363 results found
Wang L, Zhou B, Zhao Z, et al., 2021, Body-mass index and obesity in urban and rural China: findings from consecutive nationally representative surveys during 2004-18., Lancet, Vol: 398, Pages: 53-63
BACKGROUND: In China, mean body-mass index (BMI) and obesity in adults have increased steadily since the early 1980s. However, to our knowledge, there has been no reliable assessment of recent trends, nationally, regionally, or in certain population subgroups. To address this evidence gap, we present detailed analyses of relevant data from six consecutive nationally representative health surveys done between 2004 and 2018. We aimed to examine the long-term and recent trends in mean BMI and prevalence of obesity among Chinese adults, with specific emphasis on changes before and after 2010 (when various national non-communicable disease prevention programmes were initiated), assess how these trends might vary by sex, age, urban-rural locality, and socioeconomic status, and estimate the number of people who were obese in 2018 compared with 2004. METHODS: We used data from the China Chronic Disease and Risk Factors Surveillance programme, which was established in 2004 with the aim to provide periodic nationwide data on the prevalence of major chronic diseases and the associated behavioural and metabolic risk factors in the general population. Between 2004 and 2018 six nationally representative surveys were done. 776 571 individuals were invited and 746 020 (96·1%) participated, including 33 051 in 2004, 51 050 in 2007, 98 174 in 2010, 189 115 in 2013, 189 754 in 2015, and 184 876 in 2018. After exclusions, 645 223 participants aged 18-69 years remained for the present analyses. The mean BMI and prevalence of obesity (BMI ≥30 kg/m2) were calculated and time trends compared by sex, age, urban-rural locality, geographical region, and socioeconomic status. FINDINGS: Standardised mean BMI levels rose from 22·7 kg/m2 (95% CI 22·5-22·9) in 2004 to 24·4 kg/m2 (24·3-24·6) in 2018 and obesity prevalence from 3·1% (2·5-3·7) to 8·1% (7·6-8·7). Between 2010 and 2018, mean BMI rose by 0&midd
Alli AS, Clark S, Hughes AF, et al., 2021, Spatial-temporal patterns of ambient fine particulate matter (PM2.5) and black carbon (BC) pollution in Accra, Environmental Research Letters, Vol: 16, Pages: 1-12, ISSN: 1748-9326
Background: Sub-Saharan Africa (SSA) is rapidly urbanizing, and ambient air pollution has emerged as a major environmental health concern in SSA cities. Yet, effective air quality management is hindered by limited data. We deployed robust, low-cost and low-power devices in a large-scale measurement campaign and characterized within-city variations in fine particulate matter (PM2.5) and black carbon (BC) pollution in Accra, Ghana. Methods: Between April 2019 and June 2020, we measured weekly gravimetric (filter-based) and minute-by-minute PM2.5 concentrations at 146 unique locations, comprising of 10 fixed (~1-year) and 136 rotating (7-day) sites covering a range of land-use and source influences. Filters were weighed for mass, and light absorbance (10−5m−1) of the filters was used as proxy for BC concentration. Year-long data at four fixed sites that were monitored in a previous study (2006-2007) were compared to assess change in PM2.5 concentrations. Results: The mean annual PM2.5 across the fixed sites ranged from 26 μg/m3 at a peri-urban site to 40 μg/m3 at commercial, business, and industrial (CBI) areas. CBI areas had the highest PM2.5 levels (mean: 37 μg/m3), followed by high-density residential neighborhoods (mean: 36 μg/m3), while peri-urban areas recorded the lowest (mean: 26 μg/m3). Both PM2.5 and BC levels were highest during the dry dusty Harmattan period (mean PM2.5: 89 μg/m3) compared to non-Harmattan season (mean PM2.5: 23 μg/m3). PM2.5 at all sites peaked at dawn and dusk, coinciding with morning and evening heavy traffic. We found about a ~50% reduction (71 vs 37 μg/m3) in mean annual PM2.5 concentrations when compared to measurements in 2006-2007 in Accra. Conclusion: Ambient PM2.5 concentrations in Accra may have plateaued at levels lower than those seen in large Asian megacities. However, levels are still 2- to 4-fold higher than the WHO guideline. Effective and equitable policies are needed to reduce pollution
Davies B, Parkes B, Bennett J, et al., 2021, Community factors and excess mortality in first wave of the COVID-19 pandemic in England, Nature Communications, ISSN: 2041-1723
Risk factors for increased risk of death from Coronavirus Disease 19 (COVID-19) have been identified but less is known on characteristics that make communities resilient or vulnerable to the mortality impacts of the pandemic. We applied a two-stage Bayesian spatial model to quantify inequalities in excess mortality at the community level during the first wave of the pandemic in England. We used geocoded data on all deaths in people aged 40 years and older during March-May 2020 compared with 2015-2019 in 6,791 local communities. Here we show that communities with an increased risk of excess mortality had a high density of care homes, and/or high proportion of residents on income support, living in overcrowded homes and/or high percent of people with a non-White ethnicity (including Black, Asian and other minority ethnic groups). Conversely, after accounting for other community characteristics, we found no association between population density or air pollution and excess mortality. Overall, the social and environmental variables accounted for around 15% of the variation in mortality at community level. Effective and timely public health and healthcare measures that target the communities at greatest risk are urgently needed if England and other industrialised countries are to avoid further widening of inequalities in mortality patterns as the pandemic progresses.
