Likelihood Of Achieving Who Leprosy Goals: An Expert Survey

Much progress has been made in the global fight against leprosy, as evidenced by dramatic declines in prevalence rates in recent years. However, leprosy has proven elusive. New case detection rates have remained fairly stable, particularly in countries with remaining pockets of high endemicity such as India, Brazil, and Indonesia. In 2012, the World Health Organization spearheaded the London Declaration, which in part aimed for the goal of interruption of transmission of global leprosy by 2020. Aggregating the opinions of experts can supplement existing data to help determine the feasibility of reaching the WHO goals. To obtain the opinions of experts, a cross-sectional survey was sent requesting experts to give probabilistic estimates on the likelihood of achieving leprosy control targets. The survey results showed that most experts do not think the 2020 leprosy control targets will be met. The majority of experts indicated enhanced case finding as the most important measure to undertake to improve leprosy control goal success. The collection of expert opinions highlights the need for continued attention on leprosy from a public health standpoint

Short-term leprosy forecasting from an expert opinion survey.

We conducted an expert survey of leprosy (Hansen's Disease) and neglected tropical disease experts in February 2016. Experts were asked to forecast the next year of reported cases for the world, for the top three countries, and for selected states and territories of India. A total of 103 respondents answered at least one forecasting question. We elicited lower and upper confidence bounds. Comparing these results to regression and exponential smoothing, we found no evidence that any forecasting method outperformed the others. We found evidence that experts who believed it was more likely to achieve global interruption of transmission goals and disability reduction goals had higher error scores for India and Indonesia, but lower for Brazil. Even for a disease whose epidemiology changes on a slow time scale, forecasting exercises such as we conducted are simple and practical. We believe they can be used on a routine basis in public health

Are We Missing an Opportunity? Prediabetes in the U.S. Military

INTRODUCTION: The prevalence of prediabetes is estimated to be one-third of Americans with approximately 80% of these individuals unaware of the diagnosis. In the active duty military population, the prevalence of prediabetes is largely unexplored. The purpose of this study was to investigate the prevalence of prediabetes in military service members by quantifying those meeting prediabetes screening criteria, those actually being screened, and those being appropriately diagnosed. MATERIALS AND METHODS: Data were analyzed from calendar years 2014 to 2018 for active duty service members 18 years of age or older. Vitals records were collected to obtain body mass index values. Composite Health Care System laboratory data were queried for hemoglobin A1c (HbA1c) results as well as fasting plasma glucose (FPG) and oral glucose tolerance test (OGTT) results. The percentage of active duty service members meeting criteria for prediabetes screening was determined by totaling members age 45 and older with members age 18- to 44-year old with a body mass index ≥25.0 kg/m2, then dividing by the total number of members for each respective military branch. The percentage of active duty service members actually screened for prediabetes was determined based on members meeting prediabetes screening criteria who in fact had FPG, OGTT, or HbA1c labs. The total number of labs meeting prediabetes criteria was determined based on those aforementioned labs with results in the prediabetes range (FPG between 100 and 125 mg/dL, OGTT between 140 and 199 mg/dL, or HbA1c range of 5.7%-6.4%). The total number of service members with appropriate prediabetes International Classification of Disease (ICD) code was determined by identifying members with ICD-9 and ICD-10 codes 790.21, 790.22, and R73.01-R73.03 in their medical record. RESULTS: From 2014 to 2018, 53.9% of 332,502, 56% of 543,081, and 47.3% of 531,313 active duty service members in the Air Force, Army and Navy, respectively, met criteria for prediabetes screening. The rates of actually screening for prediabetes were similar across the Air Force (4.8%), Army (6.7%), and Navy (5.5%). The percentage with labs meeting prediabetes criteria ranged from 17.9% to 28.4% in the Air Force, 24.2% to 30.3% in the Army, and 24.2% to 30.9% in the Navy. The rate of ICD coding for prediabetes increased from 2014 to 2018 across all branches (29.8%-65.3% for the Air Force, 24.6%-46.8% for the Army, and 40.0%-45.5% for the Navy). CONCLUSION: Screening for prediabetes in the active duty military population is grossly inadequate, and even of those screened, diagnosing those meeting prediabetes criteria is similarly inadequate. Although this scenario is not unique to the Military Health System, but reflective of a larger national problem, efforts should be made within the Military Health System to increase the screening for this common disorder. Identifying service members with prediabetes enables opportunities for targeted interventions to delay or prevent the progression to diabetes mellitus

Correction: Short-term leprosy forecasting from an expert opinion survey.

