Ovarian cancer risk among older patients with stable adnexal masses.

BACKGROUND: Few studies have evaluated the risk of cancer among older patients with stable adnexal masses in community-based settings to determine the duration of observation time needed. OBJECTIVE: This study aimed to assess the ovarian cancer risk among older patients with stable adnexal masses on ultrasound. STUDY DESIGN: This was a retrospective cohort study of patients in a large community-based health system aged ≥50 years with an adnexal mass <10 cm on ultrasound between 2016 and 2020 who had at least 1 follow-up ultrasound performed ≥6 weeks after initial ultrasound. Masses were considered stable on follow-up examination if they did not exhibit an increase of >1 cm in the greatest dimension or a change in standardized reported ultrasound characteristics. Ovarian cancer risk was determined at increasing time intervals of stability after initial ultrasound. RESULTS: Among 4061 patients with stable masses, the average age was 61 years (range, 50-99), with an initial mass size of 3.8 cm (range, 0.2-9.9). With a median follow-up of 3.7 years, 11 cancers were detected, with an absolute risk of 0.27%. Ovarian cancer risk declined with longer duration of stability, from 0.73 (95% confidence interval, 0.30-1.17) per 1000 person-years at 6 to 12 weeks, 0.63 (95% confidence interval, 0.19-1.07) at 13 to 24 weeks, 0.44 (95% confidence interval, 0.01-0.87) at 25 to 52 weeks, and 0.00 (95% confidence interval, 0.00-0.00) at >52 weeks. Expressed as number needed to reimage, ongoing ultrasound imaging would be needed for 369 patients whose masses show stability at 6 to 12 weeks, 410 patients at 13 to 24 weeks, 583 patients at 25 to 52 weeks, and >1142 patients with stable masses at 53 to 104 weeks to detect 1 case of ovarian cancer. CONCLUSION: In a diverse community-based setting, among patients aged ≥50 years with an adnexal mass that was stable for at least 6 weeks after initial ultrasound, the risk of ovarian cancer was very low at 0.27%. Longer demonstrated duration of stability was associated with progressively lower risk, with no cancer cases observed after 52 weeks of stability. These findings suggest that the benefit of ultrasound monitoring of stable masses beyond 12 months is minimal and may be outweighed by potential risks of repeated imaging.

Prenatal detection rates for congenital heart disease using abnormal obstetrical screening ultrasound alone as indication for fetal echocardiography.

OBJECTIVE: To determine the live born prenatal detection rate of significant congenital heart disease (CHD) in a large, integrated, multi-center community-based health system using a strategy of referral only of patients with significant cardiac abnormalities on obstetrical screening ultrasound for fetal echocardiography. Detection rates were assessed for screening in both radiology and maternal fetal medicine (MFM). The impact on fetal echocardiography utilization was also assessed. METHODS: This was a retrospective cohort study using an electronic health record, outside claims databases and chart review to determine all live births between 2016 and 2020 with postnatally confirmed sCHD that were prenatally detectable and resulted in cardiac surgery, intervention, or death within 1 year. RESULTS: There were 214,486 pregnancies resulting in live births. Prenatally detectable significant CHD was confirmed in 294 infants. Of those 183 were detected for an overall live-born detection rate of 62%. Detection rates in MFM were 75% and in radiology were 52%. The number of fetal echocardiograms needed to detect (NND) sCHD was 7. CONCLUSIONS: A focus on quality and standardization of obstetrical screening ultrasound with referral to fetal echocardiography for cardiac abnormalities alone achieves benchmark targets for live-born detection of significant CHD requiring fewer fetal echocardiograms.

Association of Surgical Timing with Outcomes in Early Stage Lung Cancer.

