Study protocol for a factorial-randomized controlled trial evaluating the implementation, costs, effectiveness, and sustainment of digital therapeutics for substance use disorder in primary care (DIGITS Trial).

BACKGROUND: Experts recommend that treatment for substance use disorder (SUD) be integrated into primary care. The Digital Therapeutics for Opioids and Other SUD (DIGITS) Trial tests strategies for implementing reSET® and reSET-O®, which are prescription digital therapeutics for SUD and opioid use disorder, respectively, that include the community reinforcement approach, contingency management, and fluency training to reinforce concept mastery. This purpose of this trial is to test whether two implementation strategies improve implementation success (Aim 1) and achieve better population-level cost effectiveness (Aim 2) over a standard implementation approach. METHODS/DESIGN: The DIGITS Trial is a hybrid type III cluster-randomized trial. It examines outcomes of implementation strategies, rather than studying clinical outcomes of a digital therapeutic. It includes 22 primary care clinics from a healthcare system in Washington State and patients with unhealthy substance use who visit clinics during an active implementation period (up to one year). Primary care clinics implemented reSET and reSET-O using a multifaceted implementation strategy previously used by clinical leaders to roll-out smartphone apps ("standard implementation" including discrete strategies such as clinician training, electronic health record tools). Clinics were randomized as 21 sites in a 2x2 factorial design to receive up to two added implementation strategies: (1) practice facilitation, and/or (2) health coaching. Outcome data are derived from electronic health records and logs of digital therapeutic usage. Aim 1's primary outcomes include reach of the digital therapeutics to patients and fidelity of patients' use of the digital therapeutics to clinical recommendations. Substance use and engagement in SUD care are additional outcomes. In Aim 2, population-level cost effectiveness analysis will inform the economic benefit of the implementation strategies compared to standard implementation. Implementation is monitored using formative evaluation, and sustainment will be studied for up to one year using qualitative and quantitative research methods. DISCUSSION: The DIGITS Trial uses an experimental design to test whether implementation strategies increase and improve the delivery of digital therapeutics for SUDs when embedded in a large healthcare system. It will provide data on the potential benefits and cost-effectiveness of alternative implementation strategies. CLINICALTRIALS: gov Identifier: NCT05160233 (Submitted 12/3/2021). https://clinicaltrials.gov/ct2/show/NCT05160233.

Baseline representativeness of patients in clinics enrolled in the PRimary care Opioid Use Disorders treatment (PROUD) trial: comparison of trial and non-trial clinics in the same health systems.

BACKGROUND: Pragmatic primary care trials aim to test interventions in "real world" health care settings, but clinics willing and able to participate in trials may not be representative of typical clinics. This analysis compared patients in participating and non-participating clinics from the same health systems at baseline in the PRimary care Opioid Use Disorders treatment (PROUD) trial. METHODS: This observational analysis relied on secondary electronic health record and administrative claims data in 5 of 6 health systems in the PROUD trial. The sample included patients 16-90 years at an eligible primary care visit in the 3 years before randomization. Each system contributed 2 randomized PROUD trial clinics and 4 similarly sized non-trial clinics. We summarized patient characteristics in trial and non-trial clinics in the 2 years before randomization ("baseline"). Using mixed-effect regression models, we compared trial and non-trial clinics on a baseline measure of the primary trial outcome (clinic-level patient-years of opioid use disorder (OUD) treatment, scaled per 10,000 primary care patients seen) and a baseline measure of the secondary trial outcome (patient-level days of acute care utilization among patients with OUD). RESULTS: Patients were generally similar between the 10 trial clinics (n = 248,436) and 20 non-trial clinics (n = 341,130), although trial clinics' patients were slightly younger, more likely to be Hispanic/Latinx, less likely to be white, more likely to have Medicaid/subsidized insurance, and lived in less wealthy neighborhoods. Baseline outcomes did not differ between trial and non-trial clinics: trial clinics had 1.0 more patient-year of OUD treatment per 10,000 patients (95% CI: - 2.9, 5.0) and a 4% higher rate of days of acute care utilization than non-trial clinics (rate ratio: 1.04; 95% CI: 0.76, 1.42). CONCLUSIONS: trial clinics and non-trial clinics were similar regarding most measured patient characteristics, and no differences were observed in baseline measures of trial primary and secondary outcomes. These findings suggest trial clinics were representative of comparably sized clinics within the same health systems. Although results do not reflect generalizability more broadly, this study illustrates an approach to assess representativeness of clinics in future pragmatic primary care trials.

