Health Sciences Interprofessional Collaborative: A Perspective on Migration, COVID-19, and the Impact on Indigenous Communities.

At the United States-Mexico border, the impacts of immigration policy are dynamic with political, humanitarian, and health outcomes. This article highlights the experiences at the Casa Alitas migrant shelter in Tucson, Arizona. Casa Alitas aims to meet the needs of the im/migrants it serves, including the unique needs of indigenous asylum-seekers from Central America. We highlight the importance of community-based humanitarian response to support asylum-seekers in a way that acknowledges our shared humanity and implements specific approaches (e.g., language justice and trauma informed care). The effort at Casa Alitas is unique because in addition to other partnerships, Casa Alitas established an interprofessional collaboration between the University of Arizona Health Sciences Colleges and the Arizona State University School of Social Work. The interprofessional collaboration encourages mutual education amongst our professions and the use of our extended networks to meet the needs of im/migrants and asylum seekers in our community and the United States. We recommend the development of best practices in asylum health care, the importance of creating border-wide networks to build on local resources, and highlight the importance of exposing future health practitioners to trauma informed and culturally and linguistically appropriate care.

Massachusetts Medicaid members that smoked in 2008: Characteristics associated with smoking status in 2014.

The smoking rate among non-elderly Medicaid enrollees is more than double the rate for those privately insured; smoking-related conditions account for 15% of Medicaid expenditures. Under state health reform, Massachusetts Medicaid (MassHealth) made tobacco cessation treatment available beginning in 2006. We used surveys conducted in 2008 and 2014 to examine changes in smoking abstinence rates among MassHealth members identified as smokers and to identify factors associated with being a former smoker. Members previously identified as smokers were surveyed by mail or phone; 2008 and 2014 samples included 3,116 and 2,971 members, respectively. Surveys collected demographic and health information, asked members whether they smoked cigarettes "every day, some days or not at all', and asked questions to assess smoking intensity among current smokers. The 2014 survey included an open ended-question asking members "what helped the most" in quitting or quit attempts. We observed a significant decrease in members reporting smoking "every/some days" of 15.5 percentage points (p < .0001) from 2008 to 2014, and a significant decrease in smokers reporting smoking "more than 10 cigarettes on days smoked" of 16.7 percentage points (p < .0001). Compared to smokers, former smokers more frequently reported health concerns, the influence of family members, and the use of e-cigarettes as helping the most in quitting. Expanded access to tobacco cessation treatment under the Affordable Care Act may have help to reduce the high smoking rates among Medicaid enrollees. Additionally, smokers' concerns about health and the influence of family and friends provide opportunities for targeted intervention and messaging about quitting.

Real-world impact of quitline interventions for provider-referred smokers.

BACKGROUND: The healthcare provider-referral quitline model has potential to help identify and connect more smokers to effective cessation services as compared to the self-referral model alone. However, research is limited as to whether provider-referred smokers, who may have more barriers to quitting, can have similar rates of quit success using traditional quitline interventions as self-referred smokers. PURPOSE: To (1) determine how provider-referred smokers may differ from self-referred smokers in their demographics, service utilization, and quit rates and (2) quantify the impact of traditional quitline services on provider-referred smokers' ability to quit. METHODS: Data were collected for 2,737 provider-referred and 530 self-referred Massachusetts quitline clients between November 2007 and February 2012. Analysis was performed in 2012. Wald chi-square tests and two-sample t-tests were used to identify differences between the two referral populations. A multivariable logistic regression model was used for each referral population, and smoker quit status at follow-up was the primary outcome. Tests and models were weighted using inverse probability of treatment weights propensity score weighting method. RESULTS: Compared with self-referred smokers, provider-referred smokers were more likely to be non-white, less educated, and have public insurance. They were less ready to quit and had lower service utilization and quit rates. In both referral populations, clients who used services had greater odds of quitting than those who did not. CONCLUSIONS: Expanding the provider-referral model may require quitlines to address the various risk factors associated with this population. Providers serve critical roles in preparing patients for quitline participation prior to referral.

Who underreports smoking on birth records: a Monte Carlo predictive model with validation.

BACKGROUND: Research has shown that self-reports of smoking during pregnancy may underestimate true prevalence. However, little is known about which populations have higher rates of underreporting. Availability of more accurate measures of smoking during pregnancy could greatly enhance the usefulness of existing studies on the effects of maternal smoking offspring, especially in those populations where underreporting may lead to underestimation of the impact of smoking during pregnancy. METHODS AND FINDINGS: In this paper, we develop a statistical Monte Carlo model to estimate patterns of underreporting of smoking during pregnancy, and apply it to analyze the smoking self-report data from birth certificates in the state of Massachusetts. Our results illustrate non-uniform patterns of underreporting of smoking during pregnancy among different populations. Estimates of likely underreporting of smoking during pregnancy were highest among mothers who were college-educated, married, aged 30 years or older, employed full-time, and planning to breastfeed. The model's findings are validated and compared to an existing underreporting adjustment approach in the Maternal and Infant Smoking Study of East Boston (MISSEB). CONCLUSIONS: The validation results show that when biological assays are not available, the Monte Carlo method proposed can provide a more accurate estimate of the smoking status during pregnancy than self-reports alone. Such methods hold promise for providing a better assessment of the impact of smoking during pregnancy.