| 1. |
- Austin, Peter C, et al.
(författare)
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Measures of clustering and heterogeneity in multilevel Poisson regression analyses of rates/count data
- 2018
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Ingår i: Statistics in Medicine. - : Wiley. - 1097-0258 .- 0277-6715. ; 37:4, s. 572-589
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Tidskriftsartikel (refereegranskat)abstract
- Multilevel data occur frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models. These models incorporate cluster-specific random effects that allow one to partition the total variation in the outcome into between-cluster variation and between-individual variation. The magnitude of the effect of clustering provides a measure of the general contextual effect. When outcomes are binary or time-to-event in nature, the general contextual effect can be quantified by measures of heterogeneity like the median odds ratio or the median hazard ratio, respectively, which can be calculated from a multilevel regression model. Outcomes that are integer counts denoting the number of times that an event occurred are common in epidemiological and medical research. The median (incidence) rate ratio in multilevel Poisson regression for counts that corresponds to the median odds ratio or median hazard ratio for binary or time-to-event outcomes respectively is relatively unknown and is rarely used. The median rate ratio is the median relative change in the rate of the occurrence of the event when comparing identical subjects from 2 randomly selected different clusters that are ordered by rate. We also describe how the variance partition coefficient, which denotes the proportion of the variation in the outcome that is attributable to between-cluster differences, can be computed with count outcomes. We illustrate the application and interpretation of these measures in a case study analyzing the rate of hospital readmission in patients discharged from hospital with a diagnosis of heart failure.
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| 2. |
- Axelsson Fisk, Sten, et al.
(författare)
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Chronic Obstructive Pulmonary Disease in Sweden: an intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy
- 2018
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Ingår i: SSM - Population Health. - : Elsevier BV. - 2352-8273. ; 4, s. 334-346
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Tidskriftsartikel (refereegranskat)abstract
- Socioeconomic, ethnic and gender disparities in Chronic Obstructive Pulmonary Disease (COPD) risk are well established but no studies have applied multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) within an intersectional framework to study this outcome. We study individuals at the first level of analysis and combinations of multiple social and demographic categorizations (i.e., intersectional strata) at the second level of analysis. Here we used MAIHDA to assess to what extent individual differences in the propensity of developing COPD are at the intersectional strata level. We also used MAIHDA to determine the degree of similarity in COPD incidence of individuals in the same intersectional stratum. This leads to an improved understanding of risk heterogeneity and of the social dynamics driving socioeconomic and demographic disparities in COPD incidence. Using data from 2,445,501 residents in Sweden aged 45–65, we constructed 96 intersectional strata combining categories of age, gender, income, education, civil- and migration status. The incidences of COPD ranged from 0.02% for young, native males with high income and high education who cohabited to 0.98% for older native females with low income and low education who lived alone. We calculated the intra-class correlation coefficient (ICC) that informs on the discriminatory accuracy of the categorizations. In a model that conflated additive and interaction effects, the ICC was good (20.0%). In contrast, in a model that measured only interaction effects, the ICC was poor (1.1%) suggesting that most of the observed differences in COPD incidence across strata are due to the main effects of the categories used to construct the intersectional matrix while only a minor share of the differences are attributable to intersectional interactions. We found conclusive interaction effects. The intersectional MAIHDA approach offers improved information to guide public health policies in COPD prevention, and such policies should adopt an intersectional perspective.
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| 3. |
- Evans, Clare R., et al.
