Interrupted Time Series Model Research Articles

Evolution of an epidemic: Understanding the opioid epidemic in the United States and the impact of the COVID-19 pandemic on opioid-related mortality.

We conduct this research with a two-fold aim: providing a quantitative analysis of the opioid epidemic in the United States (U.S.), and exploring the impact of the COVID-19 pandemic on opioid-related mortality. The duration and persistence of the opioid epidemic lends itself to the need for an overarching analysis with extensive scope. Additionally, studying the ramifications of these concurrent severe public health crises is vital for informing policies to avoid preventable mortality. Using data from CDC WONDER, we consider opioid-related deaths grouped by Census Region spanning January 1999 to October 2022 inclusive, and later add on a demographic component with gender-stratification. Through the lens of key events in the opioid epidemic, we build an interrupted time series model to reveal statistically significant drivers of opioid-related mortality. We then employ a counterfactual to approximate trends in the absence of COVID-19, and estimate excess opioid-related deaths (defined as observed opioid-related deaths minus projected opioid-related deaths) associated with the pandemic. According to our model, the proliferation of fentanyl contributed to sustained increases in opioid-related death rates across three of the four U.S. census regions, corroborating existing knowledge in the field. Critically, each region has an immediate increase to its opioid-related monthly death rate of at least 0.31 deaths per 100,000 persons at the start of the pandemic, highlighting the nationwide knock-on effects of COVID-19. There are consistent positive deviations from the expected monthly opioid-related death rate and a sizable burden from cumulative excess opioid-related deaths, surpassing 60,000 additional deaths nationally from March 2020 to October 2022, ∼70% of which were male. These results suggest that robust, multi-faceted measures are even more important in light of the COVID-19 pandemic to prevent overdoses and educate users on the risks associated with potent synthetic opioids such as fentanyl.

A causal machine-learning framework for studying policy impact on air pollution: A case-study in COVID-19 lockdowns.

When studying the impact of policy interventions or natural experiments on air pollution, such as new environmental policies and opening or closing an industrial facility, careful statistical analysis is needed to separate causal changes from other confounding factors. Using COVID-19 lockdowns as a case-study, we present a comprehensive framework for estimating and validating causal changes from such perturbations. We propose using flexible machine learning-based comparative interrupted time series (CITS) models for estimating such a causal effect. We outline the assumptions required to identify causal effects, showing that many common methods rely on strong assumptions that are relaxed by machine learning models. For empirical validation, we also propose a simple diagnostic criterion, guarding against false effects in baseline years when there was no intervention. The framework is applied to study the impact of COVID-19 lockdowns on NO2 in the eastern US. The machine learning approaches guard against false effects better than common methods and suggest decreases in NO2 in Boston, New York City, Baltimore, and Washington D.C. The study showcases the importance of our validation framework in selecting a suitable method and the utility of a machine learning based CITS model for studying causal changes in air pollution time series.

The effects of the first UK lockdown for the COVID-19 pandemic on primary-care-recorded cancer and type-2 diabetes mellitus records: A population-based quasi-experimental time series study

BackgroundCOVID-19 disrupted consulting behaviour, healthcare delivery and cancer diagnostic services. This study quantifies the cancer incidence coded in UK general practice electronic health records and deviations from historical trends after the March 2020 national lockdown. For comparison, we study the coded incidence of type-2 diabetes mellitus, which is diagnosed almost entirely within primary care. MethodsPoisson interrupted time series models investigated the coded incidence of diagnoses in adults aged ≥ 18 years in the Clinical Practice Research Datalink before (01/03/2017–29/02/2020) and after (01/03/2020–28/02/2022) the first lockdown. Datasets were stratified by age, sex, and general practice per 28-day aggregation period. Models captured incidence changes associated with lockdown, both immediately and over time based on historical trends. ResultsWe studied 189,457 incident cancer and 191,915 incident diabetes records in 1480 general practices over 52,374,197 person-years at risk. During 01/03/2020–28/02/2022, there were fewer incident records of cancer (n = 22,199, 10.49 %, 10.44–10.53 %) and diabetes (n = 15,709, 7.57 %, 7.53–7.61 %) than expected. Within cancers, impacts ranged from no effect (e.g. unknown primary, pancreas, and ovary), to small effects for lung (n = 773, 3.11 %, 3.09–3.13 % fewer records) and female breast (n = 2686, 6.77 %, 6.73–6.81 %), to the greatest effect for bladder (n = 2874, 31.15 %, 31.00–31.31 %). Diabetes and cancer records recovered maximally to 86 % (95 %CI 80.3–92.7 %) and 74 % (95 %CI 70.3–78.6 %) in July 2021 and May 2021, respectively, of their expected values, declining again until the study end. ConclusionThe “missing” cancer and diabetes diagnoses in primary care may comprise delayed or missed diagnoses, reduced incidence associated with excess deaths from COVID-19, and potentially increased non-coded recording of diagnoses. Future validation studies must quantify the concordance between primary care and National Cancer Registration Data and Hospital Episode Statistics over the pandemic era.

