The socioeconomic gradient of alcohol use: an analysis of nationally representative survey data from 55 low-income and middle-income countries.

BACKGROUND: Alcohol is a leading risk factor for over 200 conditions and an important contributor to socioeconomic health inequalities. However, little is known about the associations between individuals' socioeconomic circumstances and alcohol consumption, especially heavy episodic drinking (HED; ≥5 drinks on one occasion) in low-income or middle-income countries. We investigated the association between individual and household level socioeconomic status, and alcohol drinking habits in these settings. METHODS: In this pooled analysis of individual-level data, we used available nationally representative surveys-mainly WHO Stepwise Approach to Surveillance surveys-conducted in 55 low-income and middle-income countries between 2005 and 2017 reporting on alcohol use. Surveys from participants aged 15 years or older were included. Logistic regression models controlling for age, country, and survey year stratified by sex and country income groups were used to investigate associations between two indicators of socioeconomic status (individual educational attainment and household wealth) and alcohol use (current drinking and HED amongst current drinkers). FINDINGS: Surveys from 336 287 participants were included in the analysis. Among males, the highest prevalence of both current drinking and HED was found in lower-middle-income countries (L-MICs; current drinking 49·9% [95% CI 48·7-51·2] and HED 63·3% [61·0-65·7]). Among females, the prevalence of current drinking was highest in upper-middle-income countries (U-MIC; 29·5% [26·1-33·2]), and the prevalence of HED was highest in low-income countries (LICs; 36·8% [33·6-40·2]). Clear gradients in the prevalence of current drinking were observed across all country income groups, with a higher prevalence among participants with high socioeconomic status. However, in U-MICs, current drinkers with low socioeconomic status were more likely to engage in HED than participants with high socioeconomic status; the opposite was observed in LICs, and no association between socioeconomic status and HED was found in L-MICs. INTERPRETATION: The findings call for urgent alcohol control policies and interventions in LICs and L-MICs to reduce harmful HED. Moreover, alcohol control policies need to be targeted at socially disadvantaged groups in U-MICs. FUNDING: Deutsche Forschungsgemeinschaft and the National Center for Advancing Translational Sciences of the US National Institutes of Health.

Association between country preparedness indicators and quality clinical care for cardiovascular disease risk factors in 44 lower- and middle-income countries: A multicountry analysis of survey data.

BACKGROUND: Cardiovascular diseases are leading causes of death, globally, and health systems that deliver quality clinical care are needed to manage an increasing number of people with risk factors for these diseases. Indicators of preparedness of countries to manage cardiovascular disease risk factors (CVDRFs) are regularly collected by ministries of health and global health agencies. We aimed to assess whether these indicators are associated with patient receipt of quality clinical care. METHODS AND FINDINGS: We did a secondary analysis of cross-sectional, nationally representative, individual-patient data from 187,552 people with hypertension (mean age 48.1 years, 53.5% female) living in 43 low- and middle-income countries (LMICs) and 40,795 people with diabetes (mean age 52.2 years, 57.7% female) living in 28 LMICs on progress through cascades of care (condition diagnosed, treated, or controlled) for diabetes or hypertension, to indicate outcomes of provision of quality clinical care. Data were extracted from national-level World Health Organization (WHO) Stepwise Approach to Surveillance (STEPS), or other similar household surveys, conducted between July 2005 and November 2016. We used mixed-effects logistic regression to estimate associations between each quality clinical care outcome and indicators of country development (gross domestic product [GDP] per capita or Human Development Index [HDI]); national capacity for the prevention and control of noncommunicable diseases ('NCD readiness indicators' from surveys done by WHO); health system finance (domestic government expenditure on health [as percentage of GDP], private, and out-of-pocket expenditure on health [both as percentage of current]); and health service readiness (number of physicians, nurses, or hospital beds per 1,000 people) and performance (neonatal mortality rate). All models were adjusted for individual-level predictors including age, sex, and education. In an exploratory analysis, we tested whether national-level data on facility preparedness for diabetes were positively associated with outcomes. Associations were inconsistent between indicators and quality clinical care outcomes. For hypertension, GDP and HDI were both positively associated with each outcome. Of the 33 relationships tested between NCD readiness indicators and outcomes, only two showed a significant positive association: presence of guidelines with being diagnosed (odds ratio [OR], 1.86 [95% CI 1.08-3.21], p = 0.03) and availability of funding with being controlled (OR, 2.26 [95% CI 1.09-4.69], p = 0.03). Hospital beds (OR, 1.14 [95% CI 1.02-1.27], p = 0.02), nurses/midwives (OR, 1.24 [95% CI 1.06-1.44], p = 0.006), and physicians (OR, 1.21 [95% CI 1.11-1.32], p < 0.001) per 1,000 people were positively associated with being diagnosed and, similarly, with being treated; and the number of physicians was additionally associated with being controlled (OR, 1.12 [95% CI 1.01-1.23], p = 0.03). For diabetes, no positive associations were seen between NCD readiness indicators and outcomes. There was no association between country development, health service finance, or health service performance and readiness indicators and any outcome, apart from GDP (OR, 1.70 [95% CI 1.12-2.59], p = 0.01), HDI (OR, 1.21 [95% CI 1.01-1.44], p = 0.04), and number of physicians per 1,000 people (OR, 1.28 [95% CI 1.09-1.51], p = 0.003), which were associated with being diagnosed. Six countries had data on cascades of care and nationwide-level data on facility preparedness. Of the 27 associations tested between facility preparedness indicators and outcomes, the only association that was significant was having metformin available, which was positively associated with treatment (OR, 1.35 [95% CI 1.01-1.81], p = 0.04). The main limitation was use of blood pressure measurement on a single occasion to diagnose hypertension and a single blood glucose measurement to diagnose diabetes. CONCLUSION: In this study, we observed that indicators of country preparedness to deal with CVDRFs are poor proxies for quality clinical care received by patients for hypertension and diabetes. The major implication is that assessments of countries' preparedness to manage CVDRFs should not rely on proxies; rather, it should involve direct assessment of quality clinical care.

