key: cord-1049479-t3240r2i authors: Harris, Richard title: Exploring the neighbourhood-level correlates of Covid-19 deaths in London using a difference across spatial boundaries method date: 2020-09-29 journal: Health Place DOI: 10.1016/j.healthplace.2020.102446 sha: c9e238b0aecc723985fc2c9a7b42044536f03ae0 doc_id: 1049479 cord_uid: t3240r2i This paper explores neighbourhood-level correlates of the Covid-19 deaths in London during the initial rise and peak of the pandemic within the UK – the period March 1 to April 17, 2020. It asks whether the person-level predictors of Covid-19 that are identified in reports by Public Health England and by the Office of National Statistics also hold at a neighbourhood scale, remaining evident in the differences between neighbours. In examining this, the paper focuses on localised differences in the number of deaths, putting forward an innovative method of analysis that looks at the differences between places that share a border. Specifically, a difference across spatial boundaries method is employed to consider whether a higher number of deaths in one neighbourhood, when compared to its neighbours, is related to other differences between those contiguous locations. It is also used to map localised ‘hot spots’ and to look for spatial variation in the regression coefficients. The results are compared to those for a later period, April 18 – May 31. The findings show that despite some spatial diffusion of the disease, a greater number of deaths continues to be associated with Asian and Black ethnic groups, socio-economic disadvantage, very large households (likely indicative of residential overcrowding), and fewer from younger age groups. The analysis adds to the evidence showing that age, wealth/deprivation and ethnicity are key risk factors associated with higher mortality rates from Covid-19. mortality rates for Covid-19 related deaths were all London Boroughs. A later report, by Public Health England (dated June 2020), confirms that London had the highest age-standardised death rate from Covid-19 of any region as of May 13, as well as the highest number of deaths each week until the week ending April 18 when it was exceeded by the North West region. Following the relaxing of the national 'lockdown' in England, in June, regional spikes of Covid-19 infection have tended to be outside the capital, including parts of Leicestershire, Great Manchester, West Yorkshire, and East Lancashire where local lockdowns have been enforced. This paper does not consider those infections but focuses on London in the earlier period. A note on the present situation is left to the conclusion. That the wave impacted London sooner than other regions reflects the capital's position as a global city with a total population, population density, and geographical connectivity (both to the UK and to the rest of the world) greater than for other UK settlements. Similar claims could be made of New York and some of the geographical contributors to the Covid-19 outbreak there. Nevertheless, it was, at the time, a reversal of the usual mortality pattern for England because London's population typically is younger and, in parts, much more affluent than in other regions. Public Health England (op. cit., p.30) noted this, observing that "regional inequalities in Covid-19 mortality rates are greater than those seen previously for all cause mortality and the geographic gradient is different. London had the highest Covid-19 mortality rates, but the lowest baseline all cause mortality rates." However, London is also an unequal city, a situation reflected in the Covid-19 mortality statistics: whereas the ethnically diverse and more deprived Borough of Newham had the highest agestandardised rate, with 144 deaths per 100,000 population (followed by Brent with 141.5 and Hackney with 127.4), in more affluent Kingston-upon-Thames the rate was 43 deaths per 100,000. The interest of this paper is in whether the person-level predictors of Covid-19 that are discussed in the report by Public Health England and reviewed below translate to a neighbourhood scale, remaining evident in the differences between closely located neighbourhoods. If so, then it testifies J o u r n a l P r e -p r o o f to the demographic, socio-economic and ethno-cultural geographies that shape London, their relationships to Covid-19 as risk factors, and to the geographical patterning of the mortality rates from the virus, in the capital, as well as how spatial inequalities can function at localised scales (in the differences between neighbours). To explore the neighbourhood-level relationships, a difference across spatial boundaries method is advanced. The idea, which is likened to a spatial difference-in-difference approach, is to look at the difference in the numbers of Covid-19 deaths across the boundaries of neighbouring locations and see if those are related to other differences in the compositions of those contiguous