Studying informal care during the pandemic: mental health, gender and job status.

Unexpected negative health shocks such as COVID-19 put pressure on households to provide more care to relatives and friends. This study uses data from the UK Household Longitudinal Study to investigate the impact of informal caregiving on mental health during the COVID-19 pandemic. Using a difference-in-differences analysis, we find that individuals who started providing care after the pandemic began reported more mental health issues than those who never provided care. Additionally, the gender gap in mental health widened during the pandemic, with women more likely to report mental health issues. We also find that those who began providing care during the pandemic reduced their work hours compared to those who never provided care. Our results suggest that the COVID-19 pandemic has had a negative impact on the mental health of informal caregivers, particularly for women.

Informal care, older people, and COVID-19: Evidence from the UK.

The negative health effects and mortality caused by the COVID-19 pandemic disproportionately fell upon older and disabled people. Protecting these vulnerable groups has been a key policy priority throughout the pandemic and related vaccination campaigns. Using data from the latest survey of the UK Household Longitudinal Study on COVID-19 we found that people who receive informal care have higher probability of being infected when compared to those not receiving informal care. Further, we found that care recipients who are in the lowest income groups have a higher probability of catching the virus when compared to those in the highest income groups. We also estimated the likelihood of being infected for informal carers versus those who did not provide any care during the pandemic and found no significant differences between these two groups. Our empirical findings suggest that the standard measures introduced with the aim of protecting vulnerable groups, such as closing care homes or prioritising the vaccination of their staff, were not sufficient to avoid the spread of the virus amongst disabled and older people. Informal carers play an important role in the social care sector. As such, protecting vulnerable people by investing in the informal care sector should be a priority for future health policy.

The presence of care homes and excess deaths during the COVID-19 pandemic: Evidence from Italy.

This paper explores the relationship between the spatial distribution of excess deaths and the presence of care home facilities during the first wave of the COVID-19 outbreak in Italy. Using registry-based mortality data for Lombardy, one of the areas most severely hit by the pandemic we show that the presence of a care home in a municipality is associated with significantly higher excess death rates in the population. This effect appears to be driven by excess mortality in the elderly population of 70 years old and older. Our results are robust to controlling for the number of residents in each care home, suggesting that the presence of such facilities may have acted as one of factors contributing to the diffusion of COVID-19 at the local level.

Measuring geographical disparities in England at the time of COVID-19: results using a composite indicator of population vulnerability.

OBJECTIVES: The growth of COVID-19 infections in England raises questions about system vulnerability. Several factors that vary across geographies, such as age, existing disease prevalence, medical resource availability and deprivation, can trigger adverse effects on the National Health System during a pandemic. In this paper, we present data on these factors and combine them to create an index to show which areas are more exposed. This technique can help policy makers to moderate the impact of similar pandemics. DESIGN: We combine several sources of data, which describe specific risk factors linked with the outbreak of a respiratory pathogen, that could leave local areas vulnerable to the harmful consequences of large-scale outbreaks of contagious diseases. We combine these measures to generate an index of community-level vulnerability. SETTING: 91 Clinical Commissioning Groups (CCGs) in England. MAIN OUTCOME MEASURES: We merge 15 measures spatially to generate an index of community-level vulnerability. These measures cover prevalence rates of high-risk diseases; proxies for the at-risk population density; availability of staff and quality of healthcare facilities. RESULTS: We find that 80% of CCGs that score in the highest quartile of vulnerability are located in the North of England (24 out of 30). Here, vulnerability stems from a faster rate of population ageing and from the widespread presence of underlying at-risk diseases. These same areas, especially the North-East Coast areas of Lancashire, also appear vulnerable to adverse shocks to healthcare supply due to tighter labour markets for healthcare personnel. Importantly, our index correlates with a measure of social deprivation, indicating that these communities suffer from long-standing lack of economic opportunities and are characterised by low public and private resource endowments. CONCLUSIONS: Evidence-based policy is crucial to mitigate the health impact of pandemics such as COVID-19. While current attention focuses on curbing rates of contagion, we introduce a vulnerability index combining data that can help policy makers identify the most vulnerable communities. We find that this index is positively correlated with COVID-19 deaths and it can thus be used to guide targeted capacity building. These results suggest that a stronger focus on deprived and vulnerable communities is needed to tackle future threats from emerging and re-emerging infectious disease.

Health Care Expenditure and Income: A Global Perspective.

This paper investigates the long-run economic relationship between healthcare expenditure and income in the world using data on 167 countries over the period 1995-2012, collected from the World Bank data set. The analysis is carried using panel data methods that allow one to account for unobserved heterogeneity, temporal persistence, and cross-section dependence in the form of either a common factor model or a spatial process. We estimate a global measure of income elasticity using all countries in the sample, and for sub-groups of countries, depending on their geo-political area and income. Our findings suggest that at the global level, health care is a necessity rather than a luxury. However, results vary greatly depending on the sub-sample analysed. Our findings seem to suggest that size of income elasticity depends on the position of different countries in the global income distribution, with poorer countries showing higher elasticity. Copyright © 2016 John Wiley & Sons, Ltd.

New evidence in health economics.

