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Comparative health system performance in six middle-income countries: cross-sectional analysis using World Health Organization study of global ageing and health.

ABSTRACT: Objective To assess and compare health system performance across six middle-income countries that are strengthening their health systems in pursuit of universal health coverage. Design Cross-sectional analysis from the World Health Organization Study on global AGEing and adult health, collected between 2007 and 2010. Setting Six middle-income countries: China, Ghana, India, Mexico, Russia and South Africa. Participants Nationally representative sample of adults aged 50 years and older. Main outcome measures We present achievement against key indicators of health system performance across effectiveness, cost, access, patient-centredness and equity domains. Results We found areas of poor performance in prevention and management of chronic conditions, such as hypertension control and cancer screening coverage. We also found that cost remains a barrier to healthcare access in spite of insurance schemes. Finally, we found evidence of disparities across many indicators, particularly in the effectiveness and patient centredness domains. Conclusions These findings identify important focus areas for action and shared learning as these countries move towards achieving universal health coverage.

SUBMITTER: Alshamsan R

PROVIDER: S-EPMC5987910 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Comparative health system performance in six middle-income countries: cross-sectional analysis using World Health Organization study of global ageing and health.

Alshamsan Riyadh R Lee John Tayu JT Rana Sangeeta S Areabi Hasan H Millett Christopher C

Journal of the Royal Society of Medicine 20170901 9

Objective To assess and compare health system performance across six middle-income countries that are strengthening their health systems in pursuit of universal health coverage. Design Cross-sectional analysis from the World Health Organization Study on global AGEing and adult health, collected between 2007 and 2010. Setting Six middle-income countries: China, Ghana, India, Mexico, Russia and South Africa. Participants Nationally representative sample of adults aged 50 years and older. Main outc ...[more]

PMID: 28895493

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Project description:BackgroundAccording to the most recent estimates, 842,000 deaths in low- to middle-income countries were attributable to inadequate water, sanitation and hygiene in 2012. Despite billions of dollars and decades of effort, we still lack a sound understanding of which kinds of WASH interventions are most effective in improving public health outcomes, and an important corollary-whether the right things are being measured. The World Health Organization (WHO) has made a concerted effort to compile comprehensive data on drinking water quality and sanitation in the developing world. A recent 2014 report provides information on three phenotypes (responses): Unsafe Water Deaths, Unsafe Sanitation Deaths, Unsafe Hygiene Deaths; two grouped phenotypes: Unsafe Water and Sanitation Deaths and Unsafe Water, Sanitation and Hygiene Deaths; and six explanatory variables (predictors): Improved Sanitation, Unimproved Water Source, Piped Water To Premises, Other Improved Water Source, Filtered and Bottled Water in the Household and Handwashing.Methods and findingsRegression analyses were performed to identify statistically significant associations between these mortality responses and predictors. Good fitted-model performance required: (1) the use of population-normalized death fractions as opposed to number of deaths; (2) transformed response (logit or power); and (3) square-root predictor transformation. Given the complexity and heterogeneity of the relationships and countries being studied, these models exhibited remarkable performance and explained, for example, about 85% of the observed variance in population-normalized Unsafe Sanitation Death fraction, with a high F-statistic and highly statistically significant predictor p-values. Similar performance was found for all other responses, which was an unexpected result (the expected associations between responses and predictors-i.e., water-related with water-related, etc. did not occur). The set of statistically significant predictors remains the same across all responses. That is, Unsafe Water Source (UWS), Improved Sanitation (IS) and Filtered and Bottled Water in the Household (FBH) were the only statistically significant predictors whether the response was Unsafe Sanitation Death Fraction, Unsafe Hygiene Death Fraction or Unsafe Water Death Fraction. Moreover, the fraction of variance explained for all fitted models remained relatively high (adjusted R2 ranges from 0.7605 to 0.8533). We find that two of the statistically significant predictors-Improved Sanitation and Unimproved Water Sources-are particularly influential. We also find that some predictors (Piped Water to Premises, Other Improved Water Sources) have very little explanatory power for predicting mortality and one (Other Improved Water Sources) has a counterintuitive effect on response (Unsafe Sanitary Death Fraction increases with increases in OIWS) and one predictor (Hand Washing) to have essentially no explanatory usefulness.ConclusionsOur results suggest that a higher priority may need to be given to improved sanitation than has been the case. Nevertheless, while our focus in this paper is mortality, morbidity is a staggering consequence of inadequate water, sanitation and hygiene, and lower impact on mortality may not mean a similarly low impact on morbidity. More specifically, those predictors that we found uninfluential for predicting mortality-related responses may indeed be important when morbidity is the response.