neighbours. The presumption is that, all things being equal, places that are close together ought to exhibit similar levels of mortality because the broader geographical context (their spatial setting) is the same and because the disease is transmitted through close contact with other people. Although the principle is straightforward, it induces a grouping effect in the data that is addressed through a multilevel Poisson model inspired by work addressing similar structures in migration data (Zhang et al. 2020 ). The paper proceeds with a review of what is known about the individual correlates of fatalities in England, using this as the basis to select potential predictors at the neighbourhood-level. The difference across spatial boundaries method is outlined together with a modelling strategy for handling the highly co-linear nature of the variables. The results are presented with evidence of the spatially varying nature of some of the correlates and then compared with a subsequent data release by the ONS for a period immediately after the first. Spatial 'hot spots' in the disease are mapped; areas with a statistically significant higher number of deaths than their neighbours. Whilst many of those hot spots change over the period March 1 -April 17 to April 18 -May 31, reflecting the spatial diffusion of the disease, the demographic and social composition of neighbourhoods continue to be predictive, with a greater number of deaths associated with fewer from younger populations, more from Black and Asian (but not Chinese) ethnic groups, lower average income, and with other indictors of socio-economic disadvantage, including greater percentages who are J o u r n a l P r e -p r o o f unemployed or who have never worked, and households containing large numbers of people. The analysis supports other research showing that age, wealth/deprivation and ethnicity are key risk factors associated with higher mortality rates from Covid-19. This paper draws especially on the report published by Public Health England (2020) about disparities in the risk and outcomes of Covid-19 in England. That report looks at the risk factors under eight main headings: age and sex, geography (a regional geography, highlighting the higher infections and deaths in London at that time), deprivation, ethnicity, occupation, inclusion in health groups, deaths in care homes, and comorbidities. Its conclusions extend but are consistent with the earlier analysis by the ONS (2020). The report finds that over half the deaths in confirmed cases of Covid-19 (as of May 13) were among people aged 80 years or older. The probability of death from the virus is about seventy times greater for those aged eighty or over when compared with people aged under 40 (and about three times higher for those aged 40 to 49, nine times higher for those aged 50 to 59, 27 times greater for those aged 60 to 69, and 50 times greater for those aged 70 to 79). For all age groups, the mortality rate is greater for males than for females: overall, the age-standardised rate for males is twice that for females. As well as increasing with age, the mortality rate increases with the level of neighbourhood deprivation, reaching an age standardised rate that is over twice as great in the most deprived locations (for males and for females) when compared to the least. The relationship of Covid-19 mortality to deprivation continued to be evident in the confirmed infection cases during June. Although this paper is about London, subsequent to the period of this study, other towns and cities overtook it with higher infection rates. One news outlet used the headline 'England's North-South Covid-19 divide' to comment that "only eight of the country's fifty J o u r n a l P r e -p r o o f worst-hit authorities are in the south", drawing on Public Health England data for the week June 15-21 (Chalmers 2020) . Whilst the statistic is true, for that particular week, less so is the interpretation of it because it is not really a north-south divide but an urban deprivation verses rural one. Although many of the "worst-hit" authorities are now in or towards the north of England (often locations where traditional manufacturing industries have declined), they are also in the Midlands and in parts of the South East. The absence from the list, by that time, of London, reveals the disease's spatial diffusion beyond, as well as the earlier impact of the disease in the capital. However, sub-regional inequalities remain, including within London: as of September 14, 2020 the infection rate in the London Borough of Islington was 310.4 cases per 100,000 of the population, half that of the more deprived and ethnically diverse Borough of Brent (with a rate of 627.1; the national rate, for England, was 569.2). A characteristic of the fifty 'northern' local authorities is that they are much more ethnically diverse than is typical for England -places such as Leicester (the first part of England to