The impact of coding germline variants on contralateral breast cancer risk and survival.

Evidence linking coding germline variants in breast cancer (BC)-susceptibility genes other than BRCA1, BRCA2, and CHEK2 with contralateral breast cancer (CBC) risk and breast cancer-specific survival (BCSS) is scarce. The aim of this study was to assess the association of protein-truncating variants (PTVs) and rare missense variants (MSVs) in nine known (ATM, BARD1, BRCA1, BRCA2, CHEK2, PALB2, RAD51C, RAD51D, and TP53) and 25 suspected BC-susceptibility genes with CBC risk and BCSS. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated with Cox regression models. Analyses included 34,401 women of European ancestry diagnosed with BC, including 676 CBCs and 3,449 BC deaths; the median follow-up was 10.9 years. Subtype analyses were based on estrogen receptor (ER) status of the first BC. Combined PTVs and pathogenic/likely pathogenic MSVs in BRCA1, BRCA2, and TP53 and PTVs in CHEK2 and PALB2 were associated with increased CBC risk [HRs (95% CIs): 2.88 (1.70-4.87), 2.31 (1.39-3.85), 8.29 (2.53-27.21), 2.25 (1.55-3.27), and 2.67 (1.33-5.35), respectively]. The strongest evidence of association with BCSS was for PTVs and pathogenic/likely pathogenic MSVs in BRCA2 (ER-positive BC) and TP53 and PTVs in CHEK2 [HRs (95% CIs): 1.53 (1.13-2.07), 2.08 (0.95-4.57), and 1.39 (1.13-1.72), respectively, after adjusting for tumor characteristics and treatment]. HRs were essentially unchanged when censoring for CBC, suggesting that these associations are not completely explained by increased CBC risk, tumor characteristics, or treatment. There was limited evidence of associations of PTVs and/or rare MSVs with CBC risk or BCSS for the 25 suspected BC genes. The CBC findings are relevant to treatment decisions, follow-up, and screening after BC diagnosis.

Modeling the evolution of the US opioid crisis for national policy development.

The opioid crisis is a major public health challenge in the United States, killing about 70,000 people in 2020 alone. Long delays and feedbacks between policy actions and their effects on drug-use behavior create dynamic complexity, complicating policy decision-making. In 2017, the National Academies of Sciences, Engineering, and Medicine called for a quantitative systems model to help understand and address this complexity and guide policy decisions. Here, we present SOURCE (Simulation of Opioid Use, Response, Consequences, and Effects), a dynamic simulation model developed in response to that charge. SOURCE tracks the US population aged ≥12 y through the stages of prescription and illicit opioid (e.g., heroin, illicit fentanyl) misuse and use disorder, addiction treatment, remission, and overdose death. Using data spanning from 1999 to 2020, we highlight how risks of drug use initiation and overdose have evolved in response to essential endogenous feedback mechanisms, including: 1) social influence on drug use initiation and escalation among people who use opioids; 2) risk perception and response based on overdose mortality, influencing potential new initiates; and 3) capacity limits on treatment engagement; as well as other drivers, such as 4) supply-side changes in prescription opioid and heroin availability; and 5) the competing influences of illicit fentanyl and overdose death prevention efforts. Our estimates yield a more nuanced understanding of the historical trajectory of the crisis, providing a basis for projecting future scenarios and informing policy planning.

Studying the association Between longitudinal non-dense breast tissue and breast cancer risk: a joint modelling approach.

Conflicting results have appeared in the literature on whether the amount of non-dense, adipose tissue in the breast is a risk factor or a protective factor for breast cancer, and biological hypotheses supporting both have been proposed. We suggest here that limitations in study design and statistical methodology could potentially explain the inconsistent results. Specifically, we exploit recent advances in methodology and software developed for the joint analysis of multiple longitudinal outcomes and time-to-event data to jointly analyze dense and non-dense tissue trajectories and the risk of breast cancer in a large, Swedish, screening cohort. We also perform extensive sensitivity analyses by mimicking analyses/designs of previously published studies, e.g. ignoring available longitudinal data. Overall, we did not find strong evidence supporting an association between non-dense tissue and the risk of incident breast cancer. We hypothesize that (1) previous studies have not been able to isolate the effect of non-dense tissue from dense tissue or adipose tissue elsewhere in the body, that (2) estimates of the effect of non-dense tissue on risk are strongly sensitive to modeling assumptions, or that (3) the effect size of non-dense tissue on breast cancer risk is likely to be small/not clinically relevant.

