Validity of a computerized population registry of dementia based on clinical databases

Socioeconomic and gender inequalities in mental disorders among adolescents and young adults

BACKGROUND

Socioeconomic status (SES) and gender play a key role in mental health. The objective of this study was to assess socioeconomic and gender mental health inequalities in adolescents and young adults using a population-based registry.

MATERIALS AND METHODS

We conducted a lifetime follow-up study of all residents in the Basque Country between 1 and 30 years old (n=609,381) as of 31 December 2018. Primary care, specialized outpatient, and hospital care records were searched for diagnoses. SES was assessed based on household income. We estimated disaggregated lifetime prevalence of substance use, behaviour, anxiety, depression, psychosis, and attention deficit hyperactivity disorder. The local Institute of Statistics validated the mortality data. The likelihood of risks was estimated using logistic regression.

RESULTS

Overall, 96,671 individuals (15.9%) had a diagnosed mental disorder, with clear gradients by gender and SES. Females of medium-to-high SES had the lowest prevalence of all mental disorders, except anxiety and depression. This group was followed by males of the same SES and females of low SES, while the highest prevalence of mental disorders was observed in low-SES males. The lower income categories had higher risks of psychiatric admission (adjusted odds ratio [AOR]: 3.64 for females; 6.66 for males) and death (AOR: 5.42). People with a mental health diagnosis had higher mortality (AOR: 2.38).

CONCLUSIONS

Our work evidenced important SES and gender inequalities in the mental health and premature mortality of adolescents and young adults, findings that should drive the development and implementation of early preventive interventions.

Comorbidity and household income as mediators of gender inequalities in dementia risk: a real-world data population study

BACKGROUND

Low household income (HI), comorbidities and female sex are associated with an increased risk of dementia. The aim of this study was to measure the mediating effect of comorbidity and HI on the excess risk due to gender in relation to the incidence and prevalence of dementia in the general population.

METHODS

A retrospective and observational study using real-world data analysed all people over 60 who were registered with the Basque Health Service in Gipuzkoa. The study measured HI level, the Charlson comorbidity index (CCI), age and sex. The prevalence and incidence of dementia were analysed using logistic regression and Poisson regression models, respectively, adjusted by HI, sex, comorbidity and age. We estimated the combined mediation effect of HI and comorbidity on the prevalence of dementia associated with gender.

RESULTS

Of the 221,777 individuals, 3.85% (8,549) had a diagnosis of dementia as of 31 December 2021. Classification by the CCI showed a gradient with 2.90% in CCI 0-1, 10.60% in CCI 2-3 and 18.01% in CCI > 3. Both low HI and gender were associated with a higher crude prevalence of dementia. However, in the CCI-adjusted model, women had an increased risk of dementia, while HI was no longer statistically significant. The incidence analysis produced similar results, although HI was not significant in any model. The CCI was significantly higher for men and for people with low HI. The mediation was statistically significant, and the CCI and HI explained 79% of the gender effect.

CONCLUSIONS

Comorbidity and low HI act as mediators in the increased risk of dementia associated with female sex. Given the difference in the prevalence of comorbidities by HI, individual interventions to control comorbidities could not only prevent dementia but also reduce inequalities, as the risk is greater in the most disadvantaged population.

Incidence of mental disorders in the general population aged 1-30 years disaggregated by gender and socioeconomic status

PURPOSE

The objective of this study was to estimate the incidence and age of onset of mental disorders diagnosed by gender and socioeconomic status (SES) in children, adolescents, and young adults up to 30 years of age in the whole population of the Basque Country (Spain).

METHODS

All mental health diagnoses documented in Basque Health Service records from 1 January 2003 to 31 December 2018, were classified into eight clusters: anxiety, attention deficit hyperactivity disorder (ADHD), conduct disorders, depression, psychosis/personality disorders, substance use, eating disorders, and self-harm. We calculated incidence and cumulative incidence for each cluster, disaggregated by gender, and socioeconomic status (SES). Poisson regression analyses were performed.

RESULTS

Overall, 9,486,853 person-years of observation were available for the 609,281 individuals included. ADHD and conduct disorders were diagnosed in the first decade, anxiety and depression disorders in the second and third decades, and psychosis/personality and substance use in the third. The cumulative incidence at 18 years of age for any type of disorder was 15.5%. The group with low SES had a statistically significantly higher incidence of all eight clusters. The incidence of ADHD, conduct disorders, depression, psychosis/personality disorders, and substance use was higher in males and that of anxiety, eating disorders and self-harm was higher in females.