Zhou B, Perel P, Mensah GA, et al., 2021, Global epidemiology, health burden and effective interventions for elevated blood pressure and hypertension, NATURE REVIEWS CARDIOLOGY, ISSN: 1759-5002
Clark S, Alli A, Nathvani R, et al., 2021, Space-time characterization of community noise and sound sources in Accra, Ghana, Scientific Reports, Vol: 11, Pages: 1-14, ISSN: 2045-2322
Urban noise pollution is an emerging public health concern in growing cities in sub-Saharan Africa (SSA), but the sound environment in SSA cities is understudied. We leveraged a large-scale measurement campaign to characterize the spatial and temporal patterns of measured sound levels and sound sources in Accra, Ghana. We measured sound levels and recorded audio clips at 146 representative locations, involving 7-days (136 locations) and 1-year measurements between 2019 and 2020. We calculated metrics of noise levels and intermittency and analyzed audio recordings using a pre-trained neural network to identify sources. Commercial, business, and industrial areas and areas near major roads had the highest median daily sound levels (LAeq24hr: 69 dBA and 72 dBA) and the lowest percentage of intermittent sound; the vice-versa was found for peri urban areas. Road-transport sounds dominated the overall sound environment but mixtures of other sound sources, including animals, human speech, and outdoor music, dominated in various locations and at different times. Environmental noise levels in Accra exceeded both international and national health-based guidelines. Detailed information on the acoustical environmental quality (including sound levels and types) in Accra may guide environmental policy formulation and evaluation to improve the health of urban residents.
Suel E, Bhatt S, Brauer M, et al., 2021, Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas, Remote Sensing of Environment: an interdisciplinary journal, Vol: 257, ISSN: 0034-4257
Data collected at large scale and low cost (e.g. satellite and street level imagery) have the potential to substantially improve resolution, spatial coverage, and temporal frequency of measurement of urban inequalities. Multiple types of data from different sources are often available for a given geographic area. Yet, most studies utilize a single type of input data when making measurements due to methodological difficulties in their joint use. We propose two deep learning-based methods for jointly utilizing satellite and street level imagery for measuring urban inequalities. We use London as a case study for three selected outputs, each measured in decile classes: income, overcrowding, and environmental deprivation. We compare the performances of our proposed multimodal models to corresponding unimodal ones using mean absolute error (MAE). First, satellite tiles are appended to street level imagery to enhance predictions at locations where street images are available leading to improvements in accuracy by 20, 10, and 9% in units of decile classes for income, overcrowding, and living environment. The second approach, novel to the best of our knowledge, uses a U-Net architecture to make predictions for all grid cells in a city at high spatial resolution (e.g. for 3 m × 3 m pixels in London in our experiments). It can utilize city wide availability of satellite images as well as more sparse information from street-level images where they are available leading to improvements in accuracy by 6, 10, and 11%. We also show examples of prediction maps from both approaches to visually highlight performance differences.
