Factors influencing general practitioners’ decisions in migrant patients with mental health disorder Socio-economic inequalities in mental health: a new framework and analysis across 113 countries

The identifies preventable hospitalizations as proxy of potentially low-quality of care. Previous studies showed as socioeconomic status was associated to poor diseases outcomes and to the performance of health services. Recently a particular attention was focused on the effect of the pandemic on this context. The aim of this research is to analyze the association between poor quality of primary care and socio-economic status before and during the pandemic. Methods: A retrospective observational study was conducted in Abruzzo Region, Italy. Hospital discharge records (HDR) of two different periods were selected: from April to December for 2019 and 2020. The aggregate Prevention Quality Indicator 90 (PQI-90) has been coded according to the indications of the AHRQ. The Italian socioeconomic deprivation index (DI), divided in quintiles (from 1st less deprived to the 5th most deprived) was attributed to all patient, based on the municipality of residence. A multivariate logistic regression model was performed to evaluate the association between PQI- 90 and DI. Results: Totally were analyzed 253,063 HDR, of which 14,845 attributable to the PQI-90. By correcting for gender, age and number of comorbidities, the DI was not associated with the PQI-90 during 2019. During 2020 the PQI-90 was associated to 4th DI quintile (aOR 1.19;95%CI 1.09-1.30) and 5th DI quintile (aOR 1.13; 95%CI 1.03-1.23), compared to the 1st quintile. Conclusions: The impact of the pandemic substantial. to the pre-pandemic era, the between quality Abruzzo. This evidence must be an interesting starting point for health planning in order to fight against inequalities in health services access. Key The in the primary care quality during pandemic to socioeconomic deprivation. Background: Socio-economic inequalities in common mental health disorders (CMDs) cut across each step in the cascade of care: (1) Less affluent individuals have a higher prevalence of CMDs, (2) are less likely to utilise treatment and (3) might benefit less from treatment when they do receive it. Here, we propose a new framework for distinguishing between these three types of inequalities in CMDs and test if such ‘triple inequalities’ exist globally and how they vary across countries. Background: Despite the potential of digital health tools for improving health outcomes, older adults are known to use digital health tools differently than younger adults. Focusing on needs of older populations is critical, as their numbers and proportions are projected to increase dramatically in the coming decades, both in Israel and in Taiwan. A bi-national collaboration was developed to map existing digital health resources available to older adults, as part of a larger study on digital health services use among older adults. Methods: A mapping tool was adapted from the WHO classification of digital health interventions, based on the experience in the Taiwanese and Israeli health systems. The areas included public health, prevention, self- monitoring and self-care information and services in primary and tertiary care. The mapping documented digital resources offered by governmental/ Ministry of Health, public primary care (HMOs), hospitals, and non-governmental organizations. Sources of information were institutional websites, evaluated by two specially trained reviewers for each organization who assigned a dichotomous value (yes/no) for each category. Interrater reliability was computed using a Kappa coefficient. The instrument included 17 categories and 44 sub-categories of digital resources, ranging from public health information for emergency situations to specific health service character- istics. To date, the Kappa coefficients range from 0.59-0.68 for NGO, MOH and hospital resources, considered substantial; for 3 HMOs, the values ranged from 0.41-0.49, considered moderate. The mapping tool adapted to the countries’ digital resources allowed for bi-national research to the countries’ experience. The next stage of the study will validate the results through expert interviews, followed by an end user survey with older adults to assess both reported use of services and enabling digital health literacy skills. Key messages: (cid:2) To meet the needs of aging populations, attention needs to be given to their engagement with digital health services and resources. (cid:2) Mapping digital health resources is essential for estimating how health needs are met nationally.


Background:
Patients with a migration background (MB) have more mental health disorders than those without migration background.
Yet, those patients are still underrepresented in mental healthcare services and have more unmet medical needs. Although providers' bias has been well studied, up to date, little is still known about the factors explaining those biases. We assessed the effect of general practitioners' (GPs') individual and organizational factors on their decisionmaking regarding diagnosis, treatment and referral recommendations for patient with MB with symptoms of major depression.

Methods:
An experimental study staged a video-vignette of a depressed patient with or without MB. GPs had to make decision about diagnosis, treatment and referral. We then assessed the influence of several factors on their decisions such as age, ethnicity, workload and patient confidence. ANOVA and MANOVA were used for analyses.

Results:
Overall, we found more unfavourable decisions in GPs diagnosis and treatment recommendations regarding the patient with a MB (F = 3.56, p < 0.001). In addition, they considered the symptoms of the patient with a MB as less severe (F = 7.68, p < 0.01) and would prescribe less often a medical treatment to these patients (F = 4.09, p < 0.05). Nevertheless, few factors explained these differences, except the age, the workload and the patient trustworthiness.

Conclusions:
This paper highlighted GPs biases based on apparent migration background of a patient with major depression that perpetuates ethnic inequalities in mental health care. Further research into the origins of discrimination in primary mental health care are needed to explain how and when those discriminations arise.

Key messages:
This paper shed light on pervasive unintentional discrimination still persist in mental health care among migrants in Europe. These findings may help us to further understand the role of general practitioner behaviour in primary mental health care discrimination.