The 2nd Health Econometrics Workshop took place at the Catholic University of Rome in Italy on 15-17 July 2010. The purpose of this meeting was to provide a forum where policy makers, economists and econometricians could discuss the use of statistical and econometric methods to address issues in the field of health economics. There were seven keynote speakers - leading scholars in the subject - invited to give their contributions: Alberto Holly, Stephen Hall, Badi Baltagi, William Greene, Andrew Jones, John Mullahy and Edward Norton. The meeting was attended by 50 participants from around the world, and 17 scientific papers were presented. Some of these works will be published in the forthcoming special issue of Empirical Economics.

Inequalities in healthy life years in the 25 countries of the European Union in 2005: a cross-national meta-regression analysis.

BACKGROUND: Although life expectancy in the European Union (EU) is increasing, whether most of these extra years are spent in good health is unclear. This information would be crucial to both contain health-care costs and increase labour-force participation for older people. We investigated inequalities in life expectancies and healthy life years (HLYs) at 50 years of age for the 25 countries in the EU in 2005 and the potential for increasing the proportion of older people in the labour force. METHODS: We calculated life expectancies and HLYs at 50 years of age by sex and country by the Sullivan method, which was applied to Eurostat life tables and age-specific prevalence of activity limitation from the 2005 statistics of living and income conditions survey. We investigated differences between countries through meta-regression techniques, with structural and sustainable indicators for every country. FINDINGS: In 2005, an average 50-year-old man in the 25 EU countries could expect to live until 67.3 years free of activity limitation, and a woman to 68.1 years. HLYs at 50 years for both men and women varied more between countries than did life expectancy (HLY range for men: from 9.1 years in Estonia to 23.6 years in Denmark; for women: from 10.4 years in Estonia to 24.1 years in Denmark). Gross domestic product and expenditure on elderly care were both positively associated with HLYs at 50 years in men and women (p<0.039 for both indicators and sexes); however, in men alone, long-term unemployment was negatively associated (p=0.023) and life-long learning positively associated (p=0.021) with HLYs at 50 years of age. INTERPRETATION: Substantial inequalities in HLYs at 50 years exist within EU countries. Our findings suggest that, without major improvements in population health, the target of increasing participation of older people into the labour force will be difficult to meet in all 25 EU countries. FUNDING: EU Public Health Programme.

Sur model with spatial effects: an application to mental health expenditure.

This study analyses, through the adoption of a seemingly unrelated regression approach, the temporal evolution of policy interactions among local authorities in England when allocating mental health resources. This new approach in health economics may shed light on the degree of interdependence between adjacent municipalities at a specific point in time (e.g. before, during, or after a change in policy), exploiting the information carried by the panel, rather than that of a single cross-section.

Mental health expenditure in England: a spatial panel approach.

We empirically investigate the determinants of local authority mental health expenditure in England. We adopt a reduced form demand and supply model, extended to incorporate possible interaction among authorities, as well as unobserved heterogeneity. The model is estimated using an annual panel dataset that allows us to explore both time-series and cross-municipality variation in mental health expenditure. Results are consistent with some degree of interdependence between neighbouring municipalities in spending decisions. This first attempt to apply spatial panels in investigating health expenditure offers insights and raises new questions.

Exploring the spatial pattern of mental health expenditure.

BACKGROUND: Recent years have witnessed growing interest in cross-sectional variations in municipality mental health expenditure. However, empirical work to date has not examined the links between such variability and demand and supply factors, particularly in the spatial domain. AIMS OF THE STUDY: The aim is to examine whether a local authority's spending decisions in the mental health field respond to neighbouring expenditure decisions. We explore a number of reasons why there might be interdependence between local authorities' decisions, labelling them the demonstrative, market leader, contextual, directive, shared resource and inducement effects. METHODS: Exploratory techniques from spatial data analysis are used to test for the existence of spatial structure. Drawing hypotheses from these initial exploratory analyses, we then adopt a reduced form demand and supply model, extended to incorporate possible policy interaction. The analysis of expenditure and cost variations has traditionally been based on regression models under the classical assumption that the observations are independent. But omitting the recognition that observations are interdependent might lead to erroneous statistical conclusions. Hence, we use spatial econometric techniques that explicitly take into account the potential interdependence of data in order to study the sources of spending variation between municipalities. RESULTS: The exploratory data analyses reveal the presence of positive significant spatial correlation. Per capita mental health spending distributes in clusters, with the highest concentrations in metropolitan areas such as Greater London, Greater Manchester and Birmingham. The estimated spatial regression models indicate that spatial autocorrelation characterises local expenditure decisions, consistent with some degree of policy interdependence between neighbouring municipalities. Comparing the results from our spatial model with those from a classical ('non-spatial') model suggests that the differences in the regression coefficients could be explained by the evident spatial pattern of the phenomenon, since the omission of the lagged dependent variable induces bias in the OLS estimates. IMPLICATIONS FOR HEALTH POLICIES: These results help central and local decision makers understand the factors that influence local spending levels, including variations between municipalities in their achievement of expenditure-related and perhaps other performance targets. The actual patterns of spatial interaction may well be more complex than simple contiguity (the structure assumed here), but there seems little doubt that positive interdependence is an important feature of decision making. IMPLICATIONS FOR FURTHER RESEARCH: Statistical interrogation of a panel dataset would allow exploration of both time-series and cross-municipality variation in mental health expenditure. Subsequent analysis would also benefit from more disaggregated data (e.g. at a census ward level) and the accompanying use of spatial multilevel techniques.