Impact of a Remote Primary Care Telehealth Staffing Model on Primary Care Access in the Veterans Health Administration.

The Veterans Health Administration (VHA) implemented the Clinical Resource Hub (CRH) program to fill staffing gaps in primary care (PC) clinics via telemedicine and maintain veterans' healthcare access. To evaluate PC wait times before and after CRH implementation. Comparative interrupted time series analysis among a retrospective observational cohort of PC clinics who did and did not use CRH during pre-implementation (October 2018-September 2019) and post-implementation (October 2019-February 2020) periods. Clinics completing ≥10 CRH visits per month for 2 consecutive months and propensity matched control clinics. Two measures of patient access (i.e., established, and new patient wait times) and one measure of clinic capacity (i.e., third next available appointment) were assessed. Clinics using CRH were 1:1 propensity score matched across clinical and demographic characteristics. Comparative interrupted time series models used linear mixed effects regression with random clinic-level intercepts and triple interaction (i.e., CRH use, pre- vs. post-implementation, and time) for trend and point estimations. PC clinics using CRH (N = 79) were matched to clinics not using CRH (N = 79). In the 12-month pre-implementation, third next available time increased in CRH clinics (0.16 days/month; 95% CI = [0.07, 0.25]), and decreased in the 5 months post-implementation (-0.58 days/month; 95% CI = [-0.90, -0.27]). Post-implementation third next available time also decreased in control clinics (-0.48 days/month; 95% CI = [-0.81, -0.17]). Comparative differences remained non-significant. There were no statistical differences in established or new patient wait times by CRH user status, CRH implementation, or over time. In a national VHA telemedicine program developed to provide gap coverage for PC clinics, no wait time differences were observed between clinics using and not using CRH services. This hub-and-spoke telemedicine service is an effective model to provide gap coverage while maintaining access. Further investigation of quality and long-term access remains necessary.

Evaluating the effects of supervised consumption sites on housing prices in Montreal, Canada using interrupted time series and hedonic price models

BackgroundIn 2017, three brick and mortar supervised consumption sites (SCS) opened in Montreal, Canada. Opponents argued the sites would attract people who use drugs and reduce local real estate prices. MethodsWe used interrupted time series and hedonic price models to evaluate the effects of Montreal’s SCS on local real estate prices. We linked the Quebec Professional Association of Real Estate Brokers’ housing sales data provided by Centris Inc. with census tract data and gentrification scores. Homes sold within 200m of the SCS locations between 1 January 2014 and 31 December 2021 were included. We adjusted for internal (e.g., number of bed/bathrooms, unit size) and external attributes (e.g., neighbourhood demographics), and included a spatio-temporal lag to account for correlation between sales. For sensitivity analysis we used site-specific dummy variables to better account for unmeasured neighbourhood differences, and repeated analyses using 500m and 1000m radii. ResultsWe observed a price shock after the opening of the first two SCS in June 2017 (level effect: −10.5%, 95% CI: −19.1%, −1.1%) but prices rose faster month-to-month (trend effect: 1.1%, 95% CI: 0.7%, 1.6%) after implementation. Following the implementation of the third site in November 2017 there was no immediate impact (level effect: 2.4%, 95% CI: −10.4%, 17.0%) but once more prices roses faster (0.9%, 95% CI: 0.4%, 1.5%) thereafter. When we replaced neighbourhood attributes with a site-specific dummy variable, we observed the same pattern. Sales’ prices dropped (level effect: −9.6%, 95% CI: −15.0%, −3.8%) but rose faster month-to-month (trend effect: 0.9%, 95% CI: 0.6%, 1.2%) following June 2017’s SCS implementations, with no level effect (4.9%, 95% CI: −7.3%, 18.6%) and a positive trend (0.9%, 95% CI: 0.5%, 1.3%) after November 2017’s SCS opening. In most 500m and 1000m radii models, there were no immediate shocks following SCS opening, however, positive trend effects persisted in all models. ConclusionOur models suggest homes sold near SCS may experience a price shock immediately post-implementation, with evidence of market recovery in the months that follow.

