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An Improved Measure of Deaths Due to Covid-19 in England and Wales
Abstract
We address the question of: 'how many deaths in England and Wales are due to COVID-19?' There are two approaches to measuring COVID deaths - 'COVID associated deaths' and 'excess deaths'. An excess deaths type framework is preferable, as there is substantial measurement error in COVID associated deaths, due to issues relating to the identification of deaths that are directly attributable to COVID-19. A limitation of the current excess deaths metric (a comparison of deaths to a 5 year average for the same week), is that it attributes the entirety of the variation in mortality to COVID-19. This likely means that the metric is overstated because there are a range of other drivers of mortality. We address this by estimating novel empirical Poisson models for all-cause deaths (in totality; by age category; for males; and females) that account for other drivers including the lockdown Government policy response. The models are novel because they include COVID identifier variables (which are a variation on a dummy variable). We use these identifiers to estimate weekly deviations in COVID deaths (about the mean weekly estimate pertaining to the COVID dummy variable in our baseline model). Results from two sets of identifiers indicate that, over the periods when our weekly estimates of total COVID deaths and the current excess deaths measure differ (week ending 17th or 24th April 2020 - week ending 8th May 2020), the former is considerably below the latter - on average per week 4670 deaths (54%) lower, or 4727 deaths (63%) lower, respectively.
... Yet it is a universal force of human nature -privately taking steps to reduce exposure to a new risk -that likely makes lockdown superfluous. Moreover, evidence from elsewhere suggests that lockdowns were either superfluous (Homburg, 2020;Stone, 2020) or cause total deaths to rise because of non-Covid mortality (Williams, Crookes, Glass, & Glass, 2020). ...
... In line with the current analysis, Heatley (2020) restricts attention to using data that were available to decision makers at the time of making decisions about lockdowns. 2. https://www.rnz.co.nz/news/political/412403/all-of-new-zealand-must-prepare-to-go-in-self-isolation-now -prime-minister 3. Nevertheless, deaths data have some problems of over-counting especially when positive Covid-19 tests are linked with subsequent deaths from any cause (Loke & Heneghan, 2020;Williams et al., 2020). For the county level data used here, reporting is mostly from state public health agencies who all follow the same set of CDC guidelines for Covid deaths which are available here: https://www.cdc.gov/nchs/data/nvss/vsrg/vsrg03-508.pdf 4. ...
The New Zealand policy response to Coronavirus was the most stringent in the world during the Level 4 lockdown. Up to 10 billion dollars of output (≈3.3% of GDP) was lost in moving to Level 4 rather than staying at Level 2, according to Treasury calculations. For lockdown to be optimal requires large health benefits to offset this output loss. Forecast deaths from epidemiological models are not valid counterfactuals, due to poor identification. Instead, I use empirical data, based on variation amongst United States counties, over one-fifth of which just had social distancing rather than lockdown. Political drivers of lockdown provide identification. Lockdowns do not reduce Covid-19 deaths. This pattern is visible on each date that key lockdown decisions were made in New Zealand. The apparent ineffectiveness of lockdowns suggests that New Zealand suffered large economic costs for little benefit in terms of lives saved.
... We have dedicated our careers working in this multidisciplinary field, and our statement stems from our collective expertise spanning the entire field. reports that more than half of the deaths in the said "COVID-peak" are non-COVID-19 deaths, and concludes: [7] Three key findings from our empirical analysis are as follows. First, although it has been widely reported that COVID-19 has been highly concentrated in the elderly, we find that it has been particularly concentrated in the very elderly (75-84 and 85+ years), and less so in the 65-74 age category. ...
... We have dedicated our careers working in this multidisciplinary field, and our statement stems from our collective expertise spanning the entire field. reports that more than half of the deaths in the said "COVID-peak" are non-COVID-19 deaths, and concludes: [7] Three key findings from our empirical analysis are as follows. First, although it has been widely reported that COVID-19 has been highly concentrated in the elderly, we find that it has been particularly concentrated in the very elderly (75-84 and 85+ years), and less so in the 65-74 age category. ...