Konstantinoudis G, Padellini T, Bennett J, et al., 2021, Response to "Re: Long-term exposure to air-pollution and COVID-19 mortality in England: A hierarchical spatial analysis", ENVIRONMENT INTERNATIONAL, Vol: 150, ISSN: 0160-4120
Shoari N, Ezzati M, Doyle YG, et al., 2021, Nowhere to play: available open and green space in Greater London schools, Journal of Urban Health: Bulletin of the New York Academy of Medicine, Vol: 98, Pages: 375-384, ISSN: 1099-3460
Experiencing outdoor space, especially natural space, during childhood and adolescence has beneficial physical and mental health effects, including improved cognitive and motor skills and a lower risk of obesity. Since school-age children typically spend 35–40 hours per week at schools, we quantified their access to open (non-built-up) space and green space at schools in Greater London. We linked land use information from the UK Ordnance Survey with school characteristics from the Department for Education (DfE) for schools in Greater London. We estimated open space by isolating land and water features within school boundaries and, as a subset of open space, green space defined as open space covered by vegetation. We examined the relationship of both school open and green space with distance to Central London, whether the school was fee-paying, and the percentage of pupils eligible for free school meals (as a school-level indicator of socioeconomic status). Almost 400,000 pupils (30% of all pupils in London) attended schools with less than ten square metre per pupil of open space—the minimum recommended area by DfE—and 800,000 pupils attended schools with less than ten square metre per pupil of green space. Of the latter, 70% did not have any public parks in the immediate vicinity of their schools. School green space increased with distance from Central London. There was a weak association between the school-level socioeconomic indicator and the amount of open and green space. Fee-paying schools provided less open space compared to non-fee-paying schools in central parts of London, but the provision became comparable in suburban London. Many London schools do not provide enough open and green space. There is a need to ensure regular contact with green space through safeguarding school grounds from sales, financially supporting disadvantaged schools to increase their outdoor space and providing access to off-site facilities such as sharing outdoor sp
NCD Risk Factor Collaboration NCD-RisC, Iurilli N, 2021, Heterogeneous contributions of change in population distribution of body-mass index to change in obesity and underweight, eLife, Vol: 10, ISSN: 2050-084X
From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions.
Pearson-Stuttard J, Bennett J, Vamos E, et al., 2021, Trends in predominant causes of death in individuals with and without diabetes in England from 2001 to 2018: an epidemiological analysis of linked primary care records, The Lancet Diabetes and Endocrinology, Vol: 9, Pages: 165-173, ISSN: 2213-8595
BackgroundThe prevalence of diabetes has increased in the UK and other high-income countries alongside a substantial decline in cardiovascular mortality. Yet data are scarce on how these trends have changed the causes of death in people with diabetes who have traditionally died primarily of vascular causes. We estimated how all-cause mortality and cause-specific mortality in people with diabetes have changed over time, how the composition of the mortality burden has changed, and how this composition compared with that of the non-diabetes population.MethodsIn this epidemiological analysis of primary care records, we identified 313 907 individuals with diabetes in the Clinical Practice Research Datalink, a well described primary care database, between 2001 to 2018, and linked these data to UK Office for National Statistics mortality data. We assembled serial cross sections with longitudinal follow-up to generate a mixed prevalence and incidence study population of patients with diabetes. We used discretised Poisson regression models to estimate annual death rates for deaths from all causes and 12 specific causes for men and women with diabetes. We also identified age-matched and sex matched (1:1) individuals without diabetes from the same dataset and estimated mortality rates in this group.FindingsBetween Jan 1, 2001, and Oct 31, 2018, total mortality declined by 32% in men and 31% in women with diagnosed diabetes. Death rates declined from 40·7 deaths per 1000 person-years to 27·8 deaths per 1000 person-years in men and from 42·7 deaths per 1000 person-years to 29·5 deaths per 1000 person-years in women with diagnosed diabetes. We found similar declines in individuals without diabetes, hence the gap in mortality between those with and without diabetes was maintained over the study period. Cause-specific death rates declined in ten of the 12 cause groups, with exceptions in dementia and liver disease, which increased in both populations. Th
