Mapping socio-geographical disparities in the occurrence of teenage maternity in Colombia using multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA).

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.

Multilevel modelling for measuring interaction of effects between multiple categorical variables: An illustrative application using risk factors for preeclampsia.

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.

Antidepressant use in Sweden: an intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA).

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.

The Early Social Cognition Inventory (ESCI): An examination of its psychometric properties from birth to 47 months.

Social cognition refers to a broad range of cognitive processes and skills that allow individuals to interact with and understand others, including a variety of skills from infancy through preschool and beyond, e.g., joint attention, imitation, and belief understanding. However, no measures examine socio-cognitive development from birth through preschool. Current test batteries and parent-report measures focus either on infancy, or toddlerhood through preschool (and beyond). We report six studies in which we developed and tested a new 21-item parent-report measure of social cognition targeting 0-47 months: the Early Social Cognition Inventory (ESCI). Study 1 (N = 295) revealed the ESCI has excellent internal reliability, and a two-factor structure capturing social cognition and age. Study 2 (N = 605) also showed excellent internal reliability and confirmed the two-factor structure. Study 3 (N = 84) found a medium correlation between the ESCI and a researcher-administered social cognition task battery. Study 4 (N = 46) found strong 1-month test-retest reliability. Study 5 found longitudinal stability (6 months: N = 140; 12 months: N = 39), and inter-observer reliability between parents (N = 36) was good, and children's scores increased significantly over 6 and 12 months. Study 6 showed the ESCI was internally reliable within countries (Australia, Canada, United Kingdom, United States, Trinidad and Tobago); parent ethnicity; parent education; and age groups from 4-39 months. ESCI scores positively correlated with household income (UK); children with siblings had higher scores; and Australian parents reported lower scores than American, British, and Canadian parents.

The Early Humor Survey (EHS): A reliable parent-report measure of humor development for 1- to 47-month-olds.

We created a 20-item parent-report measure of humor development from 1 to 47 months: the Early Humor Survey (EHS). We developed the EHS with Study 1 (N = 219) using exploratory factor analysis, demonstrating the EHS works with 1- to 47-month-olds with excellent reliability and a strong correlation with age, showing its developmental trajectory. We replicated the EHS with Study 2 (N = 587), revealing a one-factor structure, showing excellent reliability, and replicating a strong correlation with age. Study 3 (N = 84) found the EHS correlated with a humor experiment, however it no longer correlated once age was accounted for, suggesting low convergent validity. Subsamples of parents from Studies 2 and 3 showed excellent inter-observer reliability between both parents, and good longitudinal stability after 6 months. Combining participants from all studies, we found the EHS is reliable across countries (Australia, United Kingdom, United States), parent education levels, and children's age groups. We charted expected humor development by age (in months), and the expected proportion of children who would appreciate each humor type by age (in months). Finally, we found no demographic differences (e.g., country: Australia, Canada, United Kingdom, United States; parents' education) in humor when pooling all data. The EHS is a valuable tool that will allow researchers to understand how humor: (1) emerges; and (2) affects other aspects of life, e.g., making friends, coping with stress, and creativity. The EHS is helpful for parents, early years educators, and children's media, as it systematically charts early humor development.

Joint Modeling of Individual Trajectories, Within-Individual Variability, and a Later Outcome: Systolic Blood Pressure Through Childhood and Left Ventricular Mass in Early Adulthood.

Within-individual variability of repeatedly measured exposures might predict later outcomes (e.g., blood pressure (BP) variability (BPV) is an independent cardiovascular risk factor above and beyond mean BP). Because 2-stage methods, known to introduce bias, are typically used to investigate such associations, we introduce a joint modeling approach, examining associations of mean BP and BPV across childhood with left ventricular mass (indexed to height; LVMI) in early adulthood with data (collected 1990-2011) from the UK Avon Longitudinal Study of Parents and Children cohort. Using multilevel models, we allowed BPV to vary between individuals (a "random effect") as well as to depend on covariates (allowing for heteroskedasticity). We further distinguished within-clinic variability ("measurement error") from visit-to-visit BPV. BPV was predicted to be greater at older ages, at higher body weights, and in female participants and was positively correlated with mean BP. BPV had a weak positive association with LVMI (10% increase in within-individual BP variance was predicted to increase LVMI by 0.21%, 95% credible interval: -0.23, 0.69), but this association became negative (-0.78%, 95% credible interval: -2.54, 0.22) once the effect of mean BP on LVMI was adjusted for. This joint modeling approach offers a flexible method of relating repeatedly measured exposures to later outcomes.

