A Randomized Community-based Intervention Trial Comparing Faith Community Nurse Referrals to Telephone-Assisted Physician Appointments for Health Fair Participants with Elevated Blood Pressure

To measure the effect of faith community nurse referrals versus telephone-assisted physician appointments on blood pressure control among persons with elevated blood pressure at health fairs. Randomized community-based intervention trial conducted from October 2006 to October 2007 of 100 adults who had an average blood pressure reading equal to or above a systolic of 140 mm Hg or a diastolic of 90 mm Hg obtained at a faith community nurse-led church health event. Participants were randomized to either referral to a faith community nurse or to a telephone-assisted physician appointment. The average enrollment systolic blood pressure (SBP) was 149 ± 14 mm Hg, diastolic blood pressure (DBP) was 87 ± 11 mm Hg, 57% were uninsured and 25% were undiagnosed at the time of enrollment. The follow-up rate was 85% at 4 months. Patients in the faith community nurse referral arm had a 7 ± 15 mm Hg drop in SBP versus a 14 ± 15 mm Hg drop in the telephone-assisted physician appointment arm (p = 0.04). Twenty-seven percent of the patients in the faith community nurse referral arm had medication intensification compared to 32% in the telephone-assisted physician appointment arm (p = 0.98). Church health fairs conducted in low-income, multiethnic communities can identify many people with elevated blood pressure. Facilitating physician appointments for people with elevated blood pressure identified at health fairs confers a greater decrease in SBP than referral to a faith community nurse at four months

When One Size Does Not Fit All: A Simple Statistical Method to Deal with Across-Individual Variations of Effects

In science, it is a common experience to discover that although the investigated effect is very clear in some individuals, statistical tests are not significant because the effect is null or even opposite in other individuals. Indeed, t-tests, Anovas and linear regressions compare the average effect with respect to its inter-individual variability, so that they can fail to evidence a factor that has a high effect in many individuals (with respect to the intra-individual variability). In such paradoxical situations, statistical tools are at odds with the researcher’s aim to uncover any factor that affects individual behavior, and not only those with stereotypical effects. In order to go beyond the reductive and sometimes illusory description of the average behavior, we propose a simple statistical method: applying a Kolmogorov-Smirnov test to assess whether the distribution of p-values provided by individual tests is significantly biased towards zero. Using Monte-Carlo studies, we assess the power of this two-step procedure with respect to RM Anova and multilevel mixed-effect analyses, and probe its robustness when individual data violate the assumption of normality and homoscedasticity. We find that the method is powerful and robust even with small sample sizes for which multilevel methods reach their limits. In contrast to existing methods for combining p-values, the Kolmogorov-Smirnov test has unique resistance to outlier individuals: it cannot yield significance based on a high effect in one or two exceptional individuals, which allows drawing valid population inferences. The simplicity and ease of use of our method facilitates the identification of factors that would otherwise be overlooked because they affect individual behavior in significant but variable ways, and its power and reliability with small sample sizes (<30–50 individuals) suggest it as a tool of choice in exploratory studies

Difficult Life Events, Selective Migration and Spatial Inequalities in Mental Health in the UK

Objective: Research has indicated that people moving towards neighbourhoods with disadvantaged socio-economic status have poor health, in particular mental health, but the reasons for this are unclear. This study aims to assess why people moving towards more socio-economically deprived areas have poor mental health. It focuses upon the role of difficult life events that may both trigger moves and damage mental health. This study investigates how mental health and socio-spatial patterns of mobility vary between people moving following difficult life events and for other reasons.&lt;p&gt;&lt;/p&gt; Methods: Longitudinal analysis of British Household Panel Survey data describing adults’ moves between annual survey waves, pooled over ten years, 1996-2006 (N=122,892 observations). Respondents were defined as ‘difficult life event movers’ if they had experienced relationship breakdown, housing eviction/repossession, or job loss between waves. Respondents were categorised as moving to more or less deprived quintiles using their Census Area Statistic residential ward Carstairs score. Mental health was indicated by self-reported mental health problems. Binary logistic regression models of weighted data were adjusted for age, sex, education and social class.&lt;p&gt;&lt;/p&gt; Results: The migration rate over one year was 8.5%; 14.1% of movers had experienced a difficult life event during this time period. Adjusted regression model odds of mental health problems among difficult life event movers were 1.67 (95% CI 1.35-2.07) relative to other movers. Odds of difficult life events movers, compared to other movers, moving to a less deprived area, relative to an area with a similar level of deprivation, were 0.70 (95% CI 0.58-0.84). Odds of mental health problems among difficult life event movers relocating to more deprived areas were highly elevated at 2.40 (95% CI 1.63-3.53), relative to stayers.&lt;p&gt;&lt;/p&gt; Conclusion: Difficult life events may influence health selective patterns of migration and socio-spatial trajectories, reducing moves to less deprived neighbourhoods among people with mental illness

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)