Multiple imputation for propensity score analysis with covariates missing at random: some clarity on within and across methods.

In epidemiology and social sciences, propensity score methods are popular for estimating treatment effects using observational data, and multiple imputation is popular for handling covariate missingness. However, how to appropriately use multiple imputation for propensity score analysis is not completely clear. This paper aims to bring clarity on the consistency (or lack thereof) of methods that have been proposed, focusing on the within approach (where the effect is estimated separately in each imputed dataset and then the multiple estimates are combined) and the across approach (where typically propensity scores are averaged across imputed datasets before being used for effect estimation). We show that the within method is valid and can be used with any causal effect estimator that is consistent in the full-data setting. Existing across methods are inconsistent, but a different across method that averages the inverse probability weights across imputed datasets is consistent for propensity score weighting. We also comment on methods that rely on imputing a function of the missing covariate rather than the covariate itself, including imputation of the propensity score and of the probability weight. Based on consistency results and practical flexibility, we recommend generally using the standard within method. Throughout, we provide intuition to make the results meaningful to the broad audience of applied researchers.

Infant Deaths After Texas' 2021 Ban on Abortion in Early Pregnancy.

Importance: Prior observational research has shown that infants born in states with more abortion restrictions are more likely to die during infancy. It is unclear how recent and more severe abortion bans in the US have impacted infant mortality. Objective: To examine whether Texas Senate Bill 8 (SB8), which banned abortions after embryonic cardiac activity and did not allow exemptions for congenital anomalies, is associated with infant mortality in the state of Texas. Design, Setting, and Participants: This population-based cohort study of all recorded infant deaths from the state of Texas and 28 comparison states used a comparative interrupted time series analysis with an augmented synthetic control approach and national birth certificate data from January 1, 2018, to December 31, 2022, to estimate the difference between the number of observed and expected infant and neonatal deaths and death rates among monthly cohorts exposed to Texas' SB8. Exposure: Deaths in March 2022 were treated as the first cohort exposed to the Texas' SB8 abortion policy because these infants (if born full term) were approximately 10 to 14 weeks' gestation when SB8 went into effect on September 1, 2021. The exposure period was thus March through December 2022. Main Outcomes and Measures: Our outcomes were monthly counts and rates of infant (aged <1 year) and neonatal (aged <28 days) deaths in the exposure period in Texas. In secondary analyses, annual changes in cause-specific infant deaths between 2021 and 2022 in Texas and the rest of the US were examined. Results: Between 2018 and 2022, there were 102 391 infant deaths in the US, with 10 351 of these deaths occurring in the state of Texas. Between 2021 and 2022, infant deaths in Texas increased from 1985 to 2240, or 255 additional deaths. This corresponds to a 12.9% increase, whereas the rest of the US experienced a comparatively lower 1.8% increase. On the basis of the counterfactual analysis that used data from Texas and eligible comparison states, an excess of 216 infant deaths (95% CI, -122 to 554) was observed from March to December 2022, or a 12.7% increase above expectation. At the monthly level, significantly greater-than-expected counts were observed for 4 months between March and December 2022: April, July, September, and October. An analysis of neonatal deaths found somewhat similar patterns, with significantly greater-than-expected neonatal deaths in April and October 2022. Descriptive statistics by cause of death showed that infant deaths attributable to congenital anomalies in 2022 increased more for Texas (22.9% increase) but not the rest of the US (3.1% decrease). Conclusions and Relevance: This study found that Texas' 2021 ban on abortion in early pregnancy was associated with unexpected increases in infant and neonatal deaths in Texas between 2021 and 2022. Congenital anomalies, which are the leading cause of infant death, also increased in Texas but not the rest of the US. Although replication and further analyses are needed to understand the mechanisms behind these findings, the results suggest that restrictive abortion policies may have important unintended consequences in terms of trauma to families and medical cost as a result of increases in infant mortality. These findings are particularly relevant given the recent Dobbs v Jackson Women's Health Organization US Supreme Court decision and subsequent rollbacks of reproductive rights in many US states.

Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects.

An important strategy for identifying principal causal effects (popular estimands in settings with noncompliance) is to invoke the principal ignorability (PI) assumption. As PI is untestable, it is important to gauge how sensitive effect estimates are to its violation. We focus on this task for the common one-sided noncompliance setting where there are two principal strata, compliers and noncompliers. Under PI, compliers and noncompliers share the same outcome-mean-given-covariates function under the control condition. For sensitivity analysis, we allow this function to differ between compliers and noncompliers in several ways, indexed by an odds ratio, a generalized odds ratio, a mean ratio, or a standardized mean difference sensitivity parameter. We tailor sensitivity analysis techniques (with any sensitivity parameter choice) to several types of PI-based main analysis methods, including outcome regression, influence function (IF) based and weighting methods. We discuss range selection for the sensitivity parameter. We illustrate the sensitivity analyses with several outcome types from the JOBS II study. This application estimates nuisance functions parametrically - for simplicity and accessibility. In addition, we establish rate conditions on nonparametric nuisance estimation for IF-based estimators to be asymptotically normal - with a view to inform nonparametric inference.

Adolescents Who Do Not Endorse Risk via the Patient Health Questionnaire Before Self-Harm or Suicide.

Importance: Given that the Patient Health Questionnaire (PHQ) item 9 is commonly used to screen for risk of self-harm and suicide, it is important that clinicians recognize circumstances when at-risk adolescents may go undetected. Objective: To understand characteristics of adolescents with a history of depression who do not endorse the PHQ item 9 before a near-term intentional self-harm event or suicide. Design, Setting, and Participants: This was a retrospective cohort study design using electronic health record and claims data from January 2009 through September 2017. Settings included primary care and mental health specialty clinics across 7 integrated US health care systems. Included in the study were adolescents aged 13 to 17 years with history of depression who completed the PHQ item 9 within 30 or 90 days before self-harm or suicide. Study data were analyzed September 2022 to April 2023. Exposures: Demographic, diagnostic, treatment, and health care utilization characteristics. Main Outcome(s) and Measure(s): Responded "not at all" (score = 0) to PHQ item 9 regarding thoughts of death or self-harm within 30 or 90 days before self-harm or suicide. Results: The study included 691 adolescents (mean [SD] age, 15.3 [1.3] years; 541 female [78.3%]) in the 30-day cohort and 1024 adolescents (mean [SD] age, 15.3 [1.3] years; 791 female [77.2%]) in the 90-day cohort. A total of 197 of 691 adolescents (29%) and 330 of 1024 adolescents (32%), respectively, scored 0 before self-harm or suicide on the PHQ item 9 in the 30- and 90-day cohorts. Adolescents seen in primary care (odds ratio [OR], 1.5; 95% CI, 1.0-2.1; P = .03) and older adolescents (OR, 1.2; 95% CI, 1.0-1.3; P = .02) had increased odds of scoring 0 within 90 days of a self-harm event or suicide, and adolescents with a history of inpatient hospitalization and a mental health diagnosis had twice the odds (OR, 2.0; 95% CI, 1.3-3.0; P = .001) of scoring 0 within 30 days. Conversely, adolescents with diagnoses of eating disorders were significantly less likely to score 0 on item 9 (OR, 0.4; 95% CI, 0.2-0.8; P = .007) within 90 days. Conclusions and Relevance: Study results suggest that older age, history of an inpatient mental health encounter, or being screened in primary care were associated with at-risk adolescents being less likely to endorse having thoughts of death and self-harm on the PHQ item 9 before a self-harm event or suicide death. As use of the PHQ becomes more widespread in practice, additional research is needed for understanding reasons why many at-risk adolescents do not endorse thoughts of death and self-harm.

