Bmc Med
-
Postpartum depression (PPD) constitutes a significant mental health disorder affecting almost one fifth of pregnancies globally. Despite extensive research, the precise etiological mechanisms underlying PPD remain elusive. However, several risk factors like genetic predisposition, hormonal fluctuations, and stress-related environmental and psychosocial triggers have been found to be implicated in its development. MAIN: Recently, an increased risk of PPD has been reported to be associated with gestational diabetes mellitus (GDM), which is characterized by the disruption of glucose metabolism, primarily attributed to the emergence of insulin resistance (IR). While IR during pregnancy seems to be an evolutionary adaptative mechanism to handle the profound metabolic alterations during pregnancy, its subsequent resolution following delivery necessitates a reconfiguration of the metabolic landscape in both peripheral tissues and the central nervous system (CNS). Considering the pivotal roles of energy metabolism, particularly glucose metabolism, in CNS functions, we propose a novel model that such pronounced changes in IR and the associated glucose metabolism seen postpartum might account for PPD development. This concept is based on the profound influences from insulin and glucose metabolism on brain functions, potentially via modulating neurotransmitter actions of dopamine and serotonin. Their sudden postpartum disruption is likely to be linked to mood changes, as observed in PPD. ⋯ The detailed pathogenesis of PPD might be multifactorial and still remains to be fully elucidated. Nevertheless, our hypothesis might account in part for an additional etiological factor to PPD development. If our concept is validated, it can provide guidance for future PPD prevention, diagnosis, and intervention.
-
The use of digital health technologies to measure outcomes in clinical trials opens new opportunities as well as methodological challenges. Digital outcome measures may provide more sensitive and higher-frequency measurements but pose vital statistical challenges around how such outcomes should be defined and validated and how trials incorporating digital outcome measures should be designed and analysed. ⋯ The impact of key issues highlighted by the eight questions on a primary analysis of a trial are illustrated through a simulation study based on the 2019 Bellerophon INOPulse trial which had time spent in MVPA as a digital outcome measure. These eight questions present broad areas where methodological guidance is needed to enable wider uptake of digital outcome measures in trials.
-
Meta Analysis
Strategies to improve the implementation of preventive care in primary care: a systematic review and meta-analysis.
Action on smoking, obesity, excess alcohol, and physical inactivity in primary care is effective and cost-effective, but implementation is low. The aim was to examine the effectiveness of strategies to increase the implementation of preventive healthcare in primary care. ⋯ Multicomponent interventions may be the most effective implementation strategy. There was no evidence that implementation interventions improved behavioural outcomes.
-
Although electronic alerts are being increasingly implemented in patients with acute kidney injury (AKI), their effect remains unclear. Therefore, we conducted this meta-analysis aiming at investigating their impact on the care and outcomes of AKI patients. ⋯ Electronic alerts increased the incidence of AKI and dialysis in AKI patients, which likely reflected improved recognition and early intervention. However, these changes did not improve the survival or kidney function of AKI patients. The findings warrant further research to comprehensively evaluate the impact of electronic alerts.
-
Including structural determinants (e.g. criminalisation, stigma, inequitable gender norms) in dynamic HIV transmission models is important to help quantify their population-level impacts and guide implementation of effective interventions that reduce the burden of HIV and inequalities thereof. However, evidence-based modelling of structural determinants is challenging partly due to a limited understanding of their causal pathways and few empirical estimates of their effects on HIV acquisition and transmission. ⋯ Mathematical models can play a crucial role in elucidating the population-level impacts of structural determinants and interventions on HIV. We recommend the next generation of models reflect exposure to structural determinants dynamically and mechanistically, and reproduce the key causal pathways, based on longitudinal evidence of links between structural determinants, mediators, and HIV. This would improve the validity and usefulness of predictions of the impacts of structural determinants and interventions.