Socioeconomic predictors and consequences of depression among primary care attenders with non-communicable diseases in the Western Cape, South Africa:Cohort study within a randomised trial

Background: Socioeconomic predictors and consequences of depression and its treatment were investigated in 4393 adults with specified non-communicable diseases attending 38 public sector primary care clinics in the Eden and Overberg districts of the Western Cape, South Africa.   Methods: Participants were interviewed at baseline in 2011 and 14 months later, as part of a randomised controlled trial of a guideline-based intervention to improve diagnosis and management of chronic diseases. The 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) was used to assess depression symptoms, with higher scores representing more depressed mood. Results: Higher CESD-10 scores at baseline were independently associated with being less educated (p=0.004) and having lower income (p=0.003). CESD-10 scores at follow-up were higher in participants with less education (p=0.010) or receiving welfare grants (p=0.007) independent of their baseline scores. Participants with CESD-10 scores of 10 or more at baseline (56% of all participants) had 25% higher odds of being unemployed at follow-up (p=0.016), independently of baseline CESD-10 score and treatment status. Among participants with baseline CESD-10 scores of 10 or more, antidepressant medication at baseline was independently more likely in participants who had more education (p=0.002), higher income (p<0.001), or were unemployed (p=0.001). Antidepressant medication at follow up was independently more likely in participants with higher income (p=0.023), and in clinics with better access to pharmacists (p=0.053) and off-site drug delivery (p=0.013).  Conclusions: Socioeconomic disadvantage appears to be both a cause and consequence of depression, and may also be a barrier to treatment. There are opportunities for improving the prevention, diagnosis and treatment of depression in primary care in inequitable middle income countries like South Africa.  Trial registration: The trial is registered with Current Controlled Trials (ISRCTN20283604) and the Office for Human Research Protections Database (IRB00001938, FWA00001637)

Novel genetic analysis for case-control genome-wide association studies: quantification of power and genomic prediction accuracy

Genome-wide association studies (GWAS) are routinely conducted for both quantitative and binary (disease) traits. We present two analytical tools for use in the experimental design of GWAS. Firstly, we present power calculations quantifying power in a unified framework for a range of scenarios. In this context we consider the utility of quantitative scores (e.g. endophenotypes) that may be available on cases only or both cases and controls. Secondly, we consider, the accuracy of prediction of genetic risk from genome-wide SNPs and derive an expression for genomic prediction accuracy using a liability threshold model for disease traits in a case-control design. The expected values based on our derived equations for both power and prediction accuracy agree well with observed estimates from simulations

Socioeconomic Inequalities in Newborn Care During Facility and Home Deliveries: A Cross Sectional Analysis of Data from Demographic Surveillance Sites in Rural Bangladesh, India and Nepal

Background: In Bangladesh, India and Nepal, neonatal outcomes of poor infants are considerably worse than those of better-off infants. Understanding how these inequalities vary by country and place of delivery (home or facility) will allow targeting of interventions to those who need them most. We describe socio-economic inequalities in newborn care in rural areas of Bangladesh, Nepal and India for all deliveries and by place of delivery. Methods: We used data from surveillance sites in Bangladesh, India and from Makwanpur and Dhanusha districts in Nepal, covering periods from 2001 to 2011. We used literacy (ability to read a short text) as indicator of socioeconomic status. We developed a composite score of nine newborn care practices (score range 0–9 indicating infants received no newborn care to all nine newborn care practices). We modeled the effect of literacy and place of delivery on the newborn care score and on individual practices. Results: In all study sites (60,078 deliveries in total), use of facility delivery was higher among literate mothers. In all sites, inequalities in newborn care were observed: the difference in new born care between literate and illiterate ranged 0.35–0.80. The effect of literacy on the newborn care score reduced after adjusting for place of delivery (range score difference literate-illiterate: 0.21–0.43). Conclusion: Socioeconomic inequalities in facility care greatly contribute to inequalities in newborn care. Improving newborn care during home deliveries and improving access to facility care are a priority for addressing inequalities in newborn care and newborn mortality

Referral outcomes of individuals identified at high risk of cardiovascular disease by community health workers in Bangladesh, Guatemala, Mexico, and South Africa