Identification of complier and noncomplier average causal effects in the presence of latent missing-at-random (LMAR) outcomes: a unifying view and choices of assumptions.

The study of treatment effects is often complicated by noncompliance and missing data. In the one-sided noncompliance setting where of interest are the complier and noncomplier average causal effects, we address outcome missingness of the latent missing at random type (LMAR, also known as latent ignorability). That is, conditional on covariates and treatment assigned, the missingness may depend on compliance type. Within the instrumental variable (IV) approach to noncompliance, methods have been proposed for handling LMAR outcome that additionally invoke an exclusion restriction-type assumption on missingness, but no solution has been proposed for when a non-IV approach is used. This article focuses on effect identification in the presence of LMAR outcomes, with a view to flexibly accommodate different principal identification approaches. We show that under treatment assignment ignorability and LMAR only, effect nonidentifiability boils down to a set of two connected mixture equations involving unidentified stratum-specific response probabilities and outcome means. This clarifies that (except for a special case) effect identification generally requires two additional assumptions: a specific missingness mechanism assumption and a principal identification assumption. This provides a template for identifying effects based on separate choices of these assumptions. We consider a range of specific missingness assumptions, including those that have appeared in the literature and some new ones. Incidentally, we find an issue in the existing assumptions, and propose a modification of the assumptions to avoid the issue. Results under different assumptions are illustrated using data from the Baltimore Experience Corps Trial.

Population Neuroscience: Understanding Concepts of Generalizability and Transportability and Their Application to Improving the Public's Health.

In population neuroscience, samples are not often selected with equal or known probability from an underlying population of interest; in other words, samples are not often formally representative of a specified underlying population. This chapter provides an overview of an epidemiological approach to considering the implications of selective participation on the value of our results for population health. We discuss definitions of generalizability and transportability, given the growing recognition that generalizability and transportability are central for interpreting data that are aiming to be population-based. We provide evidence that differences in the prevalence of effect measure modifiers between a study sample and a target population will lead to a lack of generalizability and transportability. We provide an example of an association between a poly-genetic risk score and depression, showing how an internally valid association can differ based on the prevalence of effect measure modifiers. We show that when estimating associations, inferences from a study sample to a population can depend on clearly defining a target population. Given that representative sampling from explicitly defined target populations may not be feasible or realistic in many situations, especially given the sample sizes needed for statistical power for many exposures of interest (and especially when interactions are being tested), researchers should be well versed in tools available to enhance the interpretability of samples regarding target populations.

Using a Multi-Site RCT to Predict Impacts for a Single Site: Do Better Data and Methods Yield More Accurate Predictions?

Multi-site randomized controlled trials (RCTs) provide unbiased estimates of the average impact in the study sample. However, their ability to accurately predict the impact for individual sites outside the study sample, to inform local policy decisions, is largely unknown. To extend prior research on this question, we analyzed six multi-site RCTs and tested modern prediction methods-lasso regression and Bayesian Additive Regression Trees (BART)-using a wide range of moderator variables. The main study findings are that: (1) all of the methods yielded accurate impact predictions when the variation in impacts across sites was close to zero (as expected); (2) none of the methods yielded accurate impact predictions when the variation in impacts across sites was substantial; and (3) BART typically produced "less inaccurate" predictions than lasso regression or than the Sample Average Treatment Effect. These results raise concerns that when the impact of an intervention varies considerably across sites, statistical modelling using the data commonly collected by multi-site RCTs will be insufficient to explain the variation in impacts across sites and accurately predict impacts for individual sites.

Changes in Healthcare Spending Attributable to High Deductible Health Plan Offer Among Enrollees with Comorbid Substance Use Disorder and Cardiovascular Disease.

