Small Area Level Research Articles

A Multi-Level Analysis of Risky Streets and Neighbourhoods for Dissident Republican Violence in Belfast

This paper uses graph theoretical measures to analyse the relationship between street network usage, as well as other street- and area-level factors, and dissident Republican violence in Belfast. A multi-level statistical model is used. Specifically, we employ an observation-level random-effects (OLRE) Poisson regression and use variables at the street and area levels. Street- and area-level characteristics simultaneously influence where violent incidents occur. For every 10% change in the betweenness value of a street segment, the segment is expected to experience 1.32 times as many incidents. Police stations (IRR: 22.05), protestant churches (IRR: 6.19) and commercial premises (IRR: 1.44) on each street segment were also all found to significantly increase the expected number of attacks. At the small-area level, for every 10% change in the number of Catholic residents, the number of incidents is expected to be 4.45 times as many. The results indicate that along with other factors, the street network plays a role in shaping terrorist target selection. Streets that are more connected and more likely to be traversed will experience more incidents than those that are not. This has important practical implications for the policing of political violence in Northern Ireland generally and for shaping specific targeted interventions.

Depicting socioeconomic disadvantage and social gradient in quality of life in Limassol, Cyprus

Abstract Background The lack of validated and accepted area-level indices of socioeconomic disadvantage at national or regional level in Cyprus poses a barrier to “spatial thinking” among Public Health policy-makers and researchers. Methods A composite index of socioeconomic disadvantage was constructed by exploring the factor structure of 28 commonly used census indicators at city quarter (N = 38, median population 3894, IQR 2570-6169) and postcode level (N = 124, median 1320, IQR 798-2014). Predictive validity was explored in mixed random-effect regression models in term of associations with SF-36 health-related quality of life among a stratified random sample aged 45-64 (N = 450, 45 neighbourhoods). Results There was wide variability across communities in all indicators, for instance, 0.4%-25.9% unemployment, 41.1%-90.7% at most secondary level education, 3.6%-95.9% residences built prior to 1980. Two factors were identified: “socioeconomic disadvantage” (SED), with the same set of 11-12 indicators at the two levels of geographical aggregation, and “built environment”, reflecting proximity to the city centre, with nevertheless no meaningful associations with the outcome of interest. The SED index captured a stepwise decrease in physical (-1.39 95%CI -2.06, -0.72) and mental health (-1.26 95% -1.98, -0.54) scores across quartiles of postcodes with increasing disadvantage, of similar clinically significant effect size by gender. Associations attenuated slightly but remained significant after adjusting for person-based indicators of social position, with some weak evidence of cross-level interactions for physical, but not mental, health. Conclusions The composite index, tapping on educational, economic, material, occupational and social disadvantage, allowed the exploration of socioeconomic disadvantage patterning at a small-area level in the city of Limassol and documented the social gradient in health-related quality of life among a late working-life sample of residents. Key messages First-time depiction of the geographical patterning of socioeconomic disadvantage across the small geographical level of postcodes in the city of Limassol, Cyprus. Social gradient in physical and mental health in men and women across the continuum of socioeconomic disadvantage over and above person-based indicators of social position.

Social and spatial variation in heatwave-related emergency department visits in Australia

BACKGROUND AND AIM: Heatwaves are extreme weather events that cause more urgent medical care than any other hazard in Australia. Heatwave impacts vary between populations and locations and empirical evidence concerning vulnerable places and people at a local level is critical for effective policy, planning, and interventions, especially in a warming climate. This study aimed to examine heatwave-related morbidity in Australia (using emergency department (ED) presentations) at a small-area level and identify susceptibility by neighbourhood characteristics. METHODS: ED presentations data for eight Australian cities from 2013-2018 during the warm-season (October- March) were extracted from a national database. In each city, we examined the association of heatwave severity (defined using Excess Heat Factor) with ED visits at the Statistical Areas 2 geographical unit using a time-stratified case-crossover approach. Effect modification by neighbourhood characteristics were examined and city-level estimates were pooled using random-effects meta-analyses. RESULTS:A total of 23,233,374 all-cause ED presentations were included in the analysis. Pooled ED relative risk estimates indicate that ED visits increased by 2.9% (95%CI: 1.5-4.2%) and 3.4% (95%CI: 1.2-5.5%) during low-intensity and severe/extreme heatwaves, respectively. There were 67,974 excess ED presentations during heatwaves with 82% of these occurring during low-intensity events which are more frequent than severe/extreme heatwaves. The effects varied substantially by city with the highest number of excess ED presentations observed in Adelaide (28,198) and Sydney (19,899). Heat-related conditions such as dehydration and heat exhaustion/stroke were significantly greater during heatwaves and the effect increased from low-intensity to severe/extreme heatwaves. Analysis of neighbourhood characteristics on heatwave-related ED presentations revealed greater health impacts for areas with high social vulnerability e.g., household composition, language and culture, and housing quality. CONCLUSIONS:Overall, ED presentations are sensitive to heatwaves across all severity classes and the effects are stronger for individuals with certain health conditions and neighbourhood contexts. KEYWORDS: Heatwaves, integrated data, vulnerability, excess heat factor

