102 research outputs found
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
Age groups that sustain resurging COVID-19 epidemics in the United States
After initial declines, in mid-2020 a resurgence in transmission of novel coronavirus disease (COVID-19) occurred in the United States and Europe. As efforts to control COVID-19 disease are reintensified, understanding the age demographics driving transmission and how these affect the loosening of interventions is crucial. We analyze aggregated, age-specific mobility trends from more than 10 million individuals in the United States and link these mechanistically to age-specific COVID-19 mortality data. We estimate that as of October 2020, individuals aged 20 to 49 are the only age groups sustaining resurgent SARS-CoV-2 transmission with reproduction numbers well above one and that at least 65 of 100 COVID-19 infections originate from individuals aged 20 to 49 in the United States. Targeting interventions-including transmission-blocking vaccines-to adults aged 20 to 49 is an important consideration in halting resurgent epidemics and preventing COVID-19-attributable deaths
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