We developed a real-world evidence (RWE) based Markov model to project the 10-year cost of care for patients with depression from the public payer’s perspective to inform early policy and resource planning in Hong Kong.

The model considered treatment-resistant depression (TRD) and development of comorbidities along the disease course. The outcomes included costs for all-cause and psychiatric care. From our territory-wide electronic medical records, we identified 25,190 patients with newly diagnosed depression during the period from 2014 to 2016, with follow-up until December 2020 for real-world time-to-event patterns. Costs and time varying transition inputs were derived using negative binomial and parametric survival modeling. The model is available as a closed cohort, which studies a fixed cohort of incident patients, or an open cohort that introduces new patients every year. Utilities values and the number of incident cases per year were derived from published sources.

There were 9,217 new patients with depression in 2023. Our closed cohort model projected that the cumulative cost of all-cause and psychiatric care for these patients would reach USD309 million and USD58 million by 2032, respectively. In our open cohort model, 55,849 to 57,896 active prevalent cases would cost more than USD322 million and USD61 million annually in all-cause and psychiatric care, respectively. Although less than 20 percent of patients would develop TRD or its associated comorbidities, they contribute 31 to 54 percent of the costs. The key cost drivers were the number of annual incident cases and the probability of developing TRD and associated comorbidities and of becoming a low-intensity service user. These factors are relevant to early disease stages.

A small proportion of patients with depression develop TRD, but they contribute to a high proportion of the care costs. Our projection also demonstrates the application of RWE to model the long-term costs of care, which can aid policymakers in anticipating foreseeable burden and undertaking budget planning to prepare for future care needs.

Despite reports of an elevated risk of breast cancer associated with antipsychotic use in women, existing evidence remains inconclusive. We aimed to examine existing observational data in the literature and determine this hypothesised association.

We searched Embase, PubMed and Web of Science™ databases on 27 January 2022 for articles reporting relevant cohort or case-control studies published since inception, supplemented with hand searches of the reference lists of the included articles. Quality of studies was assessed using the Newcastle-Ottawa Scale. We generated the pooled odds ratio (OR) and pooled hazard ratio (HR) using a random-effects model to quantify the association. This study was registered with PROSPERO (CRD42022307913).

Nine observational studies, including five cohort and four case-control studies, were eventually included for review (N = 2 031 380) and seven for meta-analysis (N = 1 557 013). All included studies were rated as high-quality (seven to nine stars). Six studies reported a significant association of antipsychotic use with breast cancer, and a stronger association was reported when a greater extent of antipsychotic use, e.g. longer duration, was operationalised as the exposure. Pooled estimates of HRs extracted from cohort studies and ORs from case-control studies were 1.39 [95% confidence interval (CI) 1.11–1.73] and 1.37 (95% CI 0.90–2.09), suggesting a moderate association of antipsychotic use with breast cancer.

Antipsychotic use is moderately associated with breast cancer, possibly mediated by prolactin-elevating properties of certain medications. This risk should be weighed against the potential treatment effects for a balanced prescription decision.

This study examines the individual and combined association of BMI and waist-to-hip ratio (WHR) with CVD risk using genetic scores of the obesity measurements as proxies.

A 2 × 2 factorial analysis approach was applied, with participants divided into four groups of lifetime exposure to low BMI and WHR, high BMI, high WHR, and high BMI and WHR based on weighted genetic risk scores. The difference in CVD risk across groups was evaluated using multivariable logistic regression.

Cohort study.

A total of 408 003 participants were included from the prospective observational UK Biobank study.

A total of 58 429 CVD events were recorded. Compared to the low BMI and WHR genetic scores group, higher BMI or higher WHR genetic scores were associated with an increase in CVD risk (high WHR: OR, 1·07; 95 % CI (1·04, 1·10)); high BMI: OR, 1·12; 95 % CI (1·09, 1·16). A weak additive effect on CVD risk was found between BMI and WHR (high BMI and WHR: OR, 1·16; 95 % CI (1·12, 1·19)). Subgroup analysis showed similar patterns between different sex, age (<65, ≥65 years old), smoking status, Townsend deprivation index, fasting glucose level and medication uses, but lower systolic blood pressure was associated with higher CVD risk in obese participants.

High BMI and WHR were associated with increased CVD risk, and their effects are weakly additive. Even though there were overlapping of effect, both BMI and WHR are important in assessing the CVD risk in the general population.