Differences in psychological treatment outcomes by ethnicity and gender: an analysis of individual patient data.

Purpose: There are discrepancies in mental health treatment outcomes between ethnic groups, which may differ between genders. NHS Talking Therapies for anxiety and depression provide evidence-based psychological therapies for common mental disorders. This study examines the intersection between ethnicity and gender as factors associated with psychological treatment outcomes. Aims were to explore by gender: (1) differences in psychological treatment outcomes for minoritized ethnic people compared to White-British people, (2) whether differences are observed when controlling for clinical and socio-demographic factors associated with outcomes, and (3) whether organization-level factors moderate differences in outcomes between ethnic groups.

Methods: Patient data from eight NHS Talking Therapies for anxiety and depression services (n = 98,063) was used to explore associations between ethnicity and outcomes, using logistic regression. Stratified subsamples were used to separately explore factors associated with outcomes for males and females.

Results: In adjusted analyses, Asian (OR = 0.82 [95% CI 0.78; 0.87], p < .001, 'Other' (OR = 0.79 [95%CI 0.72-0.87], p < .001) and White-other (0.93 [95%CI 0.89-0.97], p < .001) ethnic groups were less likely to reliably recover than White-British people. Asian (OR = 1.48 [95% CI 1.35-1.62], p < .001), Mixed (OR = 1.18 [95% CI 1.05-1.34], p = .008), 'Other' (OR = 1.60 [95% CI 1.38-1.84], p < .001) and White-other (OR = 1.18 [95% CI 1.09-1.28], p < .001) groups were more likely to experience a reliable deterioration in symptoms. Poorer outcomes for these groups were consistent across genders. There was some evidence of interactions between ethnic groups and organization-level factors impacting outcomes, but findings were limited.

Conclusions: Across genders, Asian, 'Other' and White-other groups experienced worse treatment outcomes across several measures in adjusted models. Reducing waiting times or offering more treatment sessions might lead to increased engagement and reduced drop-out for some patient groups.