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Purpose To understand the distribution of trabeculectomies, glaucoma drainage implants (GDI) and micro-invasive glaucoma surgeries (MIGS) performed in the United States through geospatial mapping. Methods We performed an observational cohort study to evaluate glaucoma surgeries in patients age ≥65. The most recently released data from Centers for Medicare Part B Carrier Summary Files were queried to determine the number of glaucoma surgeries performed per state during the year of 2017. We created choropleth maps, titled “GlaucoMap”, to characterize the rates of various surgeries performed across the United States, defined as the number of procedures performed per 10,000 individuals. A chi-squared analysis was further used to evaluate differences in surgical preferences across geographic region. Standardized residuals (SR) were calculated to determine regional influences on surgical distribution. Results There were 174,788 glaucoma surgeries performed: 22,862 trabeculectomies (13.1%), 19,991 GDI (11.4%) and 131,935 (75.5%) MIGS. The Northeast had the highest trabeculectomy rate, GDI was highest in the Southeast and MIGS were highest in the Southwest. There was a statistically significant difference in proportional use of conventional surgeries versus MIGS across various regions in the United States (p < 0.0001). Given the high trabeculectomy and GDI rates and relatively low MIGS adoption in the Southeast, we observed a +7.03 SR for conventional surgeries and −4.01 SR for MIGS. The Southwest and Western states had the highest MIGS rate and contributed +3.29 and +3.24 SR toward disproportional MIGS preference, respectively. The preference for conventional surgeries in the Northeast (SR = +2.93) and MIGS in the Midwest (SR = +0.99) also contribute to the overall differences in glaucoma surgeries across the United States. Conclusion GlaucoMap is useful for visualizing the distribution of glaucoma surgeries in the United States. The heterogeneity in surgical preferences points to regional differences in glaucoma management.

Additional file 1: Supplemental Figure 1. County-level 7-day mean temperature distribution by state, 1991-2017. Supplemental Table 1. Rate of stillbirth across states. Supplemental Table 2. Odds ratios and 95% confidence intervals for the association between heatwaves and stillbirth in California, Florida, Georgia, Kansas, New Jersey, and Oregon. Supplemental Table 3. Odds ratios and 95% confidence intervals for the associations between heatwaves and stillbirth by maternal race, by state. Supplemental Table 4. Odds ratios and 95% confidence intervals for the association between heatwaves and stillbirth by the timing of the stillbirth, by state. Supplemental Table 5. Select estimates for continuous temperature model, reference 20°C. Supplemental Table 6. Select estimates for continuous percentile model, reference 50%. Supplemental Figure 2. Comparison of continuous temperature model versus categorical temperature model, pooled results. Supplemental Figure 3. Comparison of continuous percentile model versus categorical temperature model, pooled results. Supplemental Table 7. Odds ratios and 95% confidence intervals for categorical temperature model. Supplemental Table 8. Odds ratios and 95% confidence intervals for categorical percentile model.

Additional file 1: Supplementary Methods. Table S1. DIC scores of competing seasonality components. Table S2. ICD-9-CM-coded respiratory hospitalizations in the entire population and RSV hospitalizations among children

Funder: Public Health Research Programme; Id: http://dx.doi.org/10.13039/501100001921, Decisions about typhoid fever prevention and control are based on estimates of typhoid incidence and their uncertainty. Lack of specific clinical diagnostic criteria, poorly sensitive diagnostic tests, and scarcity of accurate and complete datasets contribute to difficulties in calculating age-specific population-level typhoid incidence. Using data from the Strategic Typhoid Alliance across Africa and Asia program, we integrated demographic censuses, healthcare utilization surveys, facility-based surveillance, and serological surveillance from Malawi, Nepal, and Bangladesh to account for under-detection of cases. We developed a Bayesian approach that adjusts the count of reported blood-culture-positive cases for blood culture detection, blood culture collection, and healthcare seeking-and how these factors vary by age-while combining information from prior published studies. We validated the model using simulated data. The ratio of observed to adjusted incidence rates was 7.7 (95% credible interval [CrI]: 6.0-12.4) in Malawi, 14.4 (95% CrI: 9.3-24.9) in Nepal, and 7.0 (95% CrI: 5.6-9.2) in Bangladesh. The probability of blood culture collection led to the largest adjustment in Malawi, while the probability of seeking healthcare contributed the most in Nepal and Bangladesh; adjustment factors varied by age. Adjusted incidence rates were within or below the seroincidence rate limits of typhoid infection. Estimates of blood-culture-confirmed typhoid fever without these adjustments results in considerable underestimation of the true incidence of typhoid fever. Our approach allows each phase of the reporting process to be synthesized to estimate the adjusted incidence of typhoid fever while correctly characterizing uncertainty, which can inform decision-making for typhoid prevention and control.

Nisha Chadha,1,2 Joshua L Warren,3 Ji Liu,2 James C Tsai,1 Christopher C Teng2 1Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York Eye and Ear, Eye and Vision Research Institute, NY 10029, USA; 2Department of Ophthalmology and Visual Science, Yale University School of Medicine, New Haven, CT 06510, USA; 3Department of Biostatistics, Yale University, New Haven, CT 06510, USA Purpose: This study evaluated US resident and fellow glaucoma surgical experience over a 7- and 8-year period, respectively.Methods: US ophthalmology resident glaucoma case logs from 2009 to 2016 and glaucoma fellow case logs from 2008 to 2016 were reviewed. The number of total, average, median, and minimum cases, along with percentile data for filtering surgery, shunting surgery, and novel procedures were analyzed for year-to-year trends.Results: Among residents training from 2009 to 2016, there was a decline in the average number of primary filtering surgeries by 20%, from 6.0±5.0 to 4.8±4.0 cases, with a concurrent increase in average primary glaucoma drainage implant (GDI) surgeries by 40%, from 4.5±4.0 to 6.3±5.0 cases, which represented an increase of 6.5% per year, P