Practical Assessment of Alcohol Use Disorder in Routine Primary Care: Performance of an Alcohol Symptom Checklist.

BACKGROUND: Alcohol use disorder (AUD) is highly prevalent but underrecognized and undertreated in primary care settings. Alcohol Symptom Checklists can engage patients and providers in discussions of AUD-related care. However, the performance of Alcohol Symptom Checklists when they are used in routine care and documented in electronic health records (EHRs) remains unevaluated. OBJECTIVE: To evaluate the psychometric performance of an Alcohol Symptom Checklist in routine primary care. DESIGN: Cross-sectional study using item response theory (IRT) and differential item functioning analyses of measurement consistency across age, sex, race, and ethnicity. PATIENTS: Patients seen in primary care in the Kaiser Permanente Washington Healthcare System who reported high-risk drinking on the Alcohol Use Disorder Identification Test Consumption screening measure (AUDIT-C ≥ 7) and subsequently completed an Alcohol Symptom Checklist between October 2015 and February 2020. MAIN MEASURE: Alcohol Symptom Checklists with 11 items assessing AUD criteria defined in the Diagnostic and Statistical Manual for Mental Disorders, 5th edition (DSM-5), completed by patients during routine medical care and documented in EHRs. KEY RESULTS: Among 11,464 patients who screened positive for high-risk drinking and completed an Alcohol Symptom Checklist (mean age 43.6 years, 30.5% female), 54.1% reported ≥ 2 DSM-5 AUD criteria (threshold for AUD diagnosis). IRT analyses demonstrated that checklist items measured a unidimensional continuum of AUD severity. Differential item functioning was observed for some demographic subgroups but had minimal impact on accurate measurement of AUD severity, with differences between demographic subgroups attributable to differential item functioning never exceeding 0.42 points of the total symptom count (of a possible range of 0-11). CONCLUSIONS: Alcohol Symptom Checklists used in routine care discriminated AUD severity consistently with current definitions of AUD and performed equitably across age, sex, race, and ethnicity. Integrating symptom checklists into routine care may help inform clinical decision-making around diagnosing and managing AUD.

Temporal variation in association between short-term exposure to fine particulate matter and hospitalisations in older adults in the USA: a long-term time-series analysis of the US Medicare dataset.

BACKGROUND: Short-term exposure to fine particulate matter (PM2·5) is associated with increased risk of hospital admissions and mortality, and health risks differ by the chemical composition of PM2·5. Policies to control PM2·5 could change its chemical composition and total mass concentration, leading to change in the subsequent health impact. However, there is little ence on whether associations between PM2·5 and health exhibit temporal variation. We investigated whether risks of hospitalisations from short-term exposure to PM2·5 varied over time in the USA. METHODS: We did a time-series analysis using a national dataset comprising daily circulatory and respiratory hospitalisation rates of Medicare beneficiaries (age ≥65 years) and PM2·5 in 173 US counties from 1999 to 2016. We fitted modified quasi-Poisson models to estimate temporal trends of associations within a county, and pooled county-level estimates using Bayesian hierarchical modelling to generate an overall estimate. FINDINGS: The study included 10 559 654 circulatory and 3 027 281 respiratory hospitalisations. We identified changes in the national average association between previous-day PM2·5 and respiratory hospitalisation over time, with a U-shape that is robust under stratification, linear, and non-linear models. The change in risk of respiratory hospitalisation per 10 µg/m3 increase in previous-day PM2·5 decreased from 0·75% (95% posterior credible interval 0·05 to 1·46) in 1999 to -0·28% (-0·79 to 0·23) in 2008, and then increased to 1·44% (0·00 to 2·91) in 2016. No statistically significant temporal change was observed for associations between same-day PM2·5 and circulatory hospitalisation. INTERPRETATION: Hospitalisation risk from PM2·5 changes over time and has increased over the past 7 years in study, especially in northeastern USA. The temporal trend differs by cause of hospitalisation. This study emphasises the necessity of evaluating temporal heterogeneity in health impacts of PM2·5 and suggests caution in applying association estimates to a different time period. FUNDING: US Environmental Protection Agency and Yale Institute for Biospheric Studies.