Prediction of opioid-related outcomes in a medicaid surgical population: Evidence to guide postoperative opiate therapy and monitoring.

BACKGROUND: Treatment of surgical pain is a common reason for opioid prescriptions. Being able to predict which patients are at risk for opioid abuse, dependence, and overdose (opioid-related adverse outcomes [OR-AE]) could help physicians make safer prescription decisions. We aimed to develop a machine-learning algorithm to predict the risk of OR-AE following surgery using Medicaid data with external validation across states. METHODS: Five machine learning models were developed and validated across seven US states (90-10 data split). The model output was the risk of OR-AE 6-months following surgery. The models were evaluated using standard metrics and area under the receiver operating characteristic curve (AUC) was used for model comparison. We assessed calibration for the top performing model and generated bootstrap estimations for standard deviations. Decision curves were generated for the top-performing model and logistic regression. RESULTS: We evaluated 96,974 surgical patients aged 15 and 64. During the 6-month period following surgery, 10,464 (10.8%) patients had an OR-AE. Outcome rates were significantly higher for patients with depression (17.5%), diabetes (13.1%) or obesity (11.1%). The random forest model achieved the best predictive performance (AUC: 0.877; F1-score: 0.57; recall: 0.69; precision:0.48). An opioid disorder diagnosis prior to surgery was the most important feature for the model, which was well calibrated and had good discrimination. CONCLUSIONS: A machine learning models to predict risk of OR-AE following surgery performed well in external validation. This work could be used to assist pain management following surgery for Medicaid beneficiaries and supports a precision medicine approach to opioid prescribing.

Random effects models of tumour growth for investigating interval breast cancer.

In Nordic countries and across Europe, breast cancer screening participation is high. However, a significant number of breast cancer cases are still diagnosed due to symptoms between screening rounds, termed "interval cancers". Radiologists use the interval cancer proportion as a proxy for the screening false negative rate (ie, 1-sensitivity). Our objective is to enhance our understanding of interval cancers by applying continuous tumour growth models to data from a study involving incident invasive breast cancer cases. Building upon previous findings regarding stationary distributions of tumour size and growth rate distributions in non-screened populations, we develop an analytical expression for the proportion of interval breast cancer cases among regularly screened women. Our approach avoids relying on estimated background cancer rates. We make specific parametric assumptions concerning tumour growth and detection processes (screening or symptoms), but our framework easily accommodates alternative assumptions. We also show how our developed analytical expression for the proportion of interval breast cancers within a screened population can be incorporated into an approach for fitting tumour growth models to incident case data. We fit a model on 3493 cases diagnosed in Sweden between 2001 and 2008. Our methodology allows us to estimate the distribution of tumour sizes at the most recent screening for interval cancers. Importantly, we find that our model-based expected incidence of interval breast cancers aligns closely with observed patterns in our study and in a large Nordic screening cohort. Finally, we evaluate the association between screening interval length and the interval cancer proportion. Our analytical expression represents a useful tool for gaining insights into the performance of population-based breast cancer screening programs.

Studying the association between longitudinal mammographic density measurements and breast cancer risk: a joint modelling approach.

BACKGROUND: Researchers have suggested that longitudinal trajectories of mammographic breast density (MD) can be used to understand changes in breast cancer (BC) risk over a woman's lifetime. Some have suggested, based on biological arguments, that the cumulative trajectory of MD encapsulates the risk of BC across time. Others have tried to connect changes in MD to the risk of BC. METHODS: To summarize the MD-BC association, we jointly model longitudinal trajectories of MD and time to diagnosis using data from a large ([Formula: see text]) mammography cohort of Swedish women aged 40-80 years. Five hundred eighteen women were diagnosed with BC during follow-up. We fitted three joint models (JMs) with different association structures; Cumulative, current value and slope, and current value association structures. RESULTS: All models showed evidence of an association between MD trajectory and BC risk ([Formula: see text] for current value of MD, [Formula: see text] and [Formula: see text] for current value and slope of MD respectively, and [Formula: see text] for cumulative value of MD). Models with cumulative association structure and with current value and slope association structure had better goodness of fit than a model based only on current value. The JM with current value and slope structure suggested that a decrease in MD may be associated with an increased (instantaneous) BC risk. It is possible that this is because of increased screening sensitivity rather than being related to biology. CONCLUSION: We argue that a JM with a cumulative association structure may be the most appropriate/biologically relevant model in this context.

Drug-related deaths among housed and homeless individuals in the UK and the USA: comparative retrospective cohort study.

