The economic impact of the COVID-19 pandemic on ethnic minorities in Manchester: lessons from the early stage of the pandemic.

This review summarizes the economic impacts of the pandemic on ethnic minorities, focusing on the city of Manchester. It utilizes multiple reporting sources to explore various dimensions of the economic shock in the UK, linking this to studies of pre-COVID-19 economic and ethnic composition in Manchester and in the combined authority area of Greater Manchester. We then make inferences about the pandemic's short-term impact specific to the city region. Greater Manchester has seen some of the highest rates of COVID-19 and as a result faced particularly stringent "lockdown" regulations. Manchester is the sixth most deprived Local Authority in England, according to 2019 English Indices of Multiple Deprivation. As a consequence, many neighborhoods in the city were always going to be less resilient to the economic shock caused by the pandemic compared with other, less-deprived, areas. Particular challenges for Manchester include the high rates of poor health, low-paid work, low qualifications, poor housing conditions and overcrowding. Ethnic minority groups also faced disparities long before the onset of the pandemic. Within the UK, ethnic minorities were found to be most disadvantaged in terms of employment and housing-particularly in large urban areas containing traditional settlement areas for ethnic minorities. Further, all Black, Asian, and Minority ethnic (BAME) groups in Greater Manchester were less likely to be employed pre-pandemic compared with White people. For example, people of Pakistani and Bangladeshi ethnic backgrounds, especially women, have the lowest levels of employment in Greater Manchester. Finally, unprecedented cuts to public spending as a result of austerity have also disproportionately affected women of an ethnic minority background alongside disabled people, the young and those with no or low-level qualifications. This environment has created and sustained a multiplicative disadvantage for Manchester's ethnic minority residents through the course of the COVID-19 pandemic.

Remittances and food security in Bangladesh: an empirical country-level analysis.

OBJECTIVE: To examine the association between remittances and food security in Bangladesh, controlling for other key factors. DESIGN: The secondary data analysis was performed on the most recent (2016) nationally representative Household Income and Expenditure Survey. We used logistic regression models to measure the association between food security of the household and remittances received. The household food security was measured based on expenditure on food items and the energy intake of the household members. The key explanatory variables included the receipt of remittances by the household and household-level socio-economic characteristics. SETTING: Bangladesh. PARTICIPANTS: Totally, 45 977 households across seven divisions of Bangladesh. RESULTS: Findings suggested that remittances have a significant positive effect on food security. Further, the households with female heads were significantly more likely to be food insecure. The wealth status and geographical locations were significantly associated with food security status in Bangladesh. CONCLUSIONS: The findings highlight the importance of considering remittance as one of the key factors, while stakeholders implement nutritional interventions in Bangladesh and other low-income settings. Future research should consider this as an important determinant while further examining food security in such settings.

Differences in beliefs about COVID-19 by gun ownership: a cross-sectional survey of Texas adults.

OBJECTIVES: We investigated the association between gun ownership and perceptions about COVID-19 among Texas adults as the pandemic emerged. We considered perceived likelihood that the pandemic would lead to civil unrest, perceived importance of taking precautions to prevent transmission and perceptions that the threat of COVID-19 has been exaggerated. METHODS: Data were collected from 5 to 12 April 2020, shortly after Texas' stay-at-home declaration. We generated a sample using random digit dial methods for a telephone survey (n=77, response rate=8%) and by randomly selecting adults from an ongoing panel to complete the survey online (n=1120, non-probability sample). We conducted a logistic regression to estimate differences in perceptions by gun ownership. To account for bias associated with use of a non-probability sample, we used Bayesian data integration and ran linear regression models to produce more accurate measures of association. RESULTS: Among the 60% of Texas adults who reported gun ownership, estimates of past 7-day gun purchases, ammunition purchases and gun carrying were 15% (n=78), 20% (n=100) and 24% (n=130), respectively. We found no evidence of an association between gun ownership with perceived importance of taking precautions to prevent transmission or with perceived likelihood of civil unrest. Results from the logistic regression (OR 1.27, 95% CI 0.99 to 1.63) and the linear regression (ß=0.18, 95% CI 0.07 to 0.29) suggest that gun owners may be more likely to believe the threat of COVID-19 was exaggerated. CONCLUSIONS: Compared with those without guns, gun owners may have been inclined to downplay the threat of COVID-19 early in the pandemic.

