Temporal trends in the physical fitness of Hong Kong adolescents between 1998 and 2015

Low physical fitness in adolescence is linked with increased cardiometabolic risk and early all-cause mortality. This study aimed to estimate temporal trends in the physical fitness of Hong Kong adolescents aged 12–17 years between 1998 and 2015. Physical fitness (9-min run/walk, sit-ups, push-ups, and sit-and-reach) and body size data in a total of 28,059 adolescents tested across five population-representative surveys of Hong Kong secondary school pupils, were reported. Temporal trends in means were estimated at the gender-age level by best-fitting sample-weighted linear regression, with national trends estimated by a post-stratified population-weighting procedure. Overall, there were small declines in 9-min run/walk (effect size (ES) = 0.29 (95%CI: 0.32, 0.26)) and sit-ups performance (ES = 0.24 (95%CI: 0.27, 0.21)), with negligible changes in push-ups and sit-and-reach performance. There were small concurrent increases in both mean height and body mass, with a negligible increase in sum of skinfolds. Trends in mean physical fitness and body size/ were not always uniform across the population distribution. The small declines in mean 9-min run/walk and sit-ups performance for Hong Kong adolescents are suggestive of corresponding declines in cardiorespiratory fitness and abdominal/core endurance, respectively. Increased national health promotion strategies are required to improve existing fitness trends.

Prevalence of SARS-CoV-2 Antibodies from a one-year National Serosurveillance of Kenyan Blood Transfusion Donors

In tropical Africa, SARS-CoV-2 epidemiology is poorly described because of lack of access to testing and weak surveillance systems. Since April 2020, we followed SARS-CoV-2 seroprevalence in plasma samples across the Kenya National Blood Transfusion Service. We developed an IgG ELISA against full length spike protein. Validated in locally-observed, PCR-positive COVID-19 cases and in pre-pandemic sera, sensitivity was 92.7% and sensitivity was 99.0%. Using sera from 9,922 donors, we estimated national seroprevalence of SARS-CoV-2 antibodies at 4.3% in April-June 2020 and 9.1% in August-September 2020. The second COVID-19 wave peaked in November 2020. Here we estimate national seroprevalence in early 2021. Between January 3 and March 15, 2021, we collected 3,062 samples from donors aged 16-64 years. Among 3,018 samples that met our study criteria 1,333 were seropositive (crude seroprevalence 44.2%, 95% CI 42.4-46.0%). After Bayesian test-performance adjustment and population weighting to represent the national population distribution, the national estimate of seroprevalence was 48.5% (95% CI 45.2-52.1%). Seroprevalence varied little by age or sex but was higher in Nairobi, the capital city, and lower in two rural regions. Almost half of Kenyan adult donors had evidence of past SARS-CoV-2 infection by March 2021. Although high, the estimate is corroborated by other population-specific estimates in country. Between March and June, 2% of the population were vaccinated against COVID-19 and the country experienced a third epidemic wave. Natural infection is outpacing vaccine delivery substantially in Africa, and this reality needs to be considered as objectives of the vaccine programme are set.

Minimizing aggregation errors when measuring potential access to services for social groups at the city scale

This research explores how to minimize aggregation errors when measuring potential access to services for social groups at the city scale. It develops a cadastral and address-based population weighting technique, the Household Space Weighting, to reduce aggregation errors caused by using population weighted centroids when applying the Have Their Centre In criterion (the Population Weighted Centroid technique). The Household Space Weighting technique is formally tested in a case study of General Practitioner practices in Newcastle upon Tyne, UK. The findings suggest that the Population Weighted Centroid technique produces inaccurate population estimates for 267 out of 910 output areas (29%) in the city. When applying the two techniques to measure access for social groups at the city scale, the absolute difference in the percentage of each social group with potential accessibility is 9–10% and the relative difference in the percentage of each social group with potential access is 18–20%, taking into account the overlay of service areas at the city scale. This suggests that if service planners or policy makers want to measure potential accessibility or potential access of social groups to services for cities, it would be useful to apply a more accurate technique, or at least be aware of the implications of applying the Population Weighted Centroid technique.

Weighting National Survey Data: why, how and which weight?