The Case Time Series Design.

Modern data linkage and technologies provide a way to reconstruct detailed longitudinal profiles of health outcomes and predictors at the individual or small-area level. Although these rich data resources offer the possibility to address epidemiologic questions that could not be feasibly examined using traditional studies, they require innovative analytical approaches. Here we present a new study design, called case time series, for epidemiologic investigations of transient health risks associated with time-varying exposures. This design combines a longitudinal structure and flexible control of time-varying confounders, typical of aggregated time series, with individual-level analysis and control-by-design of time-invariant between-subject differences, typical of self-matched methods such as case-crossover and self-controlled case series. The modeling framework is highly adaptable to various outcome and exposure definitions, and it is based on efficient estimation and computational methods that make it suitable for the analysis of highly informative longitudinal data resources. We assess the methodology in a simulation study that demonstrates its validity under defined assumptions in a wide range of data settings. We then illustrate the design in real-data examples: a first case study replicates an analysis on influenza infections and the risk of myocardial infarction using linked clinical datasets, while a second case study assesses the association between environmental exposures and respiratory symptoms using real-time measurements from a smartphone study. The case time series design represents a general and flexible tool, applicable in different epidemiologic areas for investigating transient associations with environmental factors, clinical conditions, or medications.

Age specific trends in mortality disparities by socio-economic deprivation in small geographical areas of England, 2002-2018: A retrospective registry study.

SummaryBackgroundDisparities in mortality rates according to socioeconomic position (SEP) have been rising in England. We describe the association between recent changes in socioeconomic inequality and trends in mortality disparities for different age and sex groups at small-area level in England.MethodsVital registration data from the Office for National Statistics on resident population size and number of deaths in each Lower Super Output Area (LSOA) in England from 2002 to 2018 were stratified by sex and 5-year age group. We grouped LSOA into ventiles of the Index of Multiple Deprivation (IMD), our indicator of SEP. We examined time trends in smoothed mortality rates, using 3 year moving averages for the period 2003-2017, by age across the IMD distribution. We measured mortality inequalities using the ratio of mortality rates between different deprivation groups. We calculated mortality rate ratios between the most and the least deprived 10% of areas (Total Inequality) and between the median and least deprived (Lower Inequality) 10% of areas by year, gender and age group, to examine where in the distribution of deprivation trends in mortality inequality arose.FindingsAmong <1 year olds, the inequality in mortality rates between the poorest 10% of LSOAs and the richest 10% of LSOAs fell between 2003 and 2017 by 22•7% for men and 22•8% for women. The largest inequalities were observed among 40 to 54 year olds. This inequality increased over the study period – from 3•2 times higher mortality rates for men in the most as opposed to the least deprived 10% of LSOAs in 2003 to 3•3 times in 2017. The rise was from 2•4 to 2•6 for women. Age groups ≥65 years, who experience the highest mortality risk, had low but rising inequality. Men and women aged 65 to 79 living in the most deprived LSOAs had a mortality rate 1•9 times higher than the least deprived in 2003 but this had increased to 2•2 times higher for women and 2•3 times higher for men by 2017. This was due to rising inequality in both halves of the distribution – between the top 10% of LSOA and the middle, and between the middle and the bottom 10% of LSOA.InterpretationOverall mortality inequality rose in England but there were substantial differences in the trends for specific age and sex groups. Infant and child mortality inequality fell. At older ages, mortality inequality rose across cohorts, although in different ways, as each cohort's exposure to life-course to labour market inequality has differed. Policy goals of reducing mortality inequality will be best met by a focus on the risk factors that are specific to particular age and deprivation groups.FundingEconomic and Social Research Council, through the ESRC Centre for the Microeconomic Analysis of Public Policy at the IFS. We gratefully acknowledge the support of the Nuffield Foundation, grant reference WEL/43603. The project has been funded by the Nuffield Foundation, but the views expressed are those of the authors and not necessarily the Foundation. Visit www.nuffieldfoundation.org. Research at UCL Great Ormond Street Institute of Child Health is supported by the NIHR Great Ormond Street Hospital Biomedical Research Centre.