Prevalence of prescription opioid use during pregnancy in eight US health plans during 2001-2014.

PURPOSE: To estimate prevalence of prescription opioid use during pregnancy in eight US health plans during 2001-2014. METHODS: We conducted a cohort study of singleton live birth deliveries. Maternal characteristics were ascertained from health plan and/or birth certificate data and opioids dispensed during pregnancy from health plan pharmacy records. Prevalence of prescription opioid use during pregnancy was calculated for any use, cumulative days of use, and number of dispensings. RESULTS: We examined prevalence of prescription opioid use during pregnancy in each health plan. Tennessee Medicaid had appreciably greater prevalence of use compared to the seven other health plans. Thus, results for the two groups were reported separately. In the seven health plans (n = 587 093 deliveries), prevalence of use during pregnancy was relatively stable at 9%-11% throughout 2001-2014. In Tennessee Medicaid (n = 256 724 deliveries), prevalence increased from 29% in 2001 to a peak of 36%-37% in 2004-2010, and then declined to 28% in 2014. Use for ≥30 days during pregnancy was stable at 1% in the seven health plans and increased from 2% to 7% in Tennessee Medicaid during 2001-2014. Receipt of ≥5 opioid dispensings during pregnancy increased in the seven health plans (0.3%-0.6%) and Tennessee Medicaid (3%-5%) during 2001-2014. CONCLUSION: During 2001-2014, prescription opioid use during pregnancy was more common in Tennessee Medicaid (peak prevalence in late 2000s) compared to the seven health plans (relatively stable prevalence). Although a small percentage of women had opioid use during pregnancy for ≥30 days or ≥ 5 dispensings, they represent thousands of women during 2001-2014.

Routine Assessment of Symptoms of Substance Use Disorders in Primary Care: Prevalence and Severity of Reported Symptoms.

BACKGROUND: Most patients with substance use disorders (SUDs) never receive treatment and SUDs are under-recognized in primary care (PC) where patients can be treated or linked to treatment. Asking PC patients to directly report SUD symptoms on questionnaires might help identify SUDs but to our knowledge, this approach is previously untested. OBJECTIVE: To describe the prevalence and severity of DSM-5 SUD symptoms reported by PC patients as part of routine care. DESIGN: Cross-sectional study using secondary data. PARTICIPANTS: A total of 241,265 adult patients who visited one of 25 PC sites in an integrated health system in Washington state and had alcohol, cannabis, or other drug use screening documented in their EHRs (March 2015-July 2018) were included in main analyses if they had a positive screen for high-risk substance use defined as AUDIT-C score 7-12 points, or report of past-year daily cannabis use or any other drug use. MAIN MEASURES: The main outcome was number of SUD symptoms based on Diagnostic and Statistical Manual, 5th edition (DSM-5), reported on Symptom Checklists (0-11) for alcohol or other drugs: 2-3 mild; 4-5 moderate; 6-11 severe. RESULTS: Of screened patients, 16,776 (5.7%) reported high-risk use of alcohol (2.4%), cannabis (3.9%), and/or other drugs (1.7%), and 65.0-69.9% of those completed Symptom Checklists. Of those with high-risk alcohol use, 52.5% (95% CI 50.9-54.0%) reported ≥ 2 symptoms consistent with mild-severe alcohol use disorders. Of those reporting daily cannabis use, 29.8% (28.6-30.9%) reported ≥ 2 symptoms consistent with mild-severe SUDs. Of those reporting any other drug use, 37.5% (35.7-39.3%) reported ≥ 2 symptoms consistent with mild-severe SUDs. CONCLUSIONS AND RELEVANCE: Many PC patients who screened positive for high-risk substance use reported symptoms consistent with DSM-5 SUDs on self-report Symptom Checklists. Use of SUD Symptom Checklists could support PC providers in making SUD diagnoses and initiating discussions of substance use.