The state of hypertension care in 44 low-income and middle-income countries: a cross-sectional study of nationally representative individual-level data from 1·1 million adults.

BACKGROUND: Evidence from nationally representative studies in low-income and middle-income countries (LMICs) on where in the hypertension care continuum patients are lost to care is sparse. This information, however, is essential for effective targeting of interventions by health services and monitoring progress in improving hypertension care. We aimed to determine the cascade of hypertension care in 44 LMICs-and its variation between countries and population groups-by dividing the progression in the care process, from need of care to successful treatment, into discrete stages and measuring the losses at each stage. METHODS: In this cross-sectional study, we pooled individual-level population-based data from 44 LMICs. We first searched for nationally representative datasets from the WHO Stepwise Approach to Surveillance (STEPS) from 2005 or later. If a STEPS dataset was not available for a LMIC (or we could not gain access to it), we conducted a systematic search for survey datasets; the inclusion criteria in these searches were that the survey was done in 2005 or later, was nationally representative for at least three 10-year age groups older than 15 years, included measured blood pressure data, and contained data on at least two hypertension care cascade steps. Hypertension was defined as a systolic blood pressure of at least 140 mm Hg, diastolic blood pressure of at least 90 mm Hg, or reported use of medication for hypertension. Among those with hypertension, we calculated the proportion of individuals who had ever had their blood pressure measured; had been diagnosed with hypertension; had been treated for hypertension; and had achieved control of their hypertension. We weighted countries proportionally to their population size when determining this hypertension care cascade at the global and regional level. We disaggregated the hypertension care cascade by age, sex, education, household wealth quintile, body-mass index, smoking status, country, and region. We used linear regression to predict, separately for each cascade step, a country's performance based on gross domestic product (GDP) per capita, allowing us to identify countries whose performance fell outside of the 95% prediction interval. FINDINGS: Our pooled dataset included 1 100 507 participants, of whom 192 441 (17·5%) had hypertension. Among those with hypertension, 73·6% of participants (95% CI 72·9-74·3) had ever had their blood pressure measured, 39·2% of participants (38·2-40·3) had been diagnosed with hypertension, 29·9% of participants (28·6-31·3) received treatment, and 10·3% of participants (9·6-11·0) achieved control of their hypertension. Countries in Latin America and the Caribbean generally achieved the best performance relative to their predicted performance based on GDP per capita, whereas countries in sub-Saharan Africa performed worst. Bangladesh, Brazil, Costa Rica, Ecuador, Kyrgyzstan, and Peru performed significantly better on all care cascade steps than predicted based on GDP per capita. Being a woman, older, more educated, wealthier, and not being a current smoker were all positively associated with attaining each of the four steps of the care cascade. INTERPRETATION: Our study provides important evidence for the design and targeting of health policies and service interventions for hypertension in LMICs. We show at what steps and for whom there are gaps in the hypertension care process in each of the 44 countries in our study. We also identified countries in each world region that perform better than expected from their economic development, which can direct policy makers to important policy lessons. Given the high disease burden caused by hypertension in LMICs, nationally representative hypertension care cascades, as constructed in this study, are an important measure of progress towards achieving universal health coverage. FUNDING: Harvard McLennan Family Fund, Alexander von Humboldt Foundation.