[This corrects the article DOI: 10.1371/journal.pone.0182245.]

Glycemic Benefits with Adherence to testosterone therapy in men with hypogonadism and type 2 diabetes mellitus.

BACKGROUND: While previous studies have demonstrated testosterone\u27s beneficial effects on glycemic control in men with hypogonadism and Type 2 Diabetes, the extent to which these improvements are observed based on the degree of treatment adherence has been unclear. OBJECTIVES: To evaluate the effects of long-term testosterone therapy in A1C levels in men with Type 2 Diabetes Mellitus and hypogonadism, controlling for BMI, pre-treatment A1C, and age among different testosterone therapy adherence groups. MATERIALS AND METHODS: We performed a retrospective analysis of 1737 men with diabetes and hypogonadism on testosterone therapy for 5 years of data from 2008-2018, isolating A1C, lipid panels, and BMI results for analysis. Subjects were categorized into adherence groups based on quartiles of the proportion of days covered (\u3e 75% of days, 51-75% of days, 26-50% of days and 0-25% of days), with \u3e75% of days covered considered adherent to therapy. RESULTS: Pre-treatment median A1C was 6.8%. Post-treatment median A1C was 7.1%. The adherent group, \u3e75%, was the only group notable for a decrease in A1C, with a median decrease of -0.2 (p = 0.0022). BMI improvement was associated with improved post-treatment A1C (p = 0.007). When controlling for BMI, age, and pre-treatment A1C, the \u3e75% adherence group was associated with improved post-treatment A1C (p \u3c 0.001). DISCUSSION: When controlling for all studied variables, testosterone adherence was associated with improved post-treatment A1C. The higher the initial A1C at the initiation of therapy, the higher the potential for lowering the patient\u27s A1C with \u3e75% adherence. Further, all groups showed some reduction in BMI, which may indicate that testosterone therapy may affect A1C independent of weight loss. CONCLUSION: Even when controlling for improved BMI, pre-treatment A1C, and age, testosterone positively impacted glycemic control in diabetes patients with hypogonadism, with the most benefit noted in those most adherent to therapy (\u3e75%)

Temporal trends in leprosy for the world, India, Brazil, and Indonesia for 2006–2014, together with forecast distributions for 2015.

<p>Temporal trends and regression lines are shown using large dots and dashed lines, for 2014 and before. Forecast distributions are indicated by vertical bands, with green (left) for Holt-Winters, yellow (center) for regression, and orange (right) for expert opinion. The interquartile region is shown in bright green, yellow, and orange, respectively, and above and below, the remainder of the 95 percent central coverage region is indicated in dark green, olive, and brown (respectively). The median forecast for 2015 is shown as a small white dot; the observed data for 2015 is shown as as a small red dot. Distributions were derived from Holt-Winters, regression (ordinary least squares for the world data, linear mixed effects regression for the three countries), and expert survey. The observed counts are shown in red.</p

Probabilistic forecasts for leprosy new case detection, world, and India, Brazil, and Indonesia, 2015.

<p>We show the mean and standard deviation of probabilistic forecasts using the pooled ensemble of experts, using linear mixed effects regression, and modified Holt-Winters forecasts (<i>smoothing</i>), as described in the text.</p

Probabilistic forecasts for the distribution of leprosy cases for the year 2015 for the world, India, Brazil, and Indonesia, derived from experts (orange), regression (yellow), and simple Holt-Winters (green).

<p>The vertical axis shows density; the red line indicates where the observed data fell.</p

Probabilistic forecasts for the distribution of leprosy cases for the year 2015 for each state and union territory of India derived from experts (orange, left), regression (yellow, central), and simple Holt-Winters (green, right).

<p>The median is indicated with a white dot; the bright central band (orange, yellow, green, respectively) corresponds to the interquartile region, and the remainder of the 95 percent central coverage region is indicated by the darker region (brown, olive, dark green, respectively). The observed data for 2015 are shown in red. The pseudologarithm transformation (sinh⁻¹(<i>x</i>/2)) was used for the vertical axis (asymptotically logarithmic, but finite at zero).</p