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Physical inactivity is an important modifiable risk factor for non-communicable diseases (NCDs) and mental health conditions. We aimed to estimate the public health-care costs associated with these diseases because of physical inactivity, which will help policy makers to prioritise investment in policy actions to promote and enable more people to be more active.We used a population-attributable fraction formula to estimate the direct public health-care costs of NCDs and mental health conditions for 2020-30. The disease outcomes that we included were incident cases of coronary heart disease, stroke, type 2 diabetes, hypertension, cancer (breast, colon, bladder, endometrial, oesophageal, gastric, and renal), dementia, and depression in adults aged at least 18 years. We used the most recent health and economic data evidence available for 194 countries.499·2 million new cases of preventable major NCDs would occur globally by 2030 if the prevalence of physical inactivity does not change, with direct health-care costs of INT$520 billion. The global cost of inaction on physical inactivity would reach approximately $47·6 billion per year. Although 74% of new cases of NCDs would occur in low-income and middle-countries, high-income countries would bear a larger proportion (63%) of the economic costs. The cost of treatment and management of NCDs varied-although dementia accounted for only 3% of new preventable NCDs, the disease corresponded to 22% of all costs; type 2 diabetes accounted for 2% of new preventable cases but 9% of all costs; and cancers accounted for 1% of new preventable cases but 15% of all costs.This health and economic burden of physical inactivity is avoidable. Further investments in and implementation of known and effective policy interventions will support countries to reach the Sustainable Development Goal of reduction of NCD mortality by 2030.None.

While it is well known that socioeconomic markers are associated with a higher risk of arbovirus infections, research on the relationship between socioeconomic factors and congenital Zika syndrome (CZS) remains limited. This study investigates the relationship between socioeconomic risk markers and live births with CZS in Brazil. We conducted a population-based study using data from all registered live births in Brazil (Live Births Information System) linked with the Public Health Event Record from 1 January 2015 to 31 December 2018. We used logistic regression models to estimate the OR and 95% CIs of CZS based on a three-level framework. In an analysis of 11 366 686 live births, of which 3353 had CZS, we observed that live births of self-identified black or mixed race/brown mothers (1.72 (95% CI 1.47 to 2.01) and 1.37 (95% CI 1.24 to 1.51)) were associated with a higher odds of CZS. Live births from single women compared with married women and those from women with less than 12 years of education compared with those with more than 12 years of education also had higher odds of CZS. In addition, live births following fewer prenatal care appointments had increased odds of CZS in the nationwide data. However, in the analyses conducted in the Northeast region (where the microcephaly epidemic started before the link with Zika virus was established and before preventive measures were known or disseminated), no statistical association was found between the number of prenatal care appointments and the odds of CZS. This study shows that live births of the most socially vulnerable women in Brazil had the greatest odds of CZS. This disproportionate distribution of risk places an even greater burden on already socioeconomically disadvantaged groups, and the lifelong disabilities caused by this syndrome may reinforce existing social and health inequalities.

Air pollution of fine particulates (PM2.5) is a leading cause of mortality worldwide. It is estimated that ambient PM2.5 air pollution results in between 4.1 million and 8.9 million premature deaths annually. According to the World Bank, the health effects of ambient PM2.5 air pollution had a cost of $6.4 trillion in purchasing power parity (PPP) adjusted dollars in 2019, equivalent to 4.8% of global gross domestic product (PPP adjusted) that year. Estimating the health effects and cost of ambient PM2.5 air pollution involves three steps: (1) estimating population exposure to pollution; (2) estimating the health effects of such exposure; and (3) assigning a monetary value to the illnesses and premature deaths caused by ambient air pollution. Estimating population exposure to ambient PM2,5 has gone from predominantly using ground level monitoring data mainly in larger cities to estimates of nationwide population weighted exposures based on satellite imagery and chemical transport models along with ground level monitoring data. The Global Burden of Disease 2010 (GBD 2010) provided for the first time national, regional and global estimates of exposures to ambient PM2.5. The GBD exposure estimates have also evolved substantially from 2010 to 2019, especially national estimates in South Asia, the Middle East and North Africa, Sub-Saharan Africa and Latin America and the Caribbean. Estimation of health effects of ambient PM2.5 has also undergone substantial developments during the last two decades. These developments involve: i) going from largely estimating health effects associated with variations in daily exposures to estimating health effects of annual exposure; ii) going from estimating all-cause mortality or mortality from broad disease categories (i.e., cardiopulmonary diseases) to estimating mortality from specific diseases; and iii) being able to estimate health effects over a wide range of exposure that reflect ambient and household air pollution exposure levels in low- and middle-income countries. As to monetary valuation of health effects of ambient air pollution, estimates in most low- and middle-income countries still rely on benefit transfer of values of statistical life (VSL) from high-income countries.