Alleged "COVID-19 Pandemic" mandated government enforced lockdowns of citizens, leading to massive but ignored K -12 suicide deaths of our children is connectable to Dr. Anthony Fauci.
... The global forecasts show a broad range of estimates, from 49 million people being pushed into extreme poverty , to up to 550 million (Sumner, Hoy and Ortiz-Juarez 2020). 11 All of these estimates reveal that the Covid-19 shock is likely to cause the first increase in 10. Williams et al. (2020) used a different approach based on time series Poisson modelling. They found that the lockdown in the United Kingdom resulted in more deaths, because of (non-income-related) indirect effects operating through mortality not directly caused by Covid-19. ...
The spread and threat of Covid-19 have resulted in unprecedented economic and public health responses in Indonesia and elsewhere. We analyse the direct and indirect effects of Covid-19 on life expectancy and poverty in Indonesia, and the responses to the virus. We view life expectancy and poverty as indicators of quantity and quality of life. Our analysis shows that the indirect effects on life expectancy, which operate through lower future income, exceed the direct effects of Covid-19-related deaths by at least five orders of magnitude. The reduction in long-run real income due to the Covid-19 shock may reduce life expectancy by up to 1.7 years, compared with what could otherwise be expected. In contrast, even if the Covid-19 death toll to date were 40 times worse, life expectancy would fall by just two days. Given this imbalance between direct and indirect effects, any interventions to reduce the risk of Covid-19 must be finely targeted and must consider indirect effects. Our analysis of the geographic pattern of poverty effects, which is based on near real-time mobility data, discusses how targeted interventions that are less fiscally costly could be developed. Such interventions should pose less of a threat to future growth and may help to reduce the indirect effects of the Covid-19 shock.
... The revised record of the earliest cases highlights the incomplete nature of data on cases and supports an approach based on deaths data. 3. Nevertheless, deaths data have some problems of over-counting when positive Covid-19 tests are linked with subsequent deaths from any cause (Williams, Crookes, Glass, & Glass, 2020). 4. Homburg (2020) uses a lag of 23 days but from March and early April when treatment protocols for Covid-19 were still being developed. ...
A popular narrative that New Zealand’s policy response to Coronavirus was ‘go hard, go early’ is misleading. While restrictions were the most stringent in the world during the Level 4 lockdown in March and April, these were imposed after the likely peak in new infections. I use the time path of Covid-19 deaths for each OECD country to estimate inflection points. Allowing for the typical lag from infection to death, new infections peaked before the most stringent policy responses were applied in many countries, including New Zealand. The cross-country evidence shows that restrictions imposed after the inflection point in infections is reached are ineffective in reducing total deaths. Even restrictions imposed earlier have just a modest effect; if Sweden’s more relaxed restrictions had been used, an extra 310 Covid-19 deaths are predicted for New Zealand – far fewer than the thousands of deaths in some widely reported mathematical simulations.
... There is still serious academic debate about the efficacy of lockdown as a disease control measure [20], how it should be implemented [21] and the negative and unintended consequences of these policies [22]. This work is an observational study, using jobs posted on an online job board to study the effect of COVID-19, lockdown and other disease control measures on the job market in the UK. ...
The COVID-19 global pandemic and the lockdown policies enacted to mitigate it have had profound effects on the labour market. Understanding these effects requires us to obtain and analyse data in as close to real time as possible, especially as rules change rapidly and local lockdowns are enacted. In this work we study the UK labour market by analysing data from the online job board Reed.co.uk. Using topic modelling and geo-inference methods we are able to break down the data by sector and geography. We also study how the salary, contract type and mode of work have changed since the COVID-19 crisis hit the UK in March. Overall, vacancies were down by 60 to 70% in the first weeks of lockdown. By mid September numbers had recovered slightly, though were still down by 40 to 50%. Broken down by sector, vacancies for hospitality and graduate jobs are greatly reduced, while there were more care work and nursing vacancies during lockdown. Differences by geography are less significant than between sectors, though there is some indication that local lockdowns stall recovery and less badly hit areas may have experienced a smaller reduction in vacancies. There are also small but significant changes in the median salary and number of full time and permanent jobs. In addition to these results, this work presents an open methodology that enables a rapid and detailed survey of the job market in these unsettled conditions.
