The prevalence of cardiovascular disease risk factors among adults living in extreme poverty.

Evidence on cardiovascular disease (CVD) risk factor prevalence among adults living below the World Bank's international line for extreme poverty (those with income <$1.90 per day) globally is sparse. Here we pooled individual-level data from 105 nationally representative household surveys across 78 countries, representing 85% of people living in extreme poverty globally, and sorted individuals by country-specific measures of household income or wealth to identify those in extreme poverty. CVD risk factors (hypertension, diabetes, smoking, obesity and dyslipidaemia) were present among 17.5% (95% confidence interval (CI) 16.7-18.3%), 4.0% (95% CI 3.6-4.5%), 10.6% (95% CI 9.0-12.3%), 3.1% (95% CI 2.8-3.3%) and 1.4% (95% CI 0.9-1.9%) of adults in extreme poverty, respectively. Most were not treated for CVD-related conditions (for example, among those with hypertension earning <$1.90 per day, 15.2% (95% CI 13.3-17.1%) reported taking blood pressure-lowering medication). The main limitation of the study is likely measurement error of poverty level and CVD risk factors that could have led to an overestimation of CVD risk factor prevalence among adults in extreme poverty. Nonetheless, our results could inform equity discussions for resource allocation and design of effective interventions.

Directrices para presentación de informes de ensayos clínicos sobre intervenciones con inteligencia artificial: extensión CONSORT-AI / Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension / Diretrizes para relatórios de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão CONSORT-AI

resumen está disponible en el texto completo

ABSTRACT The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

RESUMO A declaração CONSORT 2010 apresenta diretrizes mínimas para relatórios de ensaios clínicos randomizados. Seu uso generalizado tem sido fundamental para garantir a transparência na avaliação de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence) é uma nova diretriz para relatórios de ensaios clínicos que avaliam intervenções com um componente de IA. Ela foi desenvolvida em paralelo à sua declaração complementar para protocolos de ensaios clínicos, a SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 29 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão CONSORT-AI inclui 14 itens novos que, devido à sua importância para as intervenções de IA, devem ser informados rotineiramente juntamente com os itens básicos da CONSORT 2010. A CONSORT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA está inserida, considerações sobre o manuseio dos dados de entrada e saída da intervenção de IA, a interação humano-IA e uma análise dos casos de erro. A CONSORT-AI ajudará a promover a transparência e a integralidade nos relatórios de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente a qualidade do desenho do ensaio clínico e o risco de viés nos resultados relatados.

Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI / Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension / Diretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI

resumen está disponible en el texto completo

ABSTRACT The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.

RESUMO A declaração SPIRIT 2013 tem como objetivo melhorar a integralidade dos relatórios dos protocolos de ensaios clínicos, fornecendo recomendações baseadas em evidências para o conjunto mínimo de itens que devem ser abordados. Essas orientações têm sido fundamentais para promover uma avaliação transparente de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence) é uma nova diretriz de relatório para protocolos de ensaios clínicos que avaliam intervenções com um componente de IA. Essa diretriz foi desenvolvida em paralelo à sua declaração complementar para relatórios de ensaios clínicos, CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 26 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão SPIRIT-AI inclui 15 itens novos que foram considerados suficientemente importantes para os protocolos de ensaios clínicos com intervenções que utilizam IA. Esses itens novos devem constar dos relatórios de rotina, juntamente com os itens básicos da SPIRIT 2013. A SPIRIT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA será integrada, considerações sobre o manuseio dos dados de entrada e saída, a interação humano-IA e a análise de casos de erro. A SPIRIT-AI ajudará a promover a transparência e a integralidade nos protocolos de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente o delineamento e o risco de viés de um futuro estudo clínico.

Quasi-experimental evaluation of a nationwide diabetes prevention programme.