Assessing the effect of automated speed enforcement and comprehensive measures on road safety in Rwanda

Objectives Daily, approximately 3,400 traffic-related deaths occur globally, with over 90% concentrated in low and middle-income countries (LMICs). Notably, Rwanda has one of the highest road traffic death rates in the world (29.7 per 100,000 people) and is the first low-income country to implement a national Automated Speed Enforcement (ASE) policy. The primary goal of this study is to evaluate the effectiveness of ASE cameras in reducing the primary outcome of road traffic deaths and secondary outcomes of serious injury crashes and fatal crashes. Methods The study used data on road traffic deaths, and serious injury and fatal crashes collected by the Rwanda National Police between 2010 and 2022. Interrupted time series (ITS) models were fit to quantify the association between ASE and change in road traffic crash outcomes, adjusted for COVID-19-related variables (such as the start of the pandemic, the closure of schools and bars), along with exposure variables (such as GDP and population), and other concurrent road safety measures (such as road safety campaigns). Results The ITS models show that the implementation of ASE cameras significantly reduced road traffic deaths, serious injury crashes, and fatal crashes at the provincial level. For instance, the implementation of ASE cameras in the whole of Rwanda in April 2021 was significantly associated with a 0.14 (95% CI [0.072, 0.212]) reduction in monthly death incidence, equating to a 38.16% monthly decrease compared to the period before their installation (January 2010-March 2021). Conclusion This study emphasizes the significant association of ASE in Rwanda with improved road traffic crash outcomes, a result that may inform road safety policy in other LMICs. Rwanda has become the first low-income country to implement nationwide scaling of ASE in Africa, paving the way for the generation of valuable evidence on speed-related interventions. In addition to new knowledge generation, African road safety research efforts like this one are opportunities to grow academic and law enforcement cooperations while improving data systems and sources for future research benefits.

Impact of COVID-19 on road crashes in Thailand

The main goal of this study is to investigate the impact of COVID-19 on road crashes in Thailand using time series and interrupted time series analysis. To achieve the goal, road crash data from the Department of Highway (DOH), which includes total crashes, single vehicle crashes, fatalities, fatal crashes, speeding crashes, and drunk driving crashes, was obtained to conduct Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models and Interrupted Time Series (ITS) models. SARIMA models were applied to forecast the number of crashes in the absence of COVID-19 then compare them to the observed values to identify the difference. The impact of a policy change aimed at addressing the spread of COVID-19 was assessed using ITS models on a time series accident dataset. The goal was to ascertain if the intervention had a meaningful and causative impact on the outcome. The result showed that the first wave of COVID-19 caused a significant reduction in all road crash indicators instead of skyrocketing to a peak. After releasing the lockdown measures from the first wave of spreading, an increase was found in all of the crash indicators as well. However, the third wave of COVID-19, which lasted longest for nearly 7 months, also caused a decrease in the number of crashes, but not as much as the first wave of the outbreak. Moreover, the result from the interrupted time series also revealed that curfews and the closure of entertainment places are associated with a significant decrease in the number of speeding crashes and drunk driving crashes from 10 p.m. to 4 a.m., respectively. It can be observed that the COVID-19 countermeasures, such as curfews and bans on the sales of alcoholic beverages, led to a drop in the number of speeding and drunk driving crashes.

Digital Mass Hysteria during Pandemic? A Study of Twitter Communication Patterns in the US during the Stages of COVID-19 Vaccination.