have an enforced 'local lockdown', enacted in the beginning of July, at the same time as rules in the rest of the country were greatly relaxed), Bradford, Barnsley, Rochdale, Bedford, and Oldham, amongst others. Leicester and Bradford, together with Kirklees, Blackburn with Darwen, Oadby and Wigston, and Rochdale top another media list of '20 areas of England at most risk of coronavirus resurgence' as of July 2020 (Garside 2020) . Notably, these are also some of the most ethnically segregated local authorities in the country (Harris & Johnston 2020 ; although less so than in the past: Catney 2015). However, their relationship to Covid-19 is less likely a story of segregation as of the geographical correlates of segregation -deprivation, occupation types, dwellings, and so forth. If so, then we should expect to see a similar story for the earlier period in London. Ethnic diversity is also characteristic of London Boroughs like Newham, Brent, and Hackney, which the ONS' analysis identified as having the highest mortality rates in the period March 1 to April 17. These are amongst the boroughs with the highest deprivation rates in London. There is a very wide J o u r n a l P r e -p r o o f literature showing how deprivation intersects with ethnicity in the UK (for a recent assessment see Byrne et al., 2020) . That intersectionality is present in the Covid-19 statistics: in the period from March 20 to May 7, 2020, the deaths amongst Black males were 3.9 times higher than expected, compared with 2.9 and 1.7 times for Asian and White British males, respectively. The higher mortality for minority groups has been observed in other countries too (Full Fact, 2020) . In the United States, for example, non-Hispanic American Indian or Alaska Native persons have a rate approximately five times greater than that for non-Hispanic white persons, as do non-Hispanic black persons. For Hispanic or Latino persons, the increase is approximately four times (CDC, 2020) . Not all differences between the ethnic groups are explained by socio-economic dis-/advantage: even after accounting for deprivation and for the effects of sex, age, and region, the Public Health England report finds that people of Bangladeshi ethnicity have about twice the risk of a Covid-19 related death when compared to the White British. For other ethnic groups it is between 10 and 50 per cent greater. Whether it is desirable to account for deprivation is a moot point: doing so risks a statistical contrivance that dissociates an ethnic group with the reality of their lived experience (an issue of colinearity that is returned to later in this paper). Immigrants also face greater risk. The report observes that the increase in deaths, relative to the average for the same period in earlier years, is greater for migrants than for those born in the UK, especially for those born in Central and Western Africa. Only the death rate for those born in the EU is not statistically significantly higher than for those born in the UK (although it remains higher). Occupation makes a difference, with some jobs bringing people closer into contact with others, thereby increasing the risk of infection. Such occupations include frontline medical staff, the emergency services, bus and taxi drivers, teachers, and those working in the hospitality industryjobs that (in the National Health Service, for example) are reliant on international migration to the UK. Those in care homes have been most vulnerable, with the number of deaths widely reported by the UK media as a national scandal because of the lack of protective equipment and inadequate testing for the disease within those care homes (and causing further political consternation when the Prime Minister appeared to deflect the blame for those deaths on to the care homes themselves). In 2020, the care sector in England and Wales had approximately 20,000 more deaths during March and April than is usual for an average year, which equates to 2.3 times more than expected. Care homes accounted for 43 per cent of all deaths from Covid-19 in the week ending May 8. Underlying health conditions, including respiratory infections, are a contributory factor but not limited to the most elderly. The report finds that a higher percentage of Covid-19 related death certificates mention diabetes, hypertension, kidney disease, obstructive pulmonary disease, and dementia than do other (all cause) death certificates (Public Health England, 2020). Because they give the number of Covid-19 deaths per Middle Level Super Output Area (MSOA), the data published by the ONS allow for geographical modelling at a sub-regional scale, within London Boroughs. These data give only a tally of the deaths and are not age standardized. To protect confidentiality, "a small number of deaths have been reallocated between neighbouring areas" (ONS, 2020). MSOAs are the third tier of the Census geography for England and Wales (third when aggregating upwards