METHODS: In this multinational, prospective cohort study, we examined associations for 14 potentially modifiable risk factors with mortality and cardiovascular disease in 155 722 participants without a prior history of cardiovascular disease from 21 high-income, middle-income, or low-income countries (HICs, MICs, or LICs). The primary outcomes for this paper were composites of cardiovascular disease events (defined as cardiovascular death, myocardial infarction, stroke, and heart failure) and mortality. We describe the prevalence, hazard ratios (HRs), and population-attributable fractions (PAFs) for cardiovascular disease and mortality associated with a cluster of behavioural factors (ie, tobacco use, alcohol, diet, physical activity, and sodium intake), metabolic factors (ie, lipids, blood pressure, diabetes, obesity), socioeconomic and psychosocial factors (ie, education, symptoms of depression), grip strength, and household and ambient pollution. Associations between risk factors and the outcomes were established using multivariable Cox frailty models and using PAFs for the entire cohort, and also by countries grouped by income level. Associations are presented as HRs and PAFs with 95% CIs.
FINDINGS: Between Jan 6, 2005, and Dec 4, 2016, 155 722 participants were enrolled and followed up for measurement of risk factors. 17 249 (11·1%) participants were from HICs, 102 680 (65·9%) were from MICs, and 35 793 (23·0%) from LICs. Approximately 70% of cardiovascular disease cases and deaths in the overall study population were attributed to modifiable risk factors. Metabolic factors were the predominant risk factors for cardiovascular disease (41·2% of the PAF), with hypertension being the largest (22·3% of the PAF). As a cluster, behavioural risk factors contributed most to deaths (26·3% of the PAF), although the single largest risk factor was a low education level (12·5% of the PAF). Ambient air pollution was associated with 13·9% of the PAF for cardiovascular disease, although different statistical methods were used for this analysis. In MICs and LICs, household air pollution, poor diet, low education, and low grip strength had stronger effects on cardiovascular disease or mortality than in HICs.
INTERPRETATION: Most cardiovascular disease cases and deaths can be attributed to a small number of common, modifiable risk factors. While some factors have extensive global effects (eg, hypertension and education), others (eg, household air pollution and poor diet) vary by a country's economic level. Health policies should focus on risk factors that have the greatest effects on averting cardiovascular disease and death globally, with additional emphasis on risk factors of greatest importance in specific groups of countries.
FUNDING: Full funding sources are listed at the end of the paper (see Acknowledgments).
STUDY DESIGN: Cross-sectional, web-based survey administered between May and June, 2020.
RESULTS: Of 189 invited participants in 69 LMICs, we received 145 (77%) responses from 58 (84%) countries. The pandemic provides significant challenges to neonatal care, particularly in low-income countries. Respondents noted exacerbations of preexisting shortages in staffing, equipment, and isolation capabilities. In Sub-Saharan Africa, 9/35 (26%) respondents noted increased mortality in non-COVID-19-infected infants. Clinical practices on cord clamping, isolation, and breastfeeding varied widely, often not in line with World Health Organization guidelines. Most respondents noted family access restrictions, and limited shared decision-making.
CONCLUSIONS: Many LMICs face an exacerbation of preexisting resource challenges for neonatal care during the pandemic. Variable approaches to care delivery and deviations from guidelines provide opportunities for international collaborative improvement.
METHODS: PLHIV enrolled in the Therapeutics, Research, Education and AIDS Training in Asia (TREAT Asia) HIV Observational Database (TAHOD) who initiated ART with a CD4 count 1 year were censored at 12 months. Competing risk regression was used to analyse risk factors with loss to follow-up as a competing risk.
