Critical Dialogue and Capacity-Building Projects Reduced Alcohol and Substance Use in a Randomized Clinical Trial Among Formerly Incarcerated Men.

Background: Rates of alcohol and/or substance use (ASU) among residents of predominantly Black and marginalized communities are similar to ASU rates in White communities. Yet ASU has worse consequences in predominantly Black and marginalized communities (e.g., higher incarceration). Objective: We randomized participants to one of 16 intervention conditions using a 24 full factorial design to optimize a multilevel intervention reducing ASU among 602 formerly incarcerated men with substance-use-disorders (SUD). Candidate intervention components included (1) critical dialogue (CD; six weekly 2-hour-long group sessions vs. no CD sessions), (2) Quality of Life Wheel (QLW; six weekly 1-hour-long group sessions vs. no QLW sessions), (3) capacity building projects (CBP; six weekly 1-hour-long group sessions vs. no CBP sessions), and (4) delivery by a trained peer versus licensed facilitators. Outcome was percentage of days in which participants used alcohol, cocaine, opioid, and/or cannabis in previous 30 days. Results: Intent-to-treat analysis did not meet a priori component selection criteria due to low intervention attendance. After controlling for intervention group attendance (percentage of sessions attended), peer-delivered CD and CBP produced statistically and clinically significant main and interaction effects in ASU over 5 months. Per the multiphase optimization strategy framework, we selected peer-delivered CD and CBP for inclusion as the optimized version of the intervention with a cost of US$1,380 per 10 individuals. No adverse intervention effects occurred. Conclusion: CD and CBP were identified as the only potentially effective intervention components. Future research will examine strategies to improve attendance and test the optimized intervention against standard of care in a randomized-controlled-trial.

The effects of social support and self-efficacy on hopefulness in low-income older adults during COVID-19 pandemic.

BACKGROUND: Social support and self-efficacy play a significant role in improving positive psychological well-being in marginalized older adults. However, to date, there are few studies identifying the relationships during the COVID-19 pandemic. We examined the effect of social support and self-efficacy on hopefulness in a majority Black sample of marginalized low-income older adults during the COVID-19 pandemic. METHODS: This study used baseline data from a clinical trial designed to increase COVID-19 testing in Essex County, NJ, United States. The dataset involved participants 50 years old or older. We conducted: 1) cross-sectional descriptive/frequency statistics to understand the sociodemographic characteristics, 2) multivariate linear regression to investigate the direct relationships between social support subscales or self-efficacy and hopefulness, and 3) mediation analyses to examine the mediating role of self-efficacy in the relationship between social support and hopefulness. RESULTS: Our findings showed that self-efficacy had a partial mediating effect on the relationship between social support and hopefulness. After adjusting for covariate variables, social support subscales (i.e., emotional/informational, tangible, affectionate, positive social interaction social support) and self-efficacy were significantly associated with hopefulness. The indirect effect of social support via self-efficacy was positive and statistically significant. CONCLUSION: Self-efficacy mediated the relationship between social support and hopefulness in marginalized older adults aged 50 and over. Further research is needed to identify the various facets of positive psychological well-being using longitudinal data and a larger sample size.

Policies to Reduce Child Poverty and Child Maltreatment: A Scoping Review and Preliminary Estimates of Indirect Effects.

This study includes a scoping review of prior studies investigating the effects of policy changes on child poverty rates. It further conducts an empirical analysis to estimate the relationship between child poverty rates and child maltreatment report (CMR) rates, utilizing national county-level data. The study then calculates the indirect effects of policy changes on CMR rates, mediated through child poverty rates, by integrating information from previous studies with its own empirical findings. Among the policy changes explored in prior studies, those related to a child allowance and a fully refundable Child Tax Credit demonstrate the largest indirect effects but also the highest costs. The expansion of in-kinds and near-cash benefits, such as the Supplemental Nutrition Assistance Program benefits and housing vouchers, shows moderate effects with moderate costs. Tax credits like the Earned Income Tax Credit exhibit lower effects and costs when targeted at the lowest earners, and moderate effects and costs for broader expansion. Focused tax credits, such as the Child and Dependent Care Tax Credit, had lower effects and costs, even if made fully refundable. Despite certain limitations, the study's approach yields consistent estimates with a recent simulation study, indicating its potential validity. While some proposed policy changes may seem expensive, implementing them is anticipated to substantially reduce CMR rates, with the benefits outweighing the associated costs. Overall, the findings suggest that addressing child poverty to reduce CMRs is an attractive strategy with numerous potential benefits.

