We described the contribution of ultra-processed foods in the U.K. diet and its association with the overall dietary content of nutrients known to affect the risk of chronic non-communicable diseases (NCDs). Cross-sectional data from the U.K. National Diet and Nutrition Survey (2008–2014) were analysed. Food items collected using a four-day food diary were classified according to the NOVA system. The average energy intake was 1764 kcal/day, with 30.1% of calories coming from unprocessed or minimally processed foods, 4.2% from culinary ingredients, 8.8% from processed foods, and 56.8% from ultra-processed foods. As the ultra-processed food consumption increased, the dietary content of carbohydrates, free sugars, total fats, saturated fats, and sodium increased significantly while the content of protein, fibre, and potassium decreased. Increased ultra-processed food consumption had a remarkable effect on average content of free sugars, which increased from 9.9% to 15.4% of total energy from the first to the last quintile. The prevalence of people exceeding the upper limits recommended for free sugars and sodium increased by 85% and 55%, respectively, from the lowest to the highest ultra-processed food quintile. Decreasing the dietary share of ultra-processed foods may substantially improve the nutritional quality of diets and contribute to the prevention of diet-related NCDs.

Objective To investigate the risk of incident myocardial infarction, congestive heart failure, and all cause mortality associated with prescription of oral antidiabetes drugs. Design Retrospective cohort study. Setting UK general practice research database, 1990-2005. Participants 91 521 people with diabetes. Main outcome measures Incident myocardial infarction, congestive heart failure, and all cause mortality. Person time intervals for drug treatment were categorised by drug class, excluding non-drug intervals and intervals for insulin.Results 3588 incident cases of myocardial infarction, 6900 of congestive heart failure, and 18 548 deaths occurred. Compared with metformin, monotherapy with first or second generation sulphonylureas was associated with a significant 24% to 61% excess risk for all cause mortality (P<0.001) and second generation sulphonylureas with an 18% to 30% excess risk for congestive heart failure (P=0.01 and P<0.001). The thiazolidinediones were not associated with risk of myocardial infarction; pioglitazone was associated with a significant 31% to 39% lower risk of all cause mortality (P=0.02 to P<0.001) compared with metformin. Among the thiazolidinediones, rosiglitazone was associated with a 34% to 41% higher risk of all cause mortality (P=0.14 to P=0.01) compared with pioglitazone. A large number of potential confounders were accounted for in the study; however, the possibility of residual confounding or confounding by indication (differences in prognostic factors between drug groups) cannot be excluded. Conclusions Our findings suggest a relatively unfavourable risk profile of sulphonylureas compared with metformin for all outcomes examined. Pioglitazone was associated with reduced all cause mortality compared with metformin. Pioglitazone also had a favourable risk profile compared with rosiglitazone;

Background: with ageing populations and increasing exposure to risk factors for chronic diseases, the prevalence of chronic disease multimorbidity is rising globally. There is little evidence on the determinants of multimorbidity and its impact on healthcare utilisation and health status in Europe. Methods: we used cross-sectional data from the Survey of Health, Ageing and Retirement in Europe (SHARE) in 2011-12, which included nationally representative samples of persons aged 50 and older from 16 European nations. Negative binomial and logistic regression models were used to assess the association between number of chronic diseases and healthcare utilisation, self-perceived health, depression and reduction of functional capacity. Results: overall, 37.3% of participants reported multimorbidity; the lowest prevalence was in Switzerland (24.7%), the highest in Hungary (51.0%). The likelihood of having multimorbidity increased substantially with age. Number of chronic conditions was associated with greater healthcare utilisation in both primary (regression coefficient for medical doctor visits = 0.29, 95% CI = 0.27-0.30) and secondary setting (adjusted odds ratio (AOR) for having any hospitalisation in the last year = 1.49, 95% CI = 1.42-1.55) in all countries analysed. Number of chronic diseases was associated with fair/poor health status (AOR 2.13, 95% CI = 2.03-2.24), being depressed (AOR 1.48, 95% CI = 1.42-1.54) and reduced functional capacity (AOR 2.12, 95% CI = 2.02-2.22). Conclusion: multimorbidity is associated with greater healthcare utilisation, worse self-reported health status, depression and reduced functional capacity in European countries. European health systems should prioritise improving the management of patients with multimorbidity to improve their health status and increase healthcare efficiency.

