Characterizing psychosis risk traits in Africa: A longitudinal study of Kenyan adolescents

AbstractThe schizophrenia prodrome has not been extensively studied in Africa. Identification of prodromal behavioral symptoms holds promise for early intervention and prevention of disorder onset. Our goal was to investigate schizophrenia risk traits in Kenyan adolescents and identify predictors of psychosis progression.135 high-risk (HR) and 142 low-risk (LR) adolescents were identified from among secondary school students in Machakos, Kenya, using the structured interview of psychosis-risk syndromes (SIPS) and the Washington early recognition center affectivity and psychosis (WERCAP) screen. Clinical characteristics were compared across groups, and participants followed longitudinally over 0-, 4-, 7-, 14- and 20-months. Potential predictors of psychosis conversion and severity change were studied using multiple regression analyses.More psychiatric comorbidities and increased psychosocial stress were observed in HR compared to LR participants. HR participants also had worse attention and better abstraction. The psychosis conversion rate was 3.8%, with only disorganized communication severity at baseline predicting conversion (p=0.007). Decreasing psychotic symptom severity over the study period was observed in both HR and LR participants. ADHD, bipolar disorder, and major depression diagnoses, as well as poor occupational functioning and avolition were factors relating to lesser improvement in psychosis severity.Our results indicate that psychopathology and disability occur at relatively high rates in Kenyan HR adolescents. Few psychosis conversions may reflect an inadequate time to conversion, warranting longer follow-up studies to clarify risk predictors. Identifying disorganized communication and other risk factors could be useful for developing preventive strategies for HR youth in Kenya

The stigma turbine:A theoretical framework for conceptualizing and contextualizing marketplace stigma

Stigmas, or discredited personal attributes, emanate from social perceptions of physical characteristics, aspects of character, and “tribal” associations (e.g., race; Goffman 1963). Extant research emphasizes the perspective of the stigma target, with some scholars exploring how social institutions shape stigma. Yet the ways stakeholders within the socio-commercial sphere create, perpetuate, or resist stigma remain overlooked. We introduce and define marketplace stigma as the labeling, stereotyping, and devaluation by and of commercial stakeholders (consumers, companies and their employees, stockholders, institutions) and their offerings (products, services, experiences). We offer the Stigma Turbine (ST) as a unifying conceptual framework that locates marketplace stigma within the broader sociocultural context, and illuminates its relationship to forces that exacerbate or blunt stigma. In unpacking the ST, we reveal the critical role market stakeholders can play in (de)stigmatization, explore implications for marketing practice and public policy, and offer a research agenda to further our understanding of marketplace stigma and stakeholder welfare

Using a Bayesian Method to Assess Google, Twitter, and Wikipedia for ILI Surveillance

ObjectiveTo comparatively analyze Google, Twitter, and Wikipedia byevaluating how well change points detected in each web-based sourcecorrespond to change points detected in CDC ILI data.IntroductionTraditional influenza surveillance relies on reports of influenza-like illness (ILI) by healthcare providers, capturing individualswho seek medical care and missing those who may search, post,and tweet about their illnesses instead. Existing research has shownsome promise of using data from Google, Twitter, and Wikipediafor influenza surveillance, but with conflicting findings, studies haveonly evaluated these web-based sources individually or dually withoutcomparing all three of them1-5. A comparative analysis of all threeweb-based sources is needed to know which of the web-based sourcesperforms best in order to be considered to complement traditionalmethods.MethodsWe collected publicly available, de-identified data from the CDCILINet system, Google Flu Trends, HealthTweets.org, and Wikipediafor the 2012-2015 influenza seasons. Bayesian change point analysiswas the method used to detect change points, or seasonal changes,in each of the web-data sources for comparison to change pointsin CDC ILI data. All analyses was conducted using the R package‘bcp’ v4.0.0 in RStudio v0.99.484. Sensitivity and positive predictivevalues (PPV) were then calculated.ResultsDuring the 2012-2015 influenza seasons, a high sensitivity of 92%was found for Google, while the PPV for Google was 85%. A lowsensitivity of 50% was found for Twitter; a low PPV of 43% wasfound for Twitter also. Wikipedia had the lowest sensitivity of 33%and lowest PPV of 40%.ConclusionsGoogle had the best combination of sensitivity and PPV indetecting change points that corresponded with change points found inCDC data. Overall, change points in Google, Twitter, and Wikipediadata occasionally aligned well with change points captured in CDCILI data, yet these sources did not detect all changes in CDC data,which could indicate limitations of the web-based data or signify thatthe Bayesian method is not adequately sensitive. These three web-based sources need to be further studied and compared using otherstatistical methods before being incorporated as surveillance data tocomplement traditional systems.Figure 1. Detection of change points, 2012-2013 influenza seasonFigure 2. Detection of change points, 2013-2014 influenza seasonFigure 3. Detection of change points, 2014-2015 influenza seaso