... We have dedicated our careers working in this multidisciplinary field, and our statement stems from our collective expertise spanning the entire field. reports that more than half of the deaths in the said "COVID-peak" are non-COVID-19 deaths, and concludes: [7] Three key findings from our empirical analysis are as follows. First, although it has been widely reported that COVID-19 has been highly concentrated in the elderly, we find that it has been particularly concentrated in the very elderly (75-84 and 85+ years), and less so in the 65-74 age category. ...
A vile new mantra is on the lips of every public health official and politician in the global campaign to force universal masking on the general public: “there is a growing body of evidence”.
This propagandistic phrase is a vector designed to achieve five main goals:
- Give the false impression that a balance of evidence now proves that masks reduce the transmission of COVID-19
- Falsely assimilate commentary made in scientific venues with “evidence”
- Hide the fact that a decade’s worth of policy-grade evidence proves the opposite: that masks are ineffective with viral respiratory diseases
- Hide the fact that there is now direct observational proof that cloth masks do not prevent exhalation of clouds of suspended aerosol particles; above, below and through the masks
- Deter attention away from the considerable known harms and risks due to face masks, applied to entire populations
The said harms and risks include that a cloth mask becomes a culture medium for a large variety of bacterial pathogens, and a collector of viral pathogens; given the hot and humid environment and the constant source, where home fabrics are hydrophilic whereas medical masks are hydrophobic.
In short, I argue: op-eds are not “evidence”, irrelevance does not help, and more bias does not remove bias. Their mantra of “a growing body of evidence” is a self-serving contrivance that impedes good science and threatens public safety.
I prove that there is no policy-grade evidence to support forced masking on the general population, and that all the latest-decade’s policy-grade evidence points to the opposite: NOT recommending forced masking of the general population. Therefore, the politicians and health authorities are acting without legitimacy and recklessly.
The COVID-19 global pandemic and the lockdown policies enacted to mitigate it have had profound effects on the labour market. Understanding these effects requires us to obtain and analyse data in as close to real time as possible, especially as rules change rapidly and local lockdowns are enacted. This work studies the UK labour market by analysing data from the online job board Reed.co.uk, using topic modelling and geo-inference methods to break down the data by sector and geography. I also study how the salary, contract type, and mode of work have changed since the COVID-19 crisis hit the UK in March. Overall, vacancies were down by 60 to 70% in the first weeks of lockdown. By the end of the year numbers had recovered somewhat, but the total job ad deficit is measured to be over 40%. Broken down by sector, vacancies for hospitality and graduate jobs are greatly reduced, while there were more care work and nursing vacancies during lockdown. Differences by geography are less significant than between sectors, though there is some indication that local lockdowns stall recovery and less badly hit areas may have experienced a smaller reduction in vacancies. There are also small but significant changes in the salary distribution and number of full time and permanent jobs. As well as the analysis, this work presents an open methodology that enables a rapid and detailed survey of the job market in unsettled conditions and describes a web application jobtrender.com that allows others to query this data set.
This paper models the local and cross-city transmissions of the novel coronavirus in China between January 19 and February 29 in 2020. We examine the role of various socioeconomic mediating factors, including public health measures that encourage social distancing in local communities. Weather characteristics two weeks ago are used as instrumental variables for causal inference. Stringent quarantine, city lockdown, and local public health measures imposed since late January significantly decreased the virus transmission rate. The virus spread was contained by the middle of February. Population outflow from the outbreak source region posed a higher risk to the destination regions than other factors including geographic proximity and similarity in economic conditions. We quantify the effects of different public health measures in reducing the number of infections through counterfactual analyses. Over 1.4 million infections and 56,000 deaths could have been avoided as a result of the national and provincial public health measures imposed in late January in China. JEL Classification: I18, I12, C23
Background
In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases.
Methods
We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation.