BACKGROUND: The rise in prevalence of high deductible health plans (HDHPs) in the United States may raise concerns for high-need, high-utilization populations such as those with comorbid chronic conditions. In this study, we examine changes in total and out-of-pocket (OOP) spending attributable to HDHPs for enrollees with comorbid substance use disorder (SUD) and cardiovascular disease (CVD). METHODS: We used de-identified administrative claims data from 2007 to 2017. SUD and CVD were defined using algorithms of ICD 9 and 10 codes and HEDIS guidelines. The main outcome measures of interest were spending measure for all non-SUD/CVD-related services, SUD-specific services, and CVD-specific services, for all services and medications specifically. We assessed both total and OOP spending. We used an intent-to-treat two-part model approach to model spending and computed the marginal effect of HDHP offer as both the dollar change and percent change in spending attributable to HDHP offer. RESULTS: Our sample included 33,684 enrollee-years and was predominantly white and male with a mean age of 53 years. The sample had high demonstrated substantial healthcare utilization with 94% using any non-SUD/CVD services, and 84% and 78% using SUD and CVD services, respectively. HDHP offer was associated with a 17.0% (95% CI = [0.07, 0.27] increase in OOP spending for all non-SUD/CVD services, a 21.1% (95% CI = [0.11, 0.31]) increase in OOP spending for all SUD-specific services, and a 13.1% (95% CI = [0.04, 0.23]) increase in OOP spending for all CVD-specific services. HDHP offer was also associated with a significant increase in OOP spending on non-SUD/CVD-specific medications and SUD-specific medications, but not CVD-specific medications. CONCLUSIONS: This study suggests that while HDHPs do not change overall levels of annual spending among enrollees with comorbid CVD and SUD, they may increase the financial burden of healthcare services by raising OOP costs, which could negatively impact this high-need and high-utilization population.

Disparities in Unmet Health Care Needs Among US Children During the COVID-19 Pandemic.

PURPOSE: The COVID-19 pandemic disrupted pediatric health care in the United States, and this disruption layered on existing barriers to health care. We sought to characterize disparities in unmet pediatric health care needs during this period. METHODS: We analyzed data from Wave 1 (October through November 2020) and Wave 2 (March through May 2021) of the COVID Experiences Survey, a national longitudinal survey delivered online or via telephone to parents of children aged 5 through 12 years using a probability-based sample representative of the US household population. We examined 3 indicators of unmet pediatric health care needs as outcomes: forgone care and forgone well-child visits during fall 2020 through spring 2021, and no well-child visit in the past year as of spring 2021. Multivariate models examined relationships of child-, parent-, household-, and county-level characteristics with these indicators, adjusting for child's age, sex, and race/ethnicity. RESULTS: On the basis of parent report, 16.3% of children aged 5 through 12 years had forgone care, 10.9% had forgone well-child visits, and 30.1% had no well-child visit in the past year. Adjusted analyses identified disparities in indicators of pediatric health care access by characteristics at the level of the child (eg, race/ethnicity, existing health conditions, mode of school instruction), parent (eg, childcare challenges), household (eg, income), and county (eg, urban-rural classification, availability of primary care physicians). Both child and parent experiences of racism were also associated with specific indicators of unmet health care needs. CONCLUSIONS: Our findings highlight the need for continued research examining unmet health care needs and for continued efforts to optimize the clinical experience to be culturally inclusive.

Comparative effectiveness of extended release naltrexone and sublingual buprenorphine for treatment of opioid use disorder among Medicaid patients.