Diabetes is a leading cause of morbidity, mortality and cost of illness1,2. Health behaviours, particularly those related to nutrition and physical activity, play a key role in the development of type 2 diabetes mellitus3. Whereas behaviour change programmes (also known as lifestyle interventions or similar) have been found efficacious in controlled clinical trials4,5, there remains controversy about whether targeting health behaviours at the individual level is an effective preventive strategy for type 2 diabetes mellitus6 and doubt among clinicians that lifestyle advice and counselling provided in the routine health system can achieve improvements in health7-9. Here we show that being referred to the largest behaviour change programme for prediabetes globally (the English Diabetes Prevention Programme) is effective in improving key cardiovascular risk factors, including glycated haemoglobin (HbA1c), excess body weight and serum lipid levels. We do so by using a regression discontinuity design10, which uses the eligibility threshold in HbA1c for referral to the behaviour change programme, in electronic health data from about one-fifth of all primary care practices in England. We confirm our main finding, the improvement of HbA1c, using two other quasi-experimental approaches: difference-in-differences analysis exploiting the phased roll-out of the programme and instrumental variable estimation exploiting regional variation in programme coverage. This analysis provides causal, rather than associational, evidence that lifestyle advice and counselling implemented at scale in a national health system can achieve important health improvements.

Iron-fortified water: a new approach for reducing iron deficiency anemia in resource-constrained settings.

A new approach for fortification of drinking water is presented for combating iron deficiency anemia (IDA) worldwide. The idea is to leach Fe from a bed containing granular metallic iron (Fe0), primarily using ascorbic acid (AA). AA forms very stable and bioavailable complexes with ferrous iron (FeII). Calculated amounts of the FeII-AA solution can be added daily to the drinking water of households or day-care centers for children and adults (e.g. hospitals, kindergartens/schools, refugee camps) to cover the Fe needs of the populations. Granular Fe0 (e.g., sponge iron) in filters is regarded as a locally available Fe carrier in low-income settings, and, AA is also considered to be affordable in low-income countries. The primary idea of this concept is to stabilize FeII from the Fe0 filter by using an appropriate AA solution. An experiment showed that up to 12 mg Fe can be daily leached from 1.0 g of a commercial sponge iron using a 2 mM AA solution. Fe fortification of safe drinking water is a practicable, affordable and efficient method for reducing IDA in low-income communities.

Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine.

OBJECTIVE: Precision medicine requires reliable identification of variation in patient-level outcomes with different available treatments, often termed treatment effect heterogeneity. We aimed to evaluate the comparative utility of individualized treatment selection strategies based on predicted individual-level treatment effects from a causal forest machine learning algorithm and a penalized regression model. METHODS: Cohort study characterizing individual-level glucose-lowering response (6 month reduction in HbA1c) in people with type 2 diabetes initiating SGLT2-inhibitor or DPP4-inhibitor therapy. Model development set comprised 1,428 participants in the CANTATA-D and CANTATA-D2 randomised clinical trials of SGLT2-inhibitors versus DPP4-inhibitors. For external validation, calibration of observed versus predicted differences in HbA1c in patient strata defined by size of predicted HbA1c benefit was evaluated in 18,741 patients in UK primary care (Clinical Practice Research Datalink). RESULTS: Heterogeneity in treatment effects was detected in clinical trial participants with both approaches (proportion predicted to have a benefit on SGLT2-inhibitor therapy over DPP4-inhibitor therapy: causal forest: 98.6%; penalized regression: 81.7%). In validation, calibration was good with penalized regression but sub-optimal with causal forest. A strata with an HbA1c benefit > 10 mmol/mol with SGLT2-inhibitors (3.7% of patients, observed benefit 11.0 mmol/mol [95%CI 8.0-14.0]) was identified using penalized regression but not causal forest, and a much larger strata with an HbA1c benefit 5-10 mmol with SGLT2-inhibitors was identified with penalized regression (regression: 20.9% of patients, observed benefit 7.8 mmol/mol (95%CI 6.7-8.9); causal forest 11.6%, observed benefit 8.7 mmol/mol (95%CI 7.4-10.1). CONCLUSIONS: Consistent with recent results for outcome prediction with clinical data, when evaluating treatment effect heterogeneity researchers should not rely on causal forest or other similar machine learning algorithms alone, and must compare outputs with standard regression, which in this evaluation was superior.

Immunological priming of mesenchymal stromal/stem cells and their extracellular vesicles augments their therapeutic benefits in experimental graft-versus-host disease via engagement of PD-1 ligands.