Consumption of Fruits and Vegetables Among Individuals 15 Years and Older in 28 Low- and Middle-Income Countries.

BACKGROUND: The WHO recommends 400 g/d of fruits and vegetables (the equivalent of ∼5 servings/d) for the prevention of noncommunicable diseases (NCDs). However, there is limited evidence regarding individual-level correlates of meeting these recommendations in low- and middle-income countries (LMICs). In order to target policies and interventions aimed at improving intake, global monitoring of fruit and vegetable consumption by socio-demographic subpopulations is required. OBJECTIVES: The aims of this study were to 1) assess the proportion of individuals meeting the WHO recommendation and 2) evaluate socio-demographic predictors (age, sex, and educational attainment) of meeting the WHO recommendation. METHODS: Data were collected from 193,606 individuals aged ≥15 y in 28 LMICs between 2005 and 2016. The prevalence of meeting the WHO recommendation took into account the complex survey designs, and countries were weighted according to their World Bank population estimates in 2015. Poisson regression was used to estimate associations with socio-demographic characteristics. RESULTS: The proportion (95% CI) of individuals aged ≥15 y who met the WHO recommendation was 18.0% (16.6-19.4%). Mean intake of fruits was 1.15 (1.10-1.20) servings per day and for vegetables, 2.46 (2.40-2.51) servings/d. The proportion of individuals meeting the recommendation increased with increasing country gross domestic product (GDP) class (P < 0.0001) and with decreasing country FAO food price index (FPI; indicating greater stability of food prices; P < 0.0001). At the individual level, those with secondary education or greater were more likely to achieve the recommendation compared with individuals with no formal education: risk ratio (95% CI), 1.61 (1.24-2.09). CONCLUSIONS: Over 80% of individuals aged ≥15 y living in these 28 LMICs consumed lower amounts of fruits and vegetables than recommended by the WHO. Policies to promote fruit and vegetable consumption in LMICs are urgently needed to address the observed inequities in intake and prevent NCDs.

Levels and distribution of self-rated health in the Kazakh population: results from the Kazakhstan household health survey 2012.

BACKGROUND: The high and fluctuating mortality and rising health inequalities in post-Soviet countries have attracted considerable attention. However, there are very few individual-level data on distribution of health outcomes in Central Asian countries of the former Soviet Union. We analysed socioeconomic predictors of two self-rated health outcomes in a national survey in Kazakhstan. METHODS: We used data from the 2012 Kazakhstan Household Health Survey on 12,560 respondents aged 15+. Self-rated health, self-reported worsening of health, and a range of socio-demographic variables were collected in an interview. The self-rated health outcomes were dichotomized and logistic regression was used to estimate their associations with education, income, ownership of a car, second house and computer, marital status, ethnicity and urban/rural residence. RESULTS: The prevalence of poor/very poor self-rated health was 5.3%, and 11.0% of participants reported worse health compared to 1 year ago. After controlling for age, sex and region, all socio-demographic factors were related to self-rated health. After adjusting for all variables, education and car ownership showed the most consistent effects; the odds ratio of poor health and worsening of health were 0.43 (95% confidence interval 0.32-0.58) and 0.54 (0.44-0.68) for university vs. primary education, respectively, and 0.64 (0.51-0.82) and 0.68 (0.58-0.80) for car ownership, respectively. Unmarried persons, ethnic Russians and urban residents also had increased prevalence of poor health in multivariable models. CONCLUSIONS: Despite the limitations of using subjective health measures, these data suggest strong associations between two measures of self-rated health and a number of socioeconomic characteristics. Future studies and health policy initiatives in Kazakhstan and other Central Asian countries should take social determinants of health into account.