Background: For many chronic conditions, lower socioeconomic status is associated with higher rates of disease. Previous research has not investigated whether this inverse relationship exists for Parkinson's disease (PD). The purpose was to investigate the association between socioeconomic status and prevalence and incidence of PD.
Methods: The study was conducted using population-based administrative data from Manitoba, Canada. PD cases were identified from diagnoses in hospital and physician records. Area-level census data on average household income and residential postal codes in health insurance registration files were used to assign PD cases to urban and rural income quintiles. Annual adjusted prevalence and incidence estimates were calculated for fiscal years 1987/88-2006/07. Hypotheses about differences between quintiles in average estimates and average rates of change were tested using generalised linear models with generalised estimating equations.
Results: The estimated prevalence of PD increased over the 20-year-period but incidence remained unchanged. In urban regions, average prevalence and incidence estimates were significantly higher for the lowest income quintile than the highest quintile. In rural regions, average prevalence estimates were significantly higher for the lowest quintile than for the highest quintile but incidence estimates were not significantly different. The annual rate of increase in the PD prevalence was significantly different for the lowest urban and rural income quintiles.
Conclusions: There is a greater burden of PD in low-income areas. An understanding of socioeconomic inequalities is useful when formulating hypotheses about factors associated with disease onset and developing equity-oriented policies about access to healthcare resources.
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http://dx.doi.org/10.1136/jech.2008.084954 | DOI Listing |
J Neurosci Res
January 2025
Department of Psychology, University of Regensburg, Regensburg, Germany.
Anxiety and depression disorders show high prevalence rates, and stress is a significant risk factor for both. However, studies investigating the interplay between anxiety, depression, and stress regulation in the brain are scarce. The present manuscript included 124 law students from the LawSTRESS project.
View Article and Find Full Text PDFObjectives: This report describes changes in total, early, and late fetal mortality between 2022 and 2023 (provisional), as well as fetal mortality by maternal race and Hispanic origin and state of residence. Comparisons are made with findings from 2021 to 2022.
Methods: Data are based on reports of fetal death filed in the 50 states and the District of Columbia and collected via the National Vital Statistics System.
J Virol
January 2025
Institute for Medical Virology and Epidemiology of Viral Diseases, University Hospital Tübingen, Tübingen, Germany.
One key determinant of HIV-1 latency reversal is the activation of the viral long terminal repeat (LTR) by cellular transcription factors such as NF-κB and AP-1. Interestingly, the activity of these two transcription factors can be modulated by glucocorticoid receptors (GRs). Furthermore, the HIV-1 genome contains multiple binding sites for GRs.
View Article and Find Full Text PDFHum Vaccin Immunother
December 2025
Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, PR China.
Hepatitis B (Hep B) remains a critical public health issue globally, particularly in Tibet, where vaccination rates and influencing factors among college students are yet understudied. This study applies a cross-sectional design to investigate the Hep B vaccination rate among 1,126 college students in Tibet and utilizes the expanded theory of planned behavior (ETPB) to identify vaccination behavior intention (BI) and vaccination behavior (VB). Stratified cluster sampling across three universities was used to assess behavioral attitudes (BA), subjective norms (SN), perceived behavioral control (PBC), past vaccination history (PVH) and vaccination knowledge (VK), and used structural equation modeling (SEM) for model validation and multi-group comparison.
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