(författare)
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Multilevel versus single-level regression for the analysis of multilevel information : The case of quantitative intersectional analysis
- 2020
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Ingår i: Social Science and Medicine. - : Elsevier BV. - 0277-9536. ; 245
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Tidskriftsartikel (refereegranskat)abstract
- Intersectional MAIHDA involves applying multilevel models in order to estimate intercategorical inequalities. The approach has been validated thus far using both simulations and empirical applications, and has numerous methodological and theoretical advantages over single-level approaches, including parsimony and reliability for analyzing high-dimensional interactions. In this issue of SSM, Lizotte, Mahendran, Churchill and Bauer (hereafter “LMCB”) assert that there has been insufficient clarity on the interpretation of fixed effects regression coefficients in intersectional MAIHDA, and that stratum-level residuals in intersectional MAIHDA are not interpretable as interaction effects. We disagree with their second assertion; however, the authors are right to call for greater clarity. For this purpose, in this response we have three main objectives. (1) In their commentary, LMCB incorrectly describe model predictions based on MAIHDA fixed effects as estimates of “grand means” (or the mean of means), when they are actually “precision-weighted grand means.” We clarify the differences between average predicted values obtained by different models, and argue that predictions obtained by MAIHDA are more suitable to serve as reference points for residual/interaction effects. This further enables us to clarify the interpretation of residual/interaction effects in MAIHDA and conventional models. Using simple simulations, we demonstrate conditions under which the precision-weighted grand mean resembles a grand mean, and when it resembles a population mean (or the mean of all individual observations) obtained using single-level regression, explaining the results obtained by LMCB and informing future research. (2) We construct a modification to MAIHDA that constrains the fixed effects so that the resulting model predictions provide estimates of population means, which we use to demonstrate the robustness of results reported by Evans et al. (2018). We find that stratum-specific residuals obtained using the two approaches are highly correlated (Pearson corr = 0.98, p < 0.0001) and no substantive conclusions would have been affected if the preference had been for estimating population means. However, we advise researchers to use the original, unconstrained MAIHDA. (3) Finally, we outline the extent to which single-level and MAIHDA approaches address the fundamental goals of quantitative intersectional analyses and conclude that intersectional MAIHDA remains a promising new approach for the examination of inequalities.
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| 4. |
- Hernández Yumar, Aranzazu, et al.
(författare)
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Socioeconomic differences in body mass index in Spain : An intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy
- 2018
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Ingår i: PLOS ONE. - : PLOS. - 1932-6203. ; 13:12
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Tidskriftsartikel (refereegranskat)abstract
- Many studies have demonstrated the existence of simple, unidimensional socioeconomic gradients in body mass index (BMI). However, in the present paper we move beyond such traditional analyses by simultaneously considering multiple demographic and socioeconomic dimensions. Using the Spanish National Health Survey 2011–2012, we apply intersectionality theory and multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to analyze 14,190 adults nested within 108 intersectional strata defined by combining categories of gender, age, income, educational achievement and living situation. We develop two multilevel models to obtain information on stratum-specific BMI averages and the degree of clustering of BMI within strata expressed by the intra-class correlation coefficient (ICC). The first model is a simple variance components analysis that provides a detailed mapping of the BMI disparities in the population and measures the accuracy of stratum membership to predict individual BMI. The second model includes the variables used to define the intersectional strata as a way to identify stratum-specific interactions. The first model suggests moderate but meaningful clustering of individual BMI within the intersectional strata (ICC = 12.4%). Compared with the population average (BMI = 26.07 Kg/m2), the stratum of cohabiting 18-35-year-old females with medium income and high education presents the lowest BMI (-3.7 Kg/m2), while cohabiting 36-64-year-old females with low income and low education show the highest BMI (+2.6 Kg/m2). In the second model, the ICC falls to 1.9%, suggesting the existence of only very small stratum specific interaction effects. We confirm the existence of a socioeconomic gradient in BMI. Compared with traditional analyses, the intersectional MAIHDA approach provides a better mapping of socioeconomic and demographic inequalities in BMI. Because of the moderate clustering, public health policies aiming to reduce BMI in Spain should not solely focus on the intersectional strata with the highest BMI, but should also consider whole population polices.
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| 5. |
- Khalaf, Kani, et al.