Aims: To compare the real-world effectiveness of extended release naltrexone (XR-NTX) and sublingual buprenorphine (SL-BUP) for the treatment of opioid use disorder (OUD). Design: An observational active comparator, new user cohort study. Setting: Medicaid claims records for patients in New Jersey and California, 2016-2019. Participants/Cases: Adult Medicaid patients aged 18-64 years who initiated XR-NTX or SL-BUP for maintenance treatment of OUD and did not use medications for OUD in the 90-days before initiation. Comparators: New initiation with XR-NTX versus SL-BUP for the treatment of OUD. Measurements: We examined two outcomes up to 180 days after medication initiation, 1) composite of medication discontinuation and death, and 2) composite of overdose and death. Findings: Our cohort included 1,755 XR-NTX and 9,886 SL-BUP patients. In adjusted analyses, treatment with XR-NTX was more likely to result in discontinuation or death by the end of follow-up than treatment with SL-BUP: cumulative risk 76% (95% confidence interval [CI] 75%, 78%) versus 62% (95% CI 61%, 63%), respectively (risk difference 14 percentage points, 95% CI 13, 16). There was minimal difference in the cumulative risk of overdose or death by the end of follow-up: XR-NTX 3.8% (95% CI 2.9%, 4.7%) versus SL-BUP 3.3% (95% 2.9%, 3.7%); risk difference 0.5 percentage points, 95%CI -0.5, 1.5. Results were consistent across sensitivity analyses. Conclusions: Longer medication retention is important because risks of negative outcomes are elevated after discontinuation. Our results support selection of SL-BUP over XR-NTX. However, most patients discontinued medication by 6 months indicating that more effective tools are needed to improve medication retention, particularly after initiation with XR-NTX, and to identify which patients do best on which medication.

Evaluation of Dynamic Effects of Depressive Symptoms on Physical Function in Knee Osteoarthritis.

OBJECTIVE: To assess how changes in depressive symptoms influence physical function over time among those with radiographic knee osteoarthritis (OA). METHODS: Participants from the Osteoarthritis Initiative with radiographic knee OA (n = 2,212) and complete data were identified at baseline. Depressive symptoms were assessed as a time-varying score at baseline and the first three annual follow-up visits using the Center for Epidemiological Studies Depression Scale (CES-D) Scale. Physical function was measured at the first four follow-up visits using 20-meter gait speed meters per second. The following two marginal structural models were fit: one assessing the main effect of depressive symptoms on gait speed and another assessing time-specific associations. RESULTS: Time-adjusted results indicated that higher CES-D scores were significantly associated with slower gait speed (-0.0048; 95% confidence interval -0.0082 to -0.0014), and time-specific associations of CES-D were largest during the first follow-up interval (-0.0082; 95% confidence interval -0.0128 to -0.0035). During subsequent follow-up time points, the influence of depressive symptoms on gait speed diminished. CONCLUSION: The negative effect of depressive symptoms on physical function may decrease over time as knee OA progresses.

Simulated impact of mobile opioid treatment program units on increasing access to methadone for opioid use disorder.

OBJECTIVE: To model the potential impact of mobile methadone unit implementation in Louisiana on net medication for opioid use disorder (MOUD) treatment rates. DATA SOURCES/STUDY SETTING: We use secondary Louisiana Medicaid claims data between 2020 and 2021. STUDY DESIGN: We simulate the impact of mobile methadone units in Louisiana using two approaches: (1) a "Poisson regression approach," which predicts the number of opioid use disorder (OUD) patients that might use methadone at mobile locations based on the underlying association between methadone use and proximity to a brick-and-mortar methadone clinic; (2) a "policy approach," which leverages local treatment uptake rates following the expansion of methadone coverage to Louisiana Medicaid beneficiaries in 2020 to estimate methadone use following mobile unit implementation. Models were run in cases where mobile methadone operators could choose their operation locations freely and in a separate instance where they were restricted to serving rural locations. DATA COLLECTION: Our analytic sample includes 43,341 Louisiana Medicaid beneficiaries with one or more primary or secondary diagnoses for opioid dependence. PRINCIPAL FINDINGS: We predict that 10 new mobile methadone units in Louisiana would increase the net MOUD treatment rate in the state by 0.54-2.39 percentage points. If these mobile units delivered Methadone exclusively to rural areas, they could increase rural MOUD treatment by 8.54-13.67 percentage points. Further, roughly 20% of all beneficiaries residing in rural areas being treated with methadone would be an average of 24 miles closer to a methadone treatment provider following mobile unit implementation. CONCLUSIONS: Mobile methadone units represent a promising innovation in the delivery of methadone that is likely to increase methadone use, especially in underserved rural locations. However, we find significant variation in their impact conditional on where they choose to operate, and so careful location planning will be required to maximize their benefit.

