Anaemia among men in India: a nationally representative cross-sectional study

Summary Background Population-based studies on anaemia in India have mostly focused on women and children, with men with anaemia receiving much less attention despite anaemia's adverse effect on health, wellbeing, and economic productivity. This study aimed to determine the national prevalence of anaemia among men in India; how the prevalence of anaemia in men varies across India among states and districts and by sociodemographic characteristics; and whether the geographical and sociodemographic variation in the prevalence of anaemia among men is similar to that among women to inform whether anaemia reduction efforts for men should be coupled with existing efforts for women. Methods In this cross-sectional study, we analysed data from a nationally representative household survey carried out from January, 2015, to December, 2016, among men aged 15–54 years and women aged 15–49 years in all 29 states and seven Union Territories of India. Haemoglobin concentration was measured using the portable HemoCue Hb 201+ (HemoCue AB, Angelholm, Sweden) and a capillary blood sample. In addition to disaggregating anaemia prevalence (separately in men and women) by state and age group, we used mixed-effects Poisson regression to determine individual-level and district-level predictors of anaemia. Findings 106 298 men and 633 305 women were included in our analysis. In men, the prevalence of any anaemia was 23·2% (95% CI 22·7–23·7), moderate or severe anaemia was 5·1% (4·9–5·4), and severe anaemia was 0·5% (0·5–0·6). An estimated 21·7% (20·9–22·5) of men with any degree of anaemia had moderate or severe anaemia compared with 53·2% (52·9–53·5) of women with any anaemia. Men aged 20–34 years had the lowest probability of having anaemia whereas anaemia prevalence among women was similar across age groups. State-level prevalence of any anaemia in men varied from 9·2% (7·7–10·9) in Manipur to 32·9% (31·0–34·7) in Bihar. The individual-level predictors of less household wealth, lower education, living in a rural area, smoking, consuming smokeless tobacco, and being underweight and the district-level predictors of living in a district with a lower rate of primary school completion, level of urbanisation, and household wealth were all associated with a higher probability of anaemia in men. Although some important exceptions were noted, district-level and state-level prevalence of anaemia among men correlated strongly with that among women. Interpretation Anaemia among men in India is an important public health problem. Because of the similarities in the patterns of geographical and sociodemographic variation of anaemia between men and women, future efforts to reduce anaemia among men could target similar population groups as those targeted in existing efforts to reduce anaemia among women. Funding Alexander von Humboldt Foundation

On the derivation of the renewal equation from an age-dependent branching process: an epidemic modelling perspective

Renewal processes are a popular approach used in modelling infectious disease outbreaks. In a renewal process, previous infections give rise to future infections. However, while this formulation seems sensible, its application to infectious disease can be difficult to justify from first principles. It has been shown from the seminal work of Bellman and Harris that the renewal equation arises as the expectation of an age-dependent branching process. In this paper we provide a detailed derivation of the original Bellman Harris process. We introduce generalisations, that allow for time-varying reproduction numbers and the accounting of exogenous events, such as importations. We show how inference on the renewal equation is easy to accomplish within a Bayesian hierarchical framework. Using off the shelf MCMC packages, we fit to South Korea COVID-19 case data to estimate reproduction numbers and importations. Our derivation provides the mathematical fundamentals and assumptions underpinning the use of the renewal equation for modelling outbreaks

A unified machine learning approach to time series forecasting applied to demand at emergency departments

There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provide adequate quality of care while maintaining standards and productivity. Managing hospital demand effectively requires an adequate knowledge of the future rate of admission. Using 8 years of electronic admissions data from two major acute care hospitals in London, we develop a novel ensemble methodology that combines the outcomes of the best performing time series and machine learning approaches in order to make highly accurate forecasts of demand, 1, 3 and 7 days in the future. Both hospitals face an average daily demand of 208 and 106 attendances respectively and experience considerable volatility around this mean. However, our approach is able to predict attendances at these emergency departments one day in advance up to a mean absolute error of +/- 14 and +/- 10 patients corresponding to a mean absolute percentage error of 6.8% and 8.6% respectively. Our analysis compares machine learning algorithms to more traditional linear models. We find that linear models often outperform machine learning methods and that the quality of our predictions for any of the forecasting horizons of 1, 3 or 7 days are comparable as measured in MAE. In addition to comparing and combining state-of-the-art forecasting methods to predict hospital demand, we consider two different hyperparameter tuning methods, enabling a faster deployment of our models without compromising performance. We believe our framework can readily be used to forecast a wide range of policy relevant indicators

Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level

We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions. We use high-resolution crop yield maps as ground truth data to train crop and machine learning model agnostic methods at the sub-field level. We use Sentinel-2 satellite imagery as the primary modality for input data with other complementary modalities, including weather, soil, and DEM data. The proposed method uses input modalities available with global coverage, making the framework globally scalable. We explicitly highlight the importance of input modalities for crop yield prediction and emphasize that the best-performing combination of input modalities depends on region, crop, and chosen model.Comment: 4 pages, 1 figure, 3 tables, IEEE IGARSS 202

Individual characteristics associated with road traffic collisions and healthcare seeking in Low- and Middle-Income Countries and territories

Incidence of road traffic collisions (RTCs), types of users involved, and healthcare requirement afterwards are essential information for efficient policy making. We analysed individual-level data from nationally representative surveys conducted in low- or middle-income countries (LMICs) between 2008-2019. We describe the weighted incidence of non-fatal RTC in the past 12 months, type of road user involved, and incidence of traffic injuries requiring medical attention. Multivariable logistic regressions were done to evaluate associated sociodemographic and economic characteristics, and alcohol use. Data were included from 90,790 individuals from 15 countries or territories. The non-fatal RTC incidence in participants aged 24-65 years was 5.2% (95% CI: 4.6-5.9), with significant differences dependent on country income status. Drivers, passengers, pedestrians and cyclists composed 37.2%, 40.3%, 11.3% and 11.2% of RTCs, respectively. The distribution of road user type varied with country income status, with divers increasing and cyclists decreasing with increasing country income status. Type of road users involved in RTCs also varied by the age and sex of the person involved, with a greater proportion of males than females involved as drivers, and a reverse pattern for pedestrians. In multivariable analysis, RTC incidence was associated with younger age, male sex, being single, and having achieved higher levels of education; there was no association with alcohol use. In a sensitivity analysis including respondents aged 18-64 years, results were similar, however, there was an association of RTC incidence with alcohol use. The incidence of injuries requiring medical attention was 1.8% (1.6-2.1). In multivariable analyses, requiring medical attention was associated with younger age, male sex, and higher wealth quintile. We found remarkable heterogeneity in RTC incidence, the type of road users involved, and the requirement for medical attention after injuries depending on country income status and socio-demographic characteristics. Targeted data-informed approaches are needed to prevent and manage RTCs

The Socio-economic Gradient of Alcohol Use: An Analysis of Nationally Representative Survey Data from 55 Low and Middle income Countries:Socio-economic Gradient of Alcohol Use in 55 Low- 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

Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in European countries: technical description update

Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, wide-scale social distancing including local and national lockdowns. In this technical update, we extend a semi-mechanistic Bayesian hierarchical model that infers the impact of these interventions and estimates the number of infections over time. Our methods assume that changes in the reproductive number - a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from temporal data on observed to estimate the number of infections and rate of transmission that occurred several weeks prior, allowing for a probabilistic time lag between infection and death. In this update we extend our original model [Flaxman, Mishra, Gandy et al 2020, Report #13, Imperial College London] to include (a) population saturation effects, (b) prior uncertainty on the infection fatality ratio, (c) a more balanced prior on intervention effects and (d) partial pooling of the lockdown intervention covariate. We also (e) included another 3 countries (Greece, the Netherlands and Portugal). The model code is available at https://github.com/ImperialCollegeLondon/covid19model/ We are now reporting the results of our updated model online at https://mrc-ide.github.io/covid19estimates/ We estimated parameters jointly for all M=14 countries in a single hierarchical model. Inference is performed in the probabilistic programming language Stan using an adaptive Hamiltonian Monte Carlo (HMC) sampler

Variation in the proportion of adults in need of BP-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