Comparing a multivariate response Bayesian random effects logistic regression model with a latent variable item response theory model for provider profiling on multiple binary indicators simultaneously.

Provider profiling entails comparing the performance of hospitals on indicators of quality of care. Many common indicators of healthcare quality are binary (eg, short-term mortality, use of appropriate medications). Typically, provider profiling examines the variation in each indicator in isolation across hospitals. We developed Bayesian multivariate response random effects logistic regression models that allow one to simultaneously examine variation and covariation in multiple binary indicators across hospitals. Use of this model allows for (i) determining the probability that a hospital has poor performance on a single indicator; (ii) determining the probability that a hospital has poor performance on multiple indicators simultaneously; (iii) determining, by using the Mahalanobis distance, how far the performance of a given hospital is from that of an average hospital. We illustrate the utility of the method by applying it to 10 881 patients hospitalized with acute myocardial infarction at 102 hospitals. We considered six binary patient-level indicators of quality of care: use of reperfusion, assessment of left ventricular ejection fraction, measurement of cardiac troponins, use of acetylsalicylic acid within 6 hours of hospital arrival, use of beta-blockers within 12 hours of hospital arrival, and survival to 30 days after hospital admission. When considering the five measures evaluating processes of care, we found that there was a strong correlation between a hospital's performance on one indicator and its performance on a second indicator for five of the 10 possible comparisons. We compared inferences made using this approach with those obtained using a latent variable item response theory model.

Identifying Critical Points of Trajectories of Depressive Symptoms from Childhood to Young Adulthood.

Depression is a common mental illness and research has focused on late childhood and adolescence in an attempt to prevent or reduce later psychopathology and/or social impairments. It is important to establish and study population-averaged trajectories of depressive symptoms across adolescence as this could characterise specific changes in populations and help identify critical points to intervene with treatment. Multilevel growth-curve models were used to explore adolescent trajectories of depressive symptoms in 9301 individuals (57% female) from the Avon Longitudinal Study of Parents and Children, a UK based pregnancy cohort. Trajectories of depressive symptoms were constructed for males and females using the short mood and feelings questionnaire over 8 occasions, between 10 and 22 years old. Critical points of development such as age of peak velocity for depressive symptoms (the age at which depressive symptoms increase most rapidly) and the age of maximum depressive symptoms were also derived. The results suggested that from similar initial levels of depressive symptoms at age 11, females on average experienced steeper increases in depressive symptoms than males over their teenage and adolescent years until around the age of 20 when levels of depressive symptoms plateaued and started to decrease for both sexes. Females on average also had an earlier age of peak velocity of depressive symptoms that occurred at 13.5 years, compared to males who on average had an age of peak velocity at 16 years old. Evidence was less clear for a difference between the ages of maximum depressive symptoms which were on average 19.6 years for females and 20.4 for males. Identifying critical periods for different population subgroups may provide useful knowledge for treating and preventing depression and could be tailored to be time specific for certain groups. Possible explanations and recommendations are discussed.

Measures of clustering and heterogeneity in multilevel Poisson regression analyses of rates/count data.

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.

Protecting energy intakes against income shocks.

Whether and how changes in economic circumstances or household income affect individuals' diet and nutritional intakes is of substantial interest for policy purposes. This paper exploits a period of substantial income volatility in Russia to examine the extent to which, as well as how individuals protect their energy intakes in the face of unanticipated shocks to household income. Using rich data from the Russia Longitudinal Monitoring Survey, our results suggest that households use substitution, disproportionally cutting back spending on non-foods to protect spending on foods, change the composition of the consumption basket, and increase the consumption of 'cheaper' calories. Taken together, however, we find that total energy intakes as well as the nutritional composition of the diet are almost fully protected against income shocks. Specifically, we find that 12-16% of the effect of permanent income shocks on food expenditures is transmitted to energy intakes, with 84-88% protected through insurance mechanisms.

