift in use of bariatric procedures, but little is known about their long-term comparative effectiveness. Objective: To compare weight loss and safety among bariatric procedures. Design: Retrospective observational cohort study, January 2005 to September 2015. (ClinicalTrials.gov: NCT02741674). Setting: 41 health systems in the National Patient-Centered Clinical Research Network. Participants: 65 093 patients aged 20 to 79 years with body mass index (BMI) of 35 kg/m2 or greater who had bariatric procedures. Intervention: 32 208 Roux-en-Y gastric bypass (RYGB), 29 693 sleeve gastrectomy (SG), and 3192 adjustable gastric banding (AGB) procedures. Measurements: Estimated percent total weight loss (TWL) at 1, 3, and 5 years; 30-day rates of major adverse events. Results: Total numbers of eligible patients with weight measures at 1, 3, and 5 years were 44 978 (84%), 20 783 (68%), and 7159 (69%), respectively. Thirty-day rates of major adverse events were 5.0% for RYGB, 2.6% for SG, and 2.9% for AGB. One-year mean TWLs were 31.2% (95% CI, 31.1% to 31.3%) for RYGB, 25.2% (CI, 25.1% to 25.4%) for SG, and 13.7% (CI, 13.3% to 14.0%) for AGB. At 1 year, RYGB patients lost 5.9 (CI, 5.8 to 6.1) percentage points more weight than SG patients and 17.7 (CI, 17.3 to 18.1) percentage points more than AGB patients, and SG patients lost 12.0 (CI, 11.6 to 12.5) percentage points more than AGB patients. Five-year mean TWLs were 25.5% (CI, 25.1% to 25.9%) for RYGB, 18.8% (CI, 18.0% to 19.6%) for SG, and 11.7% (CI, 10.2% to 13.1%) for AGB. Patients with diabetes, those with BMI less than 50 kg/m2, those aged 65 years or older, African American patients, and Hispanic patients lost less weight than patients without those characteristics. Limitation: Potential unobserved confounding due to nonrandomized design; electronic health record databases had missing outcome data. Conclusion: Adults lost more weight with RYGB than with SG or AGB at 1, 3, and 5 years; however, RYGB had the highest 30-day rate of major adverse events. Small subgroup differences in weight loss outcomes were observed. Primary Funding Source: Patient-Centered Outcomes Research Institute.

Effect of Low Back Pain Risk-Stratification Strategy on Patient Outcomes and Care Processes: the MATCH Randomized Trial in Primary Care.

BACKGROUND: The STarT Back strategy for categorizing and treating patients with low back pain (LBP) improved patients' function while reducing costs in England. OBJECTIVE: This trial evaluated the effect of implementing an adaptation of this approach in a US setting. DESIGN: The Matching Appropriate Treatments to Consumer Healthcare needs (MATCH) trial was a pragmatic cluster randomized trial with a pre-intervention baseline period. Six primary care clinics were pair randomized, three to training in the STarT Back strategy and three to serve as controls. PARTICIPANTS: Adults receiving primary care for non-specific LBP were invited to provide data 2 weeks after their primary care visit and follow-up data 2 and 6 months (primary endpoint) later. INTERVENTIONS: The STarT Back risk-stratification strategy matches treatments for LBP to physical and psychosocial obstacles to recovery using patient-reported data (the STarT Back Tool) to categorize patients' risk of persistent disabling pain. Primary care clinicians in the intervention clinics attended six didactic sessions to improve their understanding LBP management and received in-person training in the use of the tool that had been incorporated into the electronic health record (EHR). Physical therapists received 5 days of intensive training. Control clinics received no training. MAIN MEASURES: Primary outcomes were back-related physical function and pain severity. Intervention effects were estimated by comparing mean changes in patient outcomes after 2 and 6 months between intervention and control clinics. Differences in change scores by trial arm and time period were estimated using linear mixed effect models. Secondary outcomes included healthcare utilization. KEY RESULTS: Although clinicians used the tool for about half of their patients, they did not change the treatments they recommended. The intervention had no significant effect on patient outcomes or healthcare use. CONCLUSIONS: A resource-intensive intervention to support stratified care for LBP in a US healthcare setting had no effect on patient outcomes or healthcare use. TRIAL REGISTRATION: National Clinical Trial Number NCT02286141.

A centralized mailed program with stepped increases of support increases time in compliance with colorectal cancer screening guidelines over 5 years: A randomized trial.

BACKGROUND: Screening over many years is required to optimize reductions in colorectal cancer (CRC) mortality. However, no prior trials have compared strategies for obtaining long-term adherence. METHODS: Systems of Support to Increase Colorectal Cancer Screening and Follow-Up was implemented in an integrated health care organization in Washington State. Between 2008 and 2009, 4675 individuals aged 50 to 74 years were randomized to receive the usual care (UC), which included clinic-based strategies to increase CRC screening (arm 1), or, in years 1 and 2, mailings with a call-in number for colonoscopy and mailed fecal tests (arm 2), mailings plus brief telephone assistance (arm 3), or mailings and assistance plus nurse navigation (arm 4). Active-intervention subjects (those in arms 2, 3, and 4 combined) who were still eligible for CRC screening were randomized to mailings being stopped or continued in years 3 and 5. The time in compliance with CRC screening over 5 years was compared for persons assigned to any intervention and persons assigned to UC. Screening tests contributed time on the basis of national guidelines for screening intervals (fecal tests annually, sigmoidoscopy every 5 years, and colonoscopy every 10 years). RESULTS: All participants contributed data, but they were censored at disenrollment, death, the age of 76 years, or a diagnosis of CRC. Compared with UC participants, intervention participants had 31% more adjusted covered time over 5 years (incidence rate ratio, 1.31; 95% confidence interval, 1.25-1.37; covered time, 47.5% vs 62.1%). Fecal testing accounted for almost all additional covered time. CONCLUSIONS: In a health care organization with clinic-based activities to increase CRC screening, a centralized program led to increased CRC screening adherence over 5 years. Longer term data on screening adherence and its impact on CRC outcomes are needed. Cancer 2017;123:4472-80. © 2017 American Cancer Society.

Geographic disparities in Healthy Eating Index scores (HEI-2005 and 2010) by residential property values: Findings from Seattle Obesity Study (SOS).

BACKGROUND: Higher socioeconomic status (SES) has been linked with higher-quality diets. New GIS methods allow for geographic mapping of diet quality at a very granular level. OBJECTIVE: To examine the geographic distribution of two measures of diet quality: Healthy Eating Index (HEI 2005 and HEI 2010) in relation to residential property values in Seattle-King County. METHODS: The Seattle Obesity Study (SOS) collected data from a population-based sample of King County adults in 2008-09. Socio-demographic data were obtained by 20-min telephone survey. Dietary data were obtained from food frequency questionnaires (FFQs). Home addresses were geocoded to the tax parcel and residential property values were obtained from the King County tax assessor. Multivariable regression analyses using 1116 adults tested associations between SES variables and diet quality measured (HEI scores). RESULTS: Residential property values, education, and incomes were associated with higher HEI scores in bivariate analyses. Property values were not collinear with either education or income. In adjusted multivariable models, education and residential property were better associated with HEI, compared to than income. Mapping of HEI-2005 and HEI-2010 at the census block level illustrated the geographic distribution of diet quality across Seattle-King County. CONCLUSION: The use of residential property values, an objective measure of SES, allowed for the first visual exploration of diet quality at high spatial resolution: the census block level.