BACKGROUND: Optimal time to surgery for lung cancer is not well established. We aimed to assess whether time to surgery correlates with outcomes. METHODS: We assessed patients 18-84 years old who were diagnosed with stage I/II lung cancer at our integrated healthcare system from 2009 to 2019. Time to surgery was defined to start with disease confirmation (imaging or biopsy) prior to the surgery scheduling date. Outcomes of unplanned return to care within 30 days of lung cancer surgery, all-cause mortality, and disease recurrence were compared based on time to surgery before and after 2, 4, and 12 weeks. RESULTS: Of 2861 included patients, 70% were over 65 years old and 61% were female. Time to surgery occurred in 1-2 weeks for 6%, 3-4 weeks for 31%, 5-12 weeks for 58%, and 13-26 weeks for 5% of patients. Patients with time to surgery > 4 (vs. ≤ 4) weeks had greater risk of both death (hazard ratio (HR) 1.18, 95% confidence interval (CI) 1.00-1.39) and recurrence (HR 1.33, 95% CI 1.10-1.62). Associations were not statistically significant when dichotomizing time to surgery at 2 or 12 weeks for death (2 week HR 1.23, 95% CI 0.93-1.64; 12 week HR 1.35, 95% CI 0.97-1.88) and recurrence (2 week HR 1.54, 95% CI 0.85-2.80; 12 week HR 2.28, 95% CI 0.80-6.46). CONCLUSIONS: Early stage lung cancer patients with time to surgery within 4 weeks experienced lower rates of recurrence. Optimal time to surgical resection may be shorter than previously reported.

SARS-CoV-2 Clinical Outcomes in Patients with Cancer in a Large Integrated Health Care System in Northern California.

The SARS-CoV-2 (COVID-19) pandemic continues to affect many lives globally. Patients with cancer undergoing potentially immunosuppressive therapies appear to be at particular risk for the disease and its complications. Here, we describe the experience of patients with cancer within Kaiser Permanente, a large, integrated health system in Northern California. Between February 25, 2020, and June 8, 2020, 4,627 patients were diagnosed with COVID-19, of whom 33 had active cancer treatment within 180 days and 214 had a history of cancer. Patients with active cancer treatment had a statistically higher risk of requiring noninvasive ventilation (odds ratio [OR], 2.57; confidence interval [CI], 1.10-6.01), and there was a nonsignificant trend toward higher risk of death (OR, 2.78; CI, 0.92-8.43). Those with a history of cancer had comparable outcomes to those without cancer. These data demonstrate an increased risk of complications from COVID-19 for patients with active cancer treatment.

Prior Immunosuppressive Therapy and Severe Illness Among Patients Diagnosed with SARS-CoV-2: a Community-Based Study.

BACKGROUND: An estimated 10 million people in the USA are immunocompromised, a risk factor for severe COVID-19. Data informing whether immune-mediated medications lead to more severe infection are sparse. OBJECTIVE: Determine whether outpatient immunosuppressive therapies that treat autoimmune inflammatory disease or prevent solid organ transplant rejection are associated with severe illness after diagnosis with SARS-CoV-2 DESIGN: Retrospective cohort study PARTICIPANTS: Adults with a positive PCR nasal swab for SARS-CoV-2 from February 25 to September 9, 2020, cared for within a large integrated health care organization MAIN MEASURES: Exposure was defined as an outpatient fill of prednisone, immunomodulator, small-molecule, or biologic therapy in the 105 days prior to a positive SARS-CoV-2 PCR test. The main outcome was either hospitalization, ICU admission, or death within 45 days after diagnosis of SARS-CoV-2. Multivariable logistic regression models were adjusted for age, race, gender, body mass index, comorbidities, and autoimmune disease. KEY RESULTS: A total of 39,686 adults had a positive PCR test. In the primary analysis, prior prednisone use was associated with severe illness after diagnosis with SARS-CoV-2 (odds ratio (OR) 1.31; 95% confidence interval (CI) 1.08-1.60); however, immunomodulator (OR 0.88; 95% CI 0.57-1.34) and biologic/small-molecule therapy (OR 1.26; 95% CI 0.79-2.00) were not. Secondary analyses showed variable risk among therapies: Janus-kinase inhibitors had an increased odds of severe illness (OR 3.35; 95% CI 1.16-9.67), thiopurines/conventionaldisease-modifying antirheumatic drugs had a reduced odds (OR 0.53; 95% CI 0.32-0.88), and tumor necrosis factor inhibitors were not associated (OR 0.45; 95% CI 0.18-1.08). CONCLUSIONS AND RELEVANCE: Outpatient use of prednisone is associated with severe illness after diagnosis of SARS-CoV-2. Immunomodulator and biologic/small-molecule therapy were not associated, but different risk subgroups were identified. Our findings can inform risk-benefit discussions in the clinic and risk-based recommendations for patients on these therapies.