Comparing Lung Cancer Screening Strategies in a Nationally Representative US Population Using Transportability Methods for the National Lung Cancer Screening Trial.

Importance: The National Lung Screening Trial (NLST) found that screening for lung cancer with low-dose computed tomography (CT) reduced lung cancer-specific and all-cause mortality compared with chest radiography. It is uncertain whether these results apply to a nationally representative target population. Objective: To extend inferences about the effects of lung cancer screening strategies from the NLST to a nationally representative target population of NLST-eligible US adults. Design, Setting, and Participants: This comparative effectiveness study included NLST data from US adults at 33 participating centers enrolled between August 2002 and April 2004 with follow-up through 2009 along with National Health Interview Survey (NHIS) cross-sectional household interview survey data from 2010. Eligible participants were adults aged 55 to 74 years, and were current or former smokers with at least 30 pack-years of smoking (former smokers were required to have quit within the last 15 years). Transportability analyses combined baseline covariate, treatment, and outcome data from the NLST with covariate data from the NHIS and reweighted the trial data to the target population. Data were analyzed from March 2020 to May 2023. Interventions: Low-dose CT or chest radiography screening with a screening assessment at baseline, then yearly for 2 more years. Main Outcomes and Measures: For the outcomes of lung-cancer specific and all-cause death, mortality rates, rate differences, and ratios were calculated at a median (25th percentile and 75th percentile) follow-up of 5.5 (5.2-5.9) years for lung cancer-specific mortality and 6.5 (6.1-6.9) years for all-cause mortality. Results: The transportability analysis included 51 274 NLST participants and 685 NHIS participants representing the target population (of approximately 5 700 000 individuals after survey-weighting). Compared with the target population, NLST participants were younger (median [25th percentile and 75th percentile] age, 60 [57 to 65] years vs 63 [58 to 67] years), had fewer comorbidities (eg, heart disease, 6551 of 51 274 [12.8%] vs 1 025 951 of 5 739 532 [17.9%]), and were more educated (bachelor's degree or higher, 16 349 of 51 274 [31.9%] vs 859 812 of 5 739 532 [15.0%]). In the target population, for lung cancer-specific mortality, the estimated relative rate reduction was 18% (95% CI, 1% to 33%) and the estimated absolute rate reduction with low-dose CT vs chest radiography was 71 deaths per 100 000 person-years (95% CI, 4 to 138 deaths per 100 000 person-years); for all-cause mortality the estimated relative rate reduction was 6% (95% CI, -2% to 12%). In the NLST, for lung cancer-specific mortality, the estimated relative rate reduction was 21% (95% CI, 9% to 32%) and the estimated absolute rate reduction was 67 deaths per 100 000 person-years (95% CI, 27 to 106 deaths per 100 000 person-years); for all-cause mortality, the estimated relative rate reduction was 7% (95% CI, 0% to 12%). Conclusions and Relevance: Estimates of the comparative effectiveness of low-dose CT screening compared with chest radiography in a nationally representative target population were similar to those from unweighted NLST analyses, particularly on the relative scale. Increased uncertainty around effect estimates for the target population reflects large differences in the observed characteristics of trial participants and the target population.

Comparison of methods that combine multiple randomized trials to estimate heterogeneous treatment effects.