Project description:BackgroundPrimary care is of insufficient quality in many low-income and middle-income countries. Some health facilities perform better than others despite operating in similar contexts, although the factors that characterise best performance are not well known. Existing best-performance analyses are concentrated in high-income countries and focus on hospitals. We used the positive deviance approach to identify the factors that differentiate best from worst primary care performance among health facilities across six low-resource health systems.MethodsThis positive deviance analysis used nationally representative samples of public and private health facilities from Service Provision Assessments of the Democratic Republic of the Congo, Haiti, Malawi, Nepal, Senegal, and Tanzania. Data were collected starting June 11, 2013, in Malawi and ending Feb 28, 2020, in Senegal. We assessed facility performance through completion of the Good Medical Practice Index (GMPI) of essential clinical actions (eg, taking a thorough history, conducting an adequate physical examination) according to clinical guidelines and measured with direct observations of care. We identified hospitals and clinics in the top decile of performance (defined as best performers) and conducted a quantitative, cross-national positive deviance analysis to compare them with facilities performing below the median (defined as worst performers) and identify facility-level factors that explain the gap between best and worst performance.FindingsWe identified 132 best-performing and 664 worst-performing hospitals, and 355 best-performing and 1778 worst-performing clinics based on clinical performance across countries. The mean GMPI score was 0·81 (SD 0·07) for the best-performing hospitals and 0·44 (0·09) for the worst-performing hospitals. Among clinics, mean GMPI scores were 0·75 (0·07) for the best performers and 0·34 (0·10) for the worst performers. High-quality governance, management, and community engagement were associated with best performance compared with worst performance. Private facilities out-performed government-owned hospitals and clinics.InterpretationOur findings suggest that best-performing health facilities are characterised by good management and leaders who can engage staff and community members. Governments should look to best performers to identify scalable practices and conditions for success that can improve primary care quality overall and decrease quality gaps between health facilities.FundingBill & Melinda Gates Foundation.

Project description:BackgroundIn higher income countries, work-related squatting and heavy lifting have been associated with increased arthritis risk. Here, we address the paucity of data regarding associations between arthritis and work-related physical stressors in lower- and middle-income countries.MethodsData were extracted from the Study on global AGEing and adult health (SAGE) Wave 1 (2007-10) for adults (aged ≥50 years) from Ghana, India, Russia and South Africa for whom detailed occupation data was available (n = 21,389; 49.2% women). Arthritis cases were identified using a symptom-defined algorithm (current) and self-reported doctor-diagnosis (lifetime). A sex-specific Job Exposure Matrix was used to classify work-related stressors: heavy physical work, kneeling/squatting, heavy lifting, arm elevation and awkward trunk posture. Using the International Standard Classification of Occupations, we linked SAGE and the Job Exposure Matrix. Logistic regression was used to investigate associations between arthritis and work-related stressors, adjusting for age (10 year age groupings), potential socioeconomic-related confounders, and body mass index. Excess exposure risk due to two-way interactions with other risk factors were explored.ResultsDoctor-diagnosed arthritis was associated with heavy physical work (adjusted odds ratios [OR] 1.12, 95%CI 1.01-1.23), awkward trunk posture (adjusted OR 1.23, 95%CI 1.12-1.36), kneeling or squatting (adjusted OR 1.25, 95%CI 1.12-1.38), and arm elevation (adjusted OR 1.66, 95%CI 1.37-2.00). Symptom-based arthritis was associated with kneeling or squatting (adjusted OR 1.27, 95%CI 1.08-1.50), heavy lifting (adjusted OR 1.33, 95%CI 1.11-1.58), and arm elevation (adjusted OR 2.16, 95%CI 1.63-2.86). Two-way interactions suggested excess arthritis risk existed for higher body mass index, and higher income or education.ConclusionsMinimization of occupational health risk factors is common practice in higher income countries: attention should now be directed toward reducing work-related arthritis burden in lower- and middle-income countries.

Project description:Background: Recent studies suggest the importance of distinguishing the severity levels of disability in the older adult population. However, there is still no consensus regarding an optimal classification. Few studies have estimated the prevalence of severe disability, and the results have been confined to high-income countries. There is no evidence for low- and middle-income countries (LMICs). Therefore, the aim of this study was to provide estimates of the levels of severity associated with disability in older adult populations in LMICs and to examine their relationship with health and socioeconomic factors. Methods: We used data from the Study on global AGEing and adult health (SAGE), wave 1 (2007-2010). Nationally representative samples of adults over 50 years from China, Ghana, India, Mexico, Russian Federation, and South Africa were analyzed (n = 33,641). We measured disability using the World Health Organization Disability Assessment Instrument version 2.0 (WHODAS 2.0). Disability levels according to severity were identified through the use of latent class analysis. Socioeconomic and health factors associated with severe disability were estimated using ordinal logistic regression models. Results: We identified four groups of older adult: (1) without disability, 43.4%; (2) mild disability, 33.3%; (3) moderate disability, 15.3%; and (4) severe disability, 8.0%. These results were heterogeneous for the six countries analyzed. Education and socioeconomic status were significantly associated with severe disability along with the following chronic conditions: angina, arthritis, asthma, cataracts, chronic obstructive pulmonary disease, depression, diabetes, and stroke. Severe disability was also associated with the frailty status, sarcopenia, and mild cognitive impairment. Conclusions: In this study, we estimated severity levels of disability for the older adult population in LMICs. Our results show that severe disability affects 8% of older adult, and that there are important socioeconomic and health factors associated with this condition. Measuring the severity of disability is a critical element to study the causes and consequences of aging. Moreover, the identification of older adult with severe disability is vital to design prevention programs, modify interventions, or develop enabling environments.

Dataset Information

Comparative health system performance in six middle-income countries: cross-sectional analysis using World Health Organization study of global ageing and health.

Publications

Comparative health system performance in six middle-income countries: cross-sectional analysis using World Health Organization study of global ageing and health.

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