Mesenchymal stromal cells (MSCs) and their extracellular vesicles (EVs) exert profound anti-inflammatory and regenerative effects in inflammation and tissue damage, which makes them an attractive tool for cellular therapies. In this study we have assessed the inducible immunoregulatory properties of MSCs and their EVs upon stimulation with different combinations of cytokines. First, we found that MSCs primed with IFN-γ, TNF-α and IL-1ß, upregulate the expression of PD-1 ligands, as crucial mediators of their immunomodulatory activity. Further, primed MSCs and MSC-EVs, compared to unstimulated MSCs and MSC-EVs, had increased immunosuppressive effects on activated T cells and mediated an enhanced induction of regulatory T cells, in a PD-1 dependent manner. Importantly, EVs derived from primed MSCs reduced the clinical score and prolonged the survival of mice in a model of graft-versus-host disease. These effects could be reversed in vitro and in vivo by adding neutralizing antibodies directed against PD-L1 and PD-L2 to both, MSCs and their EVs. In conclusion, our data reveal a priming strategy that potentiates the immunoregulatory function of MSCs and their EVs. This concept also provides new opportunities to improve the clinical applicability and efficiency of cellular or EV-based therapeutic MSC products.

The state of diabetes care and obstacles to better care in Aceh, Indonesia: a mixed-methods study.

BACKGROUND: Cardio-metabolic diseases are a major cause of death worldwide, including in Indonesia, where diabetes is one of the most critical diseases for the health system to manage. METHODS: We describe the characteristics, levels of control, health behavior, and diabetes-related complications of diabetes patients in Aceh, Indonesia. We use baseline data and blood testing from a randomized-controlled trial. We conducted semi-structured interviews with eight health providers from Posbindu and Prolanis programs that target diabetes and other non-communicable diseases (NCDs). We also conducted three focus group discussions with 24 diabetes patients about their experiences of living with diabetes and the existing support programs. RESULTS: The blood tests revealed average HbA1c levels indicative of poor glycemic control in 75.8 percent of patients and only 20.3 percent were free from any symptoms. Our qualitative findings suggest that patients are diagnosed after diabetes-related symptoms manifest, and that they find it hard to comply with treatment recommendations and lifestyle advice. The existing programs related to NCDs are not tailored to their needs. CONCLUSION: We identify the need to improve diabetes screening to enable earlier treatment and achieve better control of the disease. Among diagnosed patients, there are widespread beliefs about diabetes medication and alternative forms of treatment that need to be addressed in a respectful dialogue between healthcare professionals and patients. Current diabetes screening, treatment and management programs should be revised to meet the needs of the affected population and to better respond to the increasing burden of this disease.

Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI / Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension / Diretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI

resumen está disponible en el texto completo

ABSTRACT The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.

RESUMO A declaração SPIRIT 2013 tem como objetivo melhorar a integralidade dos relatórios dos protocolos de ensaios clínicos, fornecendo recomendações baseadas em evidências para o conjunto mínimo de itens que devem ser abordados. Essas orientações têm sido fundamentais para promover uma avaliação transparente de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence) é uma nova diretriz de relatório para protocolos de ensaios clínicos que avaliam intervenções com um componente de IA. Essa diretriz foi desenvolvida em paralelo à sua declaração complementar para relatórios de ensaios clínicos, CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 26 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão SPIRIT-AI inclui 15 itens novos que foram considerados suficientemente importantes para os protocolos de ensaios clínicos com intervenções que utilizam IA. Esses itens novos devem constar dos relatórios de rotina, juntamente com os itens básicos da SPIRIT 2013. A SPIRIT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA será integrada, considerações sobre o manuseio dos dados de entrada e saída, a interação humano-IA e a análise de casos de erro. A SPIRIT-AI ajudará a promover a transparência e a integralidade nos protocolos de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente o delineamento e o risco de viés de um futuro estudo clínico.

Patterns of tobacco use in low and middle income countries by tobacco product and sociodemographic characteristics: nationally representative survey data from 82 countries.