Individualized treatment decisions can improve health outcomes, but using data to make these decisions in a reliable, precise, and generalizable way is challenging with a single dataset. Leveraging multiple randomized controlled trials allows for the combination of datasets with unconfounded treatment assignment to better estimate heterogeneous treatment effects. This article discusses several nonparametric approaches for estimating heterogeneous treatment effects using data from multiple trials. We extend single-study methods to a scenario with multiple trials and explore their performance through a simulation study, with data generation scenarios that have differing levels of cross-trial heterogeneity. The simulations demonstrate that methods that directly allow for heterogeneity of the treatment effect across trials perform better than methods that do not, and that the choice of single-study method matters based on the functional form of the treatment effect. Finally, we discuss which methods perform well in each setting and then apply them to four randomized controlled trials to examine effect heterogeneity of treatments for major depressive disorder.

Trends in Opioid Use Disorder Outpatient Treatment and Telehealth Utilization Before and During the COVID-19 Pandemic.

OBJECTIVE: The authors examined trends in opioid use disorder treatment and in-person and telehealth modalities before and after COVID-19 pandemic onset among patients who had received treatment prepandemic. METHODS: The sample included 13,113 adults with commercial insurance or Medicare Advantage and receiving opioid use disorder treatment between March 2018 and February 2019. Trends in opioid use disorder outpatient treatment, treatment with medications for opioid use disorder (MOUD), and in-person and telehealth modalities were examined 1 year before pandemic onset and 2 years after (March 2019-February 2022). RESULTS: From March 2019 to February 2022, the proportion of patients with opioid use disorder outpatient and MOUD visits declined by 2.8 and 0.3 percentage points, respectively. Prepandemic, 98.6% of outpatient visits were in person; after pandemic onset, at least 34.9% of patients received outpatient care via telehealth. CONCLUSIONS: Disruptions in opioid use disorder outpatient and MOUD treatments were marginal during the pandemic, possibly because of increased telehealth utilization.

Trends in Telepsychiatry and In-Person Psychiatric Care for Depression in an Academic Health System, 2017-2022.

OBJECTIVE: The authors aimed to assess differences in appointment completion rates between telepsychiatry and in-person outpatient psychiatric care for patients with depression in an academic health system. METHODS: Electronic health records of encounters for patients (ages ≥10) with a depression diagnosis and at least one scheduled outpatient psychiatric appointment (N=586,266 appointments; November 2017-October 2022) were assessed for appointment volume and completion of telepsychiatry versus in-person sessions. RESULTS: Telepsychiatry became the dominant care modality after the onset of the COVID-19 pandemic, although the number of telepsychiatry and in-person appointments nearly converged by October 2022. Logistic regression showed that telepsychiatry appointments (July 2020-October 2022) were more likely (OR=1.30, 95% CI=1.27-1.34) to be completed than in-person appointments. CONCLUSIONS: Telepsychiatry appointments were less likely to be canceled or missed than in-person appointments, suggesting that telepsychiatry improved efficiency and continuity of care. As in-person operations resume following the pandemic, maintaining telepsychiatry services may optimize hospital-level and patient outcomes.

State Laws That Require Coprescribing Opioids and Naloxone and Codispensing Practices.

INTRODUCTION: Coprescribing naloxone with opioids could reduce the risk of overdose. By the end of 2020, 8 U.S. states implemented coprescribing laws requiring the prescription of naloxone alongside certain opioid prescriptions. This study examined the impacts of state laws that require coprescribing opioids and naloxone on codispensing practices. METHODS: Data included opioid prescriptions for commercially insured adults between 2014 and 2020. Augmented synthetic control analyses were used to examine the impacts of 8 coprescribing requirement laws implemented between 2017 and 2020 on the proportion of opioid prescription fills with a naloxone coprescription fill. Analyses were completed in spring 2023. RESULTS: Changes in the proportion of opioid prescription fills with a naloxone coprescription fill attributable to the laws varied across states. In 4 states (New Jersey, New Mexico, Rhodes Island, and Virginia), laws were associated with 0.8 (95% CI=0.3, 1.3) to 4.4 (95% CI=3.4, 5.4) percentage point increases in the proportion of opioid prescriptions with a naloxone coprescription fill (p<0.05). There were no statistically significant changes attributable to the other state laws (Arizona, Florida, Vermont, Washington). CONCLUSIONS: Laws requiring coprescribing naloxone with certain opioid prescriptions are associated with small-in-magnitude increases in codispensing in some states. Broadening the categories of opioid prescriptions covered in naloxone coprescribing requirement laws and implementing health system strategies to encourage providers to coprescribe naloxone could help to magnify the impacts of these laws.

