Reference values for digital X-ray radiogrammetry parameters in children and adolescents in comparison to estimates in patients with distal radius fractures.

The first objective of this study was to determine normative digital X-ray radiogrammetry (DXR) values, based on original digital images, in a pediatric population (aged 6-18 years). The second aim was to compare these reference data with patients suffering from distal radius fractures, whereas both cohorts originated from the same geographical region and were evaluated using the same technical parameters as well as inclusion and exclusion criteria. DXR-BMD and DXR-MCI of the metacarpal bones II-IV were assessed on standardized digital hand radiographs, without printing or scanning procedures. DXR parameters were estimated separately by gender and among six age groups; values in the fracture group were compared to age- and gender-matched normative data using Student's t tests and Z scores. In the reference cohort (150 boys, 138 girls), gender differences were found in bone mineral density (DXR-BMD), with higher values for girls from 11 to 14 years and for boys from 15 to 18 years (p < 0.05). Girls had higher normative metacarpal index (DXR-MCI) values than boys, with significant differences at 11-14 years (p < 0.05). In the case-control investigation, the fracture group (95 boys, 69 girls) presented lower DXR-BMD at 15-18 years in boys and 13-16 years in girls vs. the reference cohort (p < 0.05); DXR-MCI was lower at 11-18 years in boys and 11-16 years in girls (p < 0.05). Mean Z scores in the fracture group for DXR-BMD were -0.42 (boys) and -0.46 (girls), and for DXR-MCI were -0.51 (boys) and -0.53 (girls). These findings indicate that the fully digital DXR technique can be accurately applied in pediatric populations ≥ 6 years of age. The lower DXR-BMD and DXR-MCI values in the fracture group suggest promising early identification of individuals with increased fracture risk, without the need for additional radiation exposure, enabling the initiation of prevention strategies to possibly reduce the incidence of osteoporosis later in life.

Global Estimates of the Prevalence and Incidence of Four Curable Sexually Transmitted Infections in 2012 Based on Systematic Review and Global Reporting.

BACKGROUND Quantifying sexually transmitted infection (STI) prevalence and incidence is important for planning interventions and advocating for resources. The World Health Organization (WHO) periodically estimates global and regional prevalence and incidence of four curable STIs: chlamydia, gonorrhoea, trichomoniasis and syphilis. METHODS AND FINDINGS WHO's 2012 estimates were based upon literature reviews of prevalence data from 2005 through 2012 among general populations for genitourinary infection with chlamydia, gonorrhoea, and trichomoniasis, and nationally reported data on syphilis seroprevalence among antenatal care attendees. Data were standardized for laboratory test type, geography, age, and high risk subpopulations, and combined using a Bayesian meta-analytic approach. Regional incidence estimates were generated from prevalence estimates by adjusting for average duration of infection. In 2012, among women aged 15-49 years, the estimated global prevalence of chlamydia was 4.2% (95% uncertainty interval (UI): 3.7-4.7%), gonorrhoea 0.8% (0.6-1.0%), trichomoniasis 5.0% (4.0-6.4%), and syphilis 0.5% (0.4-0.6%); among men, estimated chlamydia prevalence was 2.7% (2.0-3.6%), gonorrhoea 0.6% (0.4-0.9%), trichomoniasis 0.6% (0.4-0.8%), and syphilis 0.48% (0.3-0.7%). These figures correspond to an estimated 131 million new cases of chlamydia (100-166 million), 78 million of gonorrhoea (53-110 million), 143 million of trichomoniasis (98-202 million), and 6 million of syphilis (4-8 million). Prevalence and incidence estimates varied by region and sex. CONCLUSIONS Estimates of the global prevalence and incidence of chlamydia, gonorrhoea, trichomoniasis, and syphilis in adult women and men remain high, with nearly one million new infections with curable STI each day. The estimates highlight the urgent need for the public health community to ensure that well-recognized effective interventions for STI prevention, screening, diagnosis, and treatment are made more widely available. Improved estimation methods are needed to allow use of more varied data and generation of estimates at the national level.

From regression estimates to document tables: output formatting using -estout-

Postestimation processing and formatting of regression estimates for input into document tables are tasks that many of us have to do. However, processing results by hand can be laborious, and is vulnerable to error. There are therefore many benefits to automation of these tasks while at the same time retaining user flexibility in terms of output format. The estout package meets these needs. estout assembles a table of coefficients, "significance stars", summary statistics, standard errors, t/z statistics, p-values, confidence intervals, and other statistics calculated for up to twenty models previously fitted and stored by estimates store. It then writes the table to the Stata log and/or to a text file. The estimates are formatted optionally in several styles: html, LaTeX, or tab-delimited (for input into MS Excel or Word). There are a large number of options regarding which output is formatted and how. This talk will take users through a range of examples, from relatively basic simple applications to complex ones.

ESTOUT: Stata module to export estimation results from estimates table

estout produces a table of regression results from one or several models for use with spreadsheets, LaTeX, HTML, or a word-processor table. eststo stores a quick copy of the active estimation results for later tabulation. esttab is a wrapper for estout. It displays a pretty looking publication-style regression table without much typing. estadd adds additional results to the e()-returns for one or several models previously fitted and stored. This package subsumes the previously circulated esto, esta, estadd, and estadd_plus. An earlier version of estout is available as estout1.