Evidence-Based Home Visiting Provisions and Child Maltreatment Report Rates: County-Level Analysis of US National Data From 2016 to 2018.

This observational ecological study examined county-level associations between evidence-based home visiting (EHV) provisions and child maltreatment report (CMR) rates, using national county-level data from 2016-2018. We found that longitudinal changes of EHV provisions were significantly negatively associated with county CMR rates while controlling for potential confounders. Our model estimated that after EHV provisions were launched in counties, their CMR rates decreased (or after they were ceased, rates increased) by 2.21 per 1000 children overall, 2.88 per 1000 children aged 0-5, 2.59 per 1000 children aged 6-11, 2.13 per 1000 male children, and 2.24 per 1000 female children. When limiting attention to EHV provisions funded by the Maternal, Infant and Early Childhood Home Visiting (MIECHV) program, we found no significant association perhaps because MIECHV-funded EHV provisions were a small subset of all EHV provisions. These findings propose potential protective impacts of county EHV provisions on overall county CMR rates. Yet, the small effect sizes suggest that EHV provisions should be considered as a part of a complete response to child maltreatment rather than in isolation. Given that EHV is provided to a very small part of the population, nevertheless, our findings suggest that expanding coverage would increase effect sizes.

Individual and structural determinants of COVID-19 vaccine uptake in a marginalized community in the United States.

Socially and medically vulnerable groups (e.g., people 65 years or older, minoritized racial groups, non-telework essential workers, and people with comorbid conditions) experience barriers to COVID-19 prevention and treatment, increased burden of disease, and increased risk of death from COVID-19. Researchers are paying increased attention to social determinants of health (SDH) in explaining inequities in COVID-19-related health outcomes and rates of vaccine uptake. The purpose of the present manuscript is to identify clinically significant predictors of COVID-19 vaccine uptake among people who were socially and medically vulnerable to SARs-CoV-2 infection. Analysis was informed by the SDH framework and included a sample of 641 baseline surveys from participants in a clinical trial designed to increase COVID-19 testing. All participants were at high risk of developing COVID-19-related complications or dying from COVID-19. Following community-based participatory research principles, a well-established community collaborative board conducted every aspect of the study. Multiple logistic regressions were conducted to examine the relationships between individual and structural factors and COVID-19 vaccine uptake. In the final time adjusted model, we found that vaccine uptake was only predicted by specific individual-level factors: being 65 years and older, living with HIV/AIDS, and having previously received a flu vaccine or a COVID-19 test. Those reporting to believe in COVID-19-conspiracy theories were less likely to get the COVID-19 vaccine. More research is needed to identify predictors of vaccine uptake among people with comorbidities that make them more vulnerable to COVID-19 complications or death.

Use of research electronic data capture (REDCap) in a sequential multiple assignment randomized trial (SMART): a practical example of automating double randomization.