(författare)
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Geographical and sociodemographic differences in discontinuation of medication for chronic obstructive pulmonary disease – a cross-classified multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)
- 2020
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Ingår i: Clinical Epidemiology. - 1179-1349. ; 12, s. 783-796
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Tidskriftsartikel (refereegranskat)abstract
- Background: While discontinuation of COPD maintenance medication is a known problem, the proportion of patients with discontinuation and its geographical and sociodemographic distribution are so far unknown in Sweden. Therefore, we analyse this question by applying an innovative approach called multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA). Patients and Methods: We analysed 49,019 patients categorized into 18 sociodemographic contexts and 21 counties of residence. All patients had a hospital COPD diagnosis and had been on inhaled maintenance medication during the 5 years before the study baseline in 2010. We defined “discontinuation” as the absolute lack of retrieval from a pharmacy of any inhaled maintenance medication during 2011. We performed a cross-classified MAIHDA and obtained the average proportion of discontinuation, as well as county and sociodemographic absolute risks, and compared them with a proposed benchmark value of 10%. We calculated the variance partition coefficient (VPC) and the area under the receiver operating characteristics curve (AUC) to quantify county and sociodemographic differences. To summarize the results, we used a framework with 15 scenarios defined by the size of the differences and the level of achievement in relation to the benchmark value. Results: Around 18% of COPD patients in Sweden discontinued maintenance medication, so the benchmark value was not achieved. There were very small county differences (VPC=0.35%, AUC=0.54). The sociodemographic differences were small (VPC=4.98%, AUC=0.57). Conclusion: Continuity of maintenance medication among COPD patients in Sweden could be improved by reducing the unjustifiably high prevalence of discontinuation. The very small county and small sociodemographic differences should motivate universal interventions across all counties and sociodemographic groups. Geographical analyses should be combined with sociodemographic analyses, and the cross-classified MAIHDA is an appropriate tool to assess health-care quality.
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| 6. |
- Kristensen, Pia Kjær, et al.
(författare)
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Disentangling the contribution of hospitals and municipalities for understanding patient level differences in one-year mortality risk after hip-fracture : A cross-classified multilevel analysis in Sweden
- 2020
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 15:6
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: One-year mortality after hip-fracture is a widely used outcome measure when comparing hospital care performance. However, traditional analyses do not explicitly consider the referral of patients to municipality care after just a few days of hospitalization. Furthermore, traditional analyses investigates hospital (or municipality) variation in patient outcomes in isolation rather than as a component of the underlying patient variation. We therefore aimed to extend the traditional approach to simultaneously estimate both case-mix adjusted hospital and municipality comparisons in order to disentangle the amount of the total patient variation in clinical outcomes that was attributable to the hospital and municipality level, respectively.METHODS: We determined 1-year mortality risk in patients aged 65 or above with hip fractures registered in Sweden between 2011 and 2014. We performed cross-classified multilevel analysis with 54,999 patients nested within 54 hospitals and 290 municipalities. We adjusted for individual demographic, socioeconomic and clinical characteristics. To quantify the size of the hospital and municipality variation we calculated the variance partition coefficient (VPC) and the area under the receiver operator characteristic curve (AUC).RESULTS: The overall 1-year mortality rate was 25.1%. The case-mix adjusted rates varied from 21.7% to 26.5% for the 54 hospitals, and from 18.9% to 29.5% for the 290 municipalities. The VPC was just 0.2% for the hospital and just 0.1% for the municipality level. Patient sociodemographic and clinical characteristics were strong predictors of 1-year mortality (AUC = 0.716), but adding the hospital and municipality levels in the cross-classified model had a minor influence (AUC = 0.718).CONCLUSIONS: Overall in Sweden, one-year mortality after hip-fracture is rather high. However, only a minor part of the patient variation is explained by the hospital and municipality levels. Therefore, a possible intervention should be nation-wide rather than directed to specific hospitals or municipalities.
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| 7. |
- Kristensen, Pia Kjær, et al.
(författare)
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Hospital differences in mortality rates after hip fracture surgery in Denmark
- 2019
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Ingår i: Clinical Epidemiology. - 1179-1349. ; 11, s. 605-614
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Tidskriftsartikel (refereegranskat)abstract
- Background: Thirty-day mortality after hip fracture is widely used when ranking hospital performance, but the reliability of such hospital ranking is seldom calculated. We aimed to quantify the variation in 30-day mortality across hospitals and to determine the hospital general contextual effect for understanding patient differences in 30-day mortality risk. Methods: Patients aged ≥65 years with an incident hip fracture registered in the Danish Multidisciplinary Fracture Registry between 2007 and 2016 were identified (n=60,004). We estimated unadjusted and patient-mix adjusted risk of 30-day mortality in 32 hospitals. We performed a multilevel analysis of individual heterogeneity and discriminatory accuracy with patients nested within hospitals. We expressed the hospital general contextual effect by the median odds ratio (MOR), the area under the receiver operating characteristics curve and the variance partition coefficient (VPC). Results: The overall 30-day mortality rate was 10%. Patient characteristics including high sociodemographic risk score, underweight, comorbidity, a subtrochanteric fracture, and living at a nursing home were strong predictors of 30-day mortality (area under the curve=0.728). The adjusted differences between hospital averages in 30-day mortality varied from 5% to 9% across the 32 hospitals, which correspond to a MOR of 1.18 (95% CI: 1.12–1.25). However, the hospital general context effect was low, as the VPC was below 1% and adding the hospital level to a single-level model with adjustment for patient-mix increased the area under the receiver operating characteristics curve by only 0.004 units. Conclusions: Only minor hospital differences were found in 30-day mortality after hip fracture. Mortality after hip fracture needs to be lowered in Denmark but possible interventions should be patient oriented and universal rather than focused on specific hospitals.