A Randomized Encouragement Trial to Increase Mail Order Pharmacy Use and Medication Adherence in Patients with Diabetes.

BACKGROUND: Mail order pharmacy (MOP) use has been linked to improved medication adherence and health outcomes among patients with diabetes. However, no large-scale intervention studies have assessed the effect of encouraging MOP use on medication adherence. OBJECTIVE: To assess an intervention to encourage MOP services to increase its use and medication adherence. DESIGN: Randomized encouragement trial. PATIENTS: 63,012 diabetes patients from three health care systems: Kaiser Permanente Northern California (KPNC), Kaiser Permanente Hawaii (KPHI), and Harvard Pilgrim Health Care (HPHC) who were poorly adherent to at least one class of cardiometabolic medications and had not used MOP in the prior 12 months. INTERVENTION: Patients were randomized to receive either usual care (control arm) or outreach encouraging MOP use consisting of a mailed letter, secure email message, and automated telephone call outlining the potential benefits of MOP use (intervention arm). HPHC intervention patients received the letter only. MEASUREMENTS: We compared the percentages of patients that began using MOP and that became adherent to cardiometabolic medication classes during a 12-month follow-up period. We also conducted a race/ethnicity-stratified analysis. RESULTS: During follow-up, 10.6% of intervention patients began using MOP vs. 9.3% of controls (p < 0.01); the percent of cardiometabolic medication delivered via mail was 42.1% vs. 39.8% (p < 0.01). Metformin adherence improved in the intervention arm relative to control at the two KP sites (52% vs. 49%, p < 0.01). Stratified analyses suggested a significant positive effect of the intervention in White (RR: 1.12, 95% CI: 1.03, 1.22) and Asian (RR: 1.30, 95% CI: 1.17, 1.45) patients. CONCLUSION: This pragmatic trial showed that simple outreach to encourage MOP modestly increased its use and improved adherence measured by refills to a key class of diabetes medications in some settings. Given its minimal cost, clinicians and health systems should consider outreach interventions to actively promote MOP use among diabetes patients. TRIAL REGISTRATION: ClinicalTrials.gov registration number: NCT02621476.

Exploiting nonsystematic covariate monitoring to broaden the scope of evidence about the causal effects of adaptive treatment strategies.

In studies based on electronic health records (EHR), the frequency of covariate monitoring can vary by covariate type, across patients, and over time, which can limit the generalizability of inferences about the effects of adaptive treatment strategies. In addition, monitoring is a health intervention in itself with costs and benefits, and stakeholders may be interested in the effect of monitoring when adopting adaptive treatment strategies. This paper demonstrates how to exploit nonsystematic covariate monitoring in EHR-based studies to both improve the generalizability of causal inferences and to evaluate the health impact of monitoring when evaluating adaptive treatment strategies. Using a real world, EHR-based, comparative effectiveness research (CER) study of patients with type II diabetes mellitus, we illustrate how the evaluation of joint dynamic treatment and static monitoring interventions can improve CER evidence and describe two alternate estimation approaches based on inverse probability weighting (IPW). First, we demonstrate the poor performance of the standard estimator of the effects of joint treatment-monitoring interventions, due to a large decrease in data support and concerns over finite-sample bias from near-violations of the positivity assumption (PA) for the monitoring process. Second, we detail an alternate IPW estimator using a no direct effect assumption. We demonstrate that this estimator can improve efficiency but at the potential cost of increase in bias from violations of the PA for the treatment process.

Comorbid Diabetes and Severe Mental Illness: Outcomes in an Integrated Health Care Delivery System.