RESULTS: A total of 1813 PLHIV were included in the study, of whom 74% were male. With 73 (4%) deaths, the overall first-year mortality rate was 4.27 per 100 person-years (PY). Thirty-eight deaths (52%) were AIDS-related, 10 (14%) were immune reconstituted inflammatory syndrome (IRIS)-related, 13 (18%) were non-AIDS-related and 12 (16%) had an unknown cause. Risk factors included having a body mass index (BMI) 100 cells/μL: SHR 0.12; 95% CI 0.05-0.26) was associated with reduced hazard for mortality compared to CD4 count ≤ 25 cells/μL.
CONCLUSIONS: Fifty-two per cent of early deaths were AIDS-related. Efforts to initiate ART at CD4 counts > 50 cell/μL are associated with improved short-term survival rates, even in those with late stages of HIV disease.
METHODS: A cross-sectional study was conducted at the National Heart Institute of Malaysia involving 503 patients who were hospitalized during the year prior to the survey.
RESULTS: The mean annual out-of-pocket health spending for IHD was MYR3045 (at the time US$761). Almost 16% (79/503) suffered from catastrophic health spending (out-of-pocket health spending ≥40% of household non-food expenditures), 29.2% (147/503) were unable to pay for medical bills, 25.0% (126/503) withdrew savings to help meet living expenses, 16.5% (83/503) reduced their monthly food consumption, 12.5% (63/503) were unable to pay utility bills and 9.0% (45/503) borrowed money to help meet living expenses.
CONCLUSIONS: Overall, the economic impact of IHD on patients in Malaysia was considerable and the prospect of economic hardship likely to persist over the years due to the long-standing nature of IHD. The findings highlight the need to evaluate the present health financing system in Malaysia and to expand its safety net coverage for vulnerable patients.
METHODS: We analysed Demographic and Health Survey data on tobacco use collected from large nationally representative samples of men and women in 54 LMICs. We estimated the weighted prevalence of any current tobacco use (including smokeless tobacco) in each country for 4 educational groups and 4 wealth groups. We calculated absolute and relative measures of inequality, that is, the slope index of inequality (SII) and relative index of inequality (RII), which take into account the distribution of prevalence across all education and wealth groups and account for population size. We also calculated the aggregate SII and RII for low-income (LIC), lower-middle-income (lMIC) and upper-middle-income (uMIC) countries as per World Bank classification.
FINDINGS: Male tobacco use was highest in Bangladesh (70.3%) and lowest in Sao Tome (7.4%), whereas female tobacco use was highest in Madagascar (21%) and lowest in Tajikistan (0.22%). Among men, educational inequalities varied widely between countries, but aggregate RII and SII showed an inverse trend by country wealth groups. RII was 3.61 (95% CI 2.83 to 4.61) in LICs, 1.99 (95% CI 1.66 to 2.38) in lMIC and 1.82 (95% CI 1.24 to 2.67) in uMIC. Wealth inequalities among men varied less between countries, but RII and SII showed an inverse pattern where RII was 2.43 (95% CI 2.05 to 2.88) in LICs, 1.84 (95% CI 1.54 to 2.21) in lMICs and 1.67 (95% CI 1.15 to 2.42) in uMICs. For educational inequalities among women, the RII varied much more than SII varied between the countries, and the aggregate RII was 14.49 (95% CI 8.87 to 23.68) in LICs, 3.05 (95% CI 1.44 to 6.47) in lMIC and 1.58 (95% CI 0.33 to 7.56) in uMIC. Wealth inequalities among women showed a pattern similar to that of men: the RII was 5.88 (95% CI 3.91 to 8.85) in LICs, 1.76 (95% CI 0.80 to 3.85) in lMIC and 0.39 (95% CI 0.09 to 1.64) in uMIC. In contrast to men, among women, the SII was pro-rich (higher smoking among the more advantaged) in 13 of the 52 countries (7 of 23 lMIC and 5 of 7 uMIC).
INTERPRETATION: Our results confirm that socioeconomic inequalities tobacco use exist in LMIC, varied widely between the countries and were much wider in the lowest income countries. These findings are important for better understanding and tackling of socioeconomic inequalities in health in LMIC.