Hospital length of stay for COVID-19 patients: Data-driven methods for forward planning.

BACKGROUND: Predicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data. METHOD: On a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy. RESULTS: All methods produced similar overall estimates of LoS for overall hospital stay, given a patient is not admitted to ICU (8.4, 9.1 and 8.0 days for AFT, TC and MS, respectively). Estimates differ more significantly between the local and national level when considering ICU. National estimates for ICU LoS from AFT and TC were 12.4 and 13.4 days, whereas in local data the MS method produced estimates of 18.9 days. CONCLUSIONS: Given the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases, instead correcting for truncation in the data, but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist.

Length of Stay in ICU of Covid-19 patients in England, March - May 2020.

INTRODUCTION: Length of Stay (LoS) in Intensive Care Units (ICUs) is an important measure for planning beds capacity during the Covid-19 pandemic. However, as the pandemic progresses and we learn more about the disease, treatment and subsequent LoS in ICU may change. OBJECTIVES: To investigate the LoS in ICUs in England associated with Covid-19, correcting for censoring, and to evaluate the effect of known predictors of Covid-19 outcomes on ICU LoS. DATA SOURCES: We used retrospective data on Covid-19 patients, admitted to ICU between 6 March and 24 May, from the "Covid-19 Hospitalisation in England Surveillance System" (CHESS) database, collected daily from England's National Health Service, and collated by Public Health England. METHODS: We used Accelerated Failure Time survival models with Weibull and log-normal distributional assumptions to investigate the effect of predictors, which are known to be associated with poor Covid-19 outcomes, on the LoS in ICU. RESULTS: Patients admitted before 25 March had significantly longer LoS in ICU (mean = 18.4 days, median = 12), controlling for age, sex, whether the patient received Extracorporeal Membrane Oxygenation, and a co-morbid risk factors score, compared with the period after 7 April (mean = 15.4, median = 10). The periods of admission reflected the changes in the ICU admission policy in England. Patients aged 50-65 had the longest LoS, while higher co-morbid risk factors score led to shorter LoS. Sex and ethnicity were not associated with ICU LoS. CONCLUSIONS: The skew of the predicted LoS suggests that a mean LoS, as compared with median, might be better suited as a measure used to assess and plan ICU beds capacity. This is important for the ongoing second and any future waves of Covid-19 cases and potential pressure on the ICU resources. Also, changes in the ICU admission policy are likely to be confounded with improvements in clinical knowledge of Covid-19.

Hierarchical Modelling of COVID-19 Death Risk in India in the Early Phase of the Pandemic.

We improve upon the modelling of India's pandemic vulnerability. Our model is multidisciplinary and recognises the nested levels of the epidemic. We create a model of the risk of severe COVID-19 and death, instead of a model of transmission. Our model allows for socio-demographic-group differentials in risk, obesity and underweight people, morbidity status and other conditioning regional and lifestyle factors. We build a hierarchical multilevel model of severe COVID-19 cases, using three different data sources: the National Family Health Survey for 2015/16, Census data for 2011 and data for COVID-19 deaths obtained cumulatively until June 2020. We provide results for 11 states of India, enabling best-yet targeting of policy actions. COVID-19 deaths in north and central India were higher in areas with older and overweight populations, and were more common among people with pre-existing health conditions, or who smoke, or who live in urban areas. Policy experts may both want to 'follow World Health Organisation advice' and yet also use disaggregated and spatially specific data to improve wellbeing outcomes during the pandemic. The future uses of our innovative data-combining model are numerous.

