BACKGROUND The built environment is a structural determinant of health and has been shown to influence health expenditures, behaviors, and outcomes. Traditional methods of assessing built environment characteristics are time-consuming and difficult to combine or compare. Google Street View (GSV) images represent a large, publicly available data source that can be used to create indicators of characteristics of the physical environment with machine learning techniques. The aim of this study is to use GSV images to measure the association of built environment features with health-related behaviors and outcomes at the census tract level. METHODS We used computer vision techniques to derive built environment indicators from approximately 31 million GSV images at 7.8 million intersections. Associations between derived indicators and health-related behaviors and outcomes on the census-tract level were assessed using multivariate regression models, controlling for demographic factors and socioeconomic position. Statistical significance was assessed at the α = 0.05 level. RESULTS Single lane roads were associated with increased diabetes and obesity, while non-single-family home buildings were associated with decreased obesity, diabetes and inactivity. Street greenness was associated with decreased prevalence of physical and mental distress, as well as decreased binge drinking, but with increased obesity. Socioeconomic disadvantage was negatively associated with binge drinking prevalence and positively associated with all other health-related behaviors and outcomes. CONCLUSIONS Structural determinants of health such as the built environment can influence population health. Our study suggests that higher levels of urban development have mixed effects on health and adds further evidence that socioeconomic distress has adverse impacts on multiple physical and mental health outcomes.BACKGROUND Birth order has been shown to affect the health of the child; less is known, however, about how birth order affects caries development in children. Thus, the present study investigated the association between birth order and dental caries development in young children. METHODS This retrospective registry-based cohort study included all children born in 2000-2003 who were residing in Stockholm County, Sweden, at age 3 years (n = 83,147). The study followed the cohort until subjects reached 7 years of age. Children with registry data on dental examinations and sociodemographic characteristics at ages 3- and 7 years constituted the final study cohort (n = 65,259). The outcome variable was "caries increment from age 3- to 7 years" (Δdeft > 0) and the key exposure, "birth order", was divided into five groups. A forward stepwise logistic binary regression was done for the multivariate analysis with adjustments for sociodemographic factors. RESULTS At age 3 years, 94% had no fillings or manifest caries lesions. During the study period, 22.5% (n = 14,711) developed dental caries. The final logistic regression analysis found a statistically significant positive association between birth order and caries increment. Further, excess risk increased with higher birth order; with the mother's first-born child as reference, risk for the second-born child was OR 1.17, 95% CI = 1.12-1.23; for the third-born child, OR 1.47, 95% CI = 1.38-1.56; for the fourth-born child, OR 1.69, 95% CI = 1.52-1.88; and for the fifth-born or higher birth-order child, OR 1.84, 95% CI = 1.58-2.14. CONCLUSIONS These findings show that birth order influences caries development in siblings, suggesting that birth order can be regarded as a predictor for caries development in young children. This factor may be helpful in assessing caries risk in preschool children and should be considered in caries prevention work in young children with older siblings.BACKGROUND Diagnosis and follow-up of retinal diseases may be improved if the thickness of the various retinal layers, in addition to the total retinal thickness, is taken into account. https://www.selleckchem.com/products/arn-509.html Here we measured the thickness of the macular retinal layers in a population-based study group to assess the normative values and their associations. METHODS Using spectral-domain optical coherence tomographic images (Spectralis®, wavelength 870 nm; Heidelberg Engineering Co, Heidelberg, Germany), we measured the thickness of the macular retinal layers in participants of the population-based Beijing Eye Study without ocular diseases and without systematic diseases, such as arterial hypertension, hyperlipidemia, diabetes mellitus, cardiovascular diseases, previous myocardial infarction, cerebral trauma and stroke. Segmentation and measurement of the retinal layers was performed automatically in each of the horizontal scans. RESULTS The study included 384 subjects (mean age60.0 ± 8.0 years). The mean thickness of the whole retinhe associations between decreasing thickness of most retinal layers with older age and the correlation of a higher thickness of some retinal layers with male gender may clinically be taken into account.BACKGROUND PCOS is a common disorder of women due to genetic, endocrine and environmental effects that manifests from puberty. The rs9939609 variant of fat mass and obesity associated (FTO) gene is linked to metabolic derangement in PCOS. We previously identified FTO (rs9939609) as a susceptibility locus for PCOS among Sri Lankan women and also explored the role of kisspeptin. Associated factors of the FTO candidate gene among South Asians with PCOS are unknown. METHODS This study aimed to determine the association between FTO (rs9939609) polymorphism with clinical (BMI, acanthosis nigricans, hirsutism) and biochemical (serum kisspeptin and testosterone levels) characteristics of PCOS in a cohort of Sri Lankan women. Genetic and clinical data including serum kisspeptin and testosterone concentrations of our previously reported cases (n = 55) and controls (n = 110) were re-analyzed, specifically for an association with rs9939609 variant of FTO gene. RESULTS Logistic regression analysis (AA - OR = 5.7, 95% CI = 2.