OBJECTIVES: To determine the prevalence and frequency of using any tobacco product and each of a detailed set of tobacco products, how tobacco use and frequency of use vary across countries, world regions, and World Bank country income groups, and the socioeconomic and demographic gradients of tobacco use and frequency of use within countries. DESIGN: Secondary analysis of nationally representative, cross-sectional, household survey data from 82 low and middle income countries collected between 1 January 2015 and 31 December 2020. SETTING: Population based survey data. PARTICIPANTS: 1 231 068 individuals aged 15 years and older. MAIN OUTCOME MEASURES: Self-reported current smoking, current daily smoking, current smokeless tobacco use, current daily smokeless tobacco use, pack years, and current use and use frequencies of each tobacco product. Products were any type of cigarette, manufactured cigarette, hand rolled cigarette, water pipe, cigar, oral snuff, nasal snuff, chewing tobacco, and betel nut (with and without tobacco). RESULTS: The smoking prevalence in the study sample was 16.5% (95% confidence interval 16.1% to 16.9%) and ranged from 1.1% (0.9% to 1.3%) in Ghana to 50.6% (45.2% to 56.1%) in Kiribati. The user prevalence of smokeless tobacco was 7.7% (7.5% to 8.0%) and prevalence was highest in Papua New Guinea (daily user prevalence of 65.4% (63.3% to 67.5%)). Although variation was wide between countries and by tobacco product, for many low and middle income countries, the highest prevalence and cigarette smoking frequency was reported in men, those with lower education, less household wealth, living in rural areas, and higher age. CONCLUSIONS: Both smoked and smokeless tobacco use and frequency of use vary widely across tobacco products in low and middle income countries. This study can inform the design and targeting of efforts to reduce tobacco use in low and middle income countries and serve as a benchmark for monitoring progress towards national and international goals.

Global economic burden of unmet surgical need for appendicitis.

BACKGROUND: There is a substantial gap in provision of adequate surgical care in many low- and middle-income countries. This study aimed to identify the economic burden of unmet surgical need for the common condition of appendicitis. METHODS: Data on the incidence of appendicitis from 170 countries and two different approaches were used to estimate numbers of patients who do not receive surgery: as a fixed proportion of the total unmet surgical need per country (approach 1); and based on country income status (approach 2). Indirect costs with current levels of access and local quality, and those if quality were at the standards of high-income countries, were estimated. A human capital approach was applied, focusing on the economic burden resulting from premature death and absenteeism. RESULTS: Excess mortality was 4185 per 100 000 cases of appendicitis using approach 1 and 3448 per 100 000 using approach 2. The economic burden of continuing current levels of access and local quality was US $92 492 million using approach 1 and $73 141 million using approach 2. The economic burden of not providing surgical care to the standards of high-income countries was $95 004 million using approach 1 and $75 666 million using approach 2. The largest share of these costs resulted from premature death (97.7 per cent) and lack of access (97.0 per cent) in contrast to lack of quality. CONCLUSION: For a comparatively non-complex emergency condition such as appendicitis, increasing access to care should be prioritized. Although improving quality of care should not be neglected, increasing provision of care at current standards could reduce societal costs substantially.

Health System Performance for Multimorbid Cardiometabolic Disease in India: A Population-Based Cross-Sectional Study.

