The role of opioid treatment programs' crisis response on client perceptions of risk and impact

BMC Public Health · 2025-12-12

Organizational responses to crises can profoundly impact the operations and functioning of programs. Specifically, the COVID-19 pandemic led to an 18% increase in drug overdoses and necessitating significant protocol adjustments. We examined opioid treatment programs (OTPs) responses to the pandemic, and associations with clients' perceptions of COVID-19 concerns and perceptions of effect and overall impact. Data from 2023 encompassing 92 OTPs and 435 client surveys were analyzed using multilevel regression models. Dependent variables measured clients COVID-19 exposure concerns, and perception of the pandemic’s broader impact. Independent variables included types of response, staff composition, funding, and accreditation. Clients in programs with higher proportions of African Americans, 1.02 (95% Confidence Interval CI = 1.00—1.03) or Latino staff, 1.03 (CI = 1.01—1.04) expressed significantly greater concern about COVID-19 exposure. Conversely, clients in publicly funded programs reported significantly lower concern about exposure, 0.37 (CI = 0.15—0.90). On the other hand, programs with more administrative responsiveness, 1.44 (CI = 0.07—2.80), or accreditation by the Commission on Accreditation of Rehabilitation Facilities, 1.90 (CI = 0.13—3.67), were associated with significantly higher perceived overall impact of the pandemic, respectively. This study highlights the intricate connection between program characteristics and organizational responses during public health crises. Our findings underscore the importance of culturally sensitive approaches and effective communication to address client COVID-19 concerns and perceptions, particularly within disproportionately affected minority communities. These insights emphasize the necessity for OTPs to adapt to meet the evolving needs of clients, ensuring that they receive the support and care required during uncertainties. • Clients of OTPs with a higher proportion of minority staff reported greater COVID-19 concerns • Publicly funded programs were associated with lower client concerns about exposure • Greater administrative responsiveness was associated with higher perceived effect of COVID-19 • Greater administrative responsiveness was marginally associated with a decline in exposure concerns • Accreditation by CARF was associated with higher perceived effect of COVID-19

Testing the constancy of the variance for time series with a trend

Computational Statistics & Data Analysis · 2025-02-21

Organizational Response to the COVID-19 Crises and Associations with Client Perceptions of Risk and Impact

Research Square · 2025-09-18

On estimation of overall treatment effects in multiregional clinical trials under a discrete random effects model

Statistical Methods in Medical Research · 2025-03-20

Multiregional clinical trials (MRCTs) have become a standard strategy for pharmaceutical product development worldwide. The heterogeneity of regional treatment effects is anticipated in an MRCT. For a two-group comparative study in an MRCT, patient assignments, including regional weights and treatment allocation ratios, are predetermined under the same protocol. In practice, the observed patient assignments at the final analysis stage are often not equal to the predetermined patient assignments, which may impact the accuracy of estimating the overall treatment effect and may lead to a biased estimator. In this study, we use a discrete random effects model (DREM) to account for the heterogeneous treatment effect across regions in an MRCT and propose a bias-adjusted estimator of the overall treatment effect through a naïve estimator conditioned on ancillary statistics based on the observed patient assignments at the final analysis stage in the trial. We also perform power analysis for the overall treatment effect and determine the overall sample size for the bias-adjusted estimator with the DREM. Results of simulation studies are given to illustrate applications of the proposed approach. Finally, we provide an example to demonstrate the implementation of the proposed approach.

Statistical inference for large-scale multi-source heterogeneous data

Scientia Sinica Mathematica · 2025-02-13

In the era of digital information, the data with which people face may not only be large-scale but also heterogeneous. In this paper, we study statistical inference for the overall population mean function of large-scale multi-source heterogeneous datasets. By borrowing hierarchical sampling methods and divide-and-conquer techniques, we propose a weighted local linear estimator for the overall population mean function of multi-source heterogeneous data. Through studying the pointwise convergence properties and extreme value distribution properties of the estimator, we construct asymptotically accurate simultaneous confidence bands and pointwise confidence intervals for large-scale multi-source heterogeneous data. Our proposed methods are applicable not only to scenarios of heterogeneous data but also to scenarios of homogeneous data using divide-and-conquer methods. Numerical simulation studies show that the proposed methods perform well in analyzing both large-scale multi-source heterogeneous data and homogeneous data. As an illustration, we apply the proposed methods to hypothesis testing problems on Beijing multi-site air-quality data and U.S. census data.

Economic Consequences of the COVID-19 crisis

SSRN Electronic Journal · 2024-01-01

Oracle-efficient estimation and global inferences for variance function of functional data

Journal of Statistical Planning and Inference · 2024-07-04 · 1 citations

Optimal model averaging for partially linear models with missing response variables and error‐prone covariates

Statistics in Medicine · 2024-07-25 · 1 citations

We consider the problem of optimal model averaging for partially linear models when the responses are missing at random and some covariates are measured with error. A novel weight choice criterion based on the Mallows-type criterion is proposed for the weight vector to be used in the model averaging. The resulting model averaging estimator for the partially linear models is shown to be asymptotically optimal under some regularity conditions in terms of achieving the smallest possible squared loss. In addition, the existence of a local minimizing weight vector and its convergence rate to the risk-based optimal weight vector are established. Simulation studies suggest that the proposed model averaging method generally outperforms existing methods. As an illustration, the proposed method is applied to analyze an HIV-CD4 dataset.

The effect of the working correlation on fitting models to longitudinal data

Scandinavian Journal of Statistics · 2024-01-02

Abstract We present a detailed discussion of the theoretical properties of quadratic inference function estimators of the parameters in marginal linear regression models. We consider the effect of the choice of working correlation on fundamental questions including the existence of quadratic inference function estimators, their relationship with generalized estimating equations estimators, and the robustness and asymptotic relative efficiency of quadratic inference function and generalized estimating equations estimators. We show that the quadratic inference function estimators do not always exist and propose a way to handle this. We then show that they have unbounded influence functions and can be more or less asymptotically efficient than generalized estimating equations estimators. We also present empirical evidence to demonstrate these results. We conclude that the choice of working correlation can have surprisingly large effects.

The Limits of Immigrant Resilience

SSRN Electronic Journal · 2024-01-01