Findings
Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9–19·2) and to hospital discharge to be 24·7 days (22·9–28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56–3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23–1·53), with substantially higher ratios in older age groups (0·32% [0·27–0·38] in those aged <60 years vs 6·4% [5·7–7·2] in those aged ≥60 years), up to 13·4% (11·2–15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4–3·5] in those aged <60 years [n=360] and 4·5% [1·8–11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39–1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0–7·6) in those aged 80 years or older.
Interpretation
These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death.
Funding
UK Medical Research Council.
The aim of this study was to estimate the excess mortality associated with the influenza activity registered in Portugal between week 49 of 2008 and week 5 of 2009. For this purpose available mortality data from the Portuguese Daily Mortality Monitoring (VDM) System was used. Several estimates of excess deaths associated with the recent recorded influenza activity were determined through statistical modelling (cyclic regression) for the total population and disaggregated by gender and age group. The results show that the impact of the 2008-9 influenza season was 1,961 excess deaths, with approximately 82% of these occurring in the age group of 75 years and older.
Baseline levels of mortality in the United States in the absence of influenza epidemics were estimated using cyclical regression models applied to national vital statistics from October 1972 to May 1985. Models were developed by age and by region. Results from 1983 to 1985 are preliminary. More mortality than predicted by the theoretical baseline occurred during nine influenza seasons. Epidemics with high morbidity in children and young adults occurred in 1976/77 and 1978/79. Regional differences in the impact of influenza occurred occasionally. Total influenza-associated excess mortality in six epidemics from the winters of 1972/73 to 1980/81 was about 200,000. About 80-90 per cent of excess mortality occurred in persons over 64 years old.
The purpose of this study was to assess the impact of recent influenza epidemics on mortality in the United States and to develop an index for comparing the severity of individual epidemics.
A cyclical regression model was applied to weekly national vital statistics from 1972 through 1992 to estimate excesses in pneumonia and influenza mortality and all-cause mortality for each influenza season. Each season was categorized on the basis of increments of 2000 pneumonia and influenza excess deaths, and each of these severity categories was correlated with a range of all-cause excess mortality.
Each of the 20 influenza seasons studied was associated with an average of 5600 pneumonia and influenza excess deaths (range, 0-11,800) and 21,300 all-cause excess deaths (range, 0-47,200). Most influenza A(H3N2) seasons fell into severity categories 4 to 6 (23,000-45,000 all-cause excess deaths), whereas most A(H1N1) and B seasons were ranked in categories 1 to 3 (0-23,000 such deaths).
From 1972 through 1992, influenza epidemics accounted for a total of 426,000 deaths in the United States, many times more than those associated with recent pandemics. The influenza epidemic severity index was useful for categorizing severity and provided improved seasonal estimates of the total number of influenza-related deaths.
We conducted the study described in this paper to investigate the impact of ambient temperature on mortality in the Netherlands during 1979-1997, the impact of heat waves and cold spells on mortality in particular, and the possibility of any heat wave- or cold spell-induced forward displacement of mortality. We found a V-like relationship between mortality and temperature, with an optimum temperature value (e.g., average temperature with lowest mortality rate) of 16.5 degrees C for total mortality, cardiovascular mortality, respiratory mortality, and mortality among those [Greater and equal to] 65 year of age. For mortality due to malignant neoplasms and mortality in the youngest age group, the optimum temperatures were 15.5 degrees C and 14.5 degrees C, respectively. For temperatures above the optimum, mortality increased by 0.47, 1.86, 12.82, and 2.72% for malignant neoplasms, cardiovascular disease, respiratory diseases, and total mortality, respectively, for each degree Celsius increase above the optimum in the preceding month. For temperatures below the optimum, mortality increased 0.22, 1.69, 5.15, and 1.37%, respectively, for each degree Celsius decrease below the optimum in the preceding month. Mortality increased significantly during all of the heat waves studied, and the elderly were most effected by extreme heat. The heat waves led to increases in mortality due to all of the selected causes, especially respiratory mortality. Average total excess mortality during the heat waves studied was 12.1%, or 39.8 deaths/day. The average excess mortality during the cold spells was 12.8% or 46.6 deaths/day, which was mostly attributable to the increase in cardiovascular mortality and mortality among the elderly. The results concerning the forward displacement of deaths due to heat waves were not conclusive. We found no cold-induced forward displacement of deaths.