CONCLUSIONS

The incidence of mental disorders is high among children, adolescents, and young adults in the Basque Country underlining the need for preventive interventions. Marked differences by gender and SES highlight mental health inequalities, especially for depression and psychosis in low SES males.

Changes in Social and Clinical Determinants of COVID-19 Outcomes Achieved by the Vaccination Program: A Nationwide Cohort Study

BACKGROUND

The objective of this study was to assess changes in social and clinical determinants of COVID-19 outcomes associated with the first year of COVID-19 vaccination rollout in the Basque population.

METHODS

A retrospective study was performed using the complete database of the Basque Health Service (n = 2,343,858). We analyzed data on age, sex, socioeconomic status, the Charlson comorbidity index (CCI), hospitalization and intensive care unit (ICU) admission, and COVID-19 infection by Cox regression models and Kaplan-Meier curves.

RESULTS

Women had a higher hazard ratio (HR) of infection (1.1) and a much lower rate of hospitalization (0.7). With older age, the risk of infection fell, but the risks of hospitalization and ICU admission increased. The higher the CCI, the higher the risks of infection and hospitalization. The risk of infection was higher in high-income individuals in all periods (HR = 1.2-1.4) while their risk of hospitalization was lower in the post-vaccination period (HR = 0.451).

CONCLUSION

Despite the lifting of many control measures during the second half of 2021, restoring human mobility patterns, the situation could not be defined as syndemic, clinical determinants seeming to have more influence than social ones on COVID-19 outcomes, both before and after vaccination program implementation.

Dementia Risk Score for a Population in Southern Europe Calculated Using Competing Risk Models

Background:

Dementia prevention can be addressed if the intervention is applied early.

Objective:

The objective of this study was to develop and validate competing risk models to predict the late risk of dementia based on variables assessed in middle age in a southern European population.

Methods:

We conducted a prospective observational study of the EPIC-Spain cohort that included 25,015 participants. Dementia cases were identified from electronic health records and validated by neurologists. Data were gathered on sociodemographic characteristics and cardiovascular risk factors. To stratify dementia risk, Fine and Gray competing risk prediction models were constructed for the entire sample and for over-55-year-olds. Risk scores were calculated for low (the 30% of the sample with the lowest risk), moderate (> 30% –60%), and high (> 60% –100%) risk.

Results:

The 755 cases of dementia identified represented a cumulative incidence of 3.1% throughout the study period. The AUC of the model for over-55-year-olds was much higher (80.8%) than the overall AUC (68.5%) in the first 15 years of follow-up and remained that way in the subsequent follow-up. The weight of the competing risk of death was greater than that of dementia and especially when the entire population was included.

Conclusion:

This study presents the first dementia risk score calculated in a southern European population in mid-life and followed up for 20 years. The score makes it feasible to achieve the early identification of individuals in a southern European population who could be targeted for the prevention of dementia based on the intensive control of risk factors.

Validation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Data

BACKGROUND

Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated.

OBJECTIVE

The objective of the study was to validate predictive models to separately identify psychotic and depressive symptoms in patients diagnosed with dementia using clinical databases representing the whole population to inform decision-makers.

METHODS

First, we searched the electronic health records of 4,003 patients with dementia to identify NPS. Second, machine learning (random forest) algorithms were applied to build separate predictive models for psychotic and depressive symptom clusters in the training set (N = 3,003). Third, calibration and discrimination were assessed in the test set (N = 1,000) to assess the performance of the models.

RESULTS

Neuropsychiatric symptoms were noted in the electronic health record of 58% of patients. The area under the receiver operating curve reached 0.80 for the psychotic cluster model and 0.74 for the depressive cluster model. The Kappa index and accuracy also showed better discrimination in the psychotic model. Calibration plots indicated that both types of model had less predictive accuracy when the probability of neuropsychiatric symptoms was <25%. The most important variables in the psychotic cluster model were use of risperidone, level of sedation, use of quetiapine and haloperidol and the number of antipsychotics prescribed. In the depressive cluster model, the most important variables were number of antidepressants prescribed, escitalopram use, level of sedation, and age.

CONCLUSION

Given their relatively good performance, the predictive models can be used to estimate prevalence of NPS in population databases.