Orthodontic pain trajectories in adolescents: Between-subject and within-subject variability in pain perception.

INTRODUCTION: The objective of this study was to assess the effects of age, sex, and the age-sex interaction on mean pain trajectories and individual variations in the pain experienced by adolescents after orthodontic separator placement. METHODS: We included 115 subjects (mean age, 14.99 years; SD, ±1.90 years; 56 boys, 48.7%; 59 girls, 51.3%) in this study. Orthodontic separators were placed in the mesial and distal contact points of the maxillary and mandibular first molars. A 100-mm visual analog scale was used for pain assessment at 11 prespecified times: 1 hour and 2, 4, 12, 24, 36, 48, 72, 96, 120, and 144 hours. A mixed-effects location scale model was used for the data analysis to directly model between-subject and within-subject variabilities in pain in addition to the usual modeling of mean pain as a function of age, sex, and time. RESULTS: Mean initial pain after 1 hour of separator placement for the 12- to 15-year-old male group was 13.52 mm on the visual analog scale, which initially increased rapidly (linear estimate, 9.16; P = 0.000; 95% confidence interval [CI], -8.65 to 9.67) but decelerated with time (quadratic estimate, -0.95; P = 0.000; 95% CI, -1.0 to -0.90), suggesting an inverted U-shaped mean pain trajectory. Age, sex, and age-sex interaction effects did not significantly influence initial pain. Compared with the 12- to 15-year-old male group, the 15- to 18-year-old female group reported the steepest rise in pain (estimate, 8.55; P = 0.00; 95% CI, 7.40 to 9.70) and, as a result, experienced the most overall pain. The 12- to 15-year-old male group reported minimum between-subjects variations (SD, ±4.6 mm) as well as within-subjects variations (SD, ±5.5 mm). The between-subjects variations were highest for the 12- to 15-year-old female group (SD, ±9.8 mm), whereas the within-subjects variations were highest for the 15- to 18-year-old female group (SD, ±10.1 mm). CONCLUSIONS: The 12- to 15-year-old boys reported the lowest mean average pain intensity and a minimum subjective variation in between-subject and within-subject variances. The 15- to 18-year-old girls experienced maximum mean pain intensity and the highest daily fluctuations in pain intensity. The 12- to 15-year-old girls were the most different from one another in their overall pain experience.

Multilevel modeling analysis of dyadic network data with an application to Ye'kwana food sharing.

Behavioral ecologists have recently begun using multilevel modeling for the analysis of social behavior. We present a multilevel modeling formulation of the Social Relations Model that is well suited for the analysis of dyadic network data. This model, which we adapt for count data and small datasets, can be fitted using standard multilevel modeling software packages. We illustrate this model with an analysis of meal sharing among Ye'kwana horticulturalists in Venezuela. In this setting, meal sharing among households is predicted by an association index, which reflects the amount of time that members of the households are interacting. This result replicates recent findings that interhousehold food sharing is especially prevalent among households that interact and cooperate in multiple ways. We discuss opportunities for human behavioral ecologists to expand their focus to the multiple currencies and cooperative behaviors that characterize interpersonal relationships in preindustrial societies. We discuss possible extensions to this statistical modeling approach and applications to research by human behavioral ecologists and primatologists.

Extending intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to study individual longitudinal trajectories, with application to mental health in the UK.

The intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) approach is gaining prominence in health sciences and beyond, as a robust quantitative method for identifying intersectional inequalities in a range of individual outcomes. However, it has so far not been applied to longitudinal data, despite the availability of such data, and growing recognition that intersectional social processes and determinants are not static, unchanging phenomena. Drawing on intersectionality and life course theories, we develop a longitudinal version of the intersectional MAIHDA approach, allowing the analysis not just of intersectional inequalities in static individual differences, but also of life course trajectories. We discuss the conceptualization of intersectional groups in this context: how they are changeable over the life course, appropriate treatment of generational differences, and relevance of the age-period-cohort identification problem. We illustrate the approach with a study of mental health using United Kingdom Household Longitudinal Study data (2009-2021). The results reveal important differences in trajectories between generations and intersectional strata, and show that trajectories are partly multiplicative but mostly additive in their intersectional inequalities. This article provides an important and much needed methodological contribution, enabling rigorous quantitative, longitudinal, intersectional analyses in social epidemiology and beyond.

