1337 A Regional Review of The Epidemiology and Pathological Characteristics of Malignant Melanoma in Northern Ireland (NI), And Correlation with Socio-Economic Status

Introduction The incidence of cutaneous malignant melanoma is increasing. Internationally, there is evidence of an association between melanoma incidence and higher socioeconomic status (SES). This study aims to assess the characteristics of patients with melanoma in NI, and correlate to SES. Method Retrospective review of electronic records for patients undergoing surgery for melanoma at the Northern Ireland Plastic Surgery Unit from August 2015 to March 2020. Patients were identified from theatre records and a prospectively collected sentinel lymph node database. The NI Multiple Deprivation Measure 2017 was used to measure SES. Results 440 patients were included (F = 54%, M = 46%). Mean age=63 (M = 67, F = 59). Mean Breslow Depth (BD) = 2.61mm (Range 0.17 – 27mm). Females had significantly thinner tumours at presentation (mean BD 2.16mm. vs 3.1mm in males, p = 0.001). In males the commonest sites were head and neck (36%) and back (23%). In females, the commonest sites were lower limb (42%) and upper limb (23%). There was a positive correlation between higher SES and increased incidence of melanoma (correlation coefficient (CC) 0.922), but this did not correlate with an increase in Breslow depth (CC -0.020). Conclusions This study provides important information on melanoma in NI, including gender and site variances. Females were more commonly affected and were a mean of 8 years younger than males at diagnosis but presented with significantly thinner tumours. Unlike the rest of the UK, the commonest site in males was the head and neck. Higher SES was related to higher incidence of melanoma but with presentation at an earlier stage of disease.

Examining the Risks of Engaging in Population-based Surveys on Violence: Follow-up Study of the Individual Deprivation Measure in South Africa

In the South African Individual Deprivation Measure, the individual survey, included questions about two potentially highly sensitive topics—individuals' experience of violence and their control over personal decision making. In-depth follow-up interviews were conducted with 105 consenting survey participants to determine whether participating in the survey resulted in negative impacts for individuals, particularly in relation to these two topics. Several participants found that being asked about their experiences resurfaced painful memories, but we did not find any evidence that the approach of surveying every eligible individual in the dwelling resulted in any form of harm for the survey participants.

Measuring Disability Inclusion: Feasibility of Using Existing Multidimensional Poverty Data in South Africa

This paper presents a framework for measuring disability inclusion in order to examine the associations between disability severity and levels of inclusion, provides an example of its operationalization, and assesses the feasibility of using an existing dataset to measure disability inclusion using this framework. Inclusion here refers to the extent to which people with disabilities are accepted and recognized as individuals with authority, enjoy personal relationships, participate in recreation and social activities, have appropriate living conditions, are able to make productive contributions, and have required formal and informal support. Indicators for the operationalization were drawn from the Individual Deprivation Measure South Africa country study and were mapped on to the domains of inclusion (where relevant), and the Washington Group Short Set of questions were used to determine disability status (no, mild, or moderate/severe disability). The analysis indicates that individuals with disabilities experience generally worse outcomes and a comparative lack of inclusion compared to individuals without disabilities, and broadly that those with moderate or severe disabilities experience worse outcomes than those with mild disabilities. This analysis also provides insight into the limitations of using existing datasets for different purposes from their original design.

Self-Rated Health Inequalities in British Nurses

IntroductionImprovements in health in the UK are beginning to stall. Differences between the health of people living in the most and least deprived areas continue to grow. An excess in mortality, not explained by deprivation, has been observed in Scotland. Some of this difference likely results from limitations in deprivation measures. Objectives and ApproachWe seek to test whether Nurses experience health inequalities in Self-Rated Health comparable with the general population. We also aim to explore cross-national differences within the Nursing occupational group. We utilise data from Census-derived Longitudinal Studies in Scotland and England & Wales which are linked to an adjusted UK-consistent Multiple Deprivation measure. The databases can only be accessed securely, so an innovative method (eDatashield) has been used to conduct analysis as if the two were combined. Nurses are of interest as they are a large occupational group with potentially protective characteristics against inequalities including high health literacy and level of education. Socioeconomic homogeneity in this group may reduce the effect of confounding when exploring area-based deprivation measures. ResultsComparing Nurses to Non-Nurses we found they have systematically different and more homogenous characteristics. Nurses are; older, have a higher level of education, are more likely to be female, own their home, are less likely to live in deprived areas and they report better Self-Rated Health. However, inequalities persist. Comparing Self-Rated Health of Scottish with English & Welsh Nurses will determine whether an ‘excess’ in worse health outcomes exists and if so, whether the UK- consistent Deprivation Measure can account for this. Full results will be cleared for dissemination through disclosure control, prior to the conference. Conclusion / ImplicationsEven in a privileged group with characteristics which protect against poor health, inequalities remain. The methods applied here present an opportunity for improved cross-national comparison and address limitations in confounding when exploring inequalities based on area deprivation.