BACKGROUND: Diabetes prevalence is twice as high among people with severe mental illness (SMI) when compared to the general population. Despite high prevalence, care outcomes are not well understood. OBJECTIVE: To compare diabetes health outcomes received by people with and without comorbid SMI, and to understand demographic factors associated with poor diabetes control among those with SMI. DESIGN: Retrospective cohort study PARTICIPANTS: 269,243 adults with diabetes MAIN MEASURES: Primary outcomes included optimal glycemic control (A1c < 7) or poor diabetes control (A1c > 9) in 2014. Secondary outcomes included control of other cardiometabolic risk factors (hypertension, dyslipidemia, smoking) and recommended diabetes monitoring. KEY RESULTS: Among this cohort, people with SMI (N = 4,399), compared to those without SMI (N = 264,844), were more likely to have optimal glycemic control, adjusting for various covariates (adjusted relative risk (aRR) 1.25, 95% CI 1.21-1.28, p < .001) and less likely to have poor control (aRR 0.92, 95% CI 0.87-0.98, p = 0.012). Better blood pressure and lipid control was more prevalent among people with SMI when compared to those without SMI (aRR 1.03; 95% CI 1.02-1.05, p < .001; aRR 1.02; 95% CI 1.00-1.05, p = 0.044, respectively). No differences were observed in recommended A1c or LDL testing, but people with SMI were more likely to have blood pressure checked (aRR 1.02, 95% CI 1.02-1.03, p < .001) and less likely to receive retinopathy screening (aRR 0.80, 95% CI 0.71-0.91, p < .001) than those without SMI. Among people with diabetes and comorbid SMI, younger adults and Hispanics were more likely to have poor diabetes control. CONCLUSIONS: Adults with diabetes and comorbid SMI had better cardiometabolic control than people with diabetes who did not have SMI, despite lower rates of retinopathy screening. Among those with comorbid SMI, younger adults and Hispanics were more vulnerable to poor A1c control.

Predictors of Hyperkalemia and Hypokalemia in Individuals with Diabetes: a Classification and Regression Tree Analysis.

BACKGROUND: Both hyperkalemia and hypokalemia can lead to cardiac arrhythmias and are associated with increased mortality. Information on the predictors of potassium in individuals with diabetes in routine clinical practice is lacking. OBJECTIVE: To identify predictors of hyperkalemia and hypokalemia in adults with diabetes. DESIGN: Retrospective cohort study, with classification and regression tree (CART) analysis. PARTICIPANTS: 321,856 individuals with diabetes enrolled in four large integrated health care systems from 2012 to 2013. MAIN MEASURES: We used a single serum potassium result collected in 2012 or 2013. Hyperkalemia was defined as a serum potassium ≥ 5.5 mEq/L and hypokalemia as < 3.5 mEq/L. Predictors included demographic factors, laboratory measurements, comorbidities, medication use, and health care utilization. KEY RESULTS: There were 2556 hypokalemia events (0.8%) and 1517 hyperkalemia events (0.5%). In univariate analyses, we identified concordant predictors (associated with increased probability of both hyperkalemia and hypokalemia), discordant predictors, and predictors of only hyperkalemia or hypokalemia. In CART models, the hyperkalemia "tree" had 5 nodes and a c-statistic of 0.76. The nodes were defined by prior potassium results and eGFRs, and the 5 terminal "leaves" had hyperkalemia probabilities of 0.2 to 7.2%. The hypokalemia tree had 4 nodes and a c-statistic of 0.76. The hypokalemia tree included nodes defined by prior potassium results, and the 4 terminal leaves had hypokalemia probabilities of 0.3 to 17.6%. Individuals with a recent potassium between 4.0 and 5.0 mEq/L, eGFR ≥ 45 mL/min/1.73m2, and no hypokalemia in the previous year had a < 1% rate of either hypokalemia or hyperkalemia. CONCLUSIONS: The yield of routine serum potassium testing may be low in individuals with a recent serum potassium between 4.0 and 5.0 mEq/L, eGFR ≥ 45 mL/min/1.73m2, and no recent history of hypokalemia. We did not examine the effect of recent changes in clinical condition or medications on acute potassium changes.

Identifying Common Predictors of Multiple Adverse Outcomes Among Elderly Adults With Type-2 Diabetes.