BACKGROUND: Adaptive interventions are often used in individualized health care to meet the unique needs of clients. Recently, more researchers have adopted the Sequential Multiple Assignment Randomized Trial (SMART), a type of research design, to build optimal adaptive interventions. SMART requires research participants to be randomized multiple times over time, depending upon their response to earlier interventions. Despite the increasing popularity of SMART designs, conducting a successful SMART study poses unique technological and logistical challenges (e.g., effectively concealing and masking allocation sequence to investigators, involved health care providers, and subjects) in addition to other challenges common to all study designs (e.g., study invitations, eligibility screening, consenting procedures, and data confidentiality protocols). Research Electronic Data Capture (REDCap) is a secure, browser-based web application widely used by researchers for data collection. REDCap offers unique features that support researchers' ability to conduct rigorous SMARTs. This manuscript provides an effective strategy for performing automatic double randomization for SMARTs using REDCap. METHODS: Between January and March 2022, we conducted a SMART using a sample of adult (age 18 and older) New Jersey residents to optimize an adaptive intervention to increase COVID-19 testing uptake. In the current report, we discuss how we used REDCap for our SMART, which required double randomization. Further, we share our REDCap project XML file for future investigators to use when designing and conducting SMARTs. RESULTS: We report on the randomization feature that REDCap offers and describe how the study team automated an additional randomization that was required for our SMART. An application programming interface was used to automate the double randomizations in conjunction with the randomization feature provided by REDCap. CONCLUSIONS: REDCap offers powerful tools to facilitate the implementation of longitudinal data collection and SMARTs. Investigators can make use of this electronic data capturing system to reduce errors and bias in the implementation of their SMARTs by automating double randomization. TRIAL REGISTRATION: The SMART study was prospectively registered at Clinicaltrials.gov; registration number: NCT04757298, date of registration: 17/02/2021.

Longitudinal changes in the county-level relationship between opioid prescriptions and child maltreatment reports, United States, 2009-2018.

This article examines whether county opioid prescription rates were associated with county child maltreatment report (CMR) rates in the United States and whether this relationship changed over time. We linked multiple national data sets to assemble retail opioid prescription data, CMR data, rural-urban codes (to control for urbanicity), and census data (to control for other community characteristics, such as poverty rates) covering 2009-2018. Multilevel linear modeling analyzed the linked data. We found that the strength of the county-level relationship between opioid prescription rates and CMR rates increased almost linearly during the study period. The relationship was not significant in 2009-2011; it became significant in 2012 and grew stronger in the next 6 years. In 2012, there was one more CMR per 1,000 children in a county for every 14.3 more opioid prescriptions per 100 people. In 2018, the number of prescriptions related to this effect was 3.6. In other words, the county-level relationship between opioid prescriptions and CMRs was four times as strong in 2018 as it had been in 2012. This trend was also observed within all subgroups of child age and sex. By type, this trend was somewhat more pronounced for neglect, but somewhat less for sexual abuse. Our findings suggest a growing need for greater efforts to prevent child maltreatment in communities with high opioid prescription rates. Further research is warranted to reveal the underlying factors for this concerning trend. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Use of research electronic data capture (REDCap) in a sequential multiple assignment randomized trial (SMART): A practical example of automating double randomization.

Background: Adaptive interventions are often used in individualized health care to meet the unique needs of clients. Recently, more researchers have adopted the Sequential Multiple Assignment Randomized Trial (SMART), a type of research design, to build optimal adaptive interventions. SMART requires research participants to be randomized multiple times over time, depending upon their response to earlier interventions. Despite the increasing popularity of SMART designs, conducting a successful SMART study poses unique technological and logistical challenges (e.g., effectively concealing and masking allocation sequence to investigators, involved health care providers, and subjects) in addition to other challenges common to all study designs (e.g., study invitations, eligibility screening, consenting procedures, and data confidentiality protocols). Research Electronic Data Capture (REDCap) is a secure, browser-based web application widely used by researchers for data collection. REDCap offers unique features that support researchers’ ability to conduct rigorous SMARTs. This manuscript provides an effective strategy for performing automatic double randomization for SMARTs using REDCap. Methods: Between January and March 2022, we conducted a SMART using a sample of adult (age 18 and older) New Jersey residents to optimize an adaptive intervention to increase COVID-19 testing uptake. In the current report, we discuss how we used REDCap for our SMART, which required double randomization. Further, we share our REDCap project XML file for future investigators to use when designing and conducting SMARTs. Results: We report on the randomization feature that REDCap offers and describe how the study team automated an additional randomization that was required for our SMART. An application programming interface was used to automate the double randomizations in conjunction with the randomization feature provided by REDCap. Conclusions: REDCap offers powerful tools to facilitate the implementation of longitudinal data collection and SMARTs. Investigators can make use of this electronic data capturing system to reduce errors and bias in the implementation of their SMARTs by automating double randomization. Trial registration: The SMART study was prospectively registered at Clinicaltrials.gov; registration number: NCT04757298, date of registration: 17/02/2021. Keywords: Research Electronic Data Capture (REDCap), randomized controlled trials (RCT), adaptive interventions, Sequential Multiple Assignment Randomized Trial (SMART), randomization, experimental design, reducing human errors, automation.

