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  1. Santosa A, Rosengren A, Ramasundarahettige C, Rangarajan S, Chifamba J, Lear SA, et al.
    JAMA Netw Open, 2021 12 01;4(12):e2138920.
    PMID: 34910150 DOI: 10.1001/jamanetworkopen.2021.38920
    Importance: Stress may increase the risk of cardiovascular disease (CVD). Most studies on stress and CVD have been conducted in high-income Western countries, but whether stress is associated with CVD in other settings has been less well studied.

    Objective: To investigate the association of a composite measure of psychosocial stress and the development of CVD events and mortality in a large prospective study involving populations from 21 high-, middle-, and low-income countries across 5 continents.

    Design, Setting, and Participants: This population-based cohort study used data from the Prospective Urban Rural Epidemiology study, collected between January 2003 and March 2021. Participants included individuals aged 35 to 70 years living in 21 low-, middle-, and high-income countries. Data were analyzed from April 8 to June 15, 2021.

    Exposures: All participants were assessed on a composite measure of psychosocial stress assessed at study entry using brief questionnaires concerning stress at work and home, major life events, and financial stress.

    Main Outcomes and Measures: The outcomes of interest were stroke, major coronary heart disease (CHD), CVD, and all-cause mortality.

    Results: A total of 118 706 participants (mean [SD] age 50.4 [9.6] years; 69 842 [58.8%] women and 48 864 [41.2%] men) without prior CVD and with complete baseline and follow-up data were included. Of these, 8699 participants (7.3%) reported high stress, 21 797 participants (18.4%) reported moderate stress, 34 958 participants (29.4%) reported low stress, and 53 252 participants (44.8%) reported no stress. High stress, compared with no stress, was more likely with younger age (mean [SD] age, 48.9 [8.9] years vs 51.1 [9.8] years), abdominal obesity (2981 participants [34.3%] vs 10 599 participants [19.9%]), current smoking (2319 participants [26.7%] vs 10 477 participants [19.7%]) and former smoking (1571 participants [18.1%] vs 3978 participants [7.5%]), alcohol use (4222 participants [48.5%] vs 13 222 participants [24.8%]), and family history of CVD (5435 participants [62.5%] vs 20 255 participants [38.0%]). During a median (IQR) follow-up of 10.2 (8.6-11.9) years, a total of 7248 deaths occurred. During the course of follow-up, there were 5934 CVD events, 4107 CHD events, and 2880 stroke events. Compared with no stress and after adjustment for age, sex, education, marital status, location, abdominal obesity, hypertension, smoking, diabetes, and family history of CVD, as the level of stress increased, there were increases in risk of death (low stress: hazard ratio [HR], 1.09 [95% CI, 1.03-1.16]; high stress: 1.17 [95% CI, 1.06-1.29]) and CHD (low stress: HR, 1.09 [95% CI, 1.01-1.18]; high stress: HR, 1.24 [95% CI, 1.08-1.42]). High stress, but not low or moderate stress, was associated with CVD (HR, 1.22 [95% CI, 1.08-1.37]) and stroke (HR, 1.30 [95% CI, 1.09-1.56]) after adjustment.

    Conclusions and Relevance: This cohort study found that higher psychosocial stress, measured as a composite score of self-perceived stress, life events, and financial stress, was significantly associated with mortality as well as with CVD, CHD, and stroke events.

  2. Rosengren A, Smyth A, Rangarajan S, Ramasundarahettige C, Bangdiwala SI, AlHabib KF, et al.
    Lancet Glob Health, 2019 06;7(6):e748-e760.
    PMID: 31028013 DOI: 10.1016/S2214-109X(19)30045-2
    BACKGROUND: Socioeconomic status is associated with differences in risk factors for cardiovascular disease incidence and outcomes, including mortality. However, it is unclear whether the associations between cardiovascular disease and common measures of socioeconomic status-wealth and education-differ among high-income, middle-income, and low-income countries, and, if so, why these differences exist. We explored the association between education and household wealth and cardiovascular disease and mortality to assess which marker is the stronger predictor of outcomes, and examined whether any differences in cardiovascular disease by socioeconomic status parallel differences in risk factor levels or differences in management.

    METHODS: In this large-scale prospective cohort study, we recruited adults aged between 35 years and 70 years from 367 urban and 302 rural communities in 20 countries. We collected data on families and households in two questionnaires, and data on cardiovascular risk factors in a third questionnaire, which was supplemented with physical examination. We assessed socioeconomic status using education and a household wealth index. Education was categorised as no or primary school education only, secondary school education, or higher education, defined as completion of trade school, college, or university. Household wealth, calculated at the household level and with household data, was defined by an index on the basis of ownership of assets and housing characteristics. Primary outcomes were major cardiovascular disease (a composite of cardiovascular deaths, strokes, myocardial infarction, and heart failure), cardiovascular mortality, and all-cause mortality. Information on specific events was obtained from participants or their family.