Monitoring the spatiotemporal epidemiology of Covid-19 incidence and mortality: A small-area analysis in Germany

Timely monitoring of incidence risks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and associated deaths at small-area level is essential to inform containment strategies. We analysed the spatiotemporal epidemiology of the SARSCoV- 2 pandemic at district level in Germany to develop a tool for disease monitoring. We used a Bayesian spatiotemporal model to estimate the district-specific risk ratios (RR) of SARS-CoV-2 incidence and the posterior probability (PP) for exceedance of RR thresholds 1, 2 or 3. Of 220 districts (55% of 401 districts) showing a RR > 1, 188 (47%) exceed the RR threshold with sufficient certainty (PP ≥ 80%) to be considered at high risk. 47 districts show very high (RR > 2, PP ≥ 80%) and 15 extremely high (RR > 3, PP ≥ 80%) risks. The spatial approach for monitoring the risk of SARS-CoV-2 provides an informative basis for local policy planning.

Averting a public health crisis in England's coastal communities: a call for public health research and policy.

Coastal communities have received little attention in the public health literature, perhaps because our mental maps tend to associate socio-economic deprivation and health inequalities with inner cities. Mapping a range of key health indicators at small area level, this paper reveals a distinct core-periphery pattern in disease prevalence, with coastal communities experiencing a high burden of ill health across almost all conditions included in the Quality and Outcomes Framework dataset. Other sources suggest poor outcomes for children and young people living in coastal areas. Low rates of participation in higher education contrast with high rates of hospitalisation for self-harm, alcohol and substance use. Reflecting a shift in the distribution of children living in poverty since the 1990s, this may be an early indicator of a future public health crisis in these communities. Exploring reasons for the health challenges facing the periphery, this perspective piece calls for more public health research that can accommodate the complex and interlinked problems facing coastal communities and a more concerted effort to align public health with economic, education, local government and transport policies at the national level.

Risk of cardiovascular mortality, stroke and coronary heart mortality associated with aircraft noise around Congonhas airport, S\xe3o Paulo, Brazil: a small-area study

BackgroundNoise pollution is increasingly recognised as a public health hazard, yet limited evidence is available from low- and middle-income countries (LMIC), particularly for specific sources. Here, we investigated the association between day-night average (Ldn) aircraft noise and the risk of death due to cardiovascular disease (CVD), stroke and coronary heart disease (CHD) at small-area level around São Paulo‘s Congonhas airport, Brazil during the period 2011–2016.MethodsWe selected 3259 census tracts across 16 districts partially or entirely exposed to ≥50 dB aircraft noise levels around the Congonhas airport, using pre-modelled 5 dB Ldn noise bands (≤50 dB to > 65 dB). We estimated the average noise exposure per census tract using area-weighting. Age, sex and calendar year-specific death counts for CVD, stroke and CHD were calculated by census tract, according to the residential address at time of death. We fitted Poisson regression models to quantify the risk associated with aircraft noise exposure, adjusting for age, sex, calendar year and area-level covariates including socioeconomic development, ethnicity, smoking and road traffic related noise and air pollution.ResultsAfter accounting for all covariates, areas exposed to the highest levels of noise (> 65 dB) showed a relative risk (RR) for CVD and CHD of 1.06 (95% CI: 0.94; 1.20) and 1.11 (95%CI: 0.96; 1.27), respectively, compared to those exposed to reference noise levels (≤50 dB). The RR for stroke ranged between 1.05 (95%CI: 0.95;1.16) and 0.91 (95%CI: 0.78;1.11) for all the noise levels assessed. We found a statistically significant positive trend for CVD and CHD mortality risk with increasing levels of noise (p = 0.043 and p = 0.005, respectively). No significant linear trend was found for stroke. Risk estimates were generally higher after excluding road traffic density, suggesting that road traffic air and noise pollution are potentially important confounders.ConclusionsThis study provides some evidence that aircraft noise is associated with increased risk of CVD and CHD mortality in a middle-income setting. More research is needed to validate these results in other LMIC settings and to further explore the influence of residual confounding and ecological bias. Remarkably, 60% of the study population living near the Congonhas airport (~ 1.5 million) were exposed to aircraft noise levels > 50 dB, well above those recommended by the WHO (45 dB), highlighting the need for public health interventions.