Local government spending and mental health: Untangling the impacts using a dynamic modelling approach.

This study investigated the impact of local government spending on mental health in England between 2013 and 2019. Guided by the "Health in All Policies" vision, which encourages the integration of health in all decision-making areas, we explored how healthcare and multiple nonmedical budgeting decisions related to population mental health. We used random curve general cross-lagged modelling to dynamically partition effects into the short-run (from t to t + 1) and long-run (from t to t + 2) impacts, account for unobserved area-level heterogeneity and reverse causality from health outcomes to financial investments, and comprehensive modelling of budget items as an interconnected system. Our findings revealed that spending in adult social care, healthcare, and law & order predicted long-term mental health gains (0.004-0.081 SDs increase for each additional 10% in expenditure). However, these sectors exhibited negative short-term impulses (0.012-0.077 SDs decrease for each additional 10% in expenditure), markedly offsetting the long-term gains. In turn, infrastructural and environmental spending related to short-run mental health gains (0.005-0.031 SDs increase for each additional 10% in expenditure), while the long-run effects were predominantly negative (0.005-0.028 SDs decrease for each additional 10% in expenditure). The frequent occurrence of short-run and long-run negative links suggested that government resources may not be effectively reaching the areas that are most in need. In the short-term, negative effects could also imply temporary disruptions to service delivery largely uncompensated by later mental health improvements. Nonetheless, some non-health spending policies, such as law & order and infrastructure, can be related to long-lasting positive mental health impacts.

A dynamic social relations model for clustered longitudinal dyadic data with continuous or ordinal responses.

Social relations models allow the identification of cluster, actor, partner, and relationship effects when analysing clustered dyadic data on interactions between individuals or other units of analysis. We propose an extension of this model which handles longitudinal data and incorporates dynamic structure, where the response may be continuous, binary, or ordinal. This allows the disentangling of the relationship effects from temporal fluctuation and measurement error and the investigation of whether individuals respond to their partner's behaviour at the previous observation. We motivate and illustrate the model with an application to Canadian data on pairs of individuals within families observed working together on a conflict discussion task.

A tutorial for conducting intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA).

Intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA) is an innovative approach for investigating inequalities, including intersectional inequalities in health, disease, psychosocial, socioeconomic, and other outcomes. I-MAIHDA and related MAIHDA approaches have conceptual and methodological advantages over conventional single-level regression analysis. By enabling the study of inequalities produced by numerous interlocking systems of marginalization and oppression, and by addressing many of the limitations of studying interactions in conventional analyses, intersectional MAIHDA provides a valuable analytical tool in social epidemiology, health psychology, precision medicine and public health, environmental justice, and beyond. The approach allows for estimation of average differences between intersectional strata (stratum inequalities), in-depth exploration of interaction effects, as well as decomposition of the total individual variation (heterogeneity) in individual outcomes within and between strata. Specific advice for conducting and interpreting MAIHDA models has been scattered across a burgeoning literature. We consolidate this knowledge into an accessible conceptual and applied tutorial for studying both continuous and binary individual outcomes. We emphasize I-MAIHDA in our illustration, however this tutorial is also informative for understanding related approaches, such as multicategorical MAIHDA, which has been proposed for use in clinical research and beyond. The tutorial will support readers who wish to perform their own analyses and those interested in expanding their understanding of the approach. To demonstrate the methodology, we provide step-by-step analytical advice and present an illustrative health application using simulated data. We provide the data and syntax to replicate all our analyses.

Profiles of vulnerability for suicide and self-harm in UK prisoners: Neurodisability, mood disturbance, substance use, and bullying.