Background: The prevalence of multimorbidity in low- and middle-income countries (LMICs) is thought to be rising rapidly. Research on the state of healthcare for multimorbidity in LMICs is needed to provide an impetus for integration of care across conditions, a baseline to monitor progress, and information for targeting of interventions to those most in need. Focusing on multimorbid cardiometabolic disease in India, this study thus aimed to determine 1) the proportion of adults with co-morbid diabetes and hypertension who successfully completed each step of the chronic disease care continuum from diagnosis to control for both conditions, and 2) how having additional cardiovascular disease (CVD) risk factors is associated with health system performance along the care continuum for diabetes, hypertension, and co-morbid diabetes and hypertension. Methods: Using a nationally representative household survey carried out in 2015 and 2016 among women aged 15-49 years and men aged 15-54 years, we created a 'cascade of care' for diabetes, hypertension, and co-morbid diabetes and hypertension by determining the proportion of those with the condition who had been diagnosed, were on treatment, and achieved control. We used Poisson regression with a robust error structure to estimate how having additional cardiovascular disease (CVD) risk factors (diabetes, hypertension, current smoking, and obesity) was associated with reaching each cascade step for diabetes, hypertension, and co-morbid diabetes and hypertension. Findings: Seven hundred thirty-four thousand seven hundred ninety-four adults were included in the analysis. Among individuals with co-morbid diabetes and hypertension, 28·8% (95% CI, 26·7%-31·0%), 16·1% (95% CI, 14·4%-17·9%), and 3·7% (95% CI, 2·8%-4·9%) - with these proportions varying between states by a factor of 4·8, 7·9, and 56·8 - were aware, treated, and achieved control of both conditions, respectively. Men, adults with lower household wealth, and those living in rural areas were less likely to reach each cascade step. Having additional CVD risk factors generally did not increase the probability of reaching each cascade step for diabetes, hypertension, and co-morbid diabetes and hypertension, except that having concurrent diabetes increased the probability of successfully transitioning through the hypertension care cascade. Interpretation: While varying widely between states and population groups, health system performance for co-morbid diabetes and hypertension is generally low in India, and there appears to be little integration of care across CVD risk factors. Funding: European Research Council.

Maternal iron-and-folic-acid supplementation and its association with low-birth weight and neonatal mortality in India.

OBJECTIVE: The current study assessed intake of iron-and-folic-acid (IFA) tablet/syrup (grouped into none, < 100 d of IFA consumption or < 100 IFA and ≥ 100 d of IFA consumption or ≥ 100 IFA) among prospective mothers and its association with various stages of low-birth weight (ELBW, extremely low-birth weight; VLBW, very low-birth weight and LBW, low-birth weight) and neonatal mortality (death during day 0-1, 2-6, 7-27 and 0-27) in India. DESIGN: The cross-sectional, nationally representative, 2015-2016 National Family Health Survey (NFHS-4) data were used. Weighted descriptive analysis and multiple binary logistic regression modelling were used. SETTING: NFHS-4 covered 640 districts from thirty-seven states and union territories of India. PARTICIPANTS: A total of 120 374 and 143 675 index children aged 0-59 months were included to analyse LBW and neonatal mortality, respectively. RESULTS: Overall, 30·7 % mothers consumed ≥ 100 IFA in 2015-2016, and this estimate ranged from 0·0 % in Zunheboto district of Nagaland state to 89·5 % in Mahe district of Puducherry of India. Multiple regression analysis revealed that children of mothers who consumed ≥ 100 IFA had lower odds of ELBW, VLBW, LBW and neonatal mortality during day 0-1, as compared with mothers who did not buy/receive any IFA. Consumption of IFA (< 100 IFA and ≥ 100 IFA) had a protective association with neonatal death during day 7-27 and 0-27. Consumption of IFA was not associated with neonatal death during day 2-6. CONCLUSIONS: While ≥ 100 IFA consumption during pregnancy was found to be associated with preventing select types of LBW and neonatal mortality, a large variation in coverage of ≥ 100 IFA consumption across 640 districts is concerning.

Patterns of multimorbidity in India: A nationally representative cross-sectional study of individuals aged 15 to 49 years.

There is a dearth of evidence on the epidemiology of multimorbidity in low- and middle-income countries. This study aimed to determine the prevalence of multimorbidity in India and its variation among states and population groups. We analyzed data from a nationally representative household survey conducted in 2015-2016 among individuals aged 15 to 49 years. Multimorbidity was defined as having two or more conditions out of five common chronic morbidities in India: anemia, asthma, diabetes, hypertension, and obesity. We disaggregated multimorbidity prevalence by condition, state, rural versus urban areas, district-level wealth, and individual-level sociodemographic characteristics. 712,822 individuals were included in the analysis. The prevalence of multimorbidity was 7·2% (95% CI, 7·1% - 7·4%), and was higher in urban (9·7% [95% CI, 9·4% - 10·1%]) than in rural (5·8% [95% CI, 5·7% - 6·0%]) areas. The three most prevalent morbidity combinations were hypertension with obesity (2·9% [95% CI, 2·8% - 3·1%]), hypertension with anemia (2·2% [95% CI, 2·1%- 2·3%]), and obesity with anemia (1·2% [95% CI, 1·1%- 1·2%]). The age-standardized multimorbidity prevalence varied from 3·4% (95% CI: 3·0% - 3·8%) in Chhattisgarh to 16·9% (95% CI: 13·2% - 21·5%) in Puducherry. Being a woman, being married, not currently smoking, greater household wealth, and living in urban areas were all associated with a higher risk of multimorbidity. Multimorbidity is common among young and middle-aged adults in India. This study can inform screening guidelines for chronic conditions and the targeting of relevant policies and interventions to those most in need.