Integration of screening, assessment, and treatment for cannabis and other drug use disorders in primary care: An evaluation in three pilot sites.

BACKGROUND: This pilot study evaluated whether use of evidence-based implementation strategies to integrate care for cannabis and other drug use into primary care (PC) as part of Behavioral Health Integration (BHI) increased diagnosis and treatment of substance use disorders (SUDs). METHODS: Patients who visited the three pilot PC sites were eligible. Implementation strategies included practice coaching, electronic health record decision support, and performance feedback (3/2015-4/2016). BHI introduced annual screening for past-year cannabis and other drug use, a Symptom Checklist for DSM-5 SUDs, and shared decision-making about treatment options. Main analyses tested whether the proportions of PC patients diagnosed with, and treated for, new cannabis or other drug use disorders (CUDs and DUDs, respectively), differed significantly pre- and post-implementation. RESULTS: Of 39,599 eligible patients, 57% and 59% were screened for cannabis and other drug use, respectively. Among PC patients reporting daily cannabis use (2%) or any drug use (1%), 51% and 37%, respectively, completed an SUD Symptom Checklist. The proportion of PC patients with newly diagnosed CUD increased significantly post-implementation (5 v 17 per 10,000 patients, p < 0.0001), but not other DUDs (10 vs 13 per 10,000, p = 0.24). The proportion treated for newly diagnosed CUDs did not increase post-implementation (1 vs 1 per 10,000, p = 0.80), but did for those treated for newly diagnosed other DUDs (1 vs 3 per 10,000, p = 0.038). CONCLUSIONS: A pilot implementation of BHI to increase routine screening and assessment for SUDs was associated with increased new CUD diagnoses and a small increase in treatment of new other DUDs.

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.

Bayesian varying coefficient kernel machine regression to assess neurodevelopmental trajectories associated with exposure to complex mixtures.

Exposure to environmental mixtures can exert wide-ranging effects on child neurodevelopment. However, there is a lack of statistical methods that can accommodate the complex exposure-response relationship between mixtures and neurodevelopment while simultaneously estimating neurodevelopmental trajectories. We introduce Bayesian varying coefficient kernel machine regression (BVCKMR), a hierarchical model that estimates how mixture exposures at a given time point are associated with health outcome trajectories. The BVCKMR flexibly captures the exposure-response relationship, incorporates prior knowledge, and accounts for potentially nonlinear and nonadditive effects of individual exposures. This model assesses the directionality and relative importance of a mixture component on health outcome trajectories and predicts health effects for unobserved exposure profiles. Using contour plots and cross-sectional plots, BVCKMR also provides information about interactions between complex mixture components. The BVCKMR is applied to a subset of data from PROGRESS, a prospective birth cohort study in Mexico city on exposure to metal mixtures and temporal changes in neurodevelopment. The mixture include metals such as manganese, arsenic, cobalt, chromium, cesium, copper, lead, cadmium, and antimony. Results from a subset of Programming Research in Obesity, Growth, Environment and Social Stressors data provide evidence of significant positive associations between second trimester exposure to copper and Bayley Scales of Infant and Toddler Development cognition score at 24 months, and cognitive trajectories across 6-24 months. We also detect an interaction effect between second trimester copper and lead exposures for cognition at 24 months. In summary, BVCKMR provides a framework for estimating neurodevelopmental trajectories associated with exposure to complex mixtures.