Background Weighting of national data is a procedure that enables the sample to be more representative of the target population. Weighting procedure is a thorough exercise and yields several types of weights. However, considerable variation exists among authors on which weight to use leaving the researchers baffled. In this article we share our experience on weighting for a few recent national surveys in Bangladesh. Methods We generated four weights: the base weight calculated from probabilities of selection, and non-response adjustments, population calibration, and trimmed weights. Finally we checked weighted means, medians, ranges, standard errors, confidence intervals, variances, multiplicative effects, design effects and prevalence of a key variable of the survey to decide on which weight to use. Results Compared to unweighted distribution, weighting makes the sample distribution to conform to the population. Among the four calculated weights, the trimmed weight had narrow standard error and variance, and smallest design and multiplicative effects. It yielded an acceptable prevalence and distribution of a core variable. Conclusion Though weighting is a time intensive exercise, it had a favorable effect on the sample distribution to comply with the Bangladeshi population. Among the four weights, we show that the trimmed weight met all parameters of good quality and precision. Therefore, we recommend to use this weight for national level surveys in Bangladesh.

Effects of Population Weighting on PM10 Concentration Estimation

Particulate matter with an aerodynamic diameter of 10 μm or less (PM10) pollution poses a considerable threat to human health, and the first step in quantifying health impacts of human exposure to PM10 pollution is exposure assessment. Population-weighted exposure level (PWEL) estimation is one of the methods that provide a more refined exposure assessment as it includes the spatiotemporal distribution of the population into the pollution concentration estimation. This study assessed the population weighting effects on the estimated PM10 concentrations in Malaysia for years 2000, 2008, and 2013. Estimated PM10 annual mean concentrations with a spatial resolution of 5 kilometres retrieved from satellite data and population count obtained from the Gridded Population of the World version 4 (GPWv4) from the Centre for International Earth Science Information Network (CIESIN) were overlaid to generate the PWEL of PM10 for each state. The calculated PWEL of PM10 concentrations were then classified based on the World Health Organization (WHO) and the national Air Quality Guidelines (AQG) and interim targets (IT) for comparison. Results revealed that the annual mean PM10 concentrations in Malaysia ranged from 31 to 73 µg/m3 but became generally lower, ranging from 20 to 72 µg/m3 after population weighting, suggesting that the PM10 population exposure in Malaysia might have been overestimated. PWEL of PM10 distribution showed that the majority of the population lived in areas that complied with the national AQG, but were vulnerable to exposure level 3 according to the WHO AQG and IT, indicating that the population was nevertheless potentially exposed to significant health effects from long-term exposure to PM10 pollution.

Spatially Explicit Mapping of Historical Population Density with Random Forest Regression: A Case Study of Gansu Province, China, in 1820 and 2000

This study established a random forest regression model (RFRM) using terrain factors, climatic and river factors, distances to the capitals of provinces, prefectures (Fu, in Chinese Pinyin), and counties as independent variables to predict the population density. Then, using the RFRM, we explicitly reconstructed the spatial distribution of the population density of Gansu Province, China, in 1820 and 2000, at a resolution of 10 by 10 km. By comparing the explicit reconstruction with census data at the township level from 2000, we found that the RFRM-based approach mostly reproduced the spatial variability in the population density, with a determination coefficient (R2) of 0.82, a positive reduction of error (RE, 0.72) and a coefficient of efficiency (CE) of 0.65. The RFRM-based reconstructions show that the population of Gansu Province in 1820 was mostly distributed in the Lanzhou, Gongchang, Pingliang, Qinzhou, Qingyang, and Ningxia prefecture. The macro-spatial pattern of the population density in 2000 kept approximately similar with that in 1820. However, fine differences could be found. The 79.92% of the population growth of Gansu Province from 1820 to 2000 occurred in areas lower than 2500 m. As a result, the population weighting in the areas above 2500 m was ~9% in 1820 while it was greater than 14% in 2000. Moreover, in comparison to 1820, the population density intensified in Lanzhou, Xining, Yinchuan, Baiyin, Linxia, and Tianshui, while it weakened in Gongchang, Qingyang, Ganzhou, and Suzhou.

