Introduction: The clinical course of LBP is complex and chronicity is more frequent than once thought. Moreover, insufficient evidence was found in support of any specific approach at the level of the general population.
Research Question: This study aimed to evaluate the effectiveness of providing a back care package through the primary healthcare system in decreasing the rate of CLBP in the community.
Material And Methods: Clusters were primary healthcare units with the covered population as participants. The intervention package comprised both exercise and educational content in the form of booklets. Data regarding LBP were collected at baseline, 3 and 9-month follow-ups. The LBP prevalence and the incidence of CLBP in the intervention group compared to the control group were analyzed using logistic regression through GEE.
Results: Eleven clusters were randomized including 3521 enrolled subjects. At 9 months, the intervention group showed a statistically significant decrease in both the prevalence and the incidence of CLBP, compared to the control group (OR = 0.44; 95% CI = 0.30-0.65; P < 0.001 and OR = 0.48; 95% CI = 0.31-0.74; P < 0.001, respectively).
Discussion And Conclusion: The population-based intervention was effective in reducing the LBP prevalence and CLBP incidence. Our results suggest that preventing CLBP through a primary healthcare package including exercise and educational content is achievable.
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http://dx.doi.org/10.1016/j.bas.2023.101714 | DOI Listing |
J Med Internet Res
January 2025
Department of Healthcare Economics and Quality Management, School of Public Health, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Background: The COVID-19 pandemic, declared in March 2020, profoundly affected global health, societal, and economic frameworks. Vaccination became a crucial tactic in combating the virus. Simultaneously, the pandemic likely underscored the internet's role as a vital resource for seeking health information.
View Article and Find Full Text PDFImportance: Fragility fractures result in significant morbidity.
Objective: To review evidence on osteoporosis screening to inform the US Preventive Services Task Force.
Data Sources: PubMed, Embase, Cochrane Library, and trial registries through January 9, 2024; references, experts, and literature surveillance through July 31, 2024.