This study examined the public's sentiments about vaccines by analyzing Twitter data during the CDC's vaccination management planning stage in the United States. Sentiment scores were assigned to each tweet using a sentiment dictionary and the sentiment changes were analyzed over 52 weeks from November 2020 to November 2021. An interrupted time series model was used to analyze the difference in sentiment, which revealed that there was a shift. Initially, overall sentiments were negative but became positive as the stage of general vaccine supply approached. However, negative sentiments sharply rose when the vaccine supply transitioned to the phase of universalization. The results identified two dominant strategic action fields for vaccines providing polarized messages on Twitter and the negative trend was strong for most of the period. The findings highlight the importance of managing strategic action fields on social networks to prevent mass hysteria during vaccine policy implementation. This study stresses the significance of effectively managing strategic action fields on social media platforms to prevent mass hysteria while implementing vaccine policies.

Analysis of Hospitals Switching From a “Danger to Self” Question to Universal Columbia‐Suicide Severity Rating Scale Screening: Impact on Screenings, Identification of Suicide Risk, and Documented Psychiatric Care

ObjectiveSutter Health launched system‐wide general population standardized suicide screening with the Columbia‐Suicide Severity Rating Scale (C‐SSRS) screen (triage) version in 23 hospitals in 2019, replacing a one‐question “danger to self” (DTS) assessment. This study analyzed the impact of C‐SSRS implementation on screening rates, positive screenings, and documented psychiatric care within 90 days for all patients and a subgroup diagnosed with Major Depressive Disorder (MDD).MethodsAdults seen at hospitals in the pre‐period (July 1, 2017−June 30, 2019) and post‐period (July 1, 2019−December 31, 2020) were identified using electronic health records. Outcomes were compared using chi‐square statistics and interrupted time series (ITS) models.ResultsPre‐period, 92.8% (740,984/798,653) of patients were screened by DTS versus 84.6% (504,015/595,915) by C‐SSRS in the post‐period. Positive screening rates were 1.5% pre‐period and 2.2% post‐period, and 9.2% pre‐period versus 10.8% post‐period for those with MDD. Among individuals with positive screenings, 64.0% (pre‐period) had documented follow‐up psychiatric care versus 52.5% post‐period and 66.4% of those with moderate or high‐risk. Among all patients seen there was an overall increase in documentation of psychiatric care within 90 days (0.87% pre‐ to 0.96% post‐period). ITS models revealed a 9.6% decline in screening, 1.3% increase in positive screenings, and 12.9% decline in documented psychiatric care following C‐SSRS implementation (all p < 0.01).ConclusionsFollowing implementation, there was meaningful increase in suicide risk identification, and an increase in the proportion of patients with documented psychiatric care. Observed relative declines in screening warrant future research examining opportunities and barriers to general population C‐SSRS use.

What are the impacts of alcohol supply reduction measures on police-recorded adult domestic and family violence in the Northern Territory of Australia?

BackgroundDuring 2017-18, the Northern Territory (NT) introduced a Banned Drinker Register (BDR) and Minimum Unit Price (MUP) NT-wide; Police Auxiliary Liquor Inspectors (PALIs) in three regional towns; and restrictions on daily purchases/opening hours (DPOH) in one regional town. The BDR is an individual-level alcohol ban; MUP is a pricing policy; and PALIs enforce bans on restricted areas at takeaway outlets. This study examines the impact of these policies on adult domestic and family violence (DFV). MethodsWe examined DFV assaults and breaches of violence orders from January 2014 – February 2020 using interrupted time series models for NT, Greater Darwin, Katherine, Tennant Creek, and Alice Springs. To account for increasing numbers of individuals on the BDR we tested two timepoints (Sept 2017, March 2018). FindingsFollowing DPOH, assaults (78 %) and alcohol-involved assaults (92 %) decreased in Tennant Creek. After PALIs, assaults (79 %) in Tennant Creek, and breaches (39 %) and alcohol-involved breaches (58 %) in Katherine decreased. After MUP, assaults (11 %), alcohol-involved assaults (21 %) and alcohol-involved breaches (21%) decreased NT wide. After MUP/PALIs in Alice Springs, alcohol-involved assaults (33 %), breaches (42 %), and alcohol-involved breaches (57 %) decreased. BDR (Sept 2017) found increases in assaults (44 %) and alcohol-involved assaults (39 %) in Katherine and assaults (10%) and alcohol-involved assaults NT-wide (17 %). There were increases of 21 %-45 % in breaches NT-wide, in Darwin, Katherine, and Alice Springs. Following March 2018 found increases in assaults (33 %) and alcohol-involved assaults (48 %) in Katherine. There were increases - from 20 % to 56 % - in breaches in NT-wide, Katherine, and Alice Springs. ConclusionPALIs and DPOH were associated with some reductions in DFV; the BDR was associated with some increases. The upward trend commences prior to the BDR, so it is also plausible that the BDR had no effect on DFV outcomes. Although MUP was associated with reductions in the NT-wide model, there were no changes in sites without cooccurring PALIs.