Kontis V, Bennett JE, Rashid T, et al., 2021, Magnitude, demographics and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortality in 21 industrialized countries (vol 26, pg 1919, 2020), NATURE MEDICINE, Vol: 27, Pages: 562-562, ISSN: 1078-8956
Coleman NC, Ezzati M, Marshall JD, et al., 2021, Fine Particulate Matter Air Pollution and Mortality Risk Among US Cancer Patients and Survivors, JNCI CANCER SPECTRUM, Vol: 5
Chan JCN, Lim L-L, Wareham NJ, et al., 2020, The Lancet Commission on diabetes: using data to transform diabetes care and patient lives, LANCET, Vol: 396, Pages: 2019-2082, ISSN: 0140-6736
Suel E, Sorek-Hamer M, Moise I, et al., 2020, Predicting air pollution spatial variation with street-level imagery, Machine Learning in Public Health (MLPH) Workshop, 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Publisher: NeurIPS
Lee M, Carter E, Yan L, et al., 2020, Determinants of personal exposure to PM2.5 and black carbon in Chinese adults: a 1 repeated-measures study in villages using solid fuel energy, Environment International, Vol: 146, ISSN: 0160-4120
Exposure to air pollution is a leading health risk factor. The variance components and contributions of indoor versus outdoor source determinants of personal exposure to air pollution are poorly understood, especially in settings of household solid fuel use. We conducted a panel study with up to 4 days of repeated measures of integrated gravimetric personal exposure to PM2.5 and black carbon in 787 men and women (ages 40-79) living in peri-urban villages in northern (Beijing and Shanxi) and southern (Guangxi) China. We simultaneously measured outdoor PM2.5 and collected questionnaire data on sociodemographic characteristics and indoor pollution sources including tobacco smoking and solid fuel stove use. We obtained over 2000 days of personal exposure monitoring which showed higher exposures in the heating season (geometric mean (GM): 108 versus 65 μg/m3 in the non-heating season for PM2.5) and among northern participants (GM: 90 versus 59 μg/m3 in southern China in the non-heating season for PM2.5). We used mixed-effects models to estimate within- and between-participant variance components and to assess the determinants of exposures. Within-participant variance in exposure dominated the total variability (68-95%). Outdoor PM2.5 was the dominant variable for explaining within-participant variance in exposure to PM2.5 (16%). Household fuel use (PM2.5: 8%; black carbon: 10%) and smoking status (PM2.5: 27%; black carbon: 5%) explained the most between-participant variance. Indoor sources (solid fuel stoves, tobacco smoking) were associated with 13-30% higher exposures to air pollution and each 10 μg/m3 increase in outdoor PM2.5 was associated with 6-8% higher exposure. Our findings indicate that repeated measurements of daily exposure are likely needed to capture longer-term exposures in settings of household solid fuel use, even within a single season, and that reducing air pollution from both outdoor and indoor sources is likely needed to achieve measurable
Konstantinoudis G, Padellini T, Bennett J, et al., 2020, Long-term exposure to air-pollution and COVID-19 mortality in England: a hierarchical spatial analysis, Environment International, ISSN: 0160-4120
Recent studies suggested a link between long-term exposure to air-pollution and COVID-19 mortality. However, due to their ecological design based on large spatial units, they neglect the strong localised air-pollution patterns, and potentially lead to inadequate confounding adjustment. We investigated the effect of long-term exposure to NO2 and PM2.5 on COVID-19 deaths up to June 30, 2020 in England using high geographical resolution. In this nationwide cross-sectional study in England, we included 38,573 COVID-19 deaths up to June 30, 2020 at the Lower Layer Super Output Area level (n=32,844 small areas). We retrieved averaged NO2 and PM2.5 concentration during 2014-2018 from the Pollution Climate Mapping. We used Bayesian hierarchical models to quantify the effect of air-pollution while adjusting for a series of confounding and spatial autocorrelation. We find a 0.5% (95% credible interval: -0.2%, 1.2%) and 1.4% (95% CrI: -2.1%, 5.1%) increase in COVID-19 mortality risk for every 1μg/m3 increase in NO2 and PM2.5 respectively, after adjusting for confounding and spatial autocorrelation. This corresponds to a posterior probability of a positive effect equal to 0.93 and 0.78 respectively. The spatial relative risk at LSOA level revealed strong patterns, similar for the different pollutants. This potentially captures the spread of the disease during the first wave of the epidemic. Our study provides some evidence of an effect of long-term NO2 exposure on COVID-19 mortality, while the effect of PM2.5 remains more uncertain.