BACKGROUND: The UK and USA currently report their highest number of drug-related deaths since records began, with higher rates among individuals experiencing homelessness. AIMS: Given that overdose prevention in homeless populations may require unique strategies, we evaluated whether substances implicated in death differed between (a) housed decedents and those experiencing homelessness and (b) between US and UK homeless populations. METHOD: We conducted an internationally comparative retrospective cohort study utilising multilevel multinomial regression modelling of coronial/medical examiner-verified drug-related deaths from 1 January 2012 to 31 December 2021. UK data were available for England, Wales and Northern Ireland; US data were collated from eight county jurisdictions. Data were available on decedent age, sex, ethnicity, housing status and substances implicated in death. RESULTS: Homeless individuals accounted for 16.3% of US decedents versus 3.4% in the UK. Opioids were implicated in 66.3 and 50.4% of all studied drug-related deaths in the UK and the USA respectively. UK homeless decedents had a significantly increased risk of having only opioids implicated in death compared with only non-opioids implicated (relative risk ratio RRR = 1.87, 95% CI 1.76-1.98, P < 0.001); conversely, US homeless decedents had a significantly decreased risk (RRR = 0.37, 95% CI 0.29-0.48, P < 0.001). Methamphetamine was implicated in two-thirds (66.7%) of deaths among US homeless decedents compared with 0.4% in the UK. CONCLUSIONS: Both the rate and type of drug-related deaths differ significantly between homeless and housed populations in the UK and USA. The two countries also differ in drugs implicated in death. Targeted programmes for country-specific implicated drug profiles appear warranted.

Factors Associated With False-Positive Recalls in Mammography Screening.

BACKGROUND: We aimed to identify factors associated with false-positive recalls in mammography screening compared with women who were not recalled and those who received true-positive recalls. METHODS: We included 29,129 women, aged 40 to 74 years, who participated in the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) between 2011 and 2013 with follow-up until the end of 2017. Nonmammographic factors were collected from questionnaires, mammographic factors were generated from mammograms, and genotypes were determined using the OncoArray or an Illumina custom array. By the use of conditional and regular logistic regression models, we investigated the association between breast cancer risk factors and risk models and false-positive recalls. RESULTS: Women with a history of benign breast disease, high breast density, masses, microcalcifications, high Tyrer-Cuzick 10-year risk scores, KARMA 2-year risk scores, and polygenic risk scores were more likely to have mammography recalls, including both false-positive and true-positive recalls. Further analyses restricted to women who were recalled found that women with a history of benign breast disease and dense breasts had a similar risk of having false-positive and true-positive recalls, whereas women with masses, microcalcifications, high Tyrer-Cuzick 10-year risk scores, KARMA 2-year risk scores, and polygenic risk scores were more likely to have true-positive recalls than false-positive recalls. CONCLUSIONS: We found that risk factors associated with false-positive recalls were also likely, or even more likely, to be associated with true-positive recalls in mammography screening.

Novel predictions of invasive breast cancer risk in mammography screening cohorts.

Mammography screening programs are aimed at reducing mortality due to breast cancer by detecting tumors at an early stage. There is currently interest in moving away from the age-based screening programs, and toward personalized screening based on individual risk factors. To accomplish this, risk prediction models for breast cancer are needed to determine who should be screened, and when. We develop a novel approach using a (random effects) continuous growth model, which we apply to a large population-based, Swedish screening cohort. Unlike existing breast cancer prediction models, this approach explicitly incorporates each woman's individual screening visits in the prediction. It jointly models invasive breast cancer tumor onset, tumor growth rate, symptomatic detection rate, and screening sensitivity. In addition to predicting the overall risk of invasive breast cancer, this model can make separate predictions regarding specific tumor sizes, and the mode of detection (eg, detected at screening, or through symptoms between screenings). It can also predict how these risks change depending on whether or not a woman will attend her next screening. In our study, we predict, given a future diagnosis, that the probability of having a tumor less than (as opposed to greater than) 10-mm diameter, at detection, will be, on average, 2.6 times higher if a woman in the cohort attends their next screening. This indicates that the model can be used to evaluate the short-term benefit of screening attendance, at an individual level.

Assessing lead time bias due to mammography screening on estimates of loss in life expectancy.