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| 8. |
- Leckie, George, et al.
(författare)
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Partitioning variation in multilevel models for count data
- 2020
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Ingår i: Psychological Methods. - : American Psychological Association (APA). - 1082-989X .- 1939-1463. ; 25:6, s. 787-801
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Tidskriftsartikel (refereegranskat)abstract
- A first step when fitting multilevel models to continuous responses is to explore the degree of clustering in the data. Researchers fit variance-component models and then report the proportion of variation in the response that is due to systematic differences between clusters. Equally they report the response correlation between units within a cluster. These statistics are popularly referred to as variance partition coefficients (VPCs) and intraclass correlation coefficients (ICCs). When fitting multilevel models to categorical (binary, ordinal, or nominal) and count responses, these statistics prove more challenging to calculate. For categorical response models, researchers appeal to their latent response formulations and report VPCs/ICCs in terms of latent continuous responses envisaged to underly the observed categorical responses. For standard count response models, however, there are no corresponding latent response formulations. More generally, there is a paucity of guidance on how to partition the variation. As a result, applied researchers are likely to avoid or inadequately report and discuss the substantive importance of clustering and cluster effects in their studies. A recent article drew attention to a little-known exact algebraic expression for the VPC/ICC for the special case of the two-level random-intercept Poisson model. In this article, we make a substantial new contribution. First, we derive exact VPC/ICC expressions for more flexible negative binomial models that allows for overdispersion, a phenomenon which often occurs in practice. Then we derive exact VPC/ICC expressions for three-level and random-coefficient extensions to these models. We illustrate our work with an application to student absenteeism. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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| 9. |
- Ljungman, Hanna, et al.
(författare)
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Antidepressant use in Sweden : an intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)
- 2021
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Ingår i: Scandinavian Journal of Public Health. - : Sage Publications. - 1403-4948 .- 1651-1905. ; 50:3, s. 395-403
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Antidepressants are among the most commonly prescribed drugs in Sweden. However, we lack detailed knowledge on the socioeconomic and demographic distribution of antidepressant use in the population. To fill this gap, we performed an intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy.Methods: Analysing all Swedish residents older than 10 years (n=8,190,990), we measured the absolute risk of antidepressant use across 144 intersectional strata defined by combinations of age, gender, income, country of birth and psychiatric diagnosis. We calculated the strata-specific absolute risk of antidepressant use in a series of multilevel logistic regression models. By means of the variance partitioning coefficient and the area under the receiver operating characteristic curve, we quantified the discriminatory accuracy of the intersectional contexts (i.e. strata) for discerning those who use antidepressants from those who do not.Results: The absolute risk of antidepressant use ranged between 0.93% and 24.78% among those without a psychiatric diagnosis, and between 21.41% and 77.56% among those with a psychiatric diagnosis. Both the variance partitioning coefficient of 41.88% and the area under the receiver operating characteristic curve of 0.81 were considerable.Conclusions: Besides overt psychiatric diagnoses, our study shows that antidepressant use is mainly conditioned by age, which might express the embodiment of socioeconomic conditions across the individual life course. Our analysis provides a detailed and highly discriminatory mapping of the heterogeneous distribution of antidepressant use in the Swedish population, which may be useful in public health management.
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| 10. |
- Mattsson, Hedda, et al.