Claims-Based vs Agency-Reported Patient Outcomes Among Home Health Agencies, 2013-2019

Given the growth of home health agency (HHA) care, it is important to understand whether quality reporting programs, such as star ratings, are associated with improved patient outcomes. To assess the immediate and long-term association of the introduction of HHA star ratings with patient-level quality outcomes, comparing claims-based and agency-reported measures. This cross-sectional study used Medicare HHA claims and agency-reported assessments to identify sequential patient episodes (ie, spells) among US adults with traditional Medicare who received HHA care (2013-2019). An interrupted time series (ITS) model was used to measure changes in trends and levels before and after the introduction of star ratings. Statistical analysis was performed from November 2022 to September 2023. The exposure was the introduction of HHA star ratings. The postexposure period was set as starting January 1, 2016, to account for the period when both star ratings (quality of patient care and patient satisfaction rating) were publicly reported. The main outcomes included claims-based hospitalization measures (both during the patient spell and 30 days after HHA discharge) and agency-reported functional measures, such as improvement in ambulation, bathing, and bed transferring. There was also a measure to capture timely initiation of care among post-acute care HHA users, defined as HHA care initiated within 2 days of inpatient discharge. This study identified 22 958 847 patient spells to compare annual changes over time; 9 750 689 patient spells were included during the pre-star ratings period from January 1, 2013, to December 31, 2015 (6 067 113 [62.2%] female; 1 100 145 [11.3%] Black, 512 487 [5.3%] Hispanic, 7 845 197 [80.5%] White; 2 656 124 [27.2%] dual eligible; mean [SD] patient spell duration, 70.9 [124.9] days; mean [SD] age, 77.4 [12.0] years); 13 208 158 patient spells were included during the post-star ratings period from January 1, 2016, to December 31, 2019 (8 104 69 [61.4%] female; 1 385 180 [10.5%] Black, 675 536 [5.1%] Hispanic, 10 664 239 [80.7%] White; 3 318 113 [25.1%] dual eligible; mean [SD] patient spell duration, 65.3 [96.2] days; mean [SD] age, 77.7 [11.6] years). Results from the ITS models found that the introduction of star ratings was associated with an acceleration in the mean [SE] hospitalization rate during the spell (0.39% [0.05%] per year) alongside functional improvements in ambulation (2.40% [0.29%] per year), bed transferring (3.95% [0.48%] per year) and bathing (2.34% [0.19%] per year) (P < .001). This occurred alongside a 1.21% (0.12%) per year reduction in timely initiation of care (P < .001). This cross-sectional study found an observed improvement in agency-reported functional measures, which contrasted with slower increases in more objective measures such as hospitalization rates and declines in timely initiation of care. These findings suggest a complex picture of HHA quality of care after the introduction of star ratings.

Expecting the Expected? Documenting Nostalgia Effects on Baseball Card Prices

Identifying anomalies, or empirical facts that contradict theoretical predictions, is a common form of proof by contradiction in economics and finance. We identify an apparent pricing anomaly in the market for trading cards: large and persistent death effects of about 20% are observed following the death of legendary baseball players. Rational investors should anticipate each player's eventual death, so these results suggest that profit opportunities are being left on the table. The arguably causal estimates come from interrupted time series models that adjust flexibly for time pre- and post-death and the time of sale using high-frequency sales data. The effect is largest in the days immediately following the death, but prices remain elevated for at least 60 days. Generally, our findings reaffirm the presence of a causal “nostalgia effect” of celebrity deaths on consumer demand for collectibles and products associated with the celebrity.