Screening for vulnerability factors associated with historic suicidality and self-harm on entry to prison is critical to help prisons understand how to allocate extremely limited mental health resources. It has been established that having previous suicide attempts increases odds of future suicidality and self-harm in prison. We utilised administrative screening data from 665 adult male prisoners on entry to a category B prison in Wales, UK, collected using the Do-IT Profiler. This sample represents 16% of all prisoners who entered that prison during a 26-month period. 12% of prisoners reported a history of attempted suicide, 11% reported historic self-harm, and 8% reported a history of both. Historic traumatic brain injury and substance use problems were associated with a 3.3- and 1.9- times increased odds of a historic suicide attempt, respectively, but no significant increased risk of historic self-harm (95% CI: 1.51-6.60 and 1.02-3.50). However, those who were bullied at school had 2.7 times increased odds of reporting a history of self-harm (95% CI: 1.63-6.09). The most salient risk factors associated with both historic suicide and self-harm were higher levels of functional neurodisability (odds ratio 0.6 for a 1 standard deviation change in score, 95% CI: 0.35-0.75), and mood disturbance (odds ratio 2.1 for a 1 standard deviation change in score, 95% CI: 1.26-3.56). Therefore, it could be beneficial for prisons to screen for broader profiles of needs, to better understand how to provide appropriate services to prisoners vulnerable to suicide and self-harm. Multidisciplinary care pathways for prisoner mental health interventions are important, to account for complex multimorbidity. Adaptations may be needed for mental health interventions to be appropriate for, for example, a prisoner with a brain injury. Understanding this broad profile of vulnerability could also contribute to more compassionate responses to suicide and self-harm from prison staff.

Clarifications on the intersectional MAIHDA approach: A conceptual guide and response to Wilkes and Karimi (2024).

Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) has been welcomed as a new gold standard for quantitative evaluation of intersectional inequalities, and it is being rapidly adopted across the health and social sciences. In their commentary "What does the MAIHDA method explain?", Wilkes and Karimi (2024) raise methodological concerns with this approach, leading them to advocate for the continued use of conventional single-level linear regression models with fixed-effects interaction parameters for quantitative intersectional analysis. In this response, we systematically address these concerns, and ultimately find them to be unfounded, arising from a series of subtle but important misunderstandings of the MAIHDA approach and literature. Since readers new to MAIHDA may share confusion on these points, we take this opportunity to provide clarifications. Our response is organized around four important clarifications: (1) At what level are the additive main effect variables defined in intersectional MAIHDA models? (2) Do MAIHDA models have problems with collinearity? (3) Why does the Variance Partitioning Coefficient (VPC) tend to be small, and the Proportional Change in Variance (PCV) tend to be large in MAIHDA? and (4) What are the goals of MAIHDA analysis?

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.

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.

Looked after children in prison as adults: life adversity and neurodisability.

PURPOSE: Looked after children (LAC) are criminalised at five times the rate of children in the general population. Children in contact with both child welfare and child justice systems have higher rates of neurodisability and substance use problems, and LAC in general have high rates of school exclusion, homelessness and unemployment. This study aims to understand whether these factors persist in LAC who are in prison as adults. DESIGN/METHODOLOGY/APPROACH: Administrative data collected by the Do-IT profiler screening tool in a prison in Wales, UK, were analysed to compare sentenced prisoners who were LAC (n = 631) to sentenced prisoners who were not LAC (n = 2,201). The sample comprised all prisoners who were screened on entry to prison in a two-year period. FINDINGS: Prisoners who were LAC scored more poorly on a functional screener for neurodisability (effect size = 0.24), and on four self-report measures capturing traits of dyslexia (0.22), attention-deficit hyperactivity disorder (0.40), autism spectrum disorders (0.34) and developmental co-ordination disorder (0.33). Prisoners who were LAC were more likely to have been to a pupil referral unit (0.24), have substance use problems (0.16), be homeless or marginally housed (0.18) and be unemployed or unable to work due to disability (0.13). ORIGINALITY/VALUE: This study uniquely contributes to our understanding of prisoners who were LAC as a target group for intervention and support with re-integration into the community upon release. LAC in prison as adults may require additional interventions to help with employment, housing and substance use. Education programmes in prison should screen for neurodisability, to develop strategies to support engagement.