Dans le contexte du développement international, on peut améliorer la modélisation de la vulnérabilité à une pandémie en combinant différentes disciplines, en combinant des données et en reconnaissant les nombreux niveaux imbriqués de l'épidémie. Des modèles de transmission ont été élaborés à l'échelle nationale ou pour plusieurs pays. A l'inverse, nous construisons un modèle permettant de prendre en compte les différents niveaux de risque selon les groupes sociaux, ainsi que le conditionnement des facteurs régionaux et des facteurs liés au mode de vie. La forme grave de la COVID-19 est notre résultat clé innovant. Nous utilisons trois sources de données simultanément: l'enquête nationale sur la santé des familles en Inde, le recensement de la population indienne de 2011 et les décès liés à l'épidémie de COVID-19. Nous fournissons des résultats pour 11 États en Inde, ce qui permet un meilleur ciblage des actions politiques. Les utilisations futures de ces modèles sont nombreuses. Dans le nord et le centre de l'Inde, les décès liés à la COVID-19 étaient plus nombreux dans les régions avec populations âgées et populations en surpoids. Ces décès étaient plus fréquents chez les personnes ayant déjà des problèmes de santé, ou chez celles qui fument ou qui vivent dans les zones urbaines. Les experts en politiques publiques pourront souhaiter à la fois « suivre les conseils de l'Organisation mondiale de la santé ¼ tout en utilisant des données désagrégées et spatiales pour améliorer les résultats en matière de bien-être pendant la pandémie.

Applying and testing a forecasting model for age and sex patterns of immigration and emigration.

International migration flows are considered the most difficult demographic component to forecast and, for that reason, models for forecasting migration are few and relatively undeveloped. This is worrying because, in developed societies, international migration is often the largest component of population growth and most influential in debates about societal and economic change. In this paper, we address the need for better forecasting models of international migration by testing a hierarchical (bilinear) model within the Bayesian inferential framework, recently developed to forecast age and sex patterns of immigration and emigration in the United Kingdom, on other types of migration flow data: age- and sex-specific time series from Sweden, South Korea, and Australia. The performances of the forecasts are compared and assessed with the observed time-series data. The results demonstrate the generality and flexibility of the model and of Bayesian inference for forecasting migration, as well as for further research.

Migration from new-accession countries and duration expectancy in the EU-15: 2002-2008.

European Union (EU) enlargements in 2004 and 2007 were accompanied by increased migration from new-accession to established-member (EU-15) countries. The impacts of these flows depend, in part, on the amount of time that persons from the former countries live in the latter over the life course. In this paper, we develop period estimates of duration expectancy in EU-15 countries among persons from new-accession countries. Using a newly developed set of harmonised Bayesian estimates of migration flows each year from 2002 to 2008 from the Integrated Modelling of European Migration (IMEM) Project, we exploit period age patterns of country-to-country migration and mortality to summarize the average number of years that persons from new-accession countries could be expected to live in EU-15 countries over the life course. In general, the results show that the amount of time that persons from new-accession countries could be expected to live in the EU-15 nearly doubled after 2004.

Integrated modelling of age and sex patterns of European migration.

Age and sex patterns of migration are essential for understanding drivers of population change and heterogeneity of migrant groups. We develop a hierarchical Bayesian model to estimate such patterns for international migration in the European Union and European Free Trade Association from 2002 to 2008, which was a period of time when the number of members expanded from 19 to 31 countries. Our model corrects for the inadequacies and inconsistencies in the available data and estimates the missing patterns. The posterior distributions of the age and sex profiles are then combined with a matrix of origin-destination flows, resulting in a synthetic database with measures of uncertainty for migration flows and other model parameters.

Bayesian Population Forecasting: Extending the Lee-Carter Method.

In this article, we develop a fully integrated and dynamic Bayesian approach to forecast populations by age and sex. The approach embeds the Lee-Carter type models for forecasting the age patterns, with associated measures of uncertainty, of fertility, mortality, immigration, and emigration within a cohort projection model. The methodology may be adapted to handle different data types and sources of information. To illustrate, we analyze time series data for the United Kingdom and forecast the components of population change to the year 2024. We also compare the results obtained from different forecast models for age-specific fertility, mortality, and migration. In doing so, we demonstrate the flexibility and advantages of adopting the Bayesian approach for population forecasting and highlight areas where this work could be extended.