OBJECTIVE: As part of a multidisciplinary team managing patients with type-2 diabetes, pharmacists need a consistent approach of identifying and prioritizing patients at highest risk of adverse outcomes. Our objective was to identify which predictors of adverse outcomes among type-2 diabetes patients were significant and common across 7 outcomes and whether these predictors improved the performance of risk prediction models. Identifying such predictors would allow pharmacists and other health care providers to prioritize their patient panels. RESEARCH DESIGN AND METHODS: Our study population included 120,256 adults aged 65 years or older with type-2 diabetes from a large integrated health system. Through an observational retrospective cohort study design, we assessed which risk factors were associated with 7 adverse outcomes (hypoglycemia, hip fractures, syncope, emergency department visit or hospital admission, death, and 2 combined outcomes). We split (50:50) our study cohort into a test and training set. We used logistic regression to model outcomes in the test set and performed k-fold validation (k=5) of the combined outcome (without death) within the validation set. RESULTS: The most significant predictors across the 7 outcomes were: age, number of medicines, prior history of outcome within the past 2 years, chronic kidney disease, depression, and retinopathy. Experiencing an adverse outcome within the prior 2 years was the strongest predictor of future adverse outcomes (odds ratio range: 4.15-7.42). The best performing models across all outcomes included: prior history of outcome, physiological characteristics, comorbidities and pharmacy-specific factors (c-statistic range: 0.71-0.80). CONCLUSIONS: Pharmacists and other health care providers can use models with prior history of adverse event, number of medicines, chronic kidney disease, depression and retinopathy to prioritize interventions for elderly patients with type-2 diabetes.

Prompting Patients with Poorly Controlled Diabetes to Identify Visit Priorities Before Primary Care Visits: a Pragmatic Cluster Randomized Trial.

BACKGROUND: Most patients with diabetes do not meet all evidence-based goals of care, and many patients report poor communication and lack of involvement in decision-making during primary care visits. OBJECTIVE: To test the hypothesis that a "Pre-Visit Prioritization" secure email message could improve visit communication and glycemic control among patients with type 2 diabetes. DESIGN: We conducted a pragmatic, provider-randomized, multi-site clinical trial from March 2015 to October 2016 across 30 primary care practices within Kaiser Permanente Northern California (KPNC), a large integrated care delivery system. PARTICIPANTS: Eligible patients had at least 1 year of KPNC membership, type 2 diabetes with most recently measured hemoglobin A1c (HbA1c) > = 8.0%, and were registered users of the KPNC online patient portal. INTERVENTIONS: Patients in the intervention arm, upon booking an appointment, received a secure email through the KPNC online portal with a link to the EHR allowing them to submit their top one or two priorities prior to the visit. Control patients received usual care. MAIN MEASURES: Glycemic control; change in HbA1c 6 and 12 months after the initial visit; patient-reported outcomes related to patient-provider communication and patient care experiences. KEY RESULTS: During the study period, 1276 patients had at least one eligible visit. In post-visit surveys (n = 457), more intervention arm patients reported preparing questions for their visit (72% vs 63%, p = 0.048) and being given treatment choices to consider (81% vs 73%, p = 0.041). Patients in both arms had similar reductions in HbA1c over the 12-month study period (0.56% ± 1.45%), with no significant differences between arms. CONCLUSIONS: A "light touch" email-based pre-visit intervention resulted in improved measures of visit interaction but did not significantly improve glycemic control relative to usual care. Improving diabetes clinical outcomes through more effective primary care visits may require more intensive approaches to patient visit preparation. TRIAL REGISTRY: NCT02375932.

Targeted learning with daily EHR data.

Electronic health records (EHR) data provide a cost- and time-effective opportunity to conduct cohort studies of the effects of multiple time-point interventions in the diverse patient population found in real-world clinical settings. Because the computational cost of analyzing EHR data at daily (or more granular) scale can be quite high, a pragmatic approach has been to partition the follow-up into coarser intervals of pre-specified length (eg, quarterly or monthly intervals). The feasibility and practical impact of analyzing EHR data at a granular scale has not been previously evaluated. We start filling these gaps by leveraging large-scale EHR data from a diabetes study to develop a scalable targeted learning approach that allows analyses with small intervals. We then study the practical effects of selecting different coarsening intervals on inferences by reanalyzing data from the same large-scale pool of patients. Specifically, we map daily EHR data into four analytic datasets using 90-, 30-, 15-, and 5-day intervals. We apply a semiparametric and doubly robust estimation approach, the longitudinal Targeted Minimum Loss-Based Estimation (TMLE), to estimate the causal effects of four dynamic treatment rules with each dataset, and compare the resulting inferences. To overcome the computational challenges presented by the size of these data, we propose a novel TMLE implementation, the "long-format TMLE," and rely on the latest advances in scalable data-adaptive machine-learning software, xgboost and h2o, for estimation of the TMLE nuisance parameters.