Parks RM, Bennett JE, Tamura-Wicks H, et al., 2020, Reply to: Concerns over calculating injury-related deaths associated with temperature, NATURE MEDICINE, Vol: 26, ISSN: 1078-8956
Rodriguez-Martinez A, Zhou B, Sophiea MK, et al., 2020, Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants, The Lancet, Vol: 396, Pages: 1511-1524, ISSN: 0140-6736
SummaryBackgroundComparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents.MethodsFor this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence.FindingsWe pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became
Shoari N, Ezzati M, Baumgartner J, et al., 2020, Accessibility and allocation of public parks and gardens in England and Wales: a COVID-19 social distancing perspective, PLoS One, Vol: 15, Pages: 1-10, ISSN: 1932-6203
Visiting parks and gardens supports physical and mental health. We quantified access to public parks and gardens in urban areas of England and Wales, and the potential for park crowdedness 22during periods of high use. We combined data from the Office for National Statistics and Ordnance Survey to quantify(i) the number of parks within 500and 1,000metresof urban postcodes (i.e., availability), (ii) the distance of postcodes to the nearest park (i.e., accessibility), and (iii) per-capita space in each park for people living within 1,000m.We26examined variability by city and share of flats. Around 25.4 million people(~87%) can access public parks or gardens within a ten-minute walk, while 3.8 million residents (~13%) live farther away; of these 21% are children and 13% are elderly. Areas with a higher share of flats on average are closer to a park but people living in these areas visit parks that are potentially overcrowded during periods of high use. Such disparity in urban areas of England and Wales becomes particularly evident during COVID-19 pandemic and lockdown when local parks, the only available out-of-home space option, hinder social distancing requirements. Cities aiming to facilitate social distancing while keeping public green spaces safe might require implementing measures such as dedicated park times for different age groups or entry allocation systems that, combined with smartphone apps or drones, can monitor and manage the total number of people using the park.
Kontis V, Bennett JE, Rashid T, et al., 2020, Magnitude, demographics and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortality in 21 industrialized countries, Nature Medicine, Vol: 26, Pages: 1919-1928, ISSN: 1078-8956
The Coronavirus Disease 2019 (COVID-19) pandemic has changed many social, economic, environmental and healthcare determinants of health. We applied an ensemble of 16 Bayesian models to vital statistics data to estimate the all-cause mortality effect of the pandemic for 21 industrialized countries. From mid-February through May 2020, 206,000 (95% credible interval, 178,100–231,000) more people died in these countries than would have had the pandemic not occurred. The number of excess deaths, excess deaths per 100,000 people and relative increase in deaths were similar between men and women in most countries. England and Wales and Spain experienced the largest effect: ~100 excess deaths per 100,000 people, equivalent to a 37% (30–44%) relative increase in England and Wales and 38% (31–45%) in Spain. Bulgaria, New Zealand, Slovakia, Australia, Czechia, Hungary, Poland, Norway, Denmark and Finland experienced mortality changes that ranged from possible small declines to increases of 5% or less in either sex. The heterogeneous mortality effects of the COVID-19 pandemic reflect differences in how well countries have managed the pandemic and the resilience and preparedness of the health and social care system.
Bukhman G, Mocumbi AO, Atun R, et al., 2020, The Lancet NCDI Poverty Commission: bridging a gap in universal health coverage for the poorest billion, LANCET, Vol: 396, Pages: 991-1044, ISSN: 0140-6736
Coleman NC, Burnett RT, Ezzati M, et al., 2020, Fine Particulate Matter Exposure and Cancer Incidence: Analysis of SEER Cancer Registry Data from 1992-2016, ENVIRONMENTAL HEALTH PERSPECTIVES, Vol: 128, ISSN: 0091-6765
Cohorts Consortium of Latin America and the Caribbean CC-LAC, Carrillo Larco R, Gregg EW, et al., 2020, Cohort profile: The Cohorts Consortium of Latin America and the Caribbean (CC-LAC), International Journal of Epidemiology, Vol: 49, Pages: 1437-1437g, ISSN: 0300-5771
Coleman NC, Burnett RT, Ezzati M, et al., 2020, Fine Particulate Matter Exposure and Cancer Incidence: Analysis of SEER Cancer Registry Data from 1992-2016., Environ Health Perspect, Vol: 128