Medical diagnoses of heat wave-related hospital admissions in older adults.

Heat waves have been associated with adverse human health effects, including higher rates of all-cause and cardiovascular mortality, and these health effects may be exacerbated under continued climate change. However, specific causes of hospitalizations associated with heat waves have not been characterized on a national scale. We systematically estimated the risks of cause-specific hospitalizations during heat waves in a national cohort of 23.7 million Medicare enrollees residing in 1943 U.S. counties during 1999-2010. Heat waves were defined as ≥2 consecutive days exceeding the county's 99th percentile of daily temperatures, and were matched to non-heat wave periods by county and week. We considered 50 outcomes from broad disease groups previously associated with heat wave-related hospitalizations, and estimated cause-specific relative risks (RRs) of hospital admissions on heat wave days. We identified 11 diagnoses with a higher admission risk on heat wave days, with heat stroke and sunstroke having the highest risk (RR = 22.5, [95% CI 14.9-34.2]). Other diseases with elevated risks included fluid and electrolyte disorders [(Hyperosmolality RR = 1.4, [95% CI 1.1-1.3]; Hypoosmolaltiy RR = 1.2, [95% CI 1.1-1.3])] and acute kidney failure (RR = 1.1, [95% CI 1.1-1.2]). These risks tended to be higher under more severe heat wave events. In addition, risks were higher among adults in the oldest (≥85) category (reference: 65-74) for volume depletion and heat exhaustion. Several causes of hospitalization identified are preventable, and public health interventions, including early warning systems and plans targeting risk factors for these illnesses, could reduce adverse effects of heat in the present and under climate change.

Heat stroke admissions during heat waves in 1,916 US counties for the period from 1999 to 2010 and their effect modifiers.

BACKGROUND: Heat stroke is a serious heat-related illness, especially among older adults. However, little is known regarding the spatiotemporal variation of heat stroke admissions during heat waves and what factors modify the adverse effects. METHODS: We conducted a large-scale national study among 23.5 million Medicare fee-for-service beneficiaries per year residing in 1,916 US counties during 1999-2010. Heat wave days, defined as a period of at least two consecutive days with temperatures exceeding the 97th percentile of that county's temperatures, were matched to non-heat wave days by county and week. We fitted random-effects Poisson regression models to estimate the relative risk (RR) of heat stroke admissions on a heat wave day as compared to a matched non-heat wave day. A variety of effect modifiers were tested including individual-level covariates, community-level covariates, meteorological conditions, and the intensity and duration of the heat wave event. RESULTS: The RR declined substantially from 71.0 (21.3-236.2) in 1999 to 3.5 (1.9-6.5) in 2010, and was highest in the northeast and lowest in the west north central regions of the US. We found a lower RR among counties with higher central air conditioning (AC) prevalence. More severe and longer-lasting heat waves had higher RRs. CONCLUSIONS: Heat stroke hospitalizations associated with heat waves declined dramatically over time, indicating increased resilience to extreme heat among older adults. Considerable risks, however, still remain through 2010, which could be addressed through public health interventions at a regional scale to further increase central AC and monitoring heat waves.

Area-level socioeconomic deprivation, nitrogen dioxide exposure, and term birth weight in New York City.