Additional file 1: Table S1. Descriptive statistics of exposures by city, temperature metric and exposure assessment methods. Table S2. Quantile values of exposures by city and exposure metrics. Table S3. Difference in AIC and ratio of overdispersion by city, outcome and temperature exposure. Figure S1. Maps of average May-October daily Daymet 1km temperature estimates by city. Figure S2. Associations with minimum temperature between the 75th and 25th percentile of exposures. Figure S3. Associations with maximum temperature between the 75th and 25th percentile of exposures. Figure S4. Associations with average temperature between the 95th and 50h percentile of exposures. Figure S5. Associations with average temperature between the 75th and 25h percentile of exposures. Figure S6. Associations with minimum temperature with Daymet grid cell link to the monitor. Figure S7. Associations with maximum temperature with Daymet grid cell link to the monitor. Figure S8. Sensitivity analyses for different natural cubic spline degrees of freedom for minimum temperature. Figure S9. Sensitivity analyses for different natural cubic spline degrees of freedom for maximum temperature. Figure S10. Associations for 3-day moving average of minimum temperature between the 95th and 50th percentile of exposures. Figure S11. Associations for 3-day moving average of maximum temperature between the 95th and 50th percentile of exposures. Primary.csv Model results for primary analysis for same-day exposure (by city, outcome, temperature metric, exposure assessment methods, AIC, overdispersion, and exposure contrast referenced at the 50th exposure percentile). Primary.csv Model results for primary analysis for same-day exposure (by city, outcome, temperature metric, exposure assessment methods, AIC, overdispersion, and exposure contrast referenced at the 50th exposure percentile). Primary_Ref25.csv Model results for primary analysis for same-day exposure (by city, outcome, temperature metric, exposure assessment methods, AIC, overdispersion, and exposure contrast referenced at the 25th exposure percentile). PrimaryMV.csv Model results for primary analysis for 3-day moving average exposure (by city, outcome, temperature metric, exposure assessment methods, AIC, overdispersion, and exposure contrast referenced at the 50th exposure percentile).

Additional file 1: Figure S1. Map of Los Angeles-Long Beach-Santa Ana MSA. The MSA was split into Los Angeles county (mint green) and Orange county (light orange). Figure S2. Map of New York-North New Jersey-Long Island MSA. The MSA was split into the Long Island area (blue), New York City area (pink), and other New York and New Jersey counties (green). Figure S3. Illustration of the selection of pregnancies whose at-risk windows overlap with the warm season for preterm (blue) and early-term birth (orange) where pregnancy 1-4 (four arrow lines) are extreme examples determining the range of the eligible LMP dates. The at-risk window was defined as 28 weeks and 0 days to 36 weeks and 6 days for preterm birth and 37 weeks and 0 days to 38 weeks and 6 days for early-term birth. Figure S4. Flow chart of identification of preterm and early-term cases and matching control. Table S1. Detailed model specification for the analyses of preterm and early-term birth for different types of heatwave indicators. Table S2. Odds ratio estimates of heatwave indicators based on definition framework 1 (97.5th percentile of temperature) on early-term birth matching on maternal race, maternal education, and location. Table S3. Odds ratio estimates of heatwave indicators based on definition framework 2 (85th percentile of positive excessive heat factor) on early-term birth matching on maternal race, maternal education, and location. Table S4. Odds ratio estimates of heatwave indicators based on definition framework 1 (97.5th percentile of temperature) on preterm birth matching on maternal race, maternal education, and location. Table S5. Odds ratio estimates of heatwave indicators based on definition framework 2 (85th percentile of positive excessive heat factor) on preterm birth matching on maternal race, maternal education, and location. Table S6. Odds ratio estimates of heatwave indicators based on definition framework 1 (97.5th percentile of temperature) on preterm birth matching on maternal race, maternal education, and month and year of the last menstrual period. Table S7. Odds ratio estimates of heatwave indicators based on definition framework 2 (85th percentile of positive excessive heat factor) on preterm birth matching on maternal race, maternal education, and month and year of the last menstrual period. Table S8. Odds ratio estimates of heatwave indicators based on definition framework 1 (97.5th percentile of temperature) on early-term birth matching on maternal race, maternal education, and month and year of the last menstrual period. Table S9. Odds ratio estimates of heatwave indicators based on definition framework 2 (85th percentile of positive excessive heat factor) on early-term birth matching on maternal race, maternal education, and month and year of the last menstrual period. Table S10. Odds ratio estimates of heatwave indicators based on definition framework 1 (97.5th percentile of temperature) on preterm birth matching on maternal race, maternal education, and location, excluding those with missing matching factors and covariates. Table S11. Odds ratio estimates of heatwave indicators based on definition framework 1 (97.5th percentile of temperature) on early-term birth matching on maternal race, maternal education, and location, excluding those with missing matching factors and covariates. Figure S5. Odds ratio estimates of heatwave indicators on early-term birth by maternal education (

Additional file 1: Figure S1. Structure of logic regression tree of extreme heat exposures for selected warm-season ED visit outcomes in Atlanta, Georgia, 1993–2012. Table S1. Summary of alternative extreme temperature metrics and their short-term associations with warm-season emergency department visits in Atlanta, 1993 to 2012. Table S2. Summary of alternative extreme temperature metrics with consecutive lags and their short-term associations with warm-season emergency department visits in Atlanta, 1993 to 2012. Table S3. Summary of extreme heat metrics from truncated continuous versus continuous temperature metric and their short-term associations with warm-season emergency department visits in Atlanta, 1993 to 2012.