from the smallest, which are Output Areas). Although MSOAs represent a formal specification of neighbourhood designed to prevent personal information disclosure within a consistent geographical framework for the reporting and analysis of socio-economic and other data, their boundaries are not arbitrary. They were designed with the criteria of broadly equal population size, socio-economic homogeneity (based on accommodation type and tenure), and spatial compactness of the zones (Cockings et al., 2011) . The mean number of Covid-19 deaths per MSOA J o u r n a l P r e -p r o o f was 2.81 nationally but 5.03 in London over the March to April reporting period. The higher average is partly because MSOAs in London contain more people (about 9,100 residents, on average, in 2018, compared to an average of 8,200 for England and Wales) but not entirely so: the estimated death rate from Covid-19 per thousand of the adult population was 0.44 for England and Wales, 0.73 for London (about 65 per cent greater). Figure 1 maps the estimated death rate for the MSOAs across London for the period March 1 to April 17. It reveals both spatial heterogeneity and spatial clustering: the rate varies across the study region but there is a pattern of positive spatial autocorrelation with higher rates surrounded by other higher rates and lower with lower. Traditionally, Moran's value is used to quantify spatial autocorrelation. Here it is 0.17 when comparing the rate for each MSOA with its contiguous neighbours. However, the value lacks the intuition that many assume of it: it does not vary from -1 to +1 like a traditional correlation coefficient but has a range dependent on the spatial weights matrix (Brunsdon & Comber 2018 , de Jong et al. 1984 ; here it ranges from -0.70 to +1.02. A more interpretable measure is the usual Pearson correlation, calculated as the correlation between the rate for each MSOA and its spatial lag (the average rate for each MSOA's contiguous neighbours). That gives a value of +0.32, a medium sized effect (Cohen 1988 ). J o u r n a l P r e -p r o o f It is unsurprising to find geographical patterns in the death rates. Given that the risk of infection is linked to age, occupation, ethnicity, and other attributes that tend broadly to be shared by residents of the same neighbourhood so the ethno-cultural, socio-economic, and demographic geographies that are seen in London's residential patterns will be reflected, to at least some degree, in the geography of the death rates. The question is, to what degree? This might be answered by fitting a standard regression model -or, potentially, a spatial model (Ward & Gleditsch 2018) to allow for spatially autocorrelated errors and/or the mutual dependence of the death rates across the MSOAs (because of the spatial transmission of the disease by proximity) -using, for example, the predictors J o u r n a l P r e -p r o o f of Covid-19 vulnerability identified by Daras et al. (2020) , which are the proportions of the population, (a) living in care homes, (b) admitted to hospital in the past five years for a long-term health condition, (c) from an ethnic minority background, and (d) living in overcrowded housing. A limitation of doing so is that the variables are likely to be highly colinear, as they will be also with other associated factors such as age and occupation. (This co-linearity is likely why Daras et al. find income deprivation not to be statistically significantly related to Covid-19 mortality despite the known association of deprivation with vulnerability to the disease). None of the variables is strictly causal with, perhaps, the exception of pre-existing health conditions: living in a care home does not cause death by Covid-19; nor does being Black or Asian. They are generally social factors that increase exposure to the disease and therefore the fatality risk. As risks, they are co-constructed by underlying social and economic systems and the inequalities they generate. However, the key reason for using neither a 'standard' nor spatial regression model is an interest in a growing body of research looking at spatial discontinuities -the (sometimes-abrupt) differences between places that are next to each other. Given the expectation that closely located places will display similar attributes because of their shared, broader-scale context then it is geographically interesting to examine when and where that expectation is not met, here in regard to deaths although the general statement is also true: when, for example, the differences between adjacent places are large and coincide with socioeconomic differences or physical features such as housing types, or boundaries formed by roads, railways, or rivers (Mitchell & Lee 2014) . They invite examination of what created the differences and of the on-going impact they have on the populations who live there (Anciaes et al. 2016 , Kramer 2018 . They have generated interest in methods of spatial