56 research outputs found

    Relation of Growth Rate from Birth to Three Months and Four to Six Months to Body Mass Index at Ages Four to Six Years

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    Background. While rapid early weight gain are common in children who become obese later in life, so is growth faltering in the first 3 months of life. Objective. We seek to determine what relationship weight gain in the first six months of age, separated into two 3-month periods, have with the BMI of children ages 4 to 6 years in an inner-city community. Subjects. A convenience sample cohort of 154 children attending an inner-city clinic. Methods. Consecutive charts were reviewed retrospectively. Age, gender, birth weight and weight change in the first and second 3 months of life were introduced as fixed factors using mixed linear models with BMI in years 4 to 6 as the dependent variable. Results. Weight change quartile in the first 3 months of life did not predict of BMI in years 4 to 6; however, weight changes quartiles during months 4 to 6 were significant predictors for subsequent overweight. Conclusion. The data presented herein suggest that, for this specific population, weight gain can be promoted when it is most essential. It is necessary, however, to identify intermediary variables that could affect outcomes in this and other communities

    Medication deserts: survey of neighborhood disparities in availability of prescription medications

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    Background Only a small amount of research has focused on the relationship between socio-economic status (SES) and geographic access to prescription medications at community pharmacies in North America and Europe. To examine the relationship between a community’s socio-economic context and its residents’ geographic access to common medications in pharmacies, we hypothesized that differences are present in access to pharmacies across communities with different socio-economic environments, and in availability of commonly prescribed medications within pharmacies located in communities with different socio-economic status. Methods We visited 408 pharmacies located in 168 socio-economically diverse communities to assess the availability of commonly prescribed medications. We collected the following information at each pharmacy visited: hours of operation, pharmacy type, in-store medication availability, and the cash price of the 13 most commonly prescribed medications. We calculated descriptive statistics for the sample and fitted a series of hierarchical linear models to test our hypothesis that the in-stock availability of medications differs by the socio-economic conditions of the community. This was accomplished by modeling medication availability in pharmacies on the socio-economic factors operating at the community level in a socio-economically devise urban area. Results Pharmacies in poor communities had significantly higher odds of medications being out of stock, OR=1.24, 95% CI [1.02, 1.52]. There was also a significant difference in density of smaller, independent pharmacies with very limited stock and hours of operation, and larger, chain pharmacies in poor communities as compared to the middle and low-poverty communities. Conclusions The findings suggest that geographic access to a neighborhood pharmacy, the type of pharmacy, and availability of commonly prescribed medications varies significantly across communities. In extreme cases, entire communities could be deemed “medication deserts” because geographic access to pharmacies and the availability of the most prescribed medications within them were very poor. To our knowledge, this study is first to report on the relationship between SES and geographic access to medications using small area econometric analysis techniques. Our findings should be reasonably generalizable to other urban areas in North America and Europe and suggest that more research is required to better understand the relationship of socio-economic environments and access to medications to develop strategies to achieve equitable medication access

    Effects of Antiretroviral Therapy and Depressive Symptoms on All-Cause Mortality Among HIV-Infected Women

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    Abstract Depression affects up to 30% of human immunodeficiency virus (HIV)-infected individuals. We estimated joint effects of antiretroviral therapy (ART) initiation and depressive symptoms on time to death using a joint marginal structural model and data from a cohort of HIV-infected women from the Women's Interagency HIV Study (conducted in the United States) from 1998–2011. Among 848 women contributing 6,721 years of follow-up, 194 participants died during follow-up, resulting in a crude mortality rate of 2.9 per 100 women-years. Cumulative mortality curves indicated greatest mortality for women who reported depressive symptoms and had not initiated ART. The hazard ratio for depressive symptoms was 3.38 (95% confidence interval (CI): 2.15, 5.33) and for ART was 0.47 (95% CI: 0.31, 0.70). Using a reference category of women without depressive symptoms who had initiated ART, the hazard ratio for women with depressive symptoms who had initiated ART was 3.60 (95% CI: 2.02, 6.43). For women without depressive symptoms who had not started ART, the hazard ratio was 2.36 (95% CI: 1.16, 4.81). Among women reporting depressive symptoms who had not started ART, the hazard ratio was 7.47 (95% CI: 3.91, 14.3). We found a protective effect of ART initiation on mortality, as well as a harmful effect of depressive symptoms, in a cohort of HIV-infected women

    Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A

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    The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods - recursive partitioning and regression - to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios >1.5; Pcombined = 2.01 × 10-19 and 2.35 × 10-13, respectively) in addition to the established associations of the MHC class II genes. Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples. Taken together with previous studies, we conclude that MHC-class-I-mediated events, principally involving HLA-B*39, contribute to the aetiology of type 1 diabetes. ©2007 Nature Publishing Group

    TRY plant trait database – enhanced coverage and open access

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    Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives
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