Opportunities to encourage mail order pharmacy delivery service use for diabetes prescriptions: a qualitative study.

BACKGROUND: Medication non-adherence is a major contributor to poor outcomes in diabetes. Previous research has shown an association between use of mail order pharmacy delivery and better medication adherence, but little is known about the barriers and facilitators to mail order pharmacy use in diabetes patients. This qualitative study examined factors related to mail order pharmacy use versus traditional "brick and mortar" pharmacies to refill prescriptions. METHODS: We conducted four 90-min focus groups in 2016 among 28 diabetes patients in the Hawaii and Northern California regions of Kaiser Permanente, a large integrated health care delivery system. We queried participants on their preferred mode for refilling prescriptions and perceived barriers and facilitators of mail order pharmacy use. One researcher independently coded each focus group transcript, with two of these transcripts double-coded by a second researcher to promote reliability. We employed thematic analysis guided by the Capability, Opportunity, Motivation, and Behavior (COM-B) framework using NVivo 11 software. RESULTS: A total of 28 diabetes patients participated. Participants' average age was 64.1 years; 57% were female; and racial/ethnic backgrounds included Asian/Native Hawaiian/Pacific Islander (36%), Black/African-American (21%) Hispanic/Latino (7%), and non-Hispanic White (36%). Analysis uncovered 26 themes related to the decision to use mail order pharmacy, with each theme representing a barrier or facilitator mapped to the COM-B framework. Most themes (20/26) fell into the COM-B category of 'Opportunity.' Opportunity barriers to mail order pharmacy use included unpredictability of medication delivery date, concerns about mail security, and difficulty coordinating refill orders for multiple prescriptions. In contrast, facilitators included greater access and convenience (e.g., no need to wait in line or arrange transportation) compared to traditional pharmacies. Motivational facilitators to mail order pharmacy use included receiving a pharmacy benefit plan incentive of a free one-month supply of prescriptions. CONCLUSIONS: This study found that while patients with diabetes may benefit from mail order pharmacy use, they perceive numerous barriers to using the service. These findings will inform the design of interventions and quality improvement initiatives to increase mail order pharmacy use, which in turn may improve medication adherence and outcomes in diabetes patients, across health care systems.

The Relationship Between Prescription Copayments and Contraceptive Adherence in a New-user Cohort.

BACKGROUND: Contraceptive nonadherence is an important contributor to unintended pregnancy in the United States. While the elimination of patient cost sharing has been cited as means to improve contraceptive access, little is known about the relationship between cost sharing and ongoing adherence and continuation of chosen methods. The purpose of this study was to examine the relationship between copayment amount and adherence to pharmacy-dispensed contraception in young women. METHODS: We conducted a retrospective cohort study of 39,142 women ages 19-29 with a new prescription for the contraceptive pill, patch, or ring at Kaiser Permanente Northern California during 2011-2014. We examined 12-month nonadherence as measured by timely prescription refills and used multivariable Cox proportional hazards models to assess the association between copayment amount and the risk of nonadherence. RESULTS: Ninety-four percent of women used the pill, and 6% used the patch or ring. Forty percent of patients had no copayment and 25% had a copayment of ≥$30. Nearly 75% of women were nonadherent during the study period. In 2013 and 2014, women with a copayment had a 9% increased risk of nonadherence (adjusted hazard ratio, 1.09; 95% confidence interval, 1.04, 1.14) compared with women with no copayment. CONCLUSIONS: Prescription copayments may serve as a barrier to adherence of pharmacy-dispensed contraception. Given recent changes to Affordable Care Act contraceptive coverage requirement, these findings can be used to support state-level and health system-level policies for no-cost contraception, and to determine the potential public health impact of this policy change.