Forecasting of Lung Cancer Incident Cases at the Small-Area Level in Victoria, Australia.

Predicting lung cancer cases at the small-area level is helpful to quantify the lung cancer burden for health planning purposes at the local geographic level. Using Victorian Cancer Registry (2001–2018) data, this study aims to forecast lung cancer counts at the local government area (LGA) level over the next ten years (2019–2028) in Victoria, Australia. We used the Age-Period-Cohort approach to estimate the annual age-specific incidence and utilised Bayesian spatio-temporal models that account for non-linear temporal trends and area-level risk factors. Compared to 2001, lung cancer incidence increased by 28.82% from 1353 to 1743 cases for men and 78.79% from 759 to 1357 cases for women in 2018. Lung cancer counts are expected to reach 2515 cases for men and 1909 cases for women in 2028, with a corresponding 44% and 41% increase. The majority of LGAs are projected to have an increasing trend for both men and women by 2028. Unexplained area-level spatial variation substantially reduced after adjusting for the elderly population in the model. Male and female lung cancer cases are projected to rise at the state level and in each LGA in the next ten years. Population growth and an ageing population largely contributed to this rise.

Exploring dynamic process of regional shrinkage in Ohio: A Bayesian perspective on population shifts at small-area levels

Recently shrinkage phenomenon in urban areas has gained broader attention to scholars, policymakers, and practitioners. There has been considerable effort devoted to unveiling various facets of shrinkage within the literature. From a different perspective, this study explicitly concentrates on the influence of the latent effects including space, time, and spatiotemporal interaction on dynamic shrinkage process based upon neighborhood-level (census tract) population shifts in Ohio for the period 1970–2010. To explore those random influences, we hypothesize that the shrinkage of population in census tracts over previous decades is in close association with growth/decline of neighboring tracts (spatial and temporal dependencies). The study adopts a full Bayesian hierarchical analytic framework that specifically utilizes the Conditional Autoregressive (CAR) and Penalized Complexity (PC) priors in order to account for the latent effects on dynamic process. For 635 census tracts in Cleveland-Elyria Metropolitan Statistical Area, Ohio in the U.S., we have found that when the size of initial population of a tract increases, it is more likely to observe population decline on average. Similarly, the relative share of African American population, unemployment rate, and poverty rate has shown a negative correlation with population growth. Our model reveals that there exists a significant influence of the random (latent) effects: a spatial correlation from adjacent tracts, a non-linear temporal dependence, and a spatiotemporal interaction complementing the random fluctuation. Moreover, we present areas of higher risks that are census tracts with higher probabilities (close to 1) of losing 10% or more of their relative population. We believe that the findings of our study allow us to support informed decision making for devising policies of shrinking regions.

Chronic high risk of intimate partner violence against women in disadvantaged neighborhoods: An eight-year space-time analysis

We conducted a small-area ecological longitudinal study to analyze neighborhood contextual influences on the spatio-temporal variations in intimate partner violence against women (IPVAW) risk in a southern European city over an eight-year period. We used geocoded data of IPVAW cases with associated protection orders (n = 5867) in the city of Valencia, Spain (2011–2018). The city's 552 census block groups were used as the neighborhood units. Neighborhood-level covariates were: income, education, immigrant concentration, residential instability, alcohol outlet density, and criminality. We used a Bayesian autoregressive approach to spatio-temporal disease mapping. Neighborhoods with low levels of income and education and high levels of residential mobility and criminality had higher relative risk of IPVAW. Spatial patterns of high risk of IPVAW persisted over time during the eight-year period analyzed. Areas of stable low risk and with increasing or decreasing risk were also identified. Our findings link neighborhood disadvantage to the existence and persistence over time of spatial inequalities in IPVAW risk, showing that high risk of IPVAW can become chronic in disadvantaged neighborhoods. Our analytic approach provides specific risk estimates at the small-area level that are informative for intervention purposes, and can be useful to assess the effectiveness of prevention efforts in reducing IPVAW.