(författare)
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Mapping socio-geographical disparities in the occurrence of teenage maternity in Colombia using multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)
- 2024
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Ingår i: International Journal for Equity in Health. - 1475-9276. ; 23, s. 1-18
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: The prevalence of teenage pregnancy in Colombia is higher than the worldwide average. The identification of socio-geographical disparities might help to prioritize public health interventions.AIM: To describe variation in the probability of teenage maternity across geopolitical departments and socio-geographical intersectional strata in Colombia.METHODS: A cross-sectional study based on live birth certificates in Colombia. Teenage maternity was defined as a woman giving birth aged 19 or younger. Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) was applied using multilevel Poisson and logistic regression. Two different approaches were used: (1) intersectional: using strata defined by the combination of health insurance, region, area of residency, and ethnicity as the second level (2) geographical: using geopolitical departments as the second level. Null, partial, and full models were obtained. General contextual effect (GCE) based on the variance partition coefficient (VPC) was considered as the measure of disparity. Proportional change in variance (PCV) was used to identify the contribution of each variable to the between-strata variation and to identify whether this variation, if any, was due to additive or interaction effects. Residuals were used to identify strata with potential higher-order interactions.RESULTS: The prevalence of teenage mothers in Colombia was 18.30% (95% CI 18.20-18.40). The highest prevalence was observed in Vichada, 25.65% (95% CI: 23.71-27.78), and in the stratum containing mothers with Subsidized/Unaffiliated healthcare insurance, Mestizo, Rural area in the Caribbean region, 29.08% (95% CI 28.55-29.61). The VPC from the null model was 1.70% and 9.16% using the geographical and socio-geographical intersectional approaches, respectively. The higher PCV for the intersectional model was attributed to health insurance. Positive and negative interactions of effects were observed.CONCLUSION: Disparities were observed between intersectional socio-geographical strata but not between geo-political departments. Our results indicate that if resources for prevention are limited, using an intersectional socio-geographical approach would be more effective than focusing on geopolitical departments especially when focusing resources on those groups which show the highest prevalence. MAIHDA could potentially be applied to many other health outcomes where resource decisions must be made.
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| 11. |
- Merlo, Juan, et al.
(författare)
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A simple multilevel approach for analysing geographical inequalities in public health reports : The case of municipality differences in obesity
- 2019
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Ingår i: Health and Place. - : ELSEVIER SCI LTD. - 1353-8292 .- 1873-2054. ; 58
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Tidskriftsartikel (refereegranskat)abstract
- The epidemiological analysis of geographical inequalities in individual outcomes is a fundamental theme in public health research. However, many traditional studies focus on analysing area differences in averages outcomes, disregarding individual variation around such averages. In doing so, these studies may produce misleading information and lead researchers to draw incorrect conclusions. Analysing individual and municipality differences in body mass index (BMI) and overweight/obesity status, we apply an analytical approach based on the multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA). This analytical approach may be viewed as a reorganization of existing multilevel modelling concepts in order to provide a systematic approach to simultaneously considering both differences between area averages and individual heterogeneity around those averages. In doing so, MAIHDA provides an improved approach to the quantification and understanding of geographical inequalities as compared with traditional approaches.
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| 12. |
- Merlo, Juan, et al.
(författare)
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An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy : The Case of Neighbourhoods and Health
- 2016
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 11:4
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Tidskriftsartikel (refereegranskat)abstract
- Background and Aim Many multilevel logistic regression analyses of "neighbourhood and health" focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that distinguishes between "specific" (measures of association) and "general" (measures of variance) contextual effects. Performing two empirical examples we illustrate the methodology, interpret the results and discuss the implications of this kind of analysis in public health. Methods We analyse 43,291 individuals residing in 218 neighbourhoods in the city of Malm, Sweden in 2006. We study two individual outcomes (psychotropic drug use and choice of private vs. public general practitioner, GP) for which the relative importance of neighbourhood as a source of individual variation differs substantially. In Step 1 of the analysis, we evaluate the OR and the area under the receiver operating characteristic (AUC) curve for individual-level covariates (i.e., age, sex and individual low income). In Step 2, we assess general contextual effects using the AUC. Finally, in Step 3 the OR for a specific neighbourhood characteristic (i.e., neighbourhood income) is interpreted jointly with the proportional change in variance (i.e., PCV) and the proportion of ORs in the opposite direction (POOR) statistics. Results For both outcomes, information on individual characteristics (Step 1) provide a low discriminatory accuracy (AUC = 0.616 for psychotropic drugs; = 0.600 for choosing a private GP). Accounting for neighbourhood of residence (Step 2) only improved the AUC for choosing a private GP (+0.295 units). High neighbourhood income (Step 3) was strongly associated to choosing a private GP (OR = 3.50) but the PCV was only 11% and the POOR 33%. Conclusion Applying an innovative stepwise multilevel analysis, we observed that, in Malmo, the neighbourhood context per se had a negligible influence on individual use of psychotropic drugs, but appears to strongly condition individual choice of a private GP. However, the latter was only modestly explained by the socioeconomic circumstances of the neighbourhoods. Our analyses are based on real data and provide useful information for understanding neighbourhood level influences in general and on individual use of psychotropic drugs and choice of GP in particular. However, our primary aim is to illustrate how to perform and interpret a multilevel analysis of individual heterogeneity in social epidemiology and public health. Our study shows that neighbourhood "effects" are not properly quantified by reporting differences between neighbourhood averages but rather by measuring the share of the individual heterogeneity that exists at the neighbourhood level.