Community Food Insecurity and Child Maltreatment Reports: County-Level Analysis of U.S. National Data From 2009 to 2018.

Current literature suggests that food insecurity increases child maltreatment risk. Yet, existing evidence is limited to individual-level associations among low-income, high-risk populations based on local, mostly urban data. This study aims to generalize prior findings to community-level associations in general populations, using national data including all urban-rural areas. We examined, for the first time, if food insecurity rates increase child maltreatment report (CMR) rates at the county level after controlling for potential confounders. We examined both within-community longitudinal changes (i.e., within-effects) and inter-community differences (i.e., between-effects) of food insecurity rates and their associations with CMR rates. We also examined differences by age, sex, race/ethnicity, maltreatment type, and urbanicity. We constructed longitudinal county-level data by linking multiple national databases, including all substantiated and unsubstantiated CMR records, the Map the Meal Gap's community food insecurity estimates, and Census data. The data covered over 96% of U.S. counties from 2009 to 2018. For analysis, we used within-between random effects models. Regarding between-effects, we found that in inter-community comparisons, higher food insecurity rates were significantly associated with increased CMR rates. This association was consistent by age, sex, maltreatment type, and urbanicity. For within-effects, we found that the association between longitudinal changes of food insecurity rates and CMR rates significantly differed by urbanicity. Specifically, longitudinal increases of food insecurity rates significantly increased CMR rates among large urban counties, but not among small urban and rural counties. Study findings highlight the importance of conducting further research to better understand the mechanisms through which food insecurity impact child maltreatment at both individual and community levels. Our community-level findings from general populations especially have significant implications for community-based programs and large-scale policies to achieve population-level impact on child well-being.

The Critical Dialogue Cornerstone: Suggested Practices to Guide Implementation, Facilitation and Evaluation.

There has been an increased focus on utilizing critical consciousness-focused interventions to address complex, multidimensional socio-cultural problems, particularly health inequities. These interventions usually incorporate a critical dialogue component. However, there's little guidance on how to implement, facilitate and evaluate critical dialogue to develop critical consciousness (i.e., reflecting and acting on sociopolitical inequities). This conceptual paper: 1) introduces critical dialogue and the tools used to implement critical dialogue from the literature; 2) details the development of the Community Wise intervention to present how Community Wise incorporated a critical dialogue component; 3) provides a brief overview of a proposed framework of critical consciousness development that the critical dialogue component of Community Wise could support; 4) provides an anecdotal exploration of the critical dialogue sessions used in the first pilot test of the intervention through the proposed framework of Transformative Consciousness; and 5) suggests practice guidelines for group work that incorporates facilitated critical dialogue.

Do county mental health, physical health, and care provider availability predict child maltreatment report rates?

BACKGROUND: Research on community-level relationships between mental/physical health and child maltreatment is sparse. OBJECTIVE: We examined how rates of mental distress, physical distress, mental health professionals, and primary care physicians were related to child maltreatment report rates at the county level. PARTICIPANTS AND SETTING: U.S. counties from 2014 to 2017. METHODS: Within-between random effects models estimated both within-effects (i.e., longitudinal changes) and between-effects (i.e., inter-county differences) of mental distress rates, physical distress rates, mental health professional rates, and primary care physician rates and their associations with overall and age-specific maltreatment report rates, while adjusting for potential confounders. RESULTS: Longitudinal increases of mental distress rates marginally significantly (p < .10) increased overall maltreatment report rates (ß = 0.50) and significantly (p < .05) increased age 0-5 maltreatment report rates (ß = 0.84). Conversely, longitudinal increases of mental health professional rates significantly decreased overall (ß = -0.38), age 0-5 (ß = -0.59), and age 6-11 (ß = -0.31) maltreatment report rates and marginally significantly decreased age 12-17 maltreatment report rates (ß = -0.13). Between-effects of metal distress rates and mental health professional rates were mostly not significant. Neither within-effects nor between-effects of physical distress rates and primary care physician rates were significant. CONCLUSIONS: Our findings suggest that community mental distress is a risk factor for child maltreatment reports and that community availability of mental health professionals is a protective factor. Community-based strategies to address mental distress and human resource approaches to supply sufficient mental health professionals in communities may help reduce maltreatment report rates in communities. Further research is required to confirm our findings and to better understand underlying mechanisms.