BACKGROUND: An increasingly popular measure for summarising cancer prognosis is the loss in life expectancy (LLE), i.e. the reduction in life expectancy following a cancer diagnosis. The proportion of life lost (PLL) can also be derived, improving comparability across age groups as LLE is highly age-dependent. LLE and PLL are often used to assess the impact of cancer over the remaining lifespan and across groups (e.g. socioeconomic groups). However, in the presence of screening, it is unclear whether part of the differences across population groups could be attributed to lead time bias. Lead time is the extra time added due to early diagnosis, that is, the time from tumour detection through screening to the time that cancer would have been diagnosed symptomatically. It leads to artificially inflated survival estimates even when there are no real survival improvements. METHODS: In this paper, we used a simulation-based approach to assess the impact of lead time due to mammography screening on the estimation of LLE and PLL in breast cancer patients. A natural history model developed in a Swedish setting was used to simulate the growth of breast cancer tumours and age at symptomatic detection. Then, a screening programme similar to current guidelines in Sweden was imposed, with individuals aged 40-74 invited to participate every second year; different scenarios were considered for screening sensitivity and attendance. To isolate the lead time bias of screening, we assumed that screening does not affect the actual time of death. Finally, estimates of LLE and PLL were obtained in the absence and presence of screening, and their difference was used to derive the lead time bias. RESULTS: The largest absolute bias for LLE was 0.61 years for a high screening sensitivity scenario and assuming perfect screening attendance. The absolute bias was reduced to 0.46 years when the perfect attendance assumption was relaxed to allow for imperfect attendance across screening visits. Bias was also present for the PLL estimates. CONCLUSIONS: The results of the analysis suggested that lead time bias influences LLE and PLL metrics, thus requiring special consideration when interpreting comparisons across calendar time or population groups.

Random effects models of lymph node metastases in breast cancer: quantifying the roles of covariates and screening using a continuous growth model.

We recently described a joint model of breast cancer tumor size and number of affected lymph nodes, which conditions on screening history, mammographic density, and mode of detection, and can be used to infer growth rates, time to symptomatic detection, screening sensitivity, and rates of lymph node spread. The model of lymph node spread can be estimated in isolation from measurements of tumor volume and number of affected lymph nodes, giving inference identical to the joint model. Here, we extend our model to include covariate effects. We also derive theoretical results in order to study the role of screening on lymph node metastases at diagnosis. We analyze the association between hormone replacement therapy (HRT) and breast cancer lymph node spread, using data from a case-control study designed specifically to study the effects of HRT on breast cancer. Using our method, we estimate that women using HRT at time of diagnosis have a 36% lower rate of lymph node spread than nonusers (95% confidence interval [CI] =(8%,58%)). This can be contrasted with the effect of HRT on the tumor growth rate, estimated here to be 15% slower in HRT users (95% CI = (-34%,+7%)). For screen-detected cancers, we illustrate how lead time can relate to lymph node spread; and using symptomatic cancers, we illustrate the potential consequences of false negative screens in terms of lymph node spread.

Adapting the Eliminating Medications Through Patient Ownership of End Results Protocol to Promote Benzodiazepine Cessation Among US Military Veterans: Focus Group Study With US Military Veterans and National Veterans Health Administration Leaders.

BACKGROUND: Long-term dependence on prescribed benzodiazepines is a public health problem. Eliminating Medications Through Patient Ownership of End Results (EMPOWER) is a promising self-management intervention, delivered directly to patients as a printed booklet, that is effective in promoting benzodiazepine reduction and cessation in older adults. EMPOWER has high potential to benefit large health care systems such as the US Veterans Health Administration (VHA), which cares for many veterans who use benzodiazepines for extended periods. OBJECTIVE: We aimed to adapt the original EMPOWER booklet materials for electronic delivery and for use among US military veterans receiving VHA care who were long-term benzodiazepine users. METHODS: We used elements of Analysis, Design, Development, Implementation, and Evaluation, a framework commonly used in the field of instructional design, to guide a qualitative approach to iteratively adapting EMPOWER Electronic Delivery (EMPOWER-ED). We conducted 3 waves of focus groups with the same 2 groups of VHA stakeholders. Stakeholders were VHA-enrolled veterans (n=16) with medical chart evidence of long-term benzodiazepine use and national VHA leaders (n=7) with expertise in setting VHA policy for prescription benzodiazepine use and developing electronically delivered educational tools for veterans. Qualitative data collected from each wave of focus groups were analyzed using template analysis. RESULTS: Themes that emerged from the initial focus groups included veterans' anxiety about self-tapering from benzodiazepines and prior negative experiences attempting to self-taper without support. Participants also provided feedback on the protocol's look and feel, educational content, the tapering protocol, and website functionality; for example, feedback from policy leaders included listing, on the cover page, the most commonly prescribed benzodiazepines to ensure that veterans were aware of medications that qualify for self-taper using the EMPOWER-ED protocol. Both groups of stakeholders identified the importance of having access to supportive resources to help veterans manage sleep and anxiety in the absence of taking benzodiazepines. Both groups also emphasized the importance of ensuring that the self-taper could be personalized and that the taper instructions were clear. The policy leaders emphasized the importance of encouraging veterans to notify their provider of their decision to self-taper to help facilitate provider assistance, if needed, with the taper process and to help prevent medication stockpiling. CONCLUSIONS: EMPOWER-ED is the first direct-to-patient electronically delivered protocol designed to help US military veterans self-taper from long-term benzodiazepine use. We used the Analysis, Design, Development, Implementation, and Evaluation framework to guide the successful adaption of the original EMPOWER booklet for use with this population and for electronic delivery. The next step in this line of research is to evaluate EMPOWER-ED in a randomized controlled trial.