BackgroundThe burden of non-communicable disease (NCDs) has grown rapidly in low- and middle-income countries (LMICs), where populations are ageing, with rising prevalence of multimorbidity (more than two co-existing chronic conditions) that will significantly increase pressure on already stretched health systems. We assess the impact of NCD multimorbidity on healthcare utilisation and out-of-pocket expenditures in six middle-income countries: China, Ghana, India, Mexico, Russia and South Africa.MethodsSecondary analyses of cross-sectional data from adult participants (>18 years) in the WHO Study on Global Ageing and Adult Health (SAGE) 2007–2010. We used multiple logistic regression to determine socio-demographic correlates of multimorbidity. Association between the number of NCDs and healthcare utilisation as well as out-of-pocket spending was assessed using logistic, negative binominal and log-linear models.ResultsThe prevalence of multimorbidity in the adult population varied from 3∙9% in Ghana to 33∙6% in Russia. Number of visits to doctors in primary and secondary care rose substantially for persons with increasing numbers of co-existing NCDs. Multimorbidity was associated with more outpatient visits in China (coefficient for number of NCD = 0∙56, 95% CI = 0∙46, 0∙66), a higher likelihood of being hospitalised in India (AOR = 1∙59, 95% CI = 1∙45, 1∙75), higher out-of-pocket expenditures for outpatient visits in India and China, and higher expenditures for hospital visits in Russia. Medicines constituted the largest proportion of out-of-pocket expenditures in persons with multimorbidity (88∙3% for outpatient, 55∙9% for inpatient visit in China) in most countries.ConclusionMultimorbidity is associated with higher levels of healthcare utilisation and greater financial burden for individuals in middle-income countries. Our study supports the WHO call for universal health insurance and health service coverage in LMICs, particularly for vulnerable groups such as the elderly with multimorbidity.

BackgroundSince 2015, a major economic crisis in Brazil has led to increasing poverty and the implementation of long-term fiscal austerity measures that will substantially reduce expenditure on social welfare programmes as a percentage of the country’s GDP over the next 20 years. The Bolsa Família Programme (BFP)—one of the largest conditional cash transfer programmes in the world—and the nationwide primary healthcare strategy (Estratégia Saúde da Família [ESF]) are affected by fiscal austerity, despite being among the policy interventions with the strongest estimated impact on child mortality in the country. We investigated how reduced coverage of the BFP and ESF—compared to an alternative scenario where the level of social protection under these programmes is maintained—may affect the under-five mortality rate (U5MR) and socioeconomic inequalities in child health in the country until 2030, the end date of the Sustainable Development Goals.Methods and findingsWe developed and validated a microsimulation model, creating a synthetic cohort of all 5,507 Brazilian municipalities for the period 2017–2030. This model was based on the longitudinal dataset and effect estimates from a previously published study that evaluated the effects of poverty, the BFP, and the ESF on child health. We forecast the economic crisis and the effect of reductions in BFP and ESF coverage due to current fiscal austerity on the U5MR, and compared this scenario with a scenario where these programmes maintain the levels of social protection by increasing or decreasing with the size of Brazil’s vulnerable populations (policy response scenarios). We used fixed effects multivariate regression models including BFP and ESF coverage and accounting for secular trends, demographic and socioeconomic changes, and programme duration effects. With the maintenance of the levels of social protection provided by the BFP and ESF, in the most likely economic crisis scenario the U5MR is expected to be 8.57% (95% CI: 6.88%–10.24%) lower in 2030 than under fiscal austerity—a cumulative 19,732 (95% CI: 10,207–29,285) averted under-five deaths between 2017 and 2030. U5MRs from diarrhoea, malnutrition, and lower respiratory tract infections are projected to be 39.3% (95% CI: 36.9%–41.8%), 35.8% (95% CI: 31.5%–39.9%), and 8.5% (95% CI: 4.1%–12.0%) lower, respectively, in 2030 under the maintenance of BFP and ESF coverage, with 123,549 fewer under-five hospitalisations from all causes over the study period. Reduced coverage of the BFP and ESF will also disproportionately affect U5MR in the most vulnerable areas, with the U5MR in the poorest quintile of municipalities expected to be 11.0% (95% CI: 8.0%–13.8%) lower in 2030 under the maintenance of BFP and ESF levels of social protection than under fiscal austerity, compared to no difference in the richest quintile. Declines in health inequalities over the last decade will also stop under a fiscal austerity scenario: the U5MR concentration index is expected to remain stable over the period 2017–2030, compared to a 13.3...