Background:
Socio-economic inequalities in common mental health disorders (CMDs) cut across each step in the cascade of care: (1) Less affluent individuals have a higher prevalence of CMDs, (2) are less likely to utilise treatment and (3) might benefit less from treatment when they do receive it. Here, we propose a new framework for distinguishing between these three types of inequalities in CMDs and test if such 'triple inequalities' exist globally and how they vary across countries.

Methods:
We use the Wellcome Global Monitor 2020 (N = 119,088 in 113 countries) to test if socio-economic factors, psychological factors (stigma and trust) and country-level factors (GDP, GINI and health expenditure) predict CMD lifetime prevalence, utilisation and perceived helpfulness of talking therapy and medication. Multi-level logistic regression models were used.

Results:
As predicted, people with higher household income are less likely to experience anxiety or depression (OR = 0.90 for each income quintile, p < 0.01), more likely to talk to a mental health professional (OR = 1.05; OR = 1.34 for higher 15th European Public Health Conference 2022 education, p < 0.01) and more likely rate this treatment as very helpful (OR = 1.06, p = 0.02) across countries. In contrast, income is not linked with utilisation (OR = 0.99, p = 0.18) and helpfulness of 'taking medication' for CMDs (OR = 1.02, p = 0.26). In LMICs, the highly educated take less medication (OR = 0.74, p < 0.01). Local stigma reduces utilisation (OR = 0.95) and helpfulness of talking therapy (OR = 0.77), while trust in health practitioners increases both (OR = 1.07 util. and OR = 1.31 helpf., p < 0.01 in all cases).

Conclusions:
Three types of socio-economic inequalities for CMDs (in prevalence, talking therapy utilisation and helpfulness) deepen disadvantages for the less affluent across 113 countries. For pharmacological treatment, inequalities in utilisation and helpfulness are weaker and have a different social gradient in LMICs. Here, less educated people are more likely to take medication.

Key messages:
Three types of socio-economic inequalities in common mental health disorders (in prevalence, talking therapy utilisation and helpfulness) exacerbate disadvantages for less affluent individuals. These Inequalities in CMD treatment utilisation and helpfulness are stronger for talking therapies than for medication, and depend on country contexts, stigma and trust in health practitioners.

Background:
Despite the potential of digital health tools for improving health outcomes, older adults are known to use digital health tools differently than younger adults. Focusing on needs of older populations is critical, as their numbers and proportions are projected to increase dramatically in the coming decades, both in Israel and in Taiwan. A bi-national collaboration was developed to map existing digital health resources available to older adults, as part of a larger study on digital health services use among older adults.

Methods:
A mapping tool was adapted from the WHO classification of digital health interventions, based on the experience in the Taiwanese and Israeli health systems. The areas included public health, prevention, self-monitoring and self-care information and services in primary and tertiary care. The mapping documented digital resources offered by governmental/ Ministry of Health, public primary care (HMOs), hospitals, and non-governmental organizations. Sources of information were institutional websites, evaluated by two specially trained reviewers for each organization who assigned a dichotomous value (yes/no) for each category. Interrater reliability was computed using a Kappa coefficient.

Results:
The instrument included 17 categories and 44 sub-categories of digital resources, ranging from public health information for emergency situations to specific health service characteristics. To date, the Kappa coefficients range from 0.59-0.68 for NGO, MOH and hospital resources, considered substantial; for 3 HMOs, the values ranged from 0.41-0.49, considered moderate.

Conclusions:
The mapping tool adapted to the countries' digital resources allowed for bi-national research to compare/contrast the countries' experience. The next stage of the study will validate the results through expert interviews, followed by an end user survey with older adults to assess both reported use of services and enabling digital health literacy skills.

Key messages:
To meet the needs of aging populations, attention needs to be given to their engagement with digital health services and resources.
Mapping digital health resources is essential for estimating how health needs are met nationally.

Introduction:
Gonorrhoea is the second most commonly diagnosed sexually transmitted infection in England, and diagnoses among young women increased 31% between 2018 and 2019. Understanding the patterns of testing and diagnosis among young women is likely to aid prevention among the most vulnerable segments of this population.

Methods:
Data on gonorrhoea diagnoses at sexual health services among women aged 16-24 in England were obtained using the GUMCAD STI Surveillance System. We investigated the relationship between two exposure variables (deprivation and ethnicity), and two outcome variables (number of gonorrhoea tests and number of gonorrhoea diagnoses). Poisson regression was used to calculate rate ratios for the relationship between the exposure and outcome variables. The testing analysis was offset for the size of the population, and the diagnosis analysis was offset for the number of tests within the population.

Results:
Between 2012 and 2019, gonorrhoea testing and diagnosis rates were highest among women living in the most deprived areas. The rate of testing in the least deprived 10% of neighbourhoods was significantly lower than that seen in the most deprived 10% of neighbourhoods (rate ratio (RR) 0.79; 95% confidence interval 0.79 -0.80), and the rate of diagnosis in the least deprived 10% of neighbourhoods was around a third of that seen in the most deprived 10% of neighbourhoods (0.35; 0.33 -0.36). When compared to White British women, the rate