HIV stigma is a public health problem. It refers to irrational judgments and attitudes towards people living with or at risk of HIV. Among adolescents and young adults living with HIV, stigma can negatively influence help-seeking decisions and impede HIV prevention efforts. The present study aimed to explore social-ecological factors associated with HIV-related stigma using a cross-sectional study design. Data used were from the 2016 Uganda Demographic and Health Survey [UDHS] of young men (n = 2214) and young women (n = 8058) aged 15–24 years. We conducted a gender disaggregated multivariable logistic regression to understand social-ecological factors associated with HIV secondary stigma. An overwhelming majority of participants (85%) indicated that secondary HIV stigma was associated with factors such as gender, ethnicity, education, wealth, cultural attitudes, and testing experiences. The study concludes that many young people living with HIV experience stigma in Uganda. Therefore, the government of Uganda, health professionals, and researchers should consider developing interventions that address HIV stigma and discrimination. Supportive programmes, such as peer support services and community-based interventions, are needed to help young people living with HIV learn to cope with the illness.

Objective: Patient perceptions of physician trust and respect are important factors for patient satisfaction evaluations. However, perceptions are subjective by nature and can be affected by patient and physician demographic characteristics. We aim to determine the causal effect on patient–physician demographic concordance and patient perceptions of physician trust and respect in an emergency care setting. Methods: We performed a causal effect analysis in an observational study setting. A near-real-time patient satisfaction survey was sent via telephone to patients within 72 h of discharge from an emergency department (ED). Patient-trust-physician (PTP) and physician-show-respect (PSR) scores were measured. Patient and physician demographics (age, gender, race, and ethnicity) were matched. Causal effect was analyzed to determine the direct effect of patient–physician demographic concordance on PTP/PSR scores. Results: We enrolled 1815 patients. The treatment effect of patient–physician age concordance on PTP scores was − 0.119 (p = 0.036). Other treatment effect of patient–physician demographic concordance on patient perception of physician trust and respect ranged from − 0.02 to − 0.2 (p > 0.05). Conclusion: Patient–physician age concordance may cause a negative effect on patient perception of physician trust. Otherwise, patient–physician demographic concordance has no effect on patient perceptions of physician trust and respect.

Objective: We aimed to examine the role of the HEART (history, EKG, age, risk factors, and troponin) score in the evaluation of six clinical outcomes among three groups of patients in the emergency department (ED). Methods: We performed a retrospective observational study among three ED patient groups including White, Black, and Hispanic patients. ED providers used the HEART score to assess the need for patient hospital admission and for emergent cardiac imaging tests (CITs). HEART scores were measured using classification accuracy rates. Performance accuracies were measured in terms of HEART score in relation to four clinical outcomes (positive findings of CITs, ED returns, hospital readmissions, and 30-day major adverse cardiac events [MACE]). Results: A high classification accuracy rate (87%) was found for use of the HEART score to determine hospital admission. HEART scores showed moderate accuracy (area under the receiver operating characteristic curve 0.66–0.78) in predicting results of emergent CITs, 30-day hospital readmissions, and 30-day MACE outcomes. Conclusions: Providers adhered to use of the HEART score to determine hospital admission. The HEART score may be associated with emergent CIT findings, 30-day hospital readmissions, and 30-day MACE outcomes, with no differences among White, Black, and Hispanic patient populations.

Background: Engaging patients in health behaviors is critical for better outcomes, yet many patient partnership behaviors are not widely adopted. Behavioral economics–based interventions offer potential solutions, but it is challenging to assess the time and cost needed for different options. Crowdsourcing platforms can efficiently and rapidly assess the efficacy of such interventions, but it is unclear if web-based participants respond to simulated incentives in the same way as they would to actual incentives. Objective: The goals of this study were (1) to assess the feasibility of using crowdsourced surveys to evaluate behavioral economics interventions for patient partnerships by examining whether web-based participants responded to simulated incentives in the same way they would have responded to actual incentives, and (2) to assess the impact of 2 behavioral economics–based intervention designs, psychological rewards and loss of framing, on simulated medication reconciliation behaviors in a simulated primary care setting. Methods: We conducted a randomized controlled trial using a between-subject design on a crowdsourcing platform (Amazon Mechanical Turk) to evaluate the effectiveness of behavioral interventions designed to improve medication adherence in primary care visits. The study included a control group that represented the participants’ baseline behavior and 3 simulated interventions, namely monetary compensation, a status effect as a psychological reward, and a loss frame as a modification of the status effect. Participants’ willingness to bring medicines to a primary care visit was measured on a 5-point Likert scale. A reverse-coding question was included to ensure response intentionality. Results: A total of 569 study participants were recruited. There were 132 in the baseline group, 187 in the monetary compensation group, 149 in the psychological reward group, and 101 in the loss frame group. All 3 nudge interventions increased participants’ willingness to bring medicines significantly when compared to the baseline scenario. The monetary compensation intervention caused an increase of 17.51% (P<.001), psychological rewards on status increased willingness by 11.85% (P<.001), and a loss frame on psychological rewards increased willingness by 24.35% (P<.001). Responses to the reverse-coding question were consistent with the willingness questions. Conclusions: In primary care, bringing medications to office visits is a frequently advocated patient partnership behavior that is nonetheless not widely adopted. Crowdsourcing platforms such as Amazon Mechanical Turk support efforts to efficiently and rapidly reach large groups of individuals to assess the efficacy of behavioral interventions. We found that crowdsourced survey-based experiments with simulated incentives can produce valid simulated behavioral responses. The use of psychological status design, particularly with a loss framing approach, can effectively enhance patient engagement in primary care. These results support the use of crowdsourcing platforms to augment and complement traditional approaches to learning about behavioral economics for patient engagement.