analysis that do not smooth-over spatially significant microscale discontinuities (Dong et al. 2020 ) that have been described as social frontiers (Dean et al. 2019 ). Consider, for example, that there is a neighbourhood in Little Ilford, Newham (about 14 kilometres east from the centre of London) that caught the media's attention (Goodier 2020) . There were 22 J o u r n a l P r e -p r o o f Covid-19 deaths there during March 1 to April 17; a rate, measured as previously, of 4.0 -the highest of any MSOA in England and Wales for the period. In an adjoining neighbourhood, the number was zero. Why the difference? It may relate to the housing stock: the former has more terraced properties and fewer flats/apartments, with greater levels of overcrowding. However, the more plausible explanation is that it also contains a care home whereas its neighbour does not. This, then, is a difference across a boundary that relates to other differences between the attributes of the two neighbourhoods. Modelling those differences requires a departure from traditional regression; the following section outlines why. It is the most technical part of the paper and may, if preferred, be skipped with the knowledge that the analysis looks at the differences between neighbourhoods that share a border, with all of the dependent and independent variables (including the control variables) being measures of difference between pairs of MSOAs. Because most MSOAs have more than one contiguous neighbour, a multilevel model is required. In general terms, if is the number of Covid-19 deaths in neighbourhood and is the number of Predictors of those differences are created in much the same way. For example, the difference in the number of Covid-19 deaths may be related to the difference in the average house price of two contiguous neighbourhoods. For this, there is no constraint that ≥ that would parallel ≥ , where is the predictor variable (the average house price): may have more deaths than but less expensive housing. The only requirement is that it matches up with the -variable; that is, the same pairs of MSOAs are used. Where there are such predictor variables, let represent the matrix of predictors, of dimension approximately equal to 2 ⁄ by . In principle, a regression model can then be fitted of the form, = + . This is either a Poisson or negative binomial model (they produce near identical results) because contains only positive integers, with many contiguous MSOAs having no difference in their numbers of Covid-19 deaths (typically because both had zero, or both had one or two), some but fewer having a difference of one, fewer still two, and so forth. In the model, is, at minimum, conditional on the differences in the number of adults living either side of MSOA boundaries. This is required because MSOAs are of unequal population sizes (and the greater the number of people who live in an MSOA, the greater the expected number of deaths). The adult population is used because of the lower likelihood that children will die from Covid-19 (and perhaps also be infected by it; the current medical evidence is not decisive on the matter). Essentially, its inclusion creates a death rate. In the analysis, additional control variables are included. Those are described in the following section. The model can also be written as, where, as previously, etc. An advantage of the model is the potential to address omitted variable bias where it arises due to some or more (unmeasured) contextual variables impacting on any two locations that share a border. Assume that the number of Covid-19 deaths at location, is a function of some known predictor variables ( " , # , + , etc.) as well as some local, unmeasured effect, ,. Suppose the same is true of location, . Because it is the pairwise differences in the attributes of and that are modelled so the value for this dyad is ∆ = ! + " " − " + # # − # + ⋯ + , − , + &, which leads to the omitted, contextual variable being differenced to zero. This is true only if , impacts equally on and , a very strong assumption. Whilst partly bolstered by appeal to Tobler's (1970) well-known 'first law of geography' -"everything is related to everything else but near things are more related than distant things" -and remembering that and are neighbours, with a shared border, if it really were a law rather than a rule-of-thumb then the spatial discontinuities would not exist that this and other research are interested in. In practice, it is hoped that the locations are sufficiently close to share some contextual similarities. The main disadvantage of the approach is that it induces a group structure in the data. To understand why, observe that the average London MSOA has 5.7 contiguous neighbours. If location has a number of deaths that is very much higher than, say, each of five neighbours then that unusual number is going to be present in each of the five across-boundary differences because each is subtracted