Lymph node metastases in breast cancer: Investigating associations with tumor characteristics, molecular subtypes and polygenic risk score using a continuous growth model.

We investigate the association between rate of breast cancer lymph node spread and grade, estrogen receptor (ER) status, progesteron receptor status, decision tree derived PAM50 molecular subtype and a polygenic risk score (PRS), using data on 10 950 women included from two different data sources. Lymph node spread was analyzed using a novel continuous tumor progression model that adjusts for tumor volume in a biologically motivated way and that incorporates covariates of interest. Grades 2 and 3 tumors, respectively, were associated with 1.63 and 2.17 times faster rates of lymph node spread than Grade 1 tumors (P < 10-16 ). ER/PR negative breast cancer was associated with a 1.25/1.19 times faster spread than ER/PR positive breast cancer, respectively (P = .0011 and .0012). Among the molecular subtypes luminal A, luminal B, Her2-enriched and basal-like, Her2-enriched breast cancer was associated with 1.53 times faster spread than luminal A cancer (P = .00072). PRS was not associated with the rate of lymph node spread. Continuous growth models are useful for quantifying associations between lymph node spread and tumor characteristics. These may be useful for building realistic progression models for microsimulation studies used to design individualized screening programs.

Mammography features for early markers of aggressive breast cancer subtypes and tumor characteristics: A population-based cohort study.

Current breast cancer risk models identify mostly less aggressive tumors, although only women developing fatal breast cancer will greatly benefit from early identification. Here, we evaluated the use of mammography features (microcalcification clusters, computer-generated Breast Imaging Reporting and Data System [cBIRADS] density and lack of breast density reduction) as early markers of aggressive subtypes and tumor characteristics. Mammograms were retrieved from a population-based cohort of women that were diagnosed with breast cancer from 2001 to 2008 in Stockholm-Gotland County, Sweden. Tumor and patient characteristics were obtained from Stockholm Breast Cancer Quality Register and the Swedish Cancer Registry. Multinomial logistic regression was used to individually model each mammographic feature as a function of molecular subtypes, tumor characteristics and detection mode. A total of 4546 women with invasive breast cancer were included in the study. Women with microcalcification clusters in the affected breast were more likely to have human epidermal growth factor receptor 2 subtype (odds ratio [OR] 1.78; 95% confidence interval [CI] 1.24-2.54) and potentially less likely to have basal subtype (OR 0.54; 0.30-0.96) compared to Luminal A subtype. High mammographic cBIRADS showed association with larger tumor size and interval vs screen-detected cancers. Lack of density reduction was associated with interval vs screen-detected cancers (OR 1.43; 1.11-1.83) and potentially of Luminal B subtype vs Luminal A subtype (OR 1.76; 1.04-2.99). In conclusion, microcalcification clusters, cBIRADS density and lack of breast density reduction could serve as early markers of particular subtypes and tumor characteristics of breast cancer. This information has the potential to be integrated into risk models to identify women at risk for developing aggressive breast cancer in need of supplemental screening.

Association between breast cancer risk and disease aggressiveness: Characterizing underlying gene expression patterns.

