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.

This study aims to examine (1) province-level variations in the levels of cardiovascular disease (CVD) risk and behavioral risk for CVDs, (2) province-level variations in the management of cascade of care for hypertension, diabetes, and dyslipidaemia, and (3) the association of province-level economic development and individual factors with the quality of care for hypertension, diabetes, and dyslipidaemia.

Effective equity-focused health policy for hypertension in low- and middle-income countries (LMICs) requires an understanding of the condition’s current socioeconomic gradients and how these are likely to change in the future as countries develop economically.

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.

The COVID-19 pandemic and associated non-pharmaceutical interventions (NPIs) have affected all countries. With a scarcity of COVID-19 vaccines there has been a need to prioritize populations, but assessing relative needs has been challenging. The COVAX Facility allocates vaccines to cover 20% of each national population, followed by a needs assessment that considers five quantitative metrics alongside a qualitative assessment. The objective of this study was to identify the most important factors for assessing countries’ needs for vaccines, and to weight each, generating a scoring tool for prioritising countries.

Antimicrobial resistance (AMR) may negatively impact surgery patients through reducing the efficacy of treatment of surgical site infections, also known as the “primary effects” of AMR. Previous estimates of the burden of AMR have largely ignored the potential “secondary effects,” such as changes in surgical care pathways due to AMR, such as different infection prevention procedures or reduced access to surgical procedures altogether, with literature providing limited quantifications of this potential burden. Former conceptual models and approaches for quantifying such impacts are available, though they are often high-level and difficult to utilize in practice. We therefore expand on this earlier work to incorporate heterogeneity in antimicrobial usage, AMR, and causative organisms, providing a detailed decision-tree-Markov-hybrid conceptual model to estimate the burden of AMR on surgery patients. We collate available data sources in England and describe how routinely collected data could be used to parameterise such a model, providing a useful repository of data systems for future health economic evaluations. The wealth of national-level data available for England provides a case study in describing how current surveillance and administrative data capture systems could be used in the estimation of transition probability and cost parameters. However, it is recommended that such data are utilized in combination with expert opinion (for scope and scenario definitions) to robustly estimate both the primary and secondary effects of AMR over time. Though we focus on England, this discussion is useful in other settings with established and/or developing infectious diseases surveillance systems that feed into AMR National Action Plans.