From primary care to the revolving door of hospital readmission: Relevance of Geoffrey Rose's call for a population strategy

The high-risk strategy in prevention has remained the preferred approach in health care. High-profile research predominantly emphasizes specific high-risk subgroups such as those who have extremely high cholesterol and super-utilizers of emergency departments. Dr. Geoffrey Rose's alternative population approach, though well established in principle, has failed to come to fruition in primary care research, aside from a few exceptions. The population approach extends intervention efforts to more moderate-risk people, attempting to shift the overall distribution in a positive direction, effecting change in more of the population. Despite requiring more initial investment due to the larger target group, the health-related gains and downstream cost savings through a population strategy may yield greater long-term cost-effectiveness than the high-risk strategy. We describe the example of extending prevention efforts from super-utilizers (e.g. those with ≥3 readmissions per year) to include those who readmit in moderate frequency (1–2 per year) in terms of potential hospital days and associated medical costs averted. Keywords: Population health, Patient readmission, Preventive healt

Using a Bayesian Method to Assess Google, Twitter, and Wikipedia for ILI Surveillance

ObjectiveTo comparatively analyze Google, Twitter, and Wikipedia byevaluating how well change points detected in each web-based sourcecorrespond to change points detected in CDC ILI data.IntroductionTraditional influenza surveillance relies on reports of influenza-like illness (ILI) by healthcare providers, capturing individualswho seek medical care and missing those who may search, post,and tweet about their illnesses instead. Existing research has shownsome promise of using data from Google, Twitter, and Wikipediafor influenza surveillance, but with conflicting findings, studies haveonly evaluated these web-based sources individually or dually withoutcomparing all three of them1-5. A comparative analysis of all threeweb-based sources is needed to know which of the web-based sourcesperforms best in order to be considered to complement traditionalmethods.MethodsWe collected publicly available, de-identified data from the CDCILINet system, Google Flu Trends, HealthTweets.org, and Wikipediafor the 2012-2015 influenza seasons. Bayesian change point analysiswas the method used to detect change points, or seasonal changes,in each of the web-data sources for comparison to change pointsin CDC ILI data. All analyses was conducted using the R package‘bcp’ v4.0.0 in RStudio v0.99.484. Sensitivity and positive predictivevalues (PPV) were then calculated.ResultsDuring the 2012-2015 influenza seasons, a high sensitivity of 92%was found for Google, while the PPV for Google was 85%. A lowsensitivity of 50% was found for Twitter; a low PPV of 43% wasfound for Twitter also. Wikipedia had the lowest sensitivity of 33%and lowest PPV of 40%.ConclusionsGoogle had the best combination of sensitivity and PPV indetecting change points that corresponded with change points found inCDC data. Overall, change points in Google, Twitter, and Wikipediadata occasionally aligned well with change points captured in CDCILI data, yet these sources did not detect all changes in CDC data,which could indicate limitations of the web-based data or signify thatthe Bayesian method is not adequately sensitive. These three web-based sources need to be further studied and compared using otherstatistical methods before being incorporated as surveillance data tocomplement traditional systems.Figure 1. Detection of change points, 2012-2013 influenza seasonFigure 2. Detection of change points, 2013-2014 influenza seasonFigure 3. Detection of change points, 2014-2015 influenza seaso

