7 research outputs found

    Short-term effects of teriparatide versus placebo on bone biomarkers, structure, and fracture healing in women with lower-extremity stress fractures: A pilot study

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    Aims In this pilot, placebo-controlled study, we evaluated whether brief administration of teriparatide (TPTD) in premenopausal women with lower-extremity stress fractures would increase markers of bone formation in advance of bone resorption, improve bone structure, and hasten fracture healing according to magnetic resonance imaging (MRI). Methods: Premenopausal women with acute lower-extremity stress fractures were randomized to injection of TPTD 20-µg subcutaneous (s.c.) (n = 6) or placebo s.c. (n = 7) for 8 weeks. Biomarkers for bone formation N-terminal propeptide of type I procollagen (P1NP) and osteocalcin (OC) and resorption collagen type-1 cross-linked C-telopeptide (CTX) and collagen type 1 cross-linked N-telopeptide (NTX) were measured at baseline, 4 and 8 weeks. The area between the percent change of P1NP and CTX over study duration is defined as the anabolic window. To assess structural changes, peripheral quantitative computed topography (pQCT) was measured at baseline, 8 and 12 weeks at the unaffected tibia and distal radius. The MRI of the affected bone assessed stress fracture healing at baseline and 8 weeks. Results: After 8 weeks of treatment, bone biomarkers P1NP and OC increased more in the TPTD- versus placebo-treated group (both p ≤ 0.01), resulting in a marked anabolic window (p ≤ 0.05). Results from pQCT demonstrated that TPTD-treated women showed a larger cortical area and thickness compared to placebo at the weight bearing tibial site, while placebo-treated women had a greater total tibia and cortical density. No changes at the radial sites were observed between groups. According to MRI, 83.3% of the TPTD- and 57.1% of the placebo-treated group had improved or healed stress fractures (p = 0.18). Conclusions: In this randomized, pilot study, brief administration of TPTD showed anabolic effects that TPTD may help hasten fracture healing in premenopausal women with lower-extremity stress fractures. Larger prospective studies are warranted to determine the effects of TPTD treatment on stress fracture healing in premenopausal women

    Using the Meaningful Involvement of People Living with HIV/AIDS (MIPA) Framework to Assess the Engagement of Sexual Minority Men of Color in the US HIV Response: a Literature Review

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    Black and Latino sexual minority men (SMM) continue to be disproportionately impacted by HIV. We utilized eight components of the Meaningful Involvement of People Living with HIV/AIDS (MIPA) framework to assess the engagement of Black and Latino SMM. Thirty-six (36) studies were included in the literature review. Forty-two percent of studies were Black SMM-specific, followed by Latino SMM-specific (31%) studies. Twenty-eight percent of studies were conducted among both groups. Most studies (72%) were intervention-related and focused on HIV prevention. The top five most common methods of community engagement were focus groups (39%), followed by interviews (36%), community-based participatory research (14%), the utilization of community advisory boards or peer mentorship (11%), and the establishment of multi-stakeholder coalitions, observations, or surveys (8%). We documented at least 7 MIPA components in 47% of the included studies. Community-based participatory research was more commonly utilized to engage Latino SMM. Researchers were more likely to initiate the engagement across all included studies. Few studies documented how Black and Latino SMM perceived the engagement. Engagement responsiveness was a well-documented MIPA component. In terms of engagement power dynamics, there were several examples of power imbalances, especially among Black SMM-specific studies. The inclusion of Black and Latino SMM had robust impacts on HIV research and interventions. There were limited examples of engagement capacity and maintenance. This is one of the first studies focused on utilizing MIPA to document the engagement of SMM of color. MIPA served as a useful framework for understanding the engagement of SMM of color in the US HIV response. The engagement of SMM of color is critical to reducing health inequities

    The Impact of the COVID-19 Pandemic on Drug Use Behaviors, Fentanyl Exposure, and Harm Reduction Service Support among People Who Use Drugs in Rural Settings

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    Background: The COVID-19 pandemic has worsened the opioid overdose crisis in the US. Rural communities have been disproportionately affected by opioid use and people who use drugs in these settings may be acutely vulnerable to pandemic-related disruptions due to high rates of poverty, social isolation, and pervasive resource limitations. Methods: We performed a mixed-methods study to assess the impact of the pandemic in a convenience sample of people who use drugs in rural Illinois. We conducted 50 surveys capturing demographics, drug availability, drug use, sharing practices, and mental health symptoms. In total, 19 qualitative interviews were performed to further explore COVID-19 knowledge, impact on personal and community life, drug acquisition and use, overdose, and protective substance use adaptations. Results: Drug use increased during the pandemic, including the use of fentanyl products such as gel encapsulated “beans” and “buttons”. Disruptions in supply, including the decreased availability of heroin, increased methamphetamine costs and a concomitant rise in local methamphetamine production, and possible fentanyl contamination of methamphetamine was reported. Participants reported increased drug use alone, experience and/or witness of overdose, depression, anxiety, and loneliness. Consistent access to harm reduction services, including naloxone and fentanyl test strips, was highlighted as a source of hope and community resiliency. Conclusions: The COVID-19 pandemic period was characterized by changing drug availability, increased overdose risk, and other drug-related harms faced by people who use drugs in rural areas. Our findings emphasize the importance of ensuring access to harm reduction services, including overdose prevention and drug checking for this vulnerable population

    Using Machine Learning to Predict Young People's Internet Health and Social Service Information Seeking.

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    Machine learning creates new opportunities to design digital health interventions for youth at risk for acquiring HIV (YARH), capitalizing on YARH's health information seeking on the internet. To date, researchers have focused on descriptive analyses that associate individual factors with health-seeking behaviors, without estimating of the strength of these predictive models. We developed predictive models by applying machine learning methods (i.e., elastic net and lasso regression models) to YARH's self-reports of internet use. The YARH were aged 14-24 years old (N = 1287) from Los Angeles and New Orleans. Models were fit to three binary indicators of YARH's lifetime internet searches for general health, sexual and reproductive health (SRH), and social service information. YARH responses regarding internet health information seeking were fed into machine learning models with potential predictor variables based on findings from previous research, including sociodemographic characteristics, sexual and gender minority identity, healthcare access and engagement, sexual behavior, substance use, and mental health. About half of the YARH reported seeking general health and SRH information and 26% sought social service information. Areas under the ROC curve (≥ .75) indicated strong predictive models and results were consistent with the existing literature. For example, higher education and sexual minority identification was associated with seeking general health, SRH, and social service information. New findings also emerged. Cisgender identity versus transgender and non-binary identities was associated with lower odds of general health, SRH, and social service information seeking. Experiencing intimate partner violence was associated with higher odds of seeking general health, SRH, and social service information. Findings demonstrate the ability to develop predictive models to inform targeted health information dissemination strategies but underscore the need to better understand health disparities that can be operationalized as predictors in machine learning algorithms

    Annual Selected Bibliography

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