Unmet need for hypercholesterolemia care in 35 low- and middle-income countries: A cross-sectional study of nationally representative surveys.

BACKGROUND: As the prevalence of hypercholesterolemia is increasing in low- and middle-income countries (LMICs), detailed evidence is urgently needed to guide the response of health systems to this epidemic. This study sought to quantify unmet need for hypercholesterolemia care among adults in 35 LMICs. METHODS AND FINDINGS: We pooled individual-level data from 129,040 respondents aged 15 years and older from 35 nationally representative surveys conducted between 2009 and 2018. Hypercholesterolemia care was quantified using cascade of care analyses in the pooled sample and by region, country income group, and country. Hypercholesterolemia was defined as (i) total cholesterol (TC) ≥240 mg/dL or self-reported lipid-lowering medication use and, alternatively, as (ii) low-density lipoprotein cholesterol (LDL-C) ≥160 mg/dL or self-reported lipid-lowering medication use. Stages of the care cascade for hypercholesterolemia were defined as follows: screened (prior to the survey), aware of diagnosis, treated (lifestyle advice and/or medication), and controlled (TC <200 mg/dL or LDL-C <130 mg/dL). We further estimated how age, sex, education, body mass index (BMI), current smoking, having diabetes, and having hypertension are associated with cascade progression using modified Poisson regression models with survey fixed effects. High TC prevalence was 7.1% (95% CI: 6.8% to 7.4%), and high LDL-C prevalence was 7.5% (95% CI: 7.1% to 7.9%). The cascade analysis showed that 43% (95% CI: 40% to 45%) of study participants with high TC and 47% (95% CI: 44% to 50%) with high LDL-C ever had their cholesterol measured prior to the survey. About 31% (95% CI: 29% to 33%) and 36% (95% CI: 33% to 38%) were aware of their diagnosis; 29% (95% CI: 28% to 31%) and 33% (95% CI: 31% to 36%) were treated; 7% (95% CI: 6% to 9%) and 19% (95% CI: 18% to 21%) were controlled. We found substantial heterogeneity in cascade performance across countries and higher performances in upper-middle-income countries and the Eastern Mediterranean, Europe, and Americas. Lipid screening was significantly associated with older age, female sex, higher education, higher BMI, comorbid diagnosis of diabetes, and comorbid diagnosis of hypertension. Awareness of diagnosis was significantly associated with older age, higher BMI, comorbid diagnosis of diabetes, and comorbid diagnosis of hypertension. Lastly, treatment of hypercholesterolemia was significantly associated with comorbid hypertension and diabetes, and control of lipid measures with comorbid diabetes. The main limitations of this study are a potential recall bias in self-reported information on received health services as well as diminished comparability due to varying survey years and varying lipid guideline application across country and clinical settings. CONCLUSIONS: Cascade performance was poor across all stages, indicating large unmet need for hypercholesterolemia care in this sample of LMICs-calling for greater policy and research attention toward this cardiovascular disease (CVD) risk factor and highlighting opportunities for improved prevention of CVD.

Targeting Hypertension Screening in Low- and Middle-Income Countries: A Cross-Sectional Analysis of 1.2 Million Adults in 56 Countries.