Time-varying treatment effect modification of oral analgesic effectiveness by depressive symptoms in knee osteoarthritis: an application of structural nested mean models in a prospective cohort.

BACKGROUND: Depressive symptoms are common in knee osteoarthritis (OA), exacerbate knee pain severity and may influence outcomes of oral analgesic treatments. The aim was to assess whether oral analgesic effectiveness in knee OA varies by fluctuations in depressive symptoms. METHODS: The sample included Osteoarthritis Initiative (OAI) participants not treated with oral analgesics at enrolment (n = 1477), with radiographic disease at the first follow-up visit (defined as the index date). Oral analgesic treatment and depressive symptoms, assessed with the Center for Epidemiological Studies Depression [(CES-D) score ≥16] Scale, were measured over three annual visits. Knee pain severity was measured at visits adjacent to treatment and modifier using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale (rescaled range = 0-100). Structural nested mean models (SNMMs) estimated causal mean differences in knee pain severity comparing treatment versus no treatment. RESULTS: The average causal effects of treated versus not treated for observations without depressive symptoms showed negligible differences in knee pain severity. However, causal mean differences in knee pain severity comparing treatment versus no treatment among observations with depressive symptoms increased over time from -0.10 [95% confidence interval (CI): -9.94, 9.74] to -16.67 (95% CI: -26.33, -7.01). Accordingly, the difference in average causal effects regarding oral analgesic treatment for knee pain severity between person-time with and without depressive symptoms was largest (-16.53; 95% CI: -26.75, -6.31) at the last time point. Cumulative treatment for 2 or 3 years did not yield larger causal mean differences. CONCLUSIONS: Knee OA patients with persistent depressive symptoms and chronic pain may derive more analgesic treatment benefit than those without depressive symptoms and less pain.

Experiences of health care services among people with cognitive disabilities and mental health conditions.

BACKGROUND: People with cognitive disabilities such as intellectual and developmental disabilities face significant barriers to accessing high-quality health care services. Barriers may be exacerbated for those with co-occurring mental health conditions. OBJECTIVE: This study compares patient experiences of health care services between adults with and without cognitive disabilities and, among people with a cognitive disability, those with and without co-occurring mental health conditions. METHODS: Cross-sectional analyses were conducted using 2021 Medical Expenditure Panel Survey data, a national U.S. survey, to examine differences in Consumer Assessment of Healthcare Providers and Systems measures. RESULTS: Adults with cognitive disabilities reported lower satisfaction with health care services compared to the general population (7.62 (95% confidence interval (CI): 7.41-7.83) vs. 8.33 (95% CI: 8.29-8.38) on scale from 0 to 10). Adults with cognitive disabilities were less likely to report that providers listened carefully to them (odds ratio (OR): 0.55, 95% CI: 0.42-0.71), explained things in a way that was easy to understand (OR: 0.48, 95% CI: 0.35-0.66), showed respect for what they had to say (OR: 0.38, 95% CI: 0.29-0.51), spent enough time with them (OR: 0.52, 95% CI: 0.40-0.69), or gave advice that was easy to understand (OR: 0.40, 95% CI: 0.28-0.58) compared to the general population. Among adults with cognitive disabilities, there were no differences based on co-occurring mental health conditions. CONCLUSIONS: Adults with cognitive disabilities report lower satisfaction with health care services driven by worse experiences with the health care system. Policies to increase provider capacity to support this population should be prioritized.