Quantifying the small-area spatio-temporal dynamics of the Covid-19 pandemic in Scotland during a period with limited testing capacity

Modelling the small-area spatio-temporal dynamics of the Covid-19 pandemic is of major public health importance, because it allows health agencies to better understand how and why the virus spreads. However, in Scotland during the first wave of the pandemic testing capacity was severely limited, meaning that large numbers of infected people were not formally diagnosed as having the virus. As a result, data on confirmed cases are unlikely to represent the true infection rates, and due to the small numbers of positive tests these data are not available at the small-area level for confidentiality reasons. Therefore to estimate the small-area dynamics in Covid-19 incidence this paper analyses the spatio-temporal trends in telehealth data relating to Covid-19, because during the first wave of the pandemic the public were advised to call the national telehealth provider NHS 24 if they experienced symptoms of the virus. Specifically, we propose a multivariate spatio-temporal correlation model for modelling the proportions of calls classified as either relating to Covid-19 directly or having related symptoms, and provide software for fitting the model in a Bayesian setting using Markov chain Monte Carlo simulation. The model was developed in partnership with the national health agency Public Health Scotland, and here we use it to analyse the spatio-temporal dynamics of the first wave of the Covid-19 pandemic in Scotland between March and July 2020, specifically focusing on the spatial variation in the peak and the end of the first wave.

Examining Trends in the Child Social Exclusion Index in Australia from 2011 to 2016: Have Geographical Patterns of Disadvantage Persisted Over Time?

Child social exclusion (CSE) is a complex and multi-dimensional measure of social and material disadvantage in which children lack the opportunities and resources to participate fully in their communities and feel connected. The aim of this article is to provide an update of the risk of child social exclusion at small area level in Australia in 2016 and compare the trends and patterns to 2011. We found that many children are experiencing disadvantage on multiple fronts and are unable to participate fully in their community. Australia’s capital cities paint a contrasting picture having clear clusters of neighbourhoods at risk of high but also low levels of child social exclusion. Areas outside capital cities have much lower proportions of children experiencing the lowest levels of social exclusion. We found that from 2011 to 2016, the geographical patterns of the least and most excluded areas remain largely unchanged over time, with 73 per cent of the small areas in Australia remaining in the same quintile in 2011 and 2016. Of the local communities having the highest risk of child social exclusion in 2011, 87% were still in the most excluded quintile in 2016. Between 2011 and 2016, 279 small areas (14%) improved their CSE quintile, but 268 local communities shifted into a more socially excluded quintile. The key drivers of improvement in child social exclusion were ‘above-average’ improvement in the socio-economic well-being of families in these areas and in their educational attainment, and reduced exposure to increases in housing stress.

Residential area and screening venue location features associated with spatial variation in breast cancer screening invitation response rates: an observational study in Greater Sydney, Australia

ObjectivesParticipation in breast cancer screening (BCS) varies at the small-area level, which may reflect environmental influences. This study assessed small-area variation in BCS invitation response rates (IRRs) and associations between...

County-level estimates of population and economic scenarios under the shared socioeconomic pathways: A case study in Inner Mongolia, China

Contribution from socio-economic development to climate change cannot be ignored. In order to make a more reasonable comprehensive assessment of the future climate change, the Intergovernmental Panel on Climate Change (IPCC) has proposed the Shared Socioeconomic Pathways (SSPs) based on the background of climate change and the possible future socio-economic conditions, which describes the adaptation and mitigation challenges of climate change. Based on different SSP scenarios, combined with China's current population policy and the actual social and economic development in Inner Mongolia, this paper adopts PDE model and C-D model to simulate population and economic changes of various counties in Inner Mongolia from 2010 to 2050. It is founded that population and GDP varies from different counties significantly, and shows the patterns of high in east and low in west, high in south and low in north. The spatial distribution of GDP is positively related with the population. The total population increases first and then decreases from 2015 to 2050 in Inner Mongolia. By the ends of 2050, the population reaches 23.32 million (−9.46%), 24.14 million (−2.30%), 24.72 million (+0.05%), 22.37 million (−9.46%), 23.49 million (−4.93%) under SSP1, SSP2, SSP3, SSP4, SSP5 scenarios. The largest gap of population is 2.35 million between SSP3 and SSP4. The GDP grows constantly from 2010 to 2050 in Inner Mongolia, but the growth rate is slowing down. By the ends of 2050, GDP reaches 5.09 trillion (+4.75 times), 5.22 trillion (+4.90 times), 4.41 trillion (+3.98 times), 4.98 trillion (+4.62 times), 5.87 trillion (+5.63 times). Therein, the biggest gap of GDP is 1.5 trillion between SSP4 and SSP5. The results provide technical solution for population and economic projection under SSPs at small area and subnational level, and provides scientific basis for the formulation of climate change policies in order to formulate measures to deal with climate change risks.