from . Since each of these dyads has a dependence upon they cannot be independent of each other. It also faces the issue that although some neighbours are quite distinct from each other, many are not. This can entail the modelling of small differences that, in some cases, will lead to estimation errors (specifically, identification problems and the model not converging to a solution as matrices approach singularity). Changing the optimisation procedure can assist (the 'nlminbwrap' optimiser was found to be most stable when using the R package, lme4 for Spatial difference-in-difference models take various forms -compare, for example, Delgado and Florax (2015), who modify a standard econometric specification to allow for spatial interaction between proximate neighbours, with Heckert (2015) , whose modification incorporates J o u r n a l P r e -p r o o f geographically weighted regression. In the present case, the spatial element comes from the differencing across contiguous neighbours and the inclusion of the random intercepts, -! and . ! , which are similar to calculating the (conditional) difference between an MSOA and its spatial lag. In practice, neither the numbers nor rates of Covid and non-Covid deaths are independent of each other. Care homes, for example, have higher amounts of both, potentially because the cause of death becomes less distinct. The data used for this analysis and a short tutorial on how to fit the models are available at [TO BE ADDED]. Taking into consideration the risk factors reviewed in the Public Health England (2020) report, Table 1 To address these issues, a four-fold modelling strategy is employed. First, each of the variables is used 'by itself' to predict the difference in the number of Covid-19 deaths but always with the inclusion of the control variables. Those are the differences in the number of adult residents (as a first and second order polynomial), the population density, the non-Covid death rate, and in the number of residential care home beds in the contiguous neighbours. The results are shown in Table 2 , wherein those shaded in a lighter grey are positive and statistically significant at a 95 per cent confidence or above, and those shaded in a darker grey are negative and statistically significant at the same. They accord with prior expectations: areas containing more younger adults than their neighbours, more of the White British, having greater affluence, and/or more managerial or professional occupations had lower numbers of Covid-19 deaths, as did those places with more of the White Other group, Chinese populations, two person households, and/or more immigration from places other than the EU, Africa and Asia (which suggests the Americas and Australasia and perhaps a younger population working in the hospitality industry). Areas with more elderly populations than their neighbours, higher percentages of Black and Asian (except Chinese) ethnic groups, greater deprivation, more people in lower-paid jobs, who are long-term unemployed or who have never worked, and/or larger household sizes had higher numbers of deaths. It is instructive to compare the effect sizes shown in Table 2 with those obtained from using corresponding variables, also 'one at a time' but still with control variables, in the simpler, general linear model (mentioned earlier) that has the actual attributes of each location instead of their differences with contiguous neighbours. There are limits to this comparison because, as previously explained, they are modelling different outcomes. Nevertheless, it is reassuring to find that they broadly accord. The Spearman's rank correlation between the two sets of estimated effect sizes is This was not pre-ordained: in much the same way that neighbourhood relationships need not apply at an individual scale, individual risk factors need not be evident at the neighbourhood scale. That they are testifies to the socio-economic, demographic, and ethnic geographies -and inequalitiesthat characterise London's growth and development both in the past and today (Cheshire & Uberti 2014 , Whitfield 2017 ). That they are evident even between neighbours suggests that it is not only broad scale regional and sub-regional factors that contribute to health outcomes but also the J o u r n a l P r e -p r o o f 'smaller scale' differences between closely located places. Only the effects of pre-existing medical conditions (primarily due to a lack of available data) and of being an immigrant (from Africa especially) do not reveal themselves as a greater risk factor as they did in the Public Health England report. Within Table 2 represent places with significantly more deaths than their neighbours, having now accounted for the five predictor variables and for the additional control variables. This model may be extended so that, which, in Equation 3, allows the effect size for a variable, ∆ " , to vary in a random slope model, across all the pairs of