Reduction of malaria case incidence following the introduction of clothianidin-based indoor residual spraying in previously unsprayed districts: an observational analysis using health facility register data from Côte d’Ivoire, 2018–2022

BackgroundIndoor residual spraying (IRS) using neonicotinoid-based insecticides (clothianidin and combined clothianidin with deltamethrin) was deployed in two previously unsprayed districts of Côte d’Ivoire in 2020 and 2021 to complement standard...

The decrease in alcohol consumption and suicide rate during the COVID-19 pandemic and their association

Despite the considerable change in alcohol consumption during the COVID-19 pandemic, the impact of the pandemic on the suicide rate in terms of alcohol consumption was not studied. This study was performed to examine whether the change in the suicide rate during the COVID-19 pandemic was related to alcohol consumption and whether the relation was specific to suicides when compared to mortality due to other causes. We performed a comparative interrupted time series (CITS) analysis for the suicide rate of people aged 19 to 60 with three comparison groups (the suicide rate of people aged 19 and under, the cancer death rate of people aged 19 to 60, and alcohol induced death rates). The suicide rate of people aged 19 to 60 and alcohol consumption per capita, along with alcohol induced death rates, continued to decrease during the pandemic in 2020 and 2021 while the suicide rate of people aged 19 and under and the cancer death rate showed an increase. In the comparative interrupted time series model, alcohol consumption had an increasing effect on the adult suicide rate compared to comparison groups when time trends and changes associated with COVID-19 were adjusted. This study shows that the decrease in adult suicide rate in Korea during the pandemic was associated with the decrease in alcohol use among the adult population. Considering that means restriction is the most effective way of controlling suicide and that alcohol can be the most potent and final trigger to suicide, the decrease in suicides during the pandemic and its association with alcohol consumption should be understood as a call for further efforts to decrease alcohol consumption.

Veterans Crisis Line Contacts After the 988 Suicide and Crisis Lifeline Rollout

IntroductionThis study identifies changes in Veterans Crisis Line (VCL) contact volume following the 988 National Suicide Prevention Hotline rollout, and examines changes in contact volume for self-identified Veterans. MethodsVCL's Medora database was analyzed from July 2018-June 2023, fitting linear interrupted time series models to forecast trends after the July 2022 rollout of the 988 Suicide Prevention Hotline. Data analysis was performed from 2023-2024. ResultsAfter the 988 rollout, average monthly VCL contact volume increased by 5,388 contacts (8.2%). The number of contacts self-identifying as Veterans increased by 2,739 (6.2%), while the percentage of self-identifying Veteran contacts who could be linked to VHA records declined by 3.8%. ConclusionsThe 988 rollout was associated with increased VCL contact volume and broad changes in the profile of users. This underscores the importance of crisis services in adapting to dynamic user needs and highlights the potential of national suicide prevention initiatives to reach diverse populations.

Differences in inpatient performance of public general hospitals following implementation of a points-counting payment based on diagnosis-related group: a robust multiple interrupted time series study in Wenzhou, China

ObjectivesThis study measures the differences in inpatient performance after a points-counting payment policy based on diagnosis-related group (DRG) was implemented. The point value is dynamic; its change depends on the...

Impact of COVID-19 pandemic on marriage, divorce, birth, and death in Kerman province, the ninth most populous province of Iran

This study examined the impact of the COVID-19 pandemic on marriage, divorce, birth, and death rates using the Poisson regression model and an interrupted time-series Poisson regression model. Before the pandemic, marriage and birth rates were decreasing, while divorce and death rates were increasing, with only the trend in birth rates being statistically significant. The immediate effect of the pandemic was a significant decrease in the divorce rate, but there were non-significant effects on birth and marriage rates. However, in the months following the onset of the pandemic, there was a statistically significant sustained effect on increasing death and divorce rates. Forecasts based on pre-pandemic data showed that by the end of 2020, marriage, divorce, death, and birth rates were higher compared to pre-pandemic levels. In conclusion, the pandemic has greatly impacted society, particularly in terms of death and divorce rates. Birth rates were not immediately affected to the time lag between decisions and actual births. Fear of COVID-19 may have increased death rates as people avoided seeking medical help. Vaccination and effective treatment strategies are vital in reducing the pandemic's impact on mortality. Supporting families financially is important due to the role of economic issues in couples’ decisions.