Background As screening programs in low- and middle-income countries (LMICs) often do not have the resources to screen the entire population, there is frequently a need to target such efforts to easily identifiable priority groups. This study aimed to determine (1) how hypertension prevalence in LMICs varies by age, sex, body mass index, and smoking status, and (2) the ability of different combinations of these variables to accurately predict hypertension. Methods and Results We analyzed individual-level, nationally representative data from 1 170 629 participants in 56 LMICs, of whom 220 636 (18.8%) had hypertension. Hypertension was defined as systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or reporting to be taking blood pressure-lowering medication. The shape of the positive association of hypertension with age and body mass index varied across world regions. We used logistic regression and random forest models to compute the area under the receiver operating characteristic curve in each country for different combinations of age, body mass index, sex, and smoking status. The area under the receiver operating characteristic curve for the model with all 4 predictors ranged from 0.64 to 0.85 between countries, with a country-level mean of 0.76 across LMICs globally. The mean absolute increase in the area under the receiver operating characteristic curve from the model including only age to the model including all 4 predictors was 0.05. Conclusions Adding body mass index, sex, and smoking status to age led to only a minor increase in the ability to distinguish between adults with and without hypertension compared with using age alone. Hypertension screening programs in LMICs could use age as the primary variable to target their efforts.

Variation in the Proportion of Adults in Need of Blood Pressure-Lowering Medications by Hypertension Care Guideline in Low- and Middle-Income Countries: A Cross-Sectional Study of 1 037 215 Individuals From 50 Nationally Representative Surveys.

BACKGROUND: Current hypertension guidelines vary substantially in their definition of who should be offered blood pressure-lowering medications. Understanding the effect of guideline choice on the proportion of adults who require treatment is crucial for planning and scaling up hypertension care in low- and middle-income countries. METHODS: We extracted cross-sectional data on age, sex, blood pressure, hypertension treatment and diagnosis status, smoking, and body mass index for adults 30 to 70 years of age from nationally representative surveys in 50 low- and middle-income countries (N = 1 037 215). We aimed to determine the effect of hypertension guideline choice on the proportion of adults in need of blood pressure-lowering medications. We considered 4 hypertension guidelines: the 2017 American College of Cardiology/American Heart Association guideline, the commonly used 140/90 mm Hg threshold, the 2016 World Health Organization HEARTS guideline, and the 2019 UK National Institute for Health and Care Excellence guideline. RESULTS: The proportion of adults in need of blood pressure-lowering medications was highest under the American College of Cardiology/American Heart Association, followed by the 140/90 mm Hg, National Institute for Health and Care Excellence, and World Health Organization guidelines (American College of Cardiology/American Heart Association: women, 27.7% [95% CI, 27.2-28.2], men, 35.0% [95% CI, 34.4-35.7]; 140/90 mm Hg: women, 26.1% [95% CI, 25.5-26.6], men, 31.2% [95% CI, 30.6-31.9]; National Institute for Health and Care Excellence: women, 11.8% [95% CI, 11.4-12.1], men, 15.7% [95% CI, 15.3-16.2]; World Health Organization: women, 9.2% [95% CI, 8.9-9.5], men, 11.0% [95% CI, 10.6-11.4]). Individuals who were unaware that they have hypertension were the primary contributor to differences in the proportion needing treatment under different guideline criteria. Differences in the proportion needing blood pressure-lowering medications were largest in the oldest (65-69 years) age group (American College of Cardiology/American Heart Association: women, 60.2% [95% CI, 58.8-61.6], men, 70.1% [95% CI, 68.8-71.3]; World Health Organization: women, 20.1% [95% CI, 18.8-21.3], men, 24.1.0% [95% CI, 22.3-25.9]). For both women and men and across all guidelines, countries in the European and Eastern Mediterranean regions had the highest proportion of adults in need of blood pressure-lowering medicines, whereas the South and Central Americas had the lowest. CONCLUSIONS: There was substantial variation in the proportion of adults in need of blood pressure-lowering medications depending on which hypertension guideline was used. Given the great implications of this choice for health system capacity, policy makers will need to carefully consider which guideline they should adopt when scaling up hypertension care in their country.

A cross-sectional study of cardiovascular disease risk clustering at different socio-geographic levels in India.