MSOAs where MSOA, has more Covid-19 deaths than its neighbour, . From this, places that add significantly to the average effect (where values of -" are significantly greater than " ) can be identified, indicating spatial non-stationarity in the modelled effect. Presently this would be reliant on very localised estimates because an MSOA is considered in relation only to its contiguous neighbours. However, the model can be refitted to assess the differences between second-order neighbours (the neighbours of neighbours); or, third-order neighbours, fourth-order, and so forth, here to the tenth-order -a limit chosen largely for pragmatic reasons: by the tenthorder the number of neighbours is becoming very large (think of it as a circular wave, the circumference of which rises with the square of the distance from its origin as it spreads out). Together those refitted models provide ten sets of estimates of the spatial variation in the effect size (of -" . Taking those estimates that are not positive and significant at a 95% confidence interval to be zero, the values are combined into a weighted average, giving most weight to the first-order neighbours estimation, next most to the second-order neighbours, and declining to zero under a Gaussian decay at a hypothetical eleventh-order estimation (which does not need to be calculated because its weight is zero). Setting the cut-off at eleven means that the tenth-order estimation still contributes to the sum, albeit not greatly and that, in broad terms, differences in the attributes of MSOAs are considered over a distance of about 7 to 8 kilometres. The net result has similarities to J o u r n a l P r e -p r o o f To this point, the analysis has focused on the period corresponding to the rise and peak of the spring The question is whether the process of diffusion changes the relationships between the neighbourhood-level predictors of Covid-19 and the numbers of deaths, here focusing back on the differences between the attributes of each MSOA and its first-order neighbours. The results of the models are shown in Table 2 and mirror those for Table 1 (still separately regressing each variable against the differences in the numbers of Covid-19 deaths between contiguous neighbours but including the control variables in each instance). Comparing Table 2 with Table 1 , the general trend is for the magnitude of the effect sizes to decrease, which is consistent with the spatial diffusion of a contagious disease. The main exceptions are the THIRTIES, BANG, HPRICE, and SHARED3+ variables, of which the last is surprising because it is J o u r n a l P r e -p r o o f a potential measure of overcrowding. However, it is also positively correlated with younger populations and negatively correlated with older ones. Variables that are statistically significant (at a 95 per cent confidence or greater) are broadly the same as for the earlier period, except the effects of older age groups appear to have waned (perhaps because they were better isolated during lockdown). Across the domains, the key variables are sometimes a subset of those previously highlighted but immigration from Asian countries emerges as new (and associated with more deaths), as does health deprivation and the aforementioned SHARED3+ variable. Following the top down-process of variable elimination across all the domains, only three variables remain, where previously it was five, the coefficients of which (and their standard errors) are: THIRTIES, -0.049 (0.023); GROUP1, -0.094 (0.022); and HHLD8, +0.055 (0.021). Continuing to be important are the age of residents, wealth, and large household sizes within neighbourhoods. Ethnicity appears to drop out but that is because of the co-linearity -as before, it is important to keep in mind that the three remaining variables are predictive not only of Covid-19 deaths but also of other associated variables. In particular, higher income neighbourhoods are associated with the White British; lower income neighbourhoods with residents who are Black. The overall impression is one of the disease's spatial diffusion yet those from minority ethnic groups, deprived areas and/or large households remain associated with higher numbers of deaths throughout both periods. Younger populations are associated with fewer deaths. J o u r n a l P r e -p r o o f This paper has presented the results of a difference across spatial boundaries method of analysis, looking at which variables are associated with neighbourhoods in London that had higher numbers of Covid-19 deaths than their nearest, contiguous neighbours during the spring of 2020. It finds that individual risk factors identified in work by the Office of National Statistics (ONS 2020) and by Public Heath England (2020) remain evident in the differences between neighbours. Whilst undoubtedly a function of the broader-scale demographic, social economic, and ethno-cultural geographies that characterise London's neighbourhoods, it also evidences that localised differences in the attributes of neighbourhoods can have statistically significant differences in health outcomes. 