A method for modelling GP practice level deprivation scores using GIS

<p>Abstract</p> <p>Background</p> <p>A measure of general practice level socioeconomic deprivation can be used to explore the association between deprivation and other practice characteristics. An area-based categorisation is commonly chosen as the basis for such a deprivation measure. Ideally a practice population-weighted area-based deprivation score would be calculated using individual level spatially referenced data. However, these data are often unavailable. One approach is to link the practice postcode to an area-based deprivation score, but this method has limitations. This study aimed to develop a Geographical Information Systems (GIS) based model that could better predict a practice population-weighted deprivation score in the absence of patient level data than simple practice postcode linkage.</p> <p>Results</p> <p>We calculated predicted practice level Index of Multiple Deprivation (IMD) 2004 deprivation scores using two methods that did not require patient level data. Firstly we linked the practice postcode to an IMD 2004 score, and secondly we used a GIS model derived using data from Rotherham, UK. We compared our two sets of predicted scores to "gold standard" practice population-weighted scores for practices in Doncaster, Havering and Warrington. Overall, the practice postcode linkage method overestimated "gold standard" IMD scores by 2.54 points (95% CI 0.94, 4.14), whereas our modelling method showed no such bias (mean difference 0.36, 95% CI -0.30, 1.02). The postcode-linked method systematically underestimated the gold standard score in less deprived areas, and overestimated it in more deprived areas. Our modelling method showed a small underestimation in scores at higher levels of deprivation in Havering, but showed no bias in Doncaster or Warrington. The postcode-linked method showed more variability when predicting scores than did the GIS modelling method.</p> <p>Conclusion</p> <p>A GIS based model can be used to predict a practice population-weighted area-based deprivation measure in the absence of patient level data. Our modelled measure generally had better agreement with the population-weighted measure than did a postcode-linked measure. Our model may also avoid an underestimation of IMD scores in less deprived areas, and overestimation of scores in more deprived areas, seen when using postcode linked scores. The proposed method may be of use to researchers who do not have access to patient level spatially referenced data.</p

Debt income and mental disorder in the general population

Background The association between poor mental health and poverty is well known but its mechanism is not fully understood. This study tests the hypothesis that the association between low income and mental disorder is mediated by debt and its attendant financial hardship. Method The study is a cross-sectional nationally representative survey of private households in England, Scotland and Wales, which assessed 8580 participants aged 16–74 years living in general households. Psychosis, neurosis, alcohol abuse and drug abuse were identified by the Clinical Interview Schedule – Revised, the Schedule for Assessment in Neuropsychiatry (SCAN), the Alcohol Use Disorder Identification Test (AUDIT) and other measures. Detailed questions were asked about income, debt and financial hardship. Results Those with low income were more likely to have mental disorder [odds ratio (OR) 2.09, 95% confidence interval (CI) 1.68–2.59] but this relationship was attenuated after adjustment for debt (OR 1.58, 95% CI 1.25–1.97) and vanished when other sociodemographic variables were also controlled (OR 1.07, 95% CI 0.77–1.48). Of those with mental disorder, 23% were in debt (compared with 8% of those without disorder), and 10% had had a utility disconnected (compared with 3%). The more debts people had, the more likely they were to have some form of mental disorder, even after adjustment for income and other sociodemographic variables. People with six or more separate debts had a six-fold increase in mental disorder after adjustment for income (OR 6.0, 95% CI 3.5–10.3). Conclusions Both low income and debt are associated with mental illness, but the effect of income appears to be mediated largely by debt

Evaluation of stability of directly standardized rates for sparse data using simulation methods.

Background Directly standardized rates (DSRs) adjust for different age distributions in different populations and enable, say, the rates of disease between the populations to be directly compared. They are routinely published but there is concern that a DSR is not valid when it is based on a “small” number of events. The aim of this study was to determine the value at which a DSR should not be published when analyzing real data in England. Methods Standard Monte Carlo simulation techniques were used assuming the number of events in 19 age groups (i.e., 0–4, 5–9, ... 90+ years) follow independent Poisson distributions. The total number of events, age specific risks, and the population sizes in each age group were varied. For each of 10,000 simulations the DSR (using the 2013 European Standard Population weights), together with the coverage of three different methods (normal approximation, Dobson, and Tiwari modified gamma) of estimating the 95% confidence intervals (CIs), were calculated. Results The normal approximation was, as expected, not suitable for use when fewer than 100 events occurred. The Tiwari method and the Dobson method of calculating confidence intervals produced similar estimates and either was suitable when the expected or observed numbers of events were 10 or greater. The accuracy of the CIs was not influenced by the distribution of the events across categories (i.e., the degree of clustering, the age distributions of the sampling populations, and the number of categories with no events occurring in them). Conclusions DSRs should not be given when the total observed number of events is less than 10. The Dobson method might be considered the preferred method due to the formulae being simpler than that of the Tiwari method and the coverage being slightly more accurate

Differences in IMD 2004 scores (predicted score - gold standard score) against their mean for Warrington practices

<p><b>Copyright information:</b></p><p>Taken from "A method for modelling GP practice level deprivation scores using GIS"</p><p>http://www.ij-healthgeographics.com/content/6/1/38</p><p>International Journal of Health Geographics 2007;6():38-38.</p><p>Published online 6 Sep 2007</p><p>PMCID:PMC2045089.</p><p></p

Differences in IMD 2004 scores (predicted score - gold standard score) against their mean for Doncaster practices

<p><b>Copyright information:</b></p><p>Taken from "A method for modelling GP practice level deprivation scores using GIS"</p><p>http://www.ij-healthgeographics.com/content/6/1/38</p><p>International Journal of Health Geographics 2007;6():38-38.</p><p>Published online 6 Sep 2007</p><p>PMCID:PMC2045089.</p><p></p

Scatter plot of predicted IMD score (modelled and postcode linked) versus gold standard score for Warrington practices

<p><b>Copyright information:</b></p><p>Taken from "A method for modelling GP practice level deprivation scores using GIS"</p><p>http://www.ij-healthgeographics.com/content/6/1/38</p><p>International Journal of Health Geographics 2007;6():38-38.</p><p>Published online 6 Sep 2007</p><p>PMCID:PMC2045089.</p><p></p

Scatter plot of predicted IMD score (modelled and postcode linked) versus gold standard score for Havering practices

<p><b>Copyright information:</b></p><p>Taken from "A method for modelling GP practice level deprivation scores using GIS"</p><p>http://www.ij-healthgeographics.com/content/6/1/38</p><p>International Journal of Health Geographics 2007;6():38-38.</p><p>Published online 6 Sep 2007</p><p>PMCID:PMC2045089.</p><p></p