Calculating a deprivation index using census data

Background Deprivation indexes have widespread use in academic research and in local and national government applications. It is useful for people to understand their construction and to be able to calculate their own measures. Aims We provide an overview of the background to area based deprivation measures. We detail and explain a series of steps taken to calculate a deprivation index for small areas in Australia. Data and methods We use data from Australia’s 2016 Census of Population and Housing for the SA2 level of geography. After defining the set of variables used as inputs, we emulate the steps taken to calculate other census based deprivation indexes. Results The resulting scheme correlates closely with an official, but more sophisticated deprivation measure, suggesting that simple schemes have utility. Conclusions There are choices to be made for input variables and for some of the detail of the calculations. Researchers can follow the steps we describe to develop their own measures

Exploring ethnic inequalities in health: Evidence from the Health Survey for England, 1998-2011

Issues of social justice and social and spatial inequalities in health have long been researched, yet there is a relative paucity of research on ethnic inequalities in health. Given the increasing ethnic diversity of England's population and the persistence of unjust differences in health this research is timely. We used annual data from the Health Survey for England between 1998 and 2011, combined into a time-series dataset, to examine the influence of socioeconomic and spatial factors on ethnic variations in health and to explore whether inequalities have changed over time. Our analysis reveals that ethnic differences in health are largely rooted in socioeconomic or spatial difference, although variations by health outcome are observed. This work builds on existing literature which looks to socioeconomic and spatial difference for explanations of ethnic inequalities in health, rather than any supposed inherent underlying risk of poor health for minority ethnic groups

Differences in the risk of cardiovascular disease for movers and stayers in New Zealand: a survival analysis

Objectives: To explore if risk of cardiovascular disease (CVD) for participants who moved before their first CVD event is higher than for stayers, and examine whether the relationship is moderated by ethnicity. Methods: The sample comprised of 2,068,360 New Zealand (NZ) residents enrolled in any Primary Health Organisation, aged between 30-84 years, had complete demographic information, and no prior history of CVD. Cox proportional regression was used to compare CVD risk between movers and stayers. The analysis was conducted for the whole sample and stratified by ethnicity. Results: The combined analysis suggested movers have a lower risk of CVD than stayers. This is consistent for all ethnic groups with some variation according to experience of deprivation change following residential mobility. Conclusions: Although mobile groups may have a higher risk of CVD than immobile groups overall, risk of CVD in the period following a residential mobility event is lower than for stayers. Results are indicative of a short-term healthy migrant effect comparable to that observed for international migrants

Do schools differ in suicide risk? the influence of school and neighbourhood on attempted suicide, suicidal ideation and self-harm among secondary school pupils

<br>Background: Rates of suicide and poor mental health are high in environments (neighbourhoods and institutions) where individuals have only weak social ties, feel socially disconnected and experience anomie - a mismatch between individual and community norms and values. Young people spend much of their time within the school environment, but the influence of school context (school connectedness, ethos and contextual factors such as school size or denomination) on suicide-risk is understudied. Our aim is to explore if school context is associated with rates of attempted suicide and suicide-risk at age 15 and self-harm at age 19, adjusting for confounders.</br> <br>Methods: A longitudinal school-based survey of 1698 young people surveyed when aged 11, (primary school), 15 (secondary school) and in early adulthood (age 19). Participants provided data about attempted suicide and suicide-risk at age 15 and deliberate self-harm at 19. In addition, data were collected about mental health at age 11, social background (gender, religion, etc.), and at age 15, perception of local area (e.g. neighbourhood cohesion, safety/civility and facilities), school connectedness (school engagement, involvement, etc.) and school context (size, denomination, etc.). A dummy variable was created indicating a religious 'mismatch', where pupils held a different faith from their school denomination. Data were analysed using multilevel logistic regression.</br> <br>Results: After adjustment for confounders, pupils attempted suicide, suicide-risk and self-harm were all more likely among pupils with low school engagement (15-18% increase in odds for each SD change in engagement). While holding Catholic religious beliefs was protective, attending a Catholic school was a risk factor for suicidal behaviours. This pattern was explained by religious 'mismatch': pupils of a different religion from their school were approximately 2-4 times more likely to attempt suicide, be a suicide-risk or self-harm.</br> <br>Conclusions: With several caveats, we found support for the importance of school context for suicidality and self-harm. School policies promoting school connectedness are uncontroversial. Devising a policy to reduce risks to pupils holding a different faith from that of their school may be more problematic.</br&gt

Charting Disaster Recovery via Google Street View: A Social Science Perspective on Challenges Raised by the Fukushima Nuclear Disaster

There is increasing interest in using Google Street View (GSV) for research purposes, particularly with regard to “virtually auditing” the built environment to assess environmental quality. Research in this field to date generally suggests GSV is a reliable means of understanding the “real world” environment. But limitations around the dates and resolution of images have been identified. An emerging strand within this literature is also concerned with the potential of GSV to understand recovery post-disaster. Using the GSV data set for the evacuated area around the Fukushima Dai’ichi nuclear power plant as a case study, this article evaluates GSV as a means of assessing disaster recovery in a dynamic situation with remaining uncertainty and a significant value and emotive dimension. The article suggests that GSV does have value in giving a high-level overview of the post-disaster situation and has potential to track recovery and resettlement over time. Drawing on social science literature relating to Fukushima, and disasters more widely, the article also argues it is imperative for researchers using GSV to reflect carefully on the wider socio-cultural contexts that are often not represented in the photo montage