'Hot spots' of the disease shift over the period from March to May 2020 and the effect sizes of the neighbourhood-level correlates diminish slightly in the period after the peak of the pandemic in the spring 2020 wave. Both observations are consistent with the transmission and spatial diffusion of a contagious disease from which no one is (initially) immune. Nevertheless, a greater number of deaths continues to be associated with Asian and Black ethnic groups, socio-economic disadvantage, very large households, and fewer from younger age groups. The analysis therefore adds to the evidence showing that age, wealth/deprivation, and ethnicity are key risk factors associated with higher mortality rates from Covid-19. Writing, now, in the autumn of 2020, the broad regional trends in Covid-19 infections have changed. The attention is still on London, but it is asking why the capital appears to be coping relatively well in keeping the infection rate down when compared to all other regions except the South East and South West. As Harris and Cheshire (2020) observe, the percentage of jobs, in London, in financial and insurance activities is almost double that of England as a whole, as is the percentage working in information and communication. These jobs are well suited to home working, which lowers the risk of infection. It is possible that London is doing relatively well because many of its population have the ability to better self-isolate through home-working or even to sell-up and move to less densely J o u r n a l P r e -p r o o f populated locations. However, whilst these might be options for some, they are not viable for all. Consequently, spatial differences in the jobs available and inequalities in the labour market are manifest, both at regional and sub-regional scales, in rates of Covid-19 infections and fatalities. Tragically, the evidence of the human cost of Covid-19 is that whilst it places everyone at risk, places are not equally risky. Instead, those risks are magnified by geographies of social-economic inequality, some at localised scales, in the capital as elsewhere in the country. Community severance: where is it found and at what cost? An Introduction to R for Spatial Analysis & Mapping 2020) Ethnicity, Race and Inequality in the UK UK suffers second-highest death rate from coronavirus. The Financial Times Exploring a decade of small area ethnic (de-)segregation in England and Wales CDC (2020) COVID-19 in Racial and Ethnic Minority Groups England's North-South Covid-19 divide: Only EIGHT of the country's 50 worst-hit authorities are in the south, official data reveals. Mail Online LONDON: The Information Capital: 100 maps and graphics that will change how you view the city Maintaining existing zoning systems using automated zone-design techniques: methods for creating the 2011 Census output geographies for England and Wales Statistical power analysis for the behavioral sciences How does Vulnerability to COVID-19 Vary between Communities in England? Developing a Small Area Vulnerability Index (SAVI) Frontiers in Residential Segregation: understanding neighbourhood boundaries and their impacts Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction Developing a locally adaptive spatial multilevel logistic model to analyze ecological effects on health using individual census records Chichester Full Fact (2020) What do we know about Covid-19 inequalities among people from minority ethnic groups? Full Fact Revealed: 20 areas of England at most risk of coronavirus resurgence. The Observer These are the neighbourhoods hit hardest by Covid-19 Measuring changing ethnic separations in England: a spatial discontinuity approach Visualizing multicollinearity using igraph Working from home could be keeping Covid-19 at bay -for proof, look at London. The Guardian, Opinion Ethnic Segregation Between Schools. Is It Increasing or Decreasing in England? A Spatial Difference-in-Differences Approach To Studying the Effect of Greening Vacant Land on Property Values On extreme values of Moran's I and Gerry's C Testing the role of barriers in shaping segregation profiles: the importance of visualizing the local neighborhood Is there really a "Wrong Side of the Tracks" in urban areas and does It matter for spatial analysis? Statistical Bulletin: Deaths involving COVID-19 by local area and socioeconomic deprivation: deaths occurring between 1 Disparities in the risk and outcomes of COVID-19 A computer simulation of urban growth in the Detroit region Spatial Regression models London: A Life in Maps (2 nd edition) Analysing inter-provincial urban migration flows in China: A new multilevel gravity model approach Simon Burgess for drawing The Observer article to my attention. Also, to two anonymous referees for their supportive comments and queries.