We sought to reexamine the effects of the 1995 Chicago heat wave on all-cause and cause-specific mortality, including mortality displacement, using advanced time-series analysis methods.
We used Poisson regression with penalized regression splines to model excess mortality and mortality displacement over a 50-day period centered on the day in which the heat wave temperature peaked, adjusting for meteorological and other variables. We controlled for temporal trends by using daily mortality data during 1993-1997. We estimated relative risks (RRs) with reference to the first day of the 50-day period.
We estimated that there were 692 excess deaths from June 21, 1995, to August 10, 1995; 26% of these deaths were owing to mortality displacement. RR for all-cause mortality on the day with peak mortality was 1.74 (95% confidence interval=1.67, 1.81). Risk of heat-related death was significantly higher among Blacks, and mortality displacement was substantially lower.
The 1995 Chicago heat wave substantially effected all-cause and cause-specific mortality, but mortality displacement was limited. Mortality risks and displacement affected Blacks disproportionally. Appropriately targeted interventions may have a tangible effect on life expectancy.
We use a dynamic panel data model to generate density forecasts for daily active Covid-19 infections for a panel of countries/regions. Our specification that assumes the growth rate of active infections can be represented by autoregressive fluctuations around a downward sloping deterministic trend function with a break. Our fully Bayesian approach allows us to flexibly estimate the cross-sectional distribution of slopes and then implicitly use this distribution as prior to construct Bayes forecasts for the individual time series. We find some evidence that information from locations with an early outbreak can sharpen forecast accuracy for late locations. There is generally a lot of uncertainty about the evolution of active infection, due to parameter and shock uncertainty, in particular before and around the peak of the infection path. Over a one-week horizon, the empirical coverage frequency of our interval forecasts is close to the nominal credible level. Weekly forecasts from our model are published at https://laurayuliu.com/covid19-panel-forecast/.
Background:
A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities.
Objective:
To estimate the length of the incubation period of COVID-19 and describe its public health implications.
Design:
Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020.
Setting:
News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China.
Participants:
Persons with confirmed SARS-CoV-2 infection outside Hubei province, China.
Measurements:
Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization.
Results:
There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine.
Limitation:
Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases.
Conclusion:
This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases.
Primary funding source:
U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.
Background:
Studies of the effect of macroeconomic fluctuations on mortality in different socioeconomic groups are scarce and have yielded mixed findings. We analyse mortality trends in Spain before and during the Great Recession in different socioeconomic groups, quantifying the change within each group.
Methods:
We did a nationwide prospective study, in which we took data from the 2001 Census. All people living in Spain on Nov 1, 2001, were followed up until Dec 31, 2011. We included 35 951 354 people alive in 2001 who were aged between 10 and 74 years in each one of the four calendar years before the economic crisis (from 2004 to 2007) and in each one of the first four calendar years of the crisis (from 2008 to 2011), and analysed all-cause and cause-specific mortality in those people. We classified individuals by socioeconomic status (low, medium, or high) using two indicators of household wealth: household floor space (<72 m(2), 72-104 m(2), and >104 m(2)) and number of cars owned by the residents of the household (none, one, and two or more). We used Poisson regression to calculate the annual percentage reduction (APR) in mortality rates during 2004-07 (pre-crisis) and 2008-11 (crisis) in each socioeconomic group, as well as the effect size, measured by the APR difference between the pre-crisis and crisis period.
Findings:
The annual decline in all-cause mortality in the three socioeconomic groups was 1·7% (95% CI 1·2 to 2·1) for the low group, 1·7% (1·3 to 2·1) for the medium group, and 2·0% (1·4 to 2·5) for the high group in 2004-07, and 3·0% (2·5 to 3·5) for the low group, 2·8% (2·5 to 3·2) for the medium group, and 2·1% (1·6 to 2·7) for the high group in 2008-11 when individuals were classified by household floor space. The annual decline in all-cause mortality when people were classified by number of cars owned by the household was 0·3% (-0·1 to 0·8) for the low group, 1·6% (1·2 to 2·0) for the medium group, and 2·2% (1·6 to 2·8) for the high group in 2004-07, and 2·3% (1·8 to 2·8) for the low group, 2·4% (2·0 to 2·7) for the medium group and 2·5% (1·9 to 3·0) for the high group in 2008-11. The low socioeconomic group showed the largest effect size for both wealth indicators.