Deprivation (im)mobility and cause-specific premature mortality in Scotland

A common approach for measuring geographical inequalities in health has been to calculate deprivation scores for small areas and then to aggregate these into quintiles. Mortality rates may then be compared for the highest and lowest deprivation quintiles at two points in time and the change in the difference between the rates determines the extent to which inequalities have widened or narrowed. This ‘period-specific’ approach to measuring inequalities is problematic both because deprivation calculated at different points in time is not directly comparable, and because the boundaries of the areas used for such analyses often change during the study period. Using 10,058 small areas for Scotland whose boundaries do not change between 1981 and 2001 we examine the deprivation (im)mobility of areas, identifying those that are persistently well-off, stable or deprived and those that improved or worsened during the period. We focus particularly on the 638 persistently most deprived areas. We demonstrate, first and importantly, that premature mortality rates increased significantly over this twenty year period in these areas. Second, we examine which causes of death are mainly responsible for this increase; the risk of death from chronic liver disease, mental disorders due to alcohol, suicide and ‘other’ causes increased considerably. The geographical approach we describe here is novel and provides new insights into the relationship between deprivation and premature mortality. We suggest that these persistently most deprived Scottish areas deserve special attention and may be particularly appropriate sites for public health interventions related to these causes of premature death

Cardiovascular medication changes over 5 years in a national data linkage study: implications for risk prediction models

Suneela Mehta,1 Rod Jackson,1 Sue Wells,1 Jeff Harrison,2 Daniel J Exeter,1 Andrew J Kerr1,3 1Section of Epidemiology and Biostatistics, 2School of Pharmacy, University of Auckland, 3Cardiology Services, Middlemore Hospital, Auckland, New Zealand Background: Despite widespread use of cardiovascular disease (CVD) preventive medications in cohorts used to develop CVD risk prediction models, only some incorporate baseline CVD pharmacotherapy and none account for treatment changes during study follow-up. Therefore, current risk prediction scores may underestimate the true CVD event risk. We examined changes in CVD pharmacotherapy over 5 years in preparation for developing new 5-year risk prediction models.Methods: Anonymized individual-level linkage of eight national administrative health datasets enabled identification of all New Zealanders aged 30–74 years, without prior hospitalization for CVD or heart failure, who utilized publicly funded health services during 2006. We determined proportions of participants dispensed blood pressure lowering, lipid lowering, and antiplatelet/anticoagulant pharmacotherapy at baseline in 2006, and the proportion of person years of follow-up (2007–2011) where dispensing occurred.Results: The study population comprised of 1,766,584 individuals, representing ~85% of all New Zealanders aged 30–74 years without prior CVD or heart failure in 2006, with mean follow-up of 4.9 years (standard deviation 0.6 years; 8,589,931 total person years). CVD medications were dispensed to 21% of people at baseline, with most single or combination pharmacotherapies continuing for ≥80% of follow-up. Complete discontinuation of baseline treatment accounted for 2% of follow-up time while CVD pharmacotherapy that commenced after baseline accounted for 7% of total follow-up time. Conclusion: In a national primary prevention cohort of 30–74 year olds, one in five received baseline CVD primary preventive pharmacotherapy and medication changes over the subsequent 5 years were modest. Baseline medication use is an important consideration when estimating CVD risk from modern cohorts. It is currently unclear how to incorporate available methods to account for treatment changes during follow-up into risk prediction scores, but this study demonstrates that baseline therapy captures most of the effect of treatment in 5-year risk models. However, the impact of treatment changes on the more common 10-year risk models requires further investigation. Keywords: cardiovascular diseases, primary prevention, drug therapy, routine data, record linkage&nbsp

Inter-relationships between geographical scale, socio-economic data suppression and population homogeneity

Over time, technology has greatly enhanced access to vast amounts of public data in government datasets. At the same time there has been an increase in ‘neighbourhood’ level research, in which researchers typically select an administrative unit for their analysis. As the demand for data driven insights and decision making continues to rise, researchers face a tradeoff between data suppression (to protect the privacy of citizens) and homogeneity (the similarity of individuals within an area unit for given characteristics). In this paper, we explore the extent that different scales of geography impact data suppression and spatial homogeneity using the intra-class correlation and the D-Statistic. We use age, sex, ethnicity, education and income data from the 2013 New Zealand Census to assess a) the extent to which data are suppressed, and b) the spatial homogeneity of these variables across 5 scales of ‘small area’ geography available to researchers in NZ. The data used for this paper was accessed via the Integrated Data Infrastructure (IDI), a large data repository of de-identified, linked microdata obtained from government agencies, and nationally representative surveys. The scales used in this study are the 2013 Meshblock, Statistical Area 1, Data Zone, Statistical Area 2 and Area Unit, each of which can be used to analyse patterns at the ‘neighbourhood’ scale. We found that Data Zones are a suitable choice for undertaking analyses of census data as they represent a’medium’ scale geography designed to reduce data suppression while maintaining reasonable levels of population homogeneity. The policy implications for this research relate to zone design and decisions relating to the definition of ‘a small cell count’ for data dissemination for different users of sociodemographic data

A genetic algorithm-based strategic planning framework for optimising accessibility and costs of general practices in Northland, New Zealand

Shortage of general practitioners (GP) is a challenge worldwide, not only in Europe, but also in countries like New Zealand. Providing primary care in rural areas is especially challenging. In order to support decision makers, it is necessary to first assess the current GP coverage and then to determine different scenarios and plans for the future. In this paper, we first present a thorough overview of related literature on locating GP practices. Second, we propose an approach for assessing the GP coverage and determining future GP locations based on a genetic algorithm framework. As a use case, we have chosen the rural New Zealand region of Northland. We also perform a sensitivity analysis for the main input parameters