Despite its importance for the targeting of interventions, little is known about the degree to which cardiovascular disease (CVD) risk factors cluster within different socio-geographic levels in South Asia. Using two jointly nationally representative household surveys, which sampled 1,082,100 adults across India, we compute the intra-cluster correlation coefficients (ICCs) of five major CVD risk factors (raised blood glucose, raised blood pressure, smoking, overweight, and obesity) at the household, community, district, and state level. Here we show that except for smoking, the level of clustering is generally highest for households, followed by communities, districts, and then states. On average, more economically developed districts have a higher household ICC in rural areas. These findings provide critical information for sample size calculations of cluster-randomized trials and household surveys, and inform the targeting of policies and prevention programming aimed at reducing CVD in India.

Lifetime Prevalence of Cervical Cancer Screening in 55 Low- and Middle-Income Countries.

Importance: The World Health Organization is developing a global strategy to eliminate cervical cancer, with goals for screening prevalence among women aged 30 through 49 years. However, evidence on prevalence levels of cervical cancer screening in low- and middle-income countries (LMICs) is sparse. Objective: To determine lifetime cervical cancer screening prevalence in LMICs and its variation across and within world regions and countries. Design, Setting, and Participants: Analysis of cross-sectional nationally representative household surveys carried out in 55 LMICs from 2005 through 2018. The median response rate across surveys was 93.8% (range, 64.0%-99.3%). The population-based sample consisted of 1 136 289 women aged 15 years or older, of whom 6885 (0.6%) had missing information for the survey question on cervical cancer screening. Exposures: World region, country; countries' economic, social, and health system characteristics; and individuals' sociodemographic characteristics. Main Outcomes and Measures: Self-report of having ever had a screening test for cervical cancer. Results: Of the 1 129 404 women included in the analysis, 542 475 were aged 30 through 49 years. A country-level median of 43.6% (interquartile range [IQR], 13.9%-77.3%; range, 0.3%-97.4%) of women aged 30 through 49 years self-reported to have ever been screened, with countries in Latin America and the Caribbean having the highest prevalence (country-level median, 84.6%; IQR, 65.7%-91.1%; range, 11.7%-97.4%) and those in sub-Saharan Africa the lowest prevalence (country-level median, 16.9%; IQR, 3.7%-31.0%; range, 0.9%-50.8%). There was large variation in the self-reported lifetime prevalence of cervical cancer screening among countries within regions and among countries with similar levels of per capita gross domestic product and total health expenditure. Within countries, women who lived in rural areas, had low educational attainment, or had low household wealth were generally least likely to self-report ever having been screened. Conclusions and Relevance: In this cross-sectional study of data collected in 55 low- and middle-income countries from 2005 through 2018, there was wide variation between countries in the self-reported lifetime prevalence of cervical cancer screening. However, the median prevalence was only 44%, supporting the need to increase the rate of screening.

Individual and social predictors of smoking and obesity: A panel study in Germany.

This is a longitudinal study of changes in smoking behaviour as well as becoming overweight/obese (OW/OB) and the strength of their association with personal factors such as self-control, mental health, and socioeconomic status (SES) versus their connection with the behaviour of other household members. Furthermore, we investigate that in terms of roles within a household, who is more vulnerable towards the behaviour of others. We used a hybrid model that followed individual adults (person-level fixed-effect) who participated in a national representative panel survey in Germany, SOEP, between 2008 and 2016 and answered all SF-12 items (N = 6874). The count of members in a household showing the associated adverse health behaviour was the nested random-effect. Compared with other predictors, the likelihood of a person becoming OW/OB had the strongest association with the number of cohabits who were also OW/OB and it became worse as this number increased (OR 7.18, 95% CI: 2.10-24.54 and 12.44, 95% CI: 1.53-100.85, for men and women respectively, e.g. compared with being married 2.83, 95% CI: 2.28-3.53 and 1.82, 95% CI: 1.42-2.34). However, for smoking the same rapid trend was not observed. Particularly, becoming OW/OB in female (adult) children was strongly associated with the behaviour of others (compared with household head or partner). For smoking the strongest link with others was among women who were head of the household. For both behaviours, we found neither mental health nor self-control to be strong predictors. Our findings indicate that various factors do not play equal roles in changes in health behaviour and particularly for women, becoming OW/OB is strongly connected with the behaviour of others. We further discuss the potential importance of social norms that might be helpful in developing more effective policies incorporating social connections as well as norms.