The association between breast cancer risk defined by the Tyrer-Cuzick score (TC) and disease prognosis is not well established. Here, we investigated the relationship between 5-year TC and disease aggressiveness and then characterized underlying molecular processes. In a case-only study (n = 2474), we studied the association of TC with molecular subtypes and tumor characteristics. In a subset of patients (n = 672), we correlated gene expression to TC and computed a low-risk TC gene expression (TC-Gx) profile, that is, a profile expected to be negatively associated with risk, which we used to test for association with disease aggressiveness. We performed enrichment analysis to pinpoint molecular processes likely to be altered in low-risk tumors. A higher TC was found to be inversely associated with more aggressive surrogate molecular subtypes and tumor characteristics (P < .05) including Ki-67 proliferation status (P < 5 × 10-07 ). Our low-risk TC-Gx, based on the weighted sum of 37 expression values of genes strongly correlated with TC, was associated with basal-like (P < 5 × 10-13 ), HER2-enriched subtype (P < 5 × 10-07 ) and worse 10-year breast cancer-specific survival (log-rank P < 5 × 10-04 ). Associations between low-risk TC-Gx and more aggressive molecular subtypes were replicated in an independent cohort from The Cancer Genome Atlas database (n = 975). Gene expression that correlated with low TC was enriched in proliferation and oncogenic signaling pathways (FDR < 0.05). Moreover, higher proliferation was a key factor explaining the association with worse survival. Women who developed breast cancer despite having a low risk were diagnosed with more aggressive tumors and had a worse prognosis, most likely driven by increased proliferation. Our findings imply the need to establish risk factors associated with more aggressive breast cancer subtypes.

Policy Responses to the Addiction Crisis.

The COVID-19 pandemic is just one of two public health crises the new Biden administration will confront. The addiction crisis is the other. The opioid epidemic has already killed more Americans than World Wars I and II combined. And it is but the most visible sign of a broader population health challenge that includes methamphetamine, cocaine, benzodiazepines, and alcohol. This article presents practical legislative and executive actions that are required for addressing these challenges. The authors focus on two broad policy challenges: (1) improving financing and delivery of treatment for substance use disorders, and (2) reducing population exposure to addictive and lethal substances. Through both of these channels, a portfolio of well-implemented, evidence-informed policies can save many thousands of lives every year.

Depression, Alcoholics Anonymous Involvement, and Daily Drinking Among Patients with co-occurring Conditions: A Longitudinal Parallel Growth Mixture Model.

BACKGROUND: Patients with cooccurring mental health and substance use disorders often find it difficult to sustain long-term recovery. One predictor of recovery may be how depression symptoms and Alcoholics Anonymous (AA) involvement influence alcohol consumption during and after inpatient psychiatric treatment. This study utilized a parallel growth mixture model to characterize the course of alcohol use, depression, and AA involvement in patients with cooccurring diagnoses. METHODS: Participants were adults with cooccurring disorders (n = 406) receiving inpatient psychiatric care as part of a telephone monitoring clinical trial. Participants were assessed at intake, 3-, 9-, and 15-month follow-up. RESULTS: A 3-class solution was the most parsimonious based upon fit indices and clinical relevance of the classes. The classes identified were high AA involvement with normative depression (27%), high stable depression with uneven AA involvement (11%), and low AA involvement with normative depression (62%). Both the low and high AA classes reduced their drinking across time and were drinking at less than half their baseline levels at all follow-ups. The high stable depression class reported an uneven pattern of AA involvement and drank at higher daily frequencies across the study timeline. Depression symptoms and alcohol use decreased substantially from intake to 3 months and then stabilized for 90% of patients with cooccurring disorders following inpatient psychiatric treatment. CONCLUSIONS: These findings can inform future clinical interventions among patients with cooccurring mental health and substance use disorders. Specifically, patients with more severe symptoms of depression may benefit from increased AA involvement, whereas patients with less severe symptoms of depression may not.

Alcoholics Anonymous and other 12-step programs for alcohol use disorder.