Interpretation:
In Spain, probably due to the decrease in exposure to risk factors, all-cause mortality decreased more during the economic crisis than before the economic crisis, especially in low socioeconomic groups.
Funding:
None.
We examine the effect of rainfall shocks on dowry deaths using data from 583 Indian districts for 2002-2007. We find that a one standard deviation decline in annual rainfall from the local mean increases reported dowry deaths by 7.8 percent. Wet shocks have no apparent effect. We examine patterns of other crimes to investigate whether an increase in general unrest during economic downturns explains the results but do not find supportive evidence. Women’s political representation in the national parliament has no apparent mitigating effect on dowry deaths. Full text: https://www.ncbi.nlm.nih.gov/pubmed/25386044
This study examines the relative effects of population density and area-level SES on all-cause mortality in Denmark. A shared frailty model was fitted with 2.7 million persons aged 30-81 years in 2,121 parishes. Residence in areas with high population density increased all-cause mortality for all age groups. For older age groups, residence in areas with higher proportions of unemployed persons had an additional effect. Area-level factors explained considerably more variation in mortality among the elderly than among younger generations. Overall this study suggests that structural prevention efforts in neighborhoods could help reduce mortality when mediating processes between area-level socioeconomic status, population density and mortality are found.
This study develops a methodology of inference for a widely used Cliff-Ord type spatial model containing spatial lags in the dependent variable, exogenous variables, and the disturbance terms, while allowing for unknown heteroskedasticity in the innovations. We first generalize the GMM estimator suggested in Kelejian and Prucha (1998,1999) for the spatial autoregressive parameter in the disturbance process. We also define IV estimators for the regression parameters of the model and give results concerning the joint asymptotic distribution of those estimators and the GMM estimator. Much of the theory is kept general to cover a wide range of settings.
This paper analyses the effects of income and income distribution on mortality. The likely relation between income and mortality for individuals is discussed, and implications for the determinants of mortality at the community level inferred. Measures of income inequality are likely to be related to mortality on aggregate data because of the non-linearity of income effects. An international cross-section analysis is then undertaken in which different measures of income and income distribution are investigated as determinants of mortality, with life expectancy at birth and age five, and infant mortality taken as measures of the dependent variable. It is found that income distribution is consistently and strongly related to mortality; in a relatively inegalitarian country life expectancy may be between five and ten years lower than in a more egalitarian country.
This article presents and critically discusses evidence on the determinants of mortality reductions in developing countries. It argues that increases in life expectancy between 1960 and 2000 were largely independent of improvements in income. The author characterizes the age and cause-of-death profile of changes in mortality and assesses what can be learned about the determinants of these changes from the international evidence and from country-specific studies. Public health infrastructure, immunization, targeted programs, and the spread of less palpable forms of knowledge all seem to have been important factors. Finally, the article suggests that the evolution of health inequality across and within countries is intrinsically related to the process of diffusion of new technologies and to the nature of these new technologies, public or private. Copyright 2007 The Population Council, Inc..
Stalling of mortality in the United Kingdom and Europe: an analytical review of the evidence'. LSE Department of Social Policy Working Paper
- M Murphy
- M Luy
- O Torrisi
Murphy, M., M. Luy and O. Torrisi (2019). 'Stalling of mortality in the United Kingdom and Europe: an
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Provisional weekly death registrations and percentage of excess death for selected cases
ONS (2020): 'Provisional weekly death registrations and percentage of excess death for selected cases,
England and Wales: 2020'. Available at:
https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/adhocs/11683p
rovisionalweeklydeathregistrationsandpercentageofexcessdeathforselectedcausesenglandandwales2020
Did COVID-19 infections decline before UK lockdown?' Mimeo
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Wood, S. N. (2020): 'Did COVID-19 infections decline before UK lockdown?' Mimeo.