Long-term association between urban air ventilation and mortality in Hong Kong

While associations between population health outcomes and some urban design characteristics, such as green space, urban heat islands (UHI), and walkability, have been well studied, no prior studies have examined the association of urban air ventilation and health outcomes. This study used data from Hong Kong, a densely populated city, to explore the association between urban air ventilation and mortality during 2008–2014. Frontal area density (FAD), was used to measure urban ventilation, with higher FAD indicating poorer ventilation, due to structures blocking wind penetration. Negative binomial regression models were constructed to regress mortality counts for each 5-year age group, gender, and small area group, on small area level variables including green space density, population density and socioeconomic indicators. An interquartile range increase in FAD was significantly associated with a 10% (95% confidence interval (CI) 2%–19%, p = 0.019) increase in all-cause mortality and a 21% (95% CI: 2%–45%, p = 0.030) increase in asthma mortality, and non-significantly associated with a 9% (95% CI: 1%–19%, p = 0.073) in cardio-respiratory mortality. Better urban ventilation can help disperse vehicle-related pollutants and allow moderation of UHIs, and for a coastal city may allow moderation of cold temperatures. Urban planning should take ventilation into account. Further studies on urban ventilation and health outcomes from different settings are needed.

Modeling multi-level survival data in multi-center epidemiological cohort studies: Applications from the ELAPSE project

BackgroundWe evaluated methods for the analysis of multi-level survival data using a pooled dataset of 14 cohorts participating in the ELAPSE project investigating associations between residential exposure to low levels of air pollution (PM2.5 and NO2) and health (natural-cause mortality and cerebrovascular, coronary and lung cancer incidence). MethodsWe applied five approaches in a multivariable Cox model to account for the first level of clustering corresponding to cohort specification: (1) not accounting for the cohort or using (2) indicator variables, (3) strata, (4) a frailty term in frailty Cox models, (5) a random intercept under a mixed Cox, for cohort identification. We accounted for the second level of clustering due to common characteristics in the residential area by (1) a random intercept per small area or (2) applying variance correction. We assessed the stratified, frailty and mixed Cox approach through simulations under different scenarios for heterogeneity in the underlying hazards and the air pollution effects. ResultsEffect estimates were stable under approaches used to adjust for cohort but substantially differed when no adjustment was applied. Further adjustment for the small area grouping increased the effect estimates’ standard errors. Simulations confirmed identical results between the stratified and frailty models. In ELAPSE we selected a stratified multivariable Cox model to account for between-cohort heterogeneity without adjustment for small area level, due to the small number of subjects and events in the latter. ConclusionsOur study supports the need to account for between-cohort heterogeneity in multi-center collaborations using pooled individual level data.

Changing Disparities in Coronavirus Disease 2019 (COVID-19) Burden in the Ethnically Homogeneous Population of Hong Kong Through Pandemic Waves: An Observational Study.