BackgroundThere are large amounts of unstructured, free-text information about quality of health care available on the Internet in blogs, social networks, and on physician rating websites that are not captured in a systematic way. New analytical techniques, such as sentiment analysis, may allow us to understand and use this information more effectively to improve the quality of health care.ObjectiveWe attempted to use machine learning to understand patients’ unstructured comments about their care. We used sentiment analysis techniques to categorize online free-text comments by patients as either positive or negative descriptions of their health care. We tried to automatically predict whether a patient would recommend a hospital, whether the hospital was clean, and whether they were treated with dignity from their free-text description, compared to the patient’s own quantitative rating of their care.MethodsWe applied machine learning techniques to all 6412 online comments about hospitals on the English National Health Service website in 2010 using Weka data-mining software. We also compared the results obtained from sentiment analysis with the paper-based national inpatient survey results at the hospital level using Spearman rank correlation for all 161 acute adult hospital trusts in England.ResultsThere was 81%, 84%, and 89% agreement between quantitative ratings of care and those derived from free-text comments using sentiment analysis for cleanliness, being treated with dignity, and overall recommendation of hospital respectively (kappa scores: .40–.74, P<.001 for all). We observed mild to moderate associations between our machine learning predictions and responses to the large patient survey for the three categories examined (Spearman rho 0.37-0.51, P<.001 for all).ConclusionsThe prediction accuracy that we have achieved using this machine learning process suggests that we are able to predict, from free-text, a reasonably accurate assessment of patients’ opinion about different performance aspects of a hospital and that these machine learning predictions are associated with results of more conventional surveys.

BackgroundNon communicable disease (NCD) multimorbidity is increasingly becoming common in high income settings but little is known about its epidemiology and associated impacts on citizens and health systems in low and middle-income countries (LMICs). We aim to examine the socio-demographic distribution of NCD multimorbidity (≥2 diseases) and its implications for health care utilization and out-of-pocket expenditure (OOPE) in India.MethodsWe analyzed cross-sectional nationally representative data from the World Health Organisaion Study on Global Ageing and Adult Health (WHO-SAGE), conducted in India during 2007. Multiple logistic regression was used to determine socio-demographic predictors of self-reported multimorbidity. A two part model was used to assess the relationship between number of NCDs and health care utilization including OOPE.Results28.5% of the sample population had at least one NCD and 8.9% had NCD multimorbidity. The prevalence of multimorbidity increased from 1.3% in 18–29 year olds to 30.6% in those aged 70 years and above. Mean outpatient visits in the preceding 12 months increased from 2.2 to 6.2 and the percentage reporting an overnight hospital stay in the past 3 years increased from 9% to 29% in those with no NCD and ≥2 NCDs respectively (p <0.001).OOPE incurred during the last outpatient visit increased from INR 272.1 (95% CI = 249.0-295.2) in respondents with no NCDs to INR 454.1 (95% CI = 407.8-500.4) in respondents with ≥2 NCDs. However, we did not find an increase in OOPE during the last inpatient visit with number of NCDs (7865.9 INR for those with zero NCDs compared with 7301.3 for those with ≥2 NCDs). For both outpatient and inpatient OOPE, medicine constitutes the largest proportion of spending (70.7% for outpatient, 53.6% for inpatient visit), followed by spending for health care provider (14.0% for outpatient, 12.2% for inpatient visit).ConclusionNCD multimorbidity is common in the Indian adult population and is associated with substantially higher healthcare utilization and OOPE. Strategies to address the growing burden of NCDs in LMICs should include efforts to improve the management of patients with multimorbidity and reduce associated financial burden to individuals and households.

Recent years have seen increasing interest in patient-centred care and calls to focus on improving the patient experience. At the same time, a growing number of patients are using the internet to describe their experiences of healthcare. We believe the increasing availability of patients' accounts of their care on blogs, social networks, Twitter and hospital review sites presents an intriguing opportunity to advance the patient-centred care agenda and provide novel quality of care data. We describe this concept as a 'cloud of patient experience'. In this commentary, we outline the ways in which the collection and aggregation of patients' descriptions of their experiences on the internet could be used to detect poor clinical care. Over time, such an approach could also identify excellence and allow it to be built on. We suggest using the techniques of natural language processing and sentiment analysis to transform unstructured descriptions of patient experience on the internet into usable measures of healthcare performance. We consider the various sources of information that could be used, the limitations of the approach and discuss whether these new techniques could detect poor performance before conventional measures of healthcare quality.