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  1. Arku RE, Brauer M, Ahmed SH, AlHabib KF, Avezum Á, Bo J, et al.
    Environ Pollut, 2020 Jul;262:114197.
    PMID: 32146361 DOI: 10.1016/j.envpol.2020.114197
    Exposure to air pollution has been linked to elevated blood pressure (BP) and hypertension, but most research has focused on short-term (hours, days, or months) exposures at relatively low concentrations. We examined the associations between long-term (3-year average) concentrations of outdoor PM2.5 and household air pollution (HAP) from cooking with solid fuels with BP and hypertension in the Prospective Urban and Rural Epidemiology (PURE) study. Outdoor PM2.5 exposures were estimated at year of enrollment for 137,809 adults aged 35-70 years from 640 urban and rural communities in 21 countries using satellite and ground-based methods. Primary use of solid fuel for cooking was used as an indicator of HAP exposure, with analyses restricted to rural participants (n = 43,313) in 27 study centers in 10 countries. BP was measured following a standardized procedure and associations with air pollution examined with mixed-effect regression models, after adjustment for a comprehensive set of potential confounding factors. Baseline outdoor PM2.5 exposure ranged from 3 to 97 μg/m3 across study communities and was associated with an increased odds ratio (OR) of 1.04 (95% CI: 1.01, 1.07) for hypertension, per 10 μg/m3 increase in concentration. This association demonstrated non-linearity and was strongest for the fourth (PM2.5 > 62 μg/m3) compared to the first (PM2.5 
  2. Tse LA, Wang C, Rangarajan S, Liu Z, Teo K, Yusufali A, et al.
    JAMA Netw Open, 2021 06 01;4(6):e2113775.
    PMID: 34190997 DOI: 10.1001/jamanetworkopen.2021.13775
    Importance: Obesity is a growing public health threat leading to serious health consequences. Late bedtime and sleep loss are common in modern society, but their associations with specific obesity types are not well characterized.

    Objective: To assess whether sleep timing and napping behavior are associated with increased obesity, independent of nocturnal sleep length.

    Design, Setting, and Participants: This large, multinational, population-based cross-sectional study used data of participants from 60 study centers in 26 countries with varying income levels as part of the Prospective Urban Rural Epidemiology study. Participants were aged 35 to 70 years and were mainly recruited during 2005 and 2009. Data analysis occurred from October 2020 through March 2021.

    Exposures: Sleep timing (ie, bedtime and wake-up time), nocturnal sleep duration, daytime napping.

    Main Outcomes and Measures: The primary outcomes were prevalence of obesity, specified as general obesity, defined as body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) of 30 or greater, and abdominal obesity, defined as waist circumference greater than 102 cm for men or greater than 88 cm for women. Multilevel logistic regression models with random effects for study centers were performed to calculate adjusted odds ratios (AORs) and 95% CIs.

    Results: Overall, 136 652 participants (81 652 [59.8%] women; mean [SD] age, 51.0 [9.8] years) were included in analysis. A total of 27 195 participants (19.9%) had general obesity, and 37 024 participants (27.1%) had abdominal obesity. The mean (SD) nocturnal sleep duration was 7.8 (1.4) hours, and the median (interquartile range) midsleep time was 2:15 am (1:30 am-3:00 am). A total of 19 660 participants (14.4%) had late bedtime behavior (ie, midnight or later). Compared with bedtime between 8 pm and 10 pm, late bedtime was associated with general obesity (AOR, 1.20; 95% CI, 1.12-1.29) and abdominal obesity (AOR, 1.20; 95% CI, 1.12-1.28), particularly among participants who went to bed between 2 am and 6 am (general obesity: AOR, 1.35; 95% CI, 1.18-1.54; abdominal obesity: AOR, 1.38; 95% CI, 1.21-1.58). Short nocturnal sleep of less than 6 hours was associated with general obesity (eg, <5 hours: AOR, 1.27; 95% CI, 1.13-1.43), but longer napping was associated with higher abdominal obesity prevalence (eg, ≥1 hours: AOR, 1.39; 95% CI, 1.31-1.47). Neither going to bed during the day (ie, before 8pm) nor wake-up time was associated with obesity.

    Conclusions and Relevance: This cross-sectional study found that late nocturnal bedtime and short nocturnal sleep were associated with increased risk of obesity prevalence, while longer daytime napping did not reduce the risk but was associated with higher risk of abdominal obesity. Strategic weight control programs should also encourage earlier bedtime and avoid short nocturnal sleep to mitigate obesity epidemic.

  3. Murphy A, Palafox B, Walli-Attaei M, Powell-Jackson T, Rangarajan S, Alhabib KF, et al.
    BMJ Glob Health, 2020;5(2):e002040.
    PMID: 32133191 DOI: 10.1136/bmjgh-2019-002040
    BACKGROUND: Non-communicable diseases (NCDs) are the leading cause of death globally. In 2014, the United Nations committed to reducing premature mortality from NCDs, including by reducing the burden of healthcare costs. Since 2014, the Prospective Urban and Rural Epidemiology (PURE) Study has been collecting health expenditure data from households with NCDs in 18 countries.

    METHODS: Using data from the PURE Study, we estimated risk of catastrophic health spending and impoverishment among households with at least one person with NCDs (cardiovascular disease, diabetes, kidney disease, cancer and respiratory diseases; n=17 435), with hypertension only (a leading risk factor for NCDs; n=11 831) or with neither (n=22 654) by country income group: high-income countries (Canada and Sweden), upper middle income countries (UMICs: Brazil, Chile, Malaysia, Poland, South Africa and Turkey), lower middle income countries (LMICs: the Philippines, Colombia, India, Iran and the Occupied Palestinian Territory) and low-income countries (LICs: Bangladesh, Pakistan, Zimbabwe and Tanzania) and China.

    RESULTS: The prevalence of catastrophic spending and impoverishment is highest among households with NCDs in LMICs and China. After adjusting for covariates that might drive health expenditure, the absolute risk of catastrophic spending is higher in households with NCDs compared with no NCDs in LMICs (risk difference=1.71%; 95% CI 0.75 to 2.67), UMICs (0.82%; 95% CI 0.37 to 1.27) and China (7.52%; 95% CI 5.88 to 9.16). A similar pattern is observed in UMICs and China for impoverishment. A high proportion of those with NCDs in LICs, especially women (38.7% compared with 12.6% in men), reported not taking medication due to costs.

    CONCLUSIONS: Our findings show that financial protection from healthcare costs for people with NCDs is inadequate, particularly in LMICs and China. While the burden of NCD care may appear greatest in LMICs and China, the burden in LICs may be masked by care foregone due to costs. The high proportion of women reporting foregone care due to cost may in part explain gender inequality in treatment of NCDs.

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