BACKGROUND: Previous research has identified an association between fine particulate matter (PM2.5) air pollution and lung cancer. Most of the evidence for this association, however, is based on research using lung cancer mortality, not incidence. Research that examines potential associations between PM2.5 and incidence of non-lung cancers is limited. OBJECTIVES: The primary purpose of this study was to evaluate the association between the incidence of cancer and exposure to PM2.5 using >8.5 million cases of cancer incidences from U.S. registries. Secondary objectives include evaluating the sensitivity of the associations to model selection, spatial control, and latency period as well as estimating the exposure-response relationship for several cancer types. METHODS: Surveillance, Epidemiology, and End Results (SEER) program data were used to calculate incidence rates for various cancer types in 607 U.S. counties. County-level PM2.5 concentrations were estimated using integrated empirical geographic regression models. Flexible semi-nonparametric regression models were used to estimate associations between PM2.5 and cancer incidence for selected cancers while controlling for important county-level covariates. Primary time-independent models using average incidence rates from 1992-2016 and average PM2.5 from 1988-2015 were estimated. In addition, time-varying models using annual incidence rates from 2002-2011 and lagged moving averages of annual estimates for PM2.5 were also estimated. RESULTS: The incidences of all cancer and lung cancer were consistently associated with PM2.5. The incident rate ratios (IRRs), per 10-μg/m3 increase in PM2.5, for all and lung cancer were 1.09 (95% CI: 1.03, 1.14) and 1.19 (95% CI: 1.09, 1.30), respectively. Less robust associations were observed with oral, rectal, liver, skin, breast, and kidney cancers. DISCUSSION: Exposure to PM2.5 air pollution contributes to lung cancer incidence and is potentially associated with non
NCD Countdown 030 collaborators, Bennett JE, Kontis V, et al., 2020, NCD countdown 2030: pathways to achieving sustainable development goal target 3.4, The Lancet, Vol: 396, Pages: 918-934, ISSN: 0140-6736
The Sustainable Development Goal (SDG) target 3.4 is to reduce premature mortality from non-communicable diseases (NCDs) by a third by 2030 relative to 2015 levels, and to promote mental health and wellbeing. We used data on cause-specific mortality to characterise the risk and trends in NCD mortality in each country and evaluate combinations of reductions in NCD causes of death that can achieve SDG target 3.4. Among NCDs, ischaemic heart disease is responsible for the highest risk of premature death in more than half of all countries for women, and more than three-quarters for men. However, stroke, other cardiovascular diseases, and some cancers are associated with a similar risk, and in many countries, a higher risk of premature death than ischaemic heart disease. Although premature mortality from NCDs is declining in most countries, for most the pace of change is too slow to achieve SDG target 3.4. To investigate the options available to each country for achieving SDG target 3.4, we considered different scenarios, each representing a combination of fast (annual rate achieved by the tenth best performing percentile of all countries) and average (median of all countries) declines in risk of premature death from NCDs. Pathways analysis shows that every country has options for achieving SDG target 3.4. No country could achieve the target by addressing a single disease. In at least half the countries, achieving the target requires improvements in the rate of decline in at least five causes for women and in at least seven causes for men to the same rate achieved by the tenth best performing percentile of all countries. Tobacco and alcohol control and effective health-system interventions—including hypertension and diabetes treatment; primary and secondary cardiovascular disease prevention in high-risk individuals; low-dose inhaled corticosteroids and bronchodilators for asthma and chronic obstructive pulmonary disease; treatment of acute cardiovascular diseases
Chatzidiakou L, Krause A, Han Y, et al., 2020, Using low-cost sensor technologies and advanced computational methods to improve dose estimations in health panel studies: results of the AIRLESS project, Journal of Exposure Science and Environmental Epidemiology, Vol: 30, Pages: 981-989, ISSN: 1559-0631
BackgroundAir pollution epidemiology has primarily relied on fixed outdoor air quality monitoring networks and static populations.MethodsTaking advantage of recent advancements in sensor technologies and computational techniques, this paper presents a novel methodological approach that improves dose estimations of multiple air pollutants in large-scale health studies. We show the results of an intensive field campaign that measured personal exposures to gaseous pollutants and particulate matter of a health panel of 251 participants residing in urban and peri-urban Beijing with 60 personal air quality monitors (PAMs). Outdoor air pollution measurements were collected in monitoring stations close to the participants’ residential addresses. Based on parameters collected with the PAMs, we developed an advanced computational model that automatically classified time-activity-location patterns of each individual during daily life at high spatial and temporal resolution.ResultsApplying this methodological approach in two established cohorts, we found substantial differences between doses estimated from outdoor and personal air quality measurements. The PAM measurements also significantly reduced the correlation between pollutant species often observed in static outdoor measurements, reducing confounding effects.ConclusionsFuture work will utilise these improved dose estimations to investigate the underlying mechanisms of air pollution on cardio-pulmonary health outcomes using detailed medical biomarkers in a way that has not been possible before.