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| 13. |
- Merlo, Juan, et al.
(författare)
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General and specific contextual effects in multilevel regression analyses and their paradoxical relationship : A conceptual tutorial
- 2018
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Ingår i: SSM - Population Health. - : Elsevier. - 2352-8273. ; 5, s. 33-37
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Tidskriftsartikel (refereegranskat)abstract
- To be relevant for public health, a context (e.g., neighborhood, school, hospital) should influence or affect the health status of the individuals included in it. The greater the influence of the shared context, the higher the correlation of subject outcomes within that context is likely to be. This intra-context or intra-class correlation is of substantive interest and has been used to quantify the magnitude of the general contextual effect (GCE). Furthermore, ignoring the intra-class correlation in a regression analysis results in spuriously narrow 95% confidence intervals around the estimated regression coefficients of the specific contextual variables entered as covariates and, thereby, overestimates the precision of the estimated specific contextual effects (SCEs). Multilevel regression analysis is an appropriate methodology for investigating both GCEs and SCEs. However, frequently researchers only report SCEs and disregard the study of the GCE, unaware that small GCEs lead to more precise estimates of SCEs so, paradoxically, the less relevant the context is, the easier it is to detect (and publish) small but "statistically significant" SCEs. We describe this paradoxical situation and encourage researchers performing multilevel regression analysis to consider simultaneously both the GCE and SCEs when interpreting contextual influences on individual health.
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| 14. |
- Merlo, Juan, et al.
(författare)
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Geographical and sociodemographic differences in statin dispensation after acute myocardial infarction in Sweden : a register-based prospective cohort study applying analysis of individual heterogeneity and discriminatory accuracy (AIHDA) for basic comparisons of healthcare quality
- 2023
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Ingår i: BMJ Open. - 2044-6055. ; 13:9, s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: In Sweden, as in many other countries, official monitoring of healthcare quality is mostly focused on geographical disparities in relation to a desirable benchmark. However, current evaluations could be improved by considering: (1) The intersection of other relevant axes of inequity like age, sex, income and migration status; and (2) The existence of individual heterogeneity around averages. Therefore, using an established quality indicator (ie, dispensation of statins after acute myocardial infarction, AMI), we valuate both geographical and sociodemographic inequalities and illustrate how the analysis of individual heterogeneity and discriminatory accuracy (AIHDA) enhances such evaluations.POPULATION AND METHODS: We applied AIHDA and calculated the area under the receiver operating characteristics curve (AUC) of regional and sociodemographic differences in the statin dispensations of 35 044 patients from 21 Swedish regions and 24 sociodemographic strata who were discharged from the hospital with an AMI diagnosis between January 2011 and December 2013. Following the Swedish National Board of Health and Welfare, we used a benchmark value of 90%.RESULTS: Dispensation of stains after AMI in Sweden did not reach the desired target of 90%. Regional differences were absent/very small (AUC=0.537) while sociodemographic differences were small (AUC=0.618). Women, especially those with immigrant background and older than 65 years, have the lowest proportions of statin dispensations after AMI.CONCLUSIONS: As the AUC statistics are small, interventions trying to achieve the benchmark value should be universal. However, special emphasis should nevertheless be directed towards women, especially older women with immigrant backgrounds.
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| 15. |
- Persmark, Anna, et al.