Numerous studies have linked air pollution with adverse birth outcomes, but relatively few have examined differential associations across the socioeconomic gradient. To evaluate interaction effects of gestational nitrogen dioxide (NO2) and area-level socioeconomic deprivation on fetal growth, we used: (1) highly spatially-resolved air pollution data from the New York City Community Air Survey (NYCCAS); and (2) spatially-stratified principle component analysis of census variables previously associated with birth outcomes to define area-level deprivation. New York City (NYC) hospital birth records for years 2008-2010 were restricted to full-term, singleton births to non-smoking mothers (n=243,853). We used generalized additive mixed models to examine the potentially non-linear interaction of nitrogen dioxide (NO2) and deprivation categories on birth weight (and estimated linear associations, for comparison), adjusting for individual-level socio-demographic characteristics and sensitivity testing adjustment for co-pollutant exposures. Estimated NO2 exposures were highest, and most varying, among mothers residing in the most-affluent census tracts, and lowest among mothers residing in mid-range deprivation tracts. In non-linear models, we found an inverse association between NO2 and birth weight in the least-deprived and most-deprived areas (p-values<0.001 and 0.05, respectively) but no association in the mid-range of deprivation (p=0.8). Likewise, in linear models, a 10 ppb increase in NO2 was associated with a decrease in birth weight among mothers in the least-deprived and most-deprived areas of -16.2g (95% CI: -21.9 to -10.5) and -11.0 g (95% CI: -22.8 to 0.9), respectively, and a non-significant change in the mid-range areas [ß=0.5 g (95% CI: -7.7 to 8.7)]. Linear slopes in the most- and least-deprived quartiles differed from the mid-range (reference group) (p-values<0.001 and 0.09, respectively). The complex patterning in air pollution exposure and deprivation in NYC, however, precludes simple interpretation of interactive effects on birth weight, and highlights the importance of considering differential distributions of air pollution concentrations, and potential differences in susceptibility, across deprivation levels.

Cause-specific risk of hospital admission related to extreme heat in older adults.

IMPORTANCE: Heat exposure is known to have a complex set of physiological effects on multiple organ systems, but current understanding of the health effects is mostly based on studies investigating a small number of prespecified health outcomes such as cardiovascular and respiratory diseases. OBJECTIVES: To identify possible causes of hospital admissions during extreme heat events and to estimate their risks using historical data. DESIGN, SETTING, AND POPULATION: Matched analysis of time series data describing daily hospital admissions of Medicare enrollees (23.7 million fee-for-service beneficiaries [aged ≥65 years] per year; 85% of all Medicare enrollees) for the period 1999 to 2010 in 1943 counties in the United States with at least 5 summers of near-complete (>95%) daily temperature data. EXPOSURES: Heat wave periods, defined as 2 or more consecutive days with temperatures exceeding the 99th percentile of county-specific daily temperatures, matched to non-heat wave periods by county and week. MAIN OUTCOMES AND MEASURES: Daily cause-specific hospitalization rates by principal discharge diagnosis codes, grouped into 283 disease categories using a validated approach. RESULTS: Risks of hospitalization for fluid and electrolyte disorders, renal failure, urinary tract infection, septicemia, and heat stroke were statistically significantly higher on heat wave days relative to matched non-heat wave days, but risk of hospitalization for congestive heart failure was lower (P < .05). Relative risks for these disease groups were 1.18 (95% CI, 1.12-1.25) for fluid and electrolyte disorders, 1.14 (95% CI, 1.06-1.23) for renal failure, 1.10 (95% CI, 1.04-1.16) for urinary tract infections, 1.06 (95% CI, 1.00-1.11) for septicemia, and 2.54 (95% CI, 2.14-3.01) for heat stroke. Absolute risk differences were 0.34 (95% CI, 0.22-0.46) excess admissions per 100,000 individuals at risk for fluid and electrolyte disorders, 0.25 (95% CI, 0.12-0.39) for renal failure, 0.24 (95% CI, 0.09-0.39) for urinary tract infections, 0.21 (95% CI, 0.01-0.41) for septicemia, and 0.16 (95% CI, 0.10-0.22) for heat stroke. For fluid and electrolyte disorders and heat stroke, the risk of hospitalization increased during more intense and longer-lasting heat wave periods (P < .05). Risks were generally highest on the heat wave day but remained elevated for up to 5 subsequent days. CONCLUSIONS AND RELEVANCE: Among older adults, periods of extreme heat were associated with increased risk of hospitalization for fluid and electrolyte disorders, renal failure, urinary tract infection, septicemia, and heat stroke. However, the absolute risk increase was small and of uncertain clinical importance.