Community food insecurity predicts child maltreatment report rates across Illinois zip codes, 2011-2018.

PURPOSE: We examined how longitudinal changes and inter-community differences of food insecurity rates were associated with child maltreatment report (CMR) rates at the zip code level. We assessed these associations overall, by urbanicity, and within subgroups of age, sex, and maltreatment type. METHODS: We used Illinois statewide zip code-level data from 2011 to 2018. We measured CMR rates based on Illinois child protective services records and food insecurity rates from Feeding America's Map the Meal Gap. We conducted spatial linear modeling to account for spatial dependence with controls for various socioeconomic, demographic, care burden, and instability conditions of communities. RESULTS: Both longitudinal changes and inter-community differences of food insecurity rates were significantly associated with increased CMR rates overall and within all subgroups. These associations were significant among all large urban, small urban, and rural areas, while longitudinal changes of food insecurity rates had significantly stronger associations among small urban areas compared with other areas. CONCLUSIONS: Communities experiencing higher food insecurity had higher CMR rates. Increases in food insecurity over time were associated with increases in CMR rates. These associations were reproduced within subgroups of child age, sex, maltreatment type, and urbanicity. Attention and collaborative efforts are warranted for high food insecure communities.

Optimization of a new adaptive intervention using the SMART Design to increase COVID-19 testing among people at high risk in an urban community.

BACKGROUND: COVID-19 has impacted the health and social fabric of individuals and families living across the USA, and it has disproportionately affected people living in urban communities with co-morbidities, those working in high-risk settings, refusing or unable to adhere to CDC guidelines, and more. Social determinants of health (SDH), such as stigmatization, incarceration, and poverty, have been associated with increased exposure to COVID-19 and increased deaths. While vaccines and booster shots are available, it will take time to reach herd immunity, and it is unclear how long newly developed vaccines provide protection and how effective they are against emerging variants. Therefore, prevention methods recommended by the Centers for Disease and Control (CDC)-i.e., testing, hand-washing, social distancing, contact tracing, vaccination and booster shots, and quarantine-are essential to reduce the rates of COVID-19 in marginalized communities. This project will adapt and test evidence-based HIV interventions along the prevention and treatment cascade to help address COVID-19 prevention needs. METHODS: The study aims to (1) optimize an adaptive intervention that will increase rates of testing and adherence to New Jersey State COVID-19 recommendations (testing, social distancing, quarantine, hospitalization, contact tracing, and acceptance of COVID-19 vaccination and booster shots) among high-risk populations and (2) identify predictors of testing completion and adherence to New Jersey recommendations. This study follows Community Based Participatory Research (CBPR) principles to conduct a Sequential, Multiple Assignment Randomized Trial (SMART) with 670 COVID-19 medically/socially vulnerable people. Participants will be recruited using a variety of strategies including advertisements on social media, posting fliers in public places, street outreach, facility-based, and snowball sampling. Participants complete a baseline survey and are randomized to receive navigation services or an electronic brochure. They then complete a follow-up 7 days after baseline and are randomized again to either continue with their original assignment or switch to the other intervention or critical dialog or brief counseling. Participants then complete a 5-week post-baseline follow-up. Guided by the COVID-19 Continuum of Prevention, Care, and Treatment, the analysis will explore the factors associated with COVID-19 testing within 7 days of the intervention. DISCUSSION: This paper describes the protocol of the first study to use SMART following CBPR to adapt evidence-based HIV prevention interventions to COVID-19. The findings will inform the development of an effective and scalable adaptive intervention to increase COVID-19 testing and adherence to public health recommendations, including vaccination and booster shots, among a marginalized and difficult-to-engage population. TRIAL REGISTRATION: ClinicalTrials.gov NCT04757298 . Registered on February 17, 2021.