    FINDINGS: Recruitment to the study began on Jan 12, 2001, with most participants enrolled between Jan 6, 2005, and Dec 4, 2014. 160 299 (87·9%) of 182 375 participants with baseline data had available follow-up event data and were eligible for inclusion. After exclusion of 6130 (3·8%) participants without complete baseline or follow-up data, 154 169 individuals remained for analysis, from five low-income, 11 middle-income, and four high-income countries. Participants were followed-up for a mean of 7·5 years. Major cardiovascular events were more common among those with low levels of education in all types of country studied, but much more so in low-income countries. After adjustment for wealth and other factors, the HR (low level of education vs high level of education) was 1·23 (95% CI 0·96-1·58) for high-income countries, 1·59 (1·42-1·78) in middle-income countries, and 2·23 (1·79-2·77) in low-income countries (pinteraction<0·0001). We observed similar results for all-cause mortality, with HRs of 1·50 (1·14-1·98) for high-income countries, 1·80 (1·58-2·06) in middle-income countries, and 2·76 (2·29-3·31) in low-income countries (pinteraction<0·0001). By contrast, we found no or weak associations between wealth and these two outcomes. Differences in outcomes between educational groups were not explained by differences in risk factors, which decreased as the level of education increased in high-income countries, but increased as the level of education increased in low-income countries (pinteraction<0·0001). Medical care (eg, management of hypertension, diabetes, and secondary prevention) seemed to play an important part in adverse cardiovascular disease outcomes because such care is likely to be poorer in people with the lowest levels of education compared to those with higher levels of education in low-income countries; however, we observed less marked differences in care based on level of education in middle-income countries and no or minor differences in high-income countries.

    INTERPRETATION: Although people with a lower level of education in low-income and middle-income countries have higher incidence of and mortality from cardiovascular disease, they have better overall risk factor profiles. However, these individuals have markedly poorer health care. Policies to reduce health inequities globally must include strategies to overcome barriers to care, especially for those with lower levels of education.

    FUNDING: Full funding sources are listed at the end of the paper (see Acknowledgments).

  3. Chow CK, Ramasundarahettige C, Hu W, AlHabib KF, Avezum A, Cheng X, et al.
    Lancet Diabetes Endocrinol, 2018 10;6(10):798-808.
    PMID: 30170949 DOI: 10.1016/S2213-8587(18)30233-X
    BACKGROUND: Data are scarce on the availability and affordability of essential medicines for diabetes. Our aim was to examine the availability and affordability of metformin, sulfonylureas, and insulin across multiple regions of the world and explore the effect of these on medicine use.

    METHODS: In the Prospective Urban Rural Epidemiology (PURE) study, participants aged 35-70 years (n=156 625) were recruited from 110 803 households, in 604 communities and 22 countries; availability (presence of any dose of medication in the pharmacy on the day of audit) and medicine cost data were collected from pharmacies with the Environmental Profile of a Community's Health audit tool. Our primary analysis was to describe the availability and affordability of metformin and insulin and also commonly used and prescribed combinations of two medicines for diabetes management (two oral drugs, metformin plus a sulphonylurea [either glibenclamide (also known as glyburide) or gliclazide] and one oral drug plus insulin [metformin plus insulin]). Medicines were defined as affordable if the cost of medicines was less than 20% of capacity-to-pay (the household income minus food expenditure). Our analyses included data collected in pharmacies and data from representative samples of households. Data on availability were ascertained during the pharmacy audit, as were data on cost of medications. These cost data were used to estimate the cost of a month's supply of essential medicines for diabetes. We estimated affordability of medicines using income data from household surveys.

    FINDINGS: Metformin was available in 113 (100%) of 113 pharmacies from high-income countries, 112 (88·2%) of 127 pharmacies in upper-middle-income countries, 179 (86·1%) of 208 pharmacies in lower-middle-income countries, 44 (64·7%) of 68 pharmacies in low-income countries (excluding India), and 88 (100%) of 88 pharmacies in India. Insulin was available in 106 (93·8%) pharmacies in high-income countries, 51 (40·2%) pharmacies in upper-middle-income countries, 61 (29·3%) pharmacies in lower-middle-income countries, seven (10·3%) pharmacies in lower-income countries, and 67 (76·1%) of 88 pharmacies in India. We estimated 0·7% of households in high-income countries and 26·9% of households in low-income countries could not afford metformin and 2·8% of households in high-income countries and 63·0% of households in low-income countries could not afford insulin. Among the 13 569 (8·6% of PURE participants) that reported a diagnosis of diabetes, 1222 (74·0%) participants reported diabetes medicine use in high-income countries compared with 143 (29·6%) participants in low-income countries. In multilevel models, availability and affordability were significantly associated with use of diabetes medicines.

    INTERPRETATION: Availability and affordability of essential diabetes medicines are poor in low-income and middle-income countries. Awareness of these global differences might importantly drive change in access for patients with diabetes.

    FUNDING: Full funding sources listed at the end of the paper (see Acknowledgments).

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