Door-to-door Video-Enhanced Prevalence Study of Tourette Disorder Among African Americans

Tourette’s Disorder or Tourette Syndrome (TS) is a highly heritable neuropsychiatric disorder affecting about 0.5% of the population worldwide. Study and clinical samples have been heavily Caucasian, and the sparse existing data on TS prevalence in African or African American samples are highly discordant. This door-to-door and health-fair-based study in a minority community in the Midwest used community health workers (CHWs) and a video–enhanced screener to measure the prevalence of TS. Two persons screened positive for TS among the 154 adults recruited. Community screening for TS in minority populations is feasible and relatively inexpensive using CHWs and a video–enhanced screening instrument

Hearing Impairment, Mental Health Services Use, and Perceived Unmet Needs Among Adults With Serious Mental Illness: A Cross-Sectional Study

PURPOSE: Individuals with hearing impairment have higher risks of mental illnesses. We sought to develop a richer understanding of how the presence of any hearing impairment affects three types (prescription medication, outpatient services, and inpatient services) of mental health services utilization (MHSU) and perceived unmet needs for mental health care; also, we aimed to identify sociodemographic factors associated with outpatient mental health services use among those with hearing impairment and discuss potential implications under the U.S. health care system. METHOD: Using secondary data from the 2015-2019 National Survey on Drug Use and Health, our study included U.S. adults aged ≥ 18 years who reported serious mental illnesses (SMIs) in the past year. Multivariable logistic regression was used to examine associations of hearing impairment with MHSU and perceived unmet mental health care needs. RESULTS: The study sample comprised 12,541 adults with SMIs. Prevalence of MHSU (medication: 55.5% vs. 57.5%; outpatient: 37.1% vs. 44.2%; inpatient: 6.6% vs.7.1%) and unmet needs for mental health care (47.5% vs. 43.3%) were estimated among survey respondents who reported hearing impairment and those who did not, respectively. Those with hearing impairment were significantly less likely to report outpatient MHSU ( = 0.73, 95% CI [0.60, 0.90]). CONCLUSIONS: MHSU was low while perceived unmet needs for mental health care were high among individuals with SMIs, regardless of hearing status. In addition, patients with hearing impairment were significantly less likely to report outpatient MHSU than their counterparts. Enhancing communication is essential to improve access to mental health care for those with hearing impairment

Screening the “Invisible Population” of Older Adult Patients for Prescription Pain Reliever Non-Medical Use and Use Disorders

Background: In the United States, the number of older adults reporting non-medical use of prescription pain relievers (NMUPPR) between 2015 and 2019 has remained constant, while those meeting criteria for opioid use disorders (OUDs) between 2013 and 2018 increased three-fold. These rates are expected to increase due to increased life expectancy among this population coupled with higher rates of substance use. However, they have consistently lower screening rates for problematic prescription pain reliever use, compared to younger cohorts. Objectives: This commentary reviewed trends in older adult NMUPPR and OUDs and reviewed several available screening tools. We then considered reasons why providers may not be screening their patients, with a focus on older adults, for NMUPPR and OUDs. Finally, we provided recommendations to increase screenings in healthcare settings. Results: Low screening rates in older adult patients may be due to several contributing factors, such as providers’ implicit biases and lack of training, time constraints, and comorbid conditions that mask NMUPPR and OUD-related symptoms. Recommendations include incorporating more addiction-related curricula in medical schools, encouraging participation in CME training focused on substance use, attending implicit bias training, and breaking down the silos between pharmacy and geriatric, addiction, and family medicine. Conclusions: There is a growing need for older adult drug screenings, and we have provided several recommendations for improvement. By increasing screenings among older populations, providers will assist in the identification and referral of patients to appropriate and timely substance use treatment and resources to ultimately ameliorate the health of older adult patients