The sensitivities of emissions reductions for the mitigation of UK PM_2.5

The reduction of ambient concentrations of fine particulate matter (PM2.5) is a key objective for air pollution control policies in the UK and elsewhere. Long-term exposure to PM2.5 has been identified as a major contributor to adverse human health effects in epidemiological studies and underpins ambient PM2.5 legislation. As a range of emission sources and atmospheric chemistry transport processes contribute to PM2.5 concentrations, atmospheric chemistry transport models are an essential tool to assess emissions control effectiveness. The EMEP4UK atmospheric chemistry transport model was used to investigate the impact of reductions in UK anthropogenic emissions of primary PM2.5, NH3, NOx, SOx or non-methane VOC on surface concentrations of PM2.5 in the UK for a recent year (2010) and for a future current legislation emission (CLE) scenario (2030). In general, the sensitivity to UK mitigation is rather small. A 30 % reduction in UK emissions of any one of the above components yields (for the 2010 simulation) a maximum reduction in PM2.5 in any given location of ∼ 0.6 µg m−3 (equivalent to ∼ 6 % of the modelled PM2.5). On average across the UK, the sensitivity of PM2.5 concentrations to a 30 % reduction in UK emissions of individual contributing components, for both the 2010 and 2030 CLE baselines, increases in the order NMVOC, NOx, SOx, NH3 and primary PM2.5; however there are strong spatial differences in the PM2.5 sensitivities across the UK. Consequently, the sensitivity of PM2.5 to individual component emissions reductions varies between area and population weighting. Reductions in NH3 have the greatest effect on area-weighted PM2.5. A full UK population weighting places greater emphasis on reductions of primary PM2.5 emissions, which is simulated to be the most effective single-component control on PM2.5 for the 2030 scenario. An important conclusion is that weighting corresponding to the average exposure indicator metric (using data from the 45 model grids containing a monitor whose measurements are used to calculate the UK AEI) further increases the emphasis on the effectiveness of primary PM2.5 emissions reductions (and of NOx emissions reductions) relative to the effectiveness of NH3 emissions reductions. Reductions in primary PM2.5 have the largest impact on the AEI in both 2010 and the 2030 CLE scenario. The summation of the modelled reductions to the UK PM2.5 AEI from 30 % reductions in UK emissions of primary PM2.5, NH3, SOx, NOx and VOC totals 1.17 and 0.82 µg m−3 for the 2010 and 2030 CLE simulations, respectively (not accounting for non-linearity).

Sensitivities of UK PM2.5 concentrations to emissions reductions

The reduction of ambient concentrations of fine particulate matter (PM2.5) is a key objective for air pollution control policies in the UK and elsewhere. Long-term exposure to PM2.5 has been identified as a major contributor to adverse human health effects in epidemiological studies and underpins ambient PM2.5 legislation. As a range of emission sources and atmospheric chemistry transport processes contribute to PM2.5 concentrations, atmospheric chemistry transport models are an essential tool to assess emissions control effectiveness. The EMEP4UK atmospheric chemistry transport model was used to investigate the impact of reductions in UK anthropogenic emissions of primary PM2.5, NH3, NOx, SOx or non-methane VOC on surface concentrations of PM2.5 in the UK for a recent year (2010) and for a future current legislation emission scenario (2030). In general, the sensitivity to UK mitigation is rather small. A 30 % reduction in UK emissions of any one of the above components yields (for the 2010 simulation) a maximum reduction in PM2.5 in any given location of ~ 0.6 μg m−3 (equivalent to ~ 6 % of the modelled PM2.5). On average across the UK, the sensitivity of PM2.5 concentrations to a 30 % reduction in UK emissions of individual contributing components, for both the 2010 and 2030 CLE baselines, increases in the order NMVOC, NOx, SOx, NH3 and primary PM2.5, but there are strong spatial differences in the PM2.5 sensitivities across the UK. Consequently, the sensitivity of PM2.5 to individual component emissions reductions varies between area and population weighting. Reductions in NH3 have the greatest effect on area-weighted PM2.5. A full UK population weighting places greater emphasis on reductions of primary PM2.5 emissions, which is simulated to be the most effective single-component control on PM2.5 for the 2030 scenario. An important observation is that weighting corresponding to the Average Exposure Indicator metric (using data from the 45 model grids containing a monitor whose measurements are used to calculate the UK AEI) further increases the emphasis on the effectiveness of primary PM2.5 emissions reductions (and of NOx emissions reductions) relative to the effectiveness of NH3 emissions reductions. Reductions in primary PM2.5 have the largest impact on the AEI in both 2010 and the 2030 CLE scenario. The summation of the modelled reductions to the UK PM2.5 AEI from 30 % reductions in UK emissions of primary PM2.5, NH3, SOx, NOx and VOC totals 1.17 and 0.82 μg m−3 for the 2010 and 2030 CLE simulations, respectively.