Diabetes Screening among Antipsychotic-Treated Adults with Severe Mental Illness in an Integrated Delivery System: A Retrospective Cohort Study.

BACKGROUND: Severe mental illness (SMI) is associated with increased risk for type 2 diabetes, partly due to adverse metabolic effects of antipsychotic medications. In public health care settings, annual screening rates are 30%. We measured adherence to national diabetes screening guidelines for patients taking antipsychotic medications. OBJECTIVE: To estimate diabetes screening prevalence among patients with SMI within an integrated health care system, and to assess characteristics associated with lack of screening. DESIGN: Retrospective cohort study. PARTICIPANTS: Antipsychotic-treated adults with SMI. We excluded participants with known diabetes. MAIN MEASURES: Primary outcome was screening via fasting glucose test or hemoglobin A1c during a 1-year period. KEY RESULTS: In 2014, 16,754 patients with SMI diagnoses were receiving antipsychotics. Seventy-four percent of these patients' providers ordered diabetes screening tests that year, but only 55% (9247/16,754) received screening. When the observation time frame was extended to 2 years, 73% (12,250/16,754) were screened. Adjusting for sex and race/ethnicity, young adults (aged 18-29 years) were less likely to receive screening than older age groups [adjusted RR (aRR) 1.23-1.57, p < 0.0001]. Compared to whites, screening was more common for Asians (aRR 1.141, 95% CI 1.089-1.195, p < 0.0001), less common for blacks (aRR 0.946, 95% CI 0.898-0.997, p < 0.0375), and no different for Hispanics (aRR 1.030, 95% CI 0.988-1.074, p = 0.165). Smokers were less likely to be screened than non-smokers (aRR 0.93, 95% CI 0.89-0.97, p < 0.0008). Utilization of either mental health or primary care services increased the likelihood of screening. CONCLUSIONS: While almost three-fourths of adults with SMI taking antipsychotic medications received a lab order for diabetes screening, only 55% received screening within a 12-month period. Young adults and smokers were less likely to be screened, despite their disproportionate metabolic risk. Future studies should assess the barriers and facilitators with regard to diabetes screening in this vulnerable population at the patient, provider, and system levels.

Building Toward a Population-Based Approach to Diabetes Screening and Prevention for US Adults.

PURPOSE OF REVIEW: Evidence-based treatments for prediabetes can prevent and delay the development of type 2 diabetes in adults. In this review, we propose a framework for population-based diabetes prevention that links screening and prevention activities across key stakeholders. We also discuss gaps in current practice, while highlighting opportunities to improve diabetes screening and prevention efforts population-wide. RECENT FINDINGS: Awareness of diabetes risk is low, and many adults with prediabetes are not identified through existing screening efforts. Accumulating evidence and policies support expansion of the Diabetes Prevention Program (DPP) into clinical and community settings. However, the infrastructure to facilitate referrals and promote data exchange among patients, clinical settings, and community-based DPP programs is lacking. Development of evidence-driven, scalable processes for assessing diabetes risk, screening eligible adults, and delivering preventive treatments are needed to effectively improve the glycemic health of the US adult population.

Advancing Health Policy and Program Research in Diabetes: Findings from the Natural Experiments for Translation in Diabetes (NEXT-D) Network.

