Additional file 1 of The slowing pace of life expectancy gains since 1950

Table A-1-1. LEB sources, by country and year. Table A-2-1. List of countries included in the analysis, when HIV is not included as a control variable. Table A-2-2. List of countries included in the analysis, when HIV is included as a control variable. Table A-2-3. List of countries by strata using a representative year for each decade, during the analysis countries were re-stratified to each LEB stratum every single year. Table A-2-4. Summary statistics, Lowest Stratum (LEB < 51). Table A-2-5. Summary statistics, Stratum II (51 ≤ LEB < 61). Table A-2-6. Summary statistics, Stratum III (61 ≤ LEB < 71). Table A-2-7. Summary statistics, Highest Stratum (LEB ≥ 71). Table A-2-8. F-tests of equality between decadal dummies parameters, 1960–69 against 2000–09 and 1980–89 against 2000–09. Table A-2-9. The effects of LEB, income per capita, fertility, population density, CO2 emissions, and time on LEB decadal gains, 1950–2009, Fixed effects model. Table A-2-10. LEB decade gains, by region, decade and LEB strata. Table A-4-1. Countries with HIV imputed values, FE regression (imputation A) and constant prevalence (imputation B). Figure A-3-1. LEB decadal gains by decade, Stratum II (51 ≤ LEB < 61) and Stratum III (61 ≤ LEB < 71). Figure A-3-2. LEB decadal gains distribution by strata, comparison between decades (PDF 1890 kb

Temperature extremes and infant mortality in Bangladesh: Hotter months, lower mortality

<div><p>Background</p><p>Our study aims to obtain estimates of the size effects of temperature extremes on infant mortality in Bangladesh using monthly time series data.</p><p>Methods</p><p>Data on temperature, child and infant mortality were obtained for Matlab district of rural Bangladesh for January 1982 to December 2008 encompassing 49,426 infant deaths. To investigate the relationship between mortality and temperature, we adopted a regression with Autoregressive Integrated Moving Average (ARIMA) errors model of seasonally adjusted temperature and mortality data. The relationship between monthly mean and maximum temperature on infant mortality was tested at 0 and 1 month lags respectively. Furthermore, our analysis was stratified to determine if the results differed by gender (boys versus girls) and by age (neonates (≤ 30 days) versus post neonates (>30days and <153days)). Dickey Fuller tests were performed to test for stationarity, and since the time series were non-stationary, we conducted the regression analysis based on the first differences of mortality and temperature.</p><p>Results</p><p>Hotter months were associated with lower infant mortality in Bangladesh. Each degree Celsius increase in mean monthly temperature reduced monthly mortality by 3.672 (SE 1.544, p<0.05) points. A one degree increase in mean monthly temperature one month prior reduced mortality by 0.767 (SE 0.439, p<0.1) for boys and by -0.0764 (SE 0.366, NS) for girls. Beneficial effects of maximum monthly temperature were on the order of 0.623 to -0.712 and statistically significant for girls and boys respectively. Effect sizes of mean monthly temperature were larger for neonates at 1.126 (SE 0.499, p<0.05) than for post-neonates at 0.880 (SE 0.310, p<0.05) reductions in mortality per degree.</p><p>Conclusion</p><p>There is no evidence that infant survival is adversely affected by monthly temperature extremes in Bangladesh. This may reflect a more heightened sensitivity of infants to hypothermia than hyperthermia in this environment.</p></div

Relationships between mortality (under 5, female and male <153days, kids< = 30days and kids>30 days) and monthly temperature (mean and maximum) over lags 0 to 1 month.

<p>Relationships between mortality (under 5, female and male <153days, kids< = 30days and kids>30 days) and monthly temperature (mean and maximum) over lags 0 to 1 month.</p

Summary statistics of variables used in the analysis.

<p>Summary statistics of variables used in the analysis.</p

Additional file 1: Table S1. of What criteria do decision makers in Thailand use to set priorities for vaccine introduction?

Definitions of criteria and levels in the BWS study. Table S2. The conditional logistic regression results among groups of respondents. (DOCX24 kb

Annual cost of screening and isolation, and rate of infection.

<p>The annual undiscounted cost in US$ (2007) of strategies ‘Selective PCR’ (left) and ‘Selective Chromogenic’ (right) in a high prevalence setting. The first two years represent baseline (no screening and no isolation).</p

Results of the sensitivity analysis of test characteristics.

<p>The costs of selective PCR-based screening are depicted on the horizontal axis and health benefits (infections averted) on the vertical axis. The left graph shows the combined results of alternately varying the test’s sensitivity and specificity from 50% to 100%, with increments of 5%. The right graph shows the test delay varied from 0 to 5 days, with increments of 0.5 day, for different pre-emptive isolation strategies: No pre-emptive isolation (diamonds), pre-emptive isolation of ‘flagged’ patients only, i.e. the base-case scenario (squares), and full pre-emptive isolation, i.e. ‘flagged’ patients as well as ‘high risk’ patients (triangles).</p

Results of the scenario analysis.

<p>1 The number of patient days in isolation.</p><p>2 The peak percentage of total patients in isolation in 97.5% of all simulations.</p><p>3 The number of years required to reach a 50% reduction in the nosocomial prevalence.</p><p>The cumulative and discounted costs in US$(2007) and discounted effects for one hospital over 15 years, for a high (15 cost-effectiveness ratio in$ per infection averted, compared to no screening; <b>UI</b> uncertainty interval;</p

Results of one-way sensitivity analysis on key model parameters.

<p>Parameters are ranked by the magnitude of their impact on the average cost-effectiveness ratio (aCER), of selective screening with PCR (aCER: $4,600) under base-case assumptions (base-case parameter values are shown between brackets).</p

Results of screening strategies.

<p>1 The number of patient days in isolation.</p><p>2 The peak isolation capacity required by the hospital in 97.5% of all simulations.</p><p>3 The number of years required to reach a 50% reduction in the nosocomial prevalence.</p><p>The cumulative and discounted costs in US$(2007) and discounted effects for one hospital over 15 years, using base-case assumptions, for a high (15 prevalence setting.</p><p><b>NA</b> not applicable; <b>aCER</b> average cost-effectiveness ratio in$ per infection averted, compared to no screening; <b>UI</b> uncertainty interval;</p