Coleman NC, Burnett RT, Higbee JD, et al., 2020, Cancer mortality risk, fine particulate air pollution, and smoking in a large, representative cohort of US adults, CANCER CAUSES & CONTROL, Vol: 31, Pages: 767-776, ISSN: 0957-5243
NCD Risk Factor Collaboration NCD-RisC, 2020, Repositioning of the global epicentre of non-optimal cholesterol, Nature, Vol: 582, Pages: 73-77, ISSN: 0028-0836
High blood cholesterol is typically considered a feature of wealthy western countries1,2. However, dietary and behavioural determinants of blood cholesterol are changing rapidly throughout the world3 and countries are using lipid-lowering medications at varying rates. These changes can have distinct effects on the levels of high-density lipoprotein (HDL) cholesterol and non-HDL cholesterol, which have different effects on human health4,5. However, the trends of HDL and non-HDL cholesterol levels over time have not been previously reported in a global analysis. Here we pooled 1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older to estimate trends from 1980 to 2018 in mean total, non-HDL and HDL cholesterol levels for 200 countries. Globally, there was little change in total or non-HDL cholesterol from 1980 to 2018. This was a net effect of increases in low- and middle-income countries, especially in east and southeast Asia, and decreases in high-income western countries, especially those in northwestern Europe, and in central and eastern Europe. As a result, countries with the highest level of non-HDL cholesterol-which is a marker of cardiovascular risk-changed from those in western Europe such as Belgium, Finland, Greenland, Iceland, Norway, Sweden, Switzerland and Malta in 1980 to those in Asia and the Pacific, such as Tokelau, Malaysia, The Philippines and Thailand. In 2017, high non-HDL cholesterol was responsible for an estimated 3.9 million (95% credible interval 3.7 million-4.2 million) worldwide deaths, half of which occurred in east, southeast and south Asia. The global repositioning of lipid-related risk, with non-optimal cholesterol shifting from a distinct feature of high-income countries in northwestern Europe, north America and Australasia to one that affects countries in east and southeast Asia and Oceania should motivate the use of population-based policies and per
Peto J, Alwan NA, Godfrey KM, et al., 2020, Universal weekly testing as the UK COVID-19 lockdown exit strategy, The Lancet, Vol: 395, Pages: 1420-1421, ISSN: 0140-6736
Higbee JD, Lefler JS, Burnett RT, et al., 2020, Estimating long-term pollution exposure effects through inverse probability weighting methods with Cox proportional hazards models., Environ Epidemiol, Vol: 4
Background: Fine particulate matter (PM2.5) is associated with negative health outcomes in both the short and long term. However, the cohort studies that have produced many of the estimates of long-term exposure associations may fail to account for selection bias in pollution exposure as well as covariate imbalance in the study population; therefore, causal modeling techniques may be beneficial. Methods: Twenty-nine years of data from the National Health Interview Survey (NHIS) was compiled and linked to modeled annual average outdoor PM2.5 concentration and restricted-use mortality data. A series of Cox proportional hazards models, adjusted using inverse probability weights, yielded causal risk estimates of long-term exposure to ambient PM2.5 on all-cause and cardiopulmonary mortality. Results: Covariate-adjusted estimated relative risks per 10 μg/m3 increase in PM2.5 exposure were estimated to be 1.117 (1.083, 1.152) for all-cause mortality and 1.232 (1.174, 1.292) for cardiopulmonary mortality. Inverse probability weighted Cox models provide relatively consistent and robust estimates similar to those in the unweighted baseline multivariate Cox model, though they have marginally lower point estimates and higher standard errors. Conclusions: These results provide evidence that long-term exposure to PM2.5 contributes to increased mortality risk in US adults and that the estimated effects are generally robust to modeling choices. The size and robustness of estimated associations highlight the importance of clean air as a matter of public health. Estimated confounding due to measured covariates appears minimal in the NHIS cohort, and various distributional assumptions have little bearing on the magnitude or standard errors of estimated causal associations.
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