BackgroundDisparities were marked in previous pandemics, usually with higher attack rates reported for those in lower socioeconomic positions and for ethnic minorities.MethodsWe examined characteristics of laboratory-confirmed coronavirus disease 2019 (COVID-19) cases in Hong Kong, assessed associations between incidence and population-level characteristics at the level of small geographic areas, and evaluated relations between socioeconomics and work-from-home (WFH) arrangements.ResultsThe largest source of COVID-19 importations switched from students studying overseas in the second wave to foreign domestic helpers in the third. The local cases were mostly individuals not in formal employment (retirees and homemakers) and production workers who were unable to WFH. For every 10% increase in the proportion of population employed as executives or professionals in a given geographic region, there was an 84% (95% confidence interval [CI], 1–97%) reduction in the incidence of COVID-19 during the third wave. In contrast, in the first 2 waves, the same was associated with 3.69 times (95% CI, 1.02–13.33) higher incidence. Executives and professionals were more likely to implement WFH and experienced frequent changes in WFH practice compared with production workers.ConclusionsConsistent findings on the reversed socioeconomic patterning of COVID-19 burden between infection waves in Hong Kong in both individual- and population-level analyses indicated that risks of infections may be related to occupations involving high exposure frequency and WFH flexibility. Contextual determinants should be taken into account in policy planning aiming at mitigating such disparities.

Spatial-time analysis of cardiovascular emergency medical requests: enlightening policy and practice

BackgroundResponse time to cardiovascular emergency medical requests is an important indicator in reducing cardiovascular disease (CVD) -related mortality. This study aimed to visualize the spatial-time distribution of response time, scene time, and call-to-hospital time of these emergency requests. We also identified patterns of clusters of CVD-related calls.MethodsThis cross-sectional study was conducted in Mashhad, north-eastern Iran, between August 2017 and December 2019. The response time to every CVD-related emergency medical request call was computed using spatial and classical statistical analyses. The Anselin Local Moran’s I was performed to identify potential clusters in the patterns of CVD-related calls, response time, call-to-hospital arrival time, and scene-to-hospital arrival time at small area level (neighborhood level) in Mashhad, Iran.ResultsThere were 84,239 CVD-related emergency request calls, 61.64% of which resulted in the transport of patients to clinical centers by EMS, while 2.62% of callers (a total of 2218 persons) died before EMS arrival. The number of CVD-related emergency calls increased by almost 7% between 2017 and 2018, and by 19% between 2017 and 2019. The peak time for calls was between 9 p.m. and 1 a.m., and the lowest number of calls were recorded between 3 a.m. and 9 a.m. Saturday was the busiest day of the week in terms of call volume. There were statistically significant clusters in the pattern of CVD-related calls in the south-eastern region of Mashhad. Further, we found a large spatial variation in scene-to-hospital arrival time and call-to-hospital arrival time in the area under study.ConclusionThe use of geographical information systems and spatial analyses in modelling and quantifying EMS response time provides a new vein of knowledge for decision makers in emergency services management. Spatial as well as temporal clustering of EMS calls were present in the study area. The reasons for clustering of unfavorable time indices for EMS response requires further exploration. This approach enables policymakers to design tailored interventions to improve response time and reduce CVD-related mortality.

Child Protection Inequalities in New Zealand: Exploring Relationships with Social Gradient Using National Linked Administrative Datasets

IntroductionContact with child protection systems are a key site of the expression of social inequalities. ‘Child welfare inequalities’ by deprivation have been documented in a number of countries including the United Kingdom. The size and nature of this relationship is sparse in the New Zealand system context. Objectives and ApproachThe integration of data routinely collected by NZ’s Ministry for Children into Statistics NZ’s Integrated Data Infrastructure (IDI) provided the opportunity to examine how child protection data relates to deprivation and location. Anonymised person-level data in the IDI was used to obtain a population-based retrospective sample of children estimated to be resident in NZ in 2013/2014 and less than 17 years of age. Using pre-linked data in the IDI, all children with at least one of three child protection outcomes in 2013/2014 were ascertained. Deprivation was assessed using a small-area level national index derived from census data. The most recent residential address recorded in the IDI was used to assign deprivation deciles to each child based on the small area in which they lived. ResultsOf 1,016,928 children, 13,851 had had at least one substantiation of interest, the least serious of the outcomes considered. Compared to children living in the least deprived quintile of small areas, children in the most deprived quintile had 13.1 times (95%CI for Incidence Rate Ratio 12.0, 14.4) the rate of substantiation, 18.0 times (95%CI 15.4, 20.8) the rate of a family group conference, and 6.5 times (95%CI 5.6, 7.6) their rate of placement in foster care. Conclusion / ImplicationsA marked relationship was observed between small-area deprivation and child protection contact. Action is needed to address the causes of deprivation, provide services that respond to families living in poverty, and examine carefully the interactions between demand and supply of services across deprivation levels.