We have found that community health workers (CHWs) with appropriate training are able to accurately identify people at high cardiovascular disease (CVD) risk in the community who would benefit from the introduction of preventative management, in Bangladesh, Guatemala, Mexico, and South Africa. This paper examines the attendance pattern for those individuals who were so identified and referred to a health care facility for further assessment and management. Patient records from the health centres in each site were reviewed for data on diagnoses made and treatment commenced. Reasons for non-attendance were sought from participants who had not attended after being referred. Qualitative data were collected from study coordinators regarding their experiences in obtaining the records and conducting the record reviews. The perspectives of CHWs and community members, who were screened, were also obtained

EEG Correlates of Attentional Load during Multiple Object Tracking

While human subjects tracked a subset of ten identical, randomly-moving objects, event-related potentials (ERPs) were evoked at parieto-occipital sites by task-irrelevant flashes that were superimposed on either tracked (Target) or non-tracked (Distractor) objects. With ERPs as markers of attention, we investigated how allocation of attention varied with tracking load, that is, with the number of objects that were tracked. Flashes on Target discs elicited stronger ERPs than did flashes on Distractor discs; ERP amplitude (0–250 ms) decreased monotonically as load increased from two to three to four (of ten) discs. Amplitude decreased more rapidly for Target discs than Distractor discs. As a result, with increasing tracking loads, the difference between ERPs to Targets and Distractors diminished. This change in ERP amplitudes with load accords well with behavioral performance, suggesting that successful tracking depends upon the relationship between the neural signals associated with attended and non-attended objects

The contribution of genetic variants to disease depends on the ruler

Our understanding of the genetic basis of disease has evolved from descriptions of overall heritability or familiality to the identification of large numbers of risk loci. One can quantify the impact of such loci on disease using a plethora of measures, which can guide future research decisions. However, different measures can attribute varying degrees of importance to a variant. In this Analysis, we consider and contrast the most commonly used measures-specifically, the heritability of disease liability, approximate heritability, sibling recurrence risk, overall genetic variance using a logarithmic relative risk scale, the area under the receiver-operating curve for risk prediction and the population attributable fraction-and give guidelines for their use that should be explicitly considered when assessing the contribution of genetic variants to disease

SDF1-Induced Antagonism of Axonal Repulsion Requires Multiple G-Protein Coupled Signaling Components That Work in Parallel

SDF1 reduces the responsiveness of axonal growth cones to repellent guidance cues in a pertussis-toxin-sensitive, cAMP-dependent manner. Here, we show that SDF1's antirepellent effect can be blocked in embryonic chick dorsal root ganglia (DRGs) by expression of peptides or proteins inhibiting either Gαi, Gαq, or Gβγ. SDF1 antirepellent activity is also blocked by pharmacological inhibition of PLC, a common effector protein for Gαq. We also show that SDF1 antirepellent activity can be mimicked by overexpression of constitutively active Gαi, Gαq, or Gαs. These results suggest a model in which multiple G protein components cooperate to produce the cAMP levels required for SDF1 antirepellent activity

Underestimated Effect Sizes in GWAS: Fundamental Limitations of Single SNP Analysis for Dichotomous Phenotypes

Complex diseases are often highly heritable. However, for many complex traits only a small proportion of the heritability can be explained by observed genetic variants in traditional genome-wide association (GWA) studies. Moreover, for some of those traits few significant SNPs have been identified. Single SNP association methods test for association at a single SNP, ignoring the effect of other SNPs. We show using a simple multi-locus odds model of complex disease that moderate to large effect sizes of causal variants may be estimated as relatively small effect sizes in single SNP association testing. This underestimation effect is most severe for diseases influenced by numerous risk variants. We relate the underestimation effect to the concept of non-collapsibility found in the statistics literature. As described, continuous phenotypes generated with linear genetic models are not affected by this underestimation effect. Since many GWA studies apply single SNP analysis to dichotomous phenotypes, previously reported results potentially underestimate true effect sizes, thereby impeding identification of true effect SNPs. Therefore, when a multi-locus model of disease risk is assumed, a multi SNP analysis may be more appropriate