(författare)
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Intersectional inequalities and the U.S. opioid crisis : challenging dominant narratives and revealing heterogeneities
- 2019
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Ingår i: Critical Public Health. - : Taylor & Francis. - 0958-1596 .- 1469-3682. ; 30:4, s. 398-414
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Tidskriftsartikel (refereegranskat)abstract
- Dominant narratives of prescription opioid misuse (POM) in the U.S. have portrayed it as an issue primarily affecting White communities. In this study we explore POM as reported in data from the 2015 National Survey on Drug Use and Health, using an intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA). We map the risk of POM through a series of multilevel models with individuals (N = 43,409) nested within strata formed by the intersections of gender, race/ethnicity, income, and age. We find meaningful heterogeneity between and within strata. The ten strata with the greatest risk for POM were comprised of individuals identifying as White, African American, and non-White Hispanic, and included individuals of low, medium, and high income. We uncover intersections of social position with high risk for POM that are often excluded from dominant narratives, including young high-income African American women. Intersectional approaches are essential for advancing our understanding of health inequalities and unfolding epidemics such as that of POM in the U.S.
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| 16. |
- Persmark, Anna, et al.
(författare)
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Precision public health: mapping socioeconomic disparities in opioid dispensations at Swedish pharmacies by Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)
- 2019
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; , s. 1-21
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundIn light of the opioid epidemic in the United States, there is growing concern about the use of opioids in Sweden as it may lead to misuse and overuse and, in turn, severe public health problems. However, little is known about the distribution of opioid use across different demographic and socioeconomic dimensions in the Swedish general population. Therefore, we applied an intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA), to obtain an improved mapping of the risk heterogeneity of and socioeconomic inequalities in opioid prescription receipt. Methods and FindingsUsing data from 6,846,106 residents in Sweden aged 18 and above, we constructed 72 intersectional strata from combinations of gender, age, income, cohabitation status, and presence or absence of psychological distress. We modelled the absolute risk (AR) of opioid prescription receipt in a series of multilevel logistic regression models distinguishing between additive and interaction effects. By means of the Variance Partitioning Coefficient (VPC) and the area under the receiver operating characteristic curve (AUC), we quantified the discriminatory accuracy (DA) of the intersectional strata for discerning those who received opioid prescriptions from those who did not. The AR of opioid prescription receipt ranged from 2.77% (95% CI 2.69¬–2.86) among low-income men aged 18–34, living alone, without psychological distress, to 28.25% (95% CI 27.95–28.56) among medium-income women aged 65 and older, living alone, with psychological distress. In a model that conflated both additive and interaction effects, the intersectional strata had a fair DA for discerning opioid users from non-users (VPC=13.2%, AUC=0.68). However, in the model that decomposed total effects into additive and interaction effects, the VPC was very low (0.42%) indicating the existence of small interaction effects for a number of the intersectional strata. ConclusionsThe intersectional MAIHDA approach aligns with the aims of precision public health, through improving the evidence base for health policy by increasing understanding of both health inequalities and individual heterogeneity. This approach is particularly relevant for socioeconomically conditioned outcomes such as opioid prescription receipt. We have identified intersections of social position within the Swedish population at greater risk for opioid prescription receipt.
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| 17. |
- Rodriguez-Lopez, Merida, et al.
(författare)
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Cross-classified Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to evaluate hospital performance : the case of hospital differences in patient survival after acute myocardial infarction
- 2020
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Ingår i: BMJ Open. - : BMJ. - 2044-6055. ; 10:10
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: To describe a novel strategy, Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to evaluate hospital performance, by analysing differences in 30-day mortality after a first-ever acute myocardial infarction (AMI) in Sweden.DESIGN: Cross-classified study.SETTING: 68 Swedish hospitals.PARTICIPANTS: 43 247 patients admitted between 2007 and 2009, with a first-ever AMI.PRIMARY AND SECONDARY OUTCOME MEASURES: We evaluate hospital performance by analysing differences in 30-day mortality after a first-ever AMI using a cross-classified multilevel analysis. We classified the patients into 10 categories according to a risk score (RS) for 30-day mortality and created 680 strata defined by combining hospital and RS categories.RESULTS: In the cross-classified multilevel analysis the overall RS adjusted hospital 30-day mortality in Sweden was 4.78% and the between-hospital variation was very small (variance partition coefficient (VPC)=0.70%, area under the curve (AUC)=0.54). The benchmark value was therefore achieved by all hospitals. However, as expected, there were large differences between the RS categories (VPC=34.13%, AUC=0.77) CONCLUSIONS: MAIHDA is a useful tool to evaluate hospital performance. The benefit of this novel approach to adjusting for patient RS is that it allowed one to estimate separate VPCs and AUC statistics to simultaneously evaluate the influence of RS categories and hospital differences on mortality. At the time of our analysis, all hospitals in Sweden were performing homogeneously well. That is, the benchmark target for 30-day mortality was fully achieved and there were not relevant hospital differences. Therefore, possible quality interventions should be universal and oriented to maintain the high hospital quality of care.