Testing a digitally distributed method to recruit a network of community organizations to fight the consequences of the drug epidemic: A study in 13 American states.

To mitigate the opioid epidemic, a concerted effort to educate, prevent, diagnose, treat, and engage residents is required. In this study, a digitally distributed method to form a large network of organizations was tested with 99 counties in regions with high vulnerability to hepatitis C virus (HCV). The method involved a cascade of contacts going from email to phone calls, to videoconferencing and measuring the number of contacts required, amount of time taken, and the proportion of success at recruiting at least one community organization per county. A recruitment period of 5 months and 2118 contact attempts led to the recruitment of organizations from 73 out of our 99 target counties. Organizations belonging to health departments required more attempts and time to recruit but ultimately enrolled at higher rates than did other organizations such as coalitions and agencies. Organizations from counties more (vs. less) vulnerable to HCV outbreaks required more attempts to recruit and, using multiple recruitment methods (e.g., emails, phone calls, and Zoom meetings), improved enrollment success. Overall, this method proved to be successful at remotely engaging a large-scale network of communities with different levels of risk within a large geographic region.

Linking critical consciousness and health: The utility of the critical reflection about social determinants of health scale (CR_SDH).

INTRODUCTION: Critical consciousness (CC) theory has been proposed as a framework to inform health interventions targeting a wide variety of health conditions. Unfortunately, methodological limitations have made it difficult to test CC as a mediator of health outcomes. Specifically, standardized and widely accepted measures of health-related CC are needed. The goal of this study was to develop and test a measure of critical reflection on social determinants of health (SDH). This measure focused on critical reflection, an essential dimension of CC. METHODS: Community-based participatory research principles and a mixed methods design were used with three samples: (1) experts in SDH and CC, (2) 502 individuals completing online surveys, and (3) 602 men with histories of substance use disorder and incarceration. All participants were over 18 years of age. Analysis included descriptive frequencies, exploratory factor analyses (EFA), confirmatory factor analysis (CFA), generalized linear regression models, correlations, and Cronbach's alpha calculations. RESULTS: The Critical Reflection about SDH scale (CR_SDH) is a short, unidimensional, and reliable scale (α = 0.914). Construct validity was supported and known-groups validity showed that the scale discriminated different levels of CR_SDH based on political views, educational level, knowledge of health inequities, and gender. CONCLUSION: The CR_SDH is a standardized measure that can assess critical reflection about the impact of SDH on health among providers and consumers of health care. The CR_SDH can be used to identify critical reflection related training needs and inform decisions about development and testing of critical reflection related health interventions and health care policy.

Enhancing behavioral intervention science: using community-based participatory research principles with the multiphase optimization strategy.

Innovative methodological frameworks are needed in intervention science to increase efficiency, potency, and community adoption of behavioral health interventions, as it currently takes 17 years and millions of dollars to test and disseminate interventions. The multiphase optimization strategy (MOST) for developing behavioral interventions was designed to optimize efficiency, efficacy, and sustainability, while community-based participatory research (CBPR) engages community members in all research steps. Classical approaches for developing behavioral interventions include testing against control interventions in randomized controlled trials. MOST adds an optimization phase to assess performance of individual intervention components and their interactions on outcomes. This information is used to engineer interventions that meet specific optimization criteria focused on effectiveness, cost, or time. Combining CBPR and MOST facilitates development of behavioral interventions that effectively address complex health challenges, are acceptable to communities, and sustainable by maximizing resources, building community capacity and acceptance. Herein, we present a case study to illustrate the value of combining MOST and CBPR to optimize a multilevel intervention for reducing substance misuse among formerly incarcerated men, for under $250 per person. This integration merged experiential and cutting-edge scientific knowledge and methods, built community capacity, and promoted the development of efficient interventions. Integrating CBPR and MOST principles yielded a framework of intervention development/testing that is more efficient, faster, cheaper, and rigorous than traditional stage models. Combining MOST and CBPR addressed significant intervention science gaps and speeds up testing and implementation of interventions.

Interprofessional collaboration associated with frequency of life-saving links to HIV continuum of care services in the urban environment of Newark, New Jersey.