Developing a Small-Area Deprivation Measure for Brazil

This report describes the development of the BrazDep small-area deprivation measure for the whole of Brazil. The measure uses the 2010 Brazilian Population Census data and is calculated for the smallest possible geographical area level, the census sectors. It combines three variables – (1) percent of households with per capita income ≤ 1/2 minimum wage; (2) percent of people not literate, aged 7+; and (3) average of percent of people with inadequate access to sewage, water, garbage collection and no toilet and bath/shower – into a single measure. Similar measures have previously been developed at the census sector level for some states or municipalities, but the deprivation measure described in this report is the first one to be provided for census sectors for the whole of Brazil. BrazDep is a measure of relative deprivation, placing the census sectors on a scale of material well-being from the least to the most deprived. It is useful in comparing areas within Brazil in 2010, but cannot be used to make comparisons across countries or time. Categorical versions of the measure are also provided, placing census sectors into groups of similar levels of deprivation. Deprivation measures, such as the one developed here, have been developed for many countries and are popular tools in public health research for describing the social patterning of health outcomes and supporting the targeting and delivery of services to areas of higher need. The deprivation measure is exponentially distributed, with a large proportion of areas having a low deprivation score and a smaller number of areas experiencing very high deprivation. There is significant regional variation in deprivation; areas in the North and Northeast of Brazil have on average much higher deprivation compared to the South and Southeast. Deprivation levels in the Central-West region fall between those for the North and South. Differences are also great between urban and rural areas, with the former having lower levels of deprivation compared to the latter. The measure was validated by comparing it to other similar indices measuring health and social vulnerability at the census sector level in states and municipalities where it was possible, and at the municipal level for across the whole of Brazil. At the municipal level the deprivation measure was also compared to health outcomes. The different validation exercises showed that the developed measure produced expected results and could be considered validated. As the measure is an estimate of the “true” deprivation in Brazil, uncertainty exists about the exact level of deprivation for all of the areas. For the majority of census sectors the uncertainty is small enough that we can reliably place the area into a deprivation category. However, for some areas uncertainty is very high and the provided estimate is unreliable. These considerations should always be kept in mind when using the BrazDep measure in research or policy. The measure should be used as part of a toolkit, rather than a single basis for decision-making. The data together with documentation is available from the University of Glasgow http: //dx.doi.org/10.5525/gla.researchdata.980. The data and this report are distributed under Creative Commons Share-Alike license (CC BY-SA 4.0) and can be freely used by researchers, policy makers or members of public.

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.

Identifying people in poverty: a multidimensional deprivation measure for the EU

In this article, I propose a multidimensional deprivation measure of poverty for the EU. The paper stands on the claim that a deprivation measure can be adequate, both conceptually and empirically, to capture poverty in the EU defined in Townsendian terms. Yet existing deprivation scales have three conceptual problems such as data-driven specification, neglected dimensionality and missing dimensions, and four data problems such as limited extent, cross-cultural equivalization, behavioral choices and reporting error. To address conceptual problems, I offer a concept-led methodology for constructing a multidimensional measure. To address data problems, I apply a post-hoc adjustment strategy using dual criteria of income poverty and financial strain. The proposed measure has four dimensions, namely needs for basic goods, health, education, leisure and social relationships, where each dimension is evaluated separately with relevant scales. When compared to the formal EU 2020 poverty target measure, the proposed measure is more likely to capture people with needs and lower resources as well as those in less affluent countries than those in more affluent countries. The (adjusted) proposed measure can be used as a stand-alone indicator to identify a target population for policy; or the unadjusted proposed measure can be combined with an income poverty measure to identify a worst-off group within that target population.