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| 18. |
- Rodriguez-Lopez, Merida, et al.
(författare)
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Multilevel modelling for measuring interaction of effects between multiple categorical variables : An illustrative application using risk factors for preeclampsia
- 2023
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Ingår i: Paediatric and Perinatal Epidemiology. - : Wiley. - 0269-5022 .- 1365-3016. ; 37:2, s. 154-164
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Tidskriftsartikel (refereegranskat)abstract
- Background: Measuring multiple and higher-order interaction effects between multiple categorical variables proves challenging. Objectives: To illustrate a multilevel modelling approach to studying complex interactions. Methods: We apply a two-level random-intercept linear regression to a binary outcome for individuals (level-1) nested within strata (level-2) defined by all observed combinations of multiple categorical exposure variables. As a pedagogic application, we analyse 36 strata defined by five risk factors of preeclampsia (parity, previous preeclampsia, chronic hypertension, multiple pregnancies, body mass index category) among 652,603 women in the Swedish Medical Birth Registry between 2002 and 2010. Results: The absolute risk of preeclampsia was 4% but was predicted to vary from 1% to 44% across strata. The stratum discriminatory accuracy was 30% according to the variance partition coefficient (VPC) and 0.73 according to the area under the receiver operating characteristic curve (AUC). While the risk heterogeneity across strata was primarily due to the main effects of the categories defining the strata, 5% of the variation was attributable to their two- and higher-way interaction effects. One stratum presented a positive interaction, and two strata presented negative interaction. Conclusions: Multilevel modelling is an innovative tool for identifying and analysing higher-order interaction effects. Further work is needed to explore how this approach can best be applied to making causal inferences.
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| 19. |
- Zettermark, Sofia, et al.
(författare)
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Population heterogeneity in associations between hormonal contraception and antidepressant use in Sweden : a prospective cohort study applying intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)
- 2021
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Ingår i: BMJ Open. - : BMJ. - 2044-6055. ; 11, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVES: From a reproductive justice framework, we aimed to investigate how a possible association between hormonal contraceptive (HC) and antidepressants use (as a proxy for depression) is distributed across intersectional strata in the population. We aimed to visualise how intersecting power dynamics may operate in combination with HC use to increase or decrease subsequent use of antidepressants. Our main hypothesis was that the previously observed association between HC and antidepressants use would vary between strata, being more pronounced in more oppressed intersectional contexts. For this purpose, we applied an intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy approach.DESIGN: Observational prospective cohort study using record linkage of national Swedish registers.SETTING: The population of Sweden.PARTICIPANTS: All 915 954 women aged 12-30 residing in Sweden 2010, without a recent pregnancy and alive during the individual 1-year follow-up.PRIMARY OUTCOME MEASURE: Use of any antidepressant, meaning being dispensed at least one antidepressant (ATC: N06A) during follow-up.RESULTS: Previously mentally healthy HC users had an OR of 1.79 for use of antidepressants compared with non-users, whereas this number was 1.28 for women with previous mental health issues. The highest antidepressant use were uniformly found in strata with previous mental health issues, with highest usage in women aged 24-30 with no immigrant background, low income and HC use (51.4%). The largest difference in antidepressant use between HC users and non-users was found in teenagers, and in adult women of immigrant background with low income. Of the total individual variance in the latent propensity of using antidepressant 9.01% (healthy) and 8.16% (with previous mental health issues) was found at the intersectional stratum level.CONCLUSIONS: Our study suggests teenagers and women with immigrant background and low income could be more sensitive to mood effects of HC, a heterogeneity important to consider moving forward.
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