BACKGROUND: HIV continuum of care has been used as a strategy to reduce HIV transmission rates, with timely engagement in HIV testing being the first and most critical step. This study examines interprofessional-collaboration (IPC) after controlling for agency/ provider demographics, provider training and self-efficacy as a significant predictor of how frequently HIV service providers link their clients to HIV testing. METHODS: Multilevel binary logistic regression analysis was conducted to examine the effects of IPC on links to HIV testing while controlling for demographic and agency information, provider training, and standardized measures of providers' feelings, attitudes, and opinions about IPC. Cross-sectional data from 142 providers in 13 agencies offering treatment and prevention services for HIV and substance-use disorders were collected via a survey. RESULTS: Those who scored higher on the IPC scale reported significantly higher rates of linkages to HIV testing. Compared to the null model (i.e., no predictor model), the final multilevel binary logistic regression model showed a significantly improved likelihood of linkage to HIV testing by 11.4%, p. < .05. The final model correctly classified 90.2% of links to HIV testing. Providers in agencies with smaller budgets and in agencies offering substance use disorder services were more likely to link clients to HIV testing. Younger providers who received HIV training were also more likely to link clients to HIV testing. CONCLUSIONS: Findings suggest IPC training as a potential strategy to improve linkages to HIV testing for clients at risk for HIV infection. Future research is recommended to identify specific areas of IPC that might have differential effects on links to HIV testing.

Optimizing a community-engaged multi-level group intervention to reduce substance use: an application of the multiphase optimization strategy.

BACKGROUND: Rates of alcohol and illicit drug use (AIDU) are consistently similar across racial groups (Windsor and Negi, J Addict Dis 28:258-68, 2009; Keyes et al. Soc Sci Med 124:132-41, 2015). Yet AIDU has significantly higher consequences for residents in distressed communities with concentrations of African Americans (DCAA - i.e., localities with high rates of poverty and crime) who also have considerably less access to effective treatment of substance use disorders (SUD). This project is optimizing Community Wise, an innovative multi-level behavioral-health intervention created in partnership with service providers and residents of distressed communities with histories of SUD and incarceration, to reduce health inequalities related to AIDU. METHODS: Grounded in critical consciousness theory, community-based participatory research principles (CBPR), and the multiphase optimization strategy (MOST), this study employs a 2 × 2 × 2 × 2 factorial design to engineer the most efficient, effective, and scalable version of Community Wise that can be delivered for US$250 per person or less. This study is fully powered to detect change in AIDU in a sample of 528 men with a histories of SUD and incarceration, residing in Newark, NJ in the United States. A community collaborative board oversees recruitment using a variety of strategies including indigenous field worker sampling, facility-based sampling, community advertisement through fliers, and street outreach. Participants are randomly assigned to one of 16 conditions that include a combination of the following candidate intervention components: peer or licensed facilitator, group dialogue, personal goal development, and community organizing. All participants receive a core critical-thinking component. Data are collected at baseline plus five post-baseline monthly follow ups. Once the optimized Community Wise intervention is identified, it will be evaluated against an existing standard of care in a future randomized clinical trial. DISCUSSION: This paper describes the protocol of the first ever study using CBPR and MOST to optimize a substance use intervention targeting a marginalized population. Data from this study will culminate in an optimized Community Wise manual; enhanced methodological strategies to develop multi-component scalable interventions using MOST and CBPR; and a better understanding of the application of critical consciousness theory to the field of health inequalities related to AIDU. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02951455 . Registered on 1 November 2016.

Using Technology to Facilitate Collaboration in Community-Based Participatory Research (CBPR).

This study explores the use of Computer-Supported Collaborative Work (CSCW) technologies, by way of a computer-based system called iCohere. This system was used to facilitate collaboration conducting Community-Based Participatory Research (CBPR). Data was gathered from 13 members of a Community Collaborative Board (CCB). Analysis revealed that iCohere served the following functions: facilitating communication, providing a depository for information and resource sharing, and allowing for remote meeting attendance. Results indicated that while iCohere was useful in performing these functions, less expensive technologies had the potential to achieve similar goals if properly implemented. Implications for future research on CSCW systems and CBPR are discussed.