What CBT Modules Work Best for Whom? Identifying Subgroups of Depressed Youths by Their Differential Response to Specific Modules

Prior research suggests that the effects of specific cognitive-behavioral therapy (CBT) modules on symptom outcomes can be estimated. We conducted a study utilizing idiographic and nomothetic methods to clarify which CBT modules are most effective for youth depression, and for whom they are most effective. Thirty-five youths received modular CBT for depression. Interrupted time series models estimated whether the introduction of each module was associated with changes in internalizing symptoms, whereby significant symptom reduction would suggest a therapeutic response to the module. Regression models were used to explore whether participant characteristics predicted subgroups of youths based on their estimated response to certain types (e.g., cognitive) of modules, and whether group membership was associated with posttreatment outcomes. Thirty youths (86%) had at least one module associated with a significant change in internalizing symptoms from premodule delivery to postmodule delivery. The specific modules associated with these changes varied across youths. Behavioral activation was most frequently associated with symptom decreases (34% of youths). No participant characteristics predicted estimated response to module type, and group membership was not significantly associated with posttreatment outcomes. Youths display highly heterogeneous responses to treatment modules, indicating multiple pathways to symptom improvement for depressed youths.

Assessing the effectiveness of the expanded hepatitis A vaccination program in China: an interrupted time series design

IntroductionChina initialised the expanded hepatitis A vaccination programme (EHAP) in 2008. However, the effectiveness of the programme remains unclear. We aimed to comprehensively evaluate the effectiveness of EHAP in the...

Opioid Prescribing Patterns After Imposition of Setting-Specific Limits on Prescription Duration

Despite their widespread adoption across the US, policies imposing one-size-fits-all limits on the duration of prescriptions for opioids have shown modest and mixed implications for prescribing. To assess whether a prescription duration limit policy tailored to different clinical settings was associated with shorter opioid prescription lengths. This cross-sectional study examined changes in opioid prescribing patterns for opioid-naive Medicaid enrollees aged 12 to 64 years before and after implementation of a statewide prescription duration limit policy in West Virginia in June 2018. Patients with cancer or Medicare coverage were excluded. The policy assigned a 7-day duration limit to opioid prescriptions for adults treated in outpatient hospital- or office-based practices, a 4-day limit for adults treated in emergency departments, and a 3-day limit for pediatric patients younger than 18 years regardless of clinical setting. Data were examined from January 1, 2017, through September 30, 2019, and data were analyzed from June 12 to October 30, 2023. Whether a patient's initial opioid prescription was longer in days than the June 2018 policy limit for a given care setting before and after policy implementation. Interrupted time series models were used to calculate the association between the policy's implementation and outcomes. The analytic sample included 44 703 Medicaid enrollees (27 957 patients [62.5%] before policy implementation and 16 746 patients [37.5%] after policy implementation; mean [SD] age, 33.9 [13.4] years; 27 461 females [61.4%]). Among adults treated in outpatient hospital- or office-based settings, the duration limit policy was associated with a decrease of 8.83 (95% CI, -10.43 to -7.23) percentage points (P < .001), or a 56.8% relative reduction, in the proportion of prescriptions exceeding the 7-day limit. In the emergency department setting, the policy was associated with a decrease of 7.03 (95% CI, -10.38 to -3.68) percentage points (P < .001), a 37.5% relative reduction, in the proportion of prescriptions exceeding the 4-day limit. The proportion of pediatric opioid prescriptions longer than the 3-day limit decreased by 12.80 (95% CI, -17.31 to -8.37) percentage points (P < .001), a 26.5% relative reduction, after the policy's implementation. Results of this cross-sectional study suggest that opioid prescription duration limits tailored to different clinical settings are associated with reduced length of prescriptions for opioid-naive patients. Additional research is needed to evaluate whether these limits are associated with reductions in the incidence of opioid use disorder or with unintended consequences, such as shifts to illicit opioids.