PURPOSE OF REVIEW: To advance our understanding of the impacts of policies and programs aimed at improving detection, engagement, prevention, and clinical diabetes management in the USA, we synthesized findings from a network of studies that used natural experiments to evaluate diabetes health policies and programs. FINDINGS: Studies from the Natural EXperiments for Translation in Diabetes (NEXT-D) network used rigorous longitudinal quasi-experimental study designs (e.g., interrupted time series) and analytical methods (e.g., difference-in-differences) to augment causal inference. Investigators partnered with health system stakeholders to evaluate whether glucose testing rates changed from before-to-after clinic interventions (e.g., integrating electronic screening decision prompts in New York City) or employer programs (e.g., targeted messaging and waiving copayments for at-risk employees). Other studies examined participation and behavior change in low- (e.g., wellness coaching) or high-intensity lifestyle modification programs (e.g., diabetes prevention program-like interventions) offered by payers or employers. Lastly, studies assessed how employer health insurance benefits impacted healthcare utilization, adherence, and outcomes among people with diabetes. NEXT-D demonstrated that low-intensity interventions to facilitate glucose testing and enhance engagement in lifestyle modification were associated with small improvements in weight but large improvements in screening and testing when supported by electronic health record-based decision-support. Regarding high-intensity diabetes prevention program-like lifestyle programs offered by payers or employers, enrollment was modest and led to weight loss and marginally lower short-term health expenditures. Health plans that incentivize patient behaviors were associated with increases in medication adherence. Meanwhile, shifting patients to high-deductible health plans was associated with no change in medication use and preventive screenings, but patients with diabetes delayed accessing healthcare for acute complications (e.g., cellulitis). Findings were more pronounced among lower-income patients, who experienced increased rates and acuity of emergency department visits for diabetes complications and other high-severity conditions. Findings from NEXT-D studies provide informative data that can guide programs and policies to facilitate detection, prevention, and treatment of diabetes in practice.

Influence of a New Diabetes Diagnosis on the Health Behaviors of the Patient's Partner.

PURPOSE: When a person is given a diagnosis of diabetes, the changes in his or her health behaviors may influence the behaviors of his or her partner. The diabetes diagnosis may affect household members' perceptions of their own health risks, which could trigger behavioral change. The purpose of this study was to assess whether partners of persons with newly diagnosed diabetes changed their health behaviors compared with partners of persons without diabetes. METHODS: The study population consisted of Kaiser Permanente Northern California health plan members from 2007 to 2011. This cohort study assessed differences in change of 8 health behaviors. The study compared coresiding partners of persons with newly diagnosed diabetes before and after a diabetes diagnosis with a 5 to 1 matched sample of coresiding partners of persons without diabetes. RESULTS: A total of 180,910 couples were included in the analysis. After adjusting for baseline characteristics, partners of persons with newly diagnosed diabetes had significantly higher rates of participation in weight management-related health education classes (risk ratio [RR] = 1.50; 95% CI, 1.39-1.63); smoking cessation medication use (RR = 1.25; 95% CI, 1.05-1.50); glucose screening (RR = 1.07; 95% CI, 1.05-1.08); clinically meaningful weight loss (RR = 1.06; 95% CI, 1.02-1.11); lipid screening (RR = 1.05; 95% CI, 1.04-1.07); influenza vaccination (RR = 1.03; 95% CI, 1.02-1.04); and blood pressure screening (RR = 1.02; 95% CI, 1.02-1.03) compared with partners of persons without diabetes. CONCLUSIONS: There were small but significant differences in health-related behavioral changes among partners of persons with newly diagnosed diabetes compared with partners of persons without diabetes, even when no intervention occurred. This finding suggests a diabetes diagnosis within a family may be a teachable moment to improve health behaviors at the household level.

Population Health Management for Diabetes: Health Care System-Level Approaches for Improving Quality and Addressing Disparities.

PURPOSE OF REVIEW: Population care approaches for diabetes have the potential to improve the quality of care and decrease diabetes-related mortality and morbidity. Population care strategies are particularly relevant as accountable care organizations (ACOs), patient-centered medical homes (PCMH), and integrated delivery systems are increasingly focused on managing chronic disease care at the health system level. This review outlines the key elements of population care approaches for diabetes in the current health care environment. RECENT FINDINGS: Population care approaches proactively identify diabetes patients through disease registries and electronic health record data and utilize multidisciplinary care teams, personalized provider feedback, and decision support tools to target and care for patients at risk for poor outcomes. Existing evidence suggests that these strategies can improve care outcomes and potentially ameliorate existing race/ethnic disparities in health care. However, such strategies may be less effective for patients who are disengaged from the health care system. As population care for diabetes continues to evolve, future initiatives should consider ways to tailor population care to meet individual patient needs, while leveraging improvements in clinical information systems and care integration to optimally manage and prevent diabetes in the future.