BACKGROUND: Alcohol use disorder (AUD) confers a prodigious burden of disease, disability, premature mortality, and high economic costs from lost productivity, accidents, violence, incarceration, and increased healthcare utilization. For over 80 years, Alcoholics Anonymous (AA) has been a widespread AUD recovery organization, with millions of members and treatment free at the point of access, but it is only recently that rigorous research on its effectiveness has been conducted. OBJECTIVES: To evaluate whether peer-led AA and professionally-delivered treatments that facilitate AA involvement (Twelve-Step Facilitation (TSF) interventions) achieve important outcomes, specifically: abstinence, reduced drinking intensity, reduced alcohol-related consequences, alcohol addiction severity, and healthcare cost offsets. SEARCH METHODS: We searched the Cochrane Drugs and Alcohol Group Specialized Register, Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, Embase, CINAHL and PsycINFO from inception to 2 August 2019. We searched for ongoing and unpublished studies via ClinicalTrials.gov and the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) on 15 November 2018. All searches included non-English language literature. We handsearched references of topic-related systematic reviews and bibliographies of included studies. SELECTION CRITERIA: We included randomized controlled trials (RCTs), quasi-RCTs and non-randomized studies that compared AA or TSF (AA/TSF) with other interventions, such as motivational enhancement therapy (MET) or cognitive behavioral therapy (CBT), TSF treatment variants, or no treatment. We also included healthcare cost offset studies. Participants were non-coerced adults with AUD. DATA COLLECTION AND ANALYSIS: We categorized studies by: study design (RCT/quasi-RCT; non-randomized; economic); degree of standardized manualization (all interventions manualized versus some/none); and comparison intervention type (i.e. whether AA/TSF was compared to an intervention with a different theoretical orientation or an AA/TSF intervention that varied in style or intensity). For analyses, we followed Cochrane methodology calculating the standard mean difference (SMD) for continuous variables (e.g. percent days abstinent (PDA)) or the relative risk (risk ratios (RRs)) for dichotomous variables. We conducted random-effects meta-analyses to pool effects wherever possible. MAIN RESULTS: We included 27 studies containing 10,565 participants (21 RCTs/quasi-RCTs, 5 non-randomized, and 1 purely economic study). The average age of participants within studies ranged from 34.2 to 51.0 years. AA/TSF was compared with psychological clinical interventions, such as MET and CBT, and other 12-step program variants. We rated selection bias as being at high risk in 11 of the 27 included studies, unclear in three, and as low risk in 13. We rated risk of attrition bias as high risk in nine studies, unclear in 14, and low in four, due to moderate (> 20%) attrition rates in the study overall (8 studies), or in study treatment group (1 study). Risk of bias due to inadequate researcher blinding was high in one study, unclear in 22, and low in four. Risks of bias arising from the remaining domains were predominantly low or unclear. AA/TSF (manualized) compared to treatments with a different theoretical orientation (e.g. CBT) (randomized/quasi-randomized evidence) RCTs comparing manualized AA/TSF to other clinical interventions (e.g. CBT), showed AA/TSF improves rates of continuous abstinence at 12 months (risk ratio (RR) 1.21, 95% confidence interval (CI) 1.03 to 1.42; 2 studies, 1936 participants; high-certainty evidence). This effect remained consistent at both 24 and 36 months. For percentage days abstinent (PDA), AA/TSF appears to perform as well as other clinical interventions at 12 months (mean difference (MD) 3.03, 95% CI -4.36 to 10.43; 4 studies, 1999 participants; very low-certainty evidence), and better at 24 months (MD 12.91, 95% CI 7.55 to 18.29; 2 studies, 302 participants; low-certainty evidence) and 36 months (MD 6.64, 95% CI 1.54 to 11.75; 1 study, 806 participants; low-certainty evidence). For longest period of abstinence (LPA), AA/TSF may perform as well as comparison interventions at six months (MD 0.60, 95% CI -0.30 to 1.50; 2 studies, 136 participants; low-certainty evidence). For drinking intensity, AA/TSF may perform as well as other clinical interventions at 12 months, as measured by drinks per drinking day (DDD) (MD -0.17, 95% CI -1.11 to 0.77; 1 study, 1516 participants; moderate-certainty evidence) and percentage days heavy drinking (PDHD) (MD -5.51, 95% CI -14.15 to 3.13; 1 study, 91 participants; low-certainty evidence). For alcohol-related consequences, AA/TSF probably performs as well as other clinical interventions at 12 months (MD -2.88, 95% CI -6.81 to 1.04; 3 studies, 1762 participants; moderate-certainty evidence). For alcohol addiction severity, one study found evidence of a difference in favor of AA/TSF at 12 months (P < 0.05; low-certainty evidence). AA/TSF (non-manualized) compared to treatments with a different theoretical orientation (e.g. CBT) (randomized/quasi-randomized evidence) For the proportion of participants completely abstinent, non-manualized AA/TSF may perform as well as other clinical interventions at three to nine months follow-up (RR 1.71, 95% CI 0.70 to 4.18; 1 study, 93 participants; low-certainty evidence). Non-manualized AA/TSF may also perform slightly better than other clinical interventions for PDA (MD 3.00, 95% CI 0.31 to 5.69; 1 study, 93 participants; low-certainty evidence). For drinking intensity, AA/TSF may perform as well as other clinical interventions at nine months, as measured by DDD (MD -1.76, 95% CI -2.23 to -1.29; 1 study, 93 participants; very low-certainty evidence) and PDHD (MD 2.09, 95% CI -1.24 to 5.42; 1 study, 286 participants; low-certainty evidence). None of the RCTs comparing non-manualized AA/TSF to other clinical interventions assessed LPA, alcohol-related consequences, or alcohol addiction severity. Cost-effectiveness studies In three studies, AA/TSF had higher healthcare cost savings than outpatient treatment, CBT, and no AA/TSF treatment. The fourth study found that total medical care costs decreased for participants attending CBT, MET, and AA/TSF treatment, but that among participants with worse prognostic characteristics AA/TSF had higher potential cost savings than MET (moderate-certainty evidence). AUTHORS' CONCLUSIONS: There is high quality evidence that manualized AA/TSF interventions are more effective than other established treatments, such as CBT, for increasing abstinence. Non-manualized AA/TSF may perform as well as these other established treatments. AA/TSF interventions, both manualized and non-manualized, may be at least as effective as other treatments for other alcohol-related outcomes. AA/TSF probably produces substantial healthcare cost savings among people with alcohol use disorder.

Alcoholics Anonymous and 12-Step Facilitation Treatments for Alcohol Use Disorder: A Distillation of a 2020 Cochrane Review for Clinicians and Policy Makers.

AIMS: A recently completed Cochrane review assessed the effectiveness and cost-benefits of Alcoholics Anonymous (AA) and clinically delivered 12-Step Facilitation (TSF) interventions for alcohol use disorder (AUD). This paper summarizes key findings and discusses implications for practice and policy. METHODS: Cochrane review methods were followed. Searches were conducted across all major databases (e.g. Cochrane Drugs and Alcohol Group Specialized Register, PubMed, Embase, PsycINFO and ClinicalTrials.gov) from inception to 2 August 2019 and included non-English language studies. Randomized controlled trials (RCTs) and quasi-experiments that compared AA/TSF with other interventions, such as motivational enhancement therapy (MET) or cognitive behavioral therapy (CBT), TSF treatment variants or no treatment, were included. Healthcare cost offset studies were also included. Studies were categorized by design (RCT/quasi-experimental; nonrandomized; economic), degree of manualization (all interventions manualized versus some/none) and comparison intervention type (i.e. whether AA/TSF was compared to an intervention with a different theoretical orientation or an AA/TSF intervention that varied in style or intensity). Random-effects meta-analyses were used to pool effects where possible using standard mean differences (SMD) for continuous outcomes (e.g. percent days abstinent (PDA)) and the relative risk ratios (RRs) for dichotomous. RESULTS: A total of 27 studies (21 RCTs/quasi-experiments, 5 nonrandomized and 1 purely economic study) containing 10,565 participants were included. AA/TSF interventions performed at least as well as established active comparison treatments (e.g. CBT) on all outcomes except for abstinence where it often outperformed other treatments. AA/TSF also demonstrated higher health care cost savings than other AUD treatments. CONCLUSIONS: AA/TSF interventions produce similar benefits to other treatments on all drinking-related outcomes except for continuous abstinence and remission, where AA/TSF is superior. AA/TSF also reduces healthcare costs. Clinically implementing one of these proven manualized AA/TSF interventions is likely to enhance outcomes for individuals with AUD while producing health economic benefits.

How Medicaid work requirements could exacerbate the opioid epidemic.

In 2018, the Trump Administration took the unprecedented step of allowing states to impose work requirements as a condition of Medicaid eligibility. States can apply for a demonstration waiver to require Medicaid beneficiaries aged 19-64 who do not meet exemption criteria (e.g., disability, caring for a sick relative) to participate in "community engagement" activities, which include employment, volunteering, and enrollment in a qualifying education or job training program. Debate thus far has focused primarily around the important issue of whether such requirements are legal. Less attention has focused on another serious concern - namely, that work requirements could exacerbate the nation's most urgent public health crisis: the opioid epidemic. Many enrollees with opioid use disorder who are unable to meet states' community engagement criteria will not qualify for an exemption from the work requirements, and risk being dropped from Medicaid enrollment. Refusing health insurance to individuals who are unable to meet work requirements could result in significant losses in coverage among a highly vulnerable population. Implementing new barriers to Medicaid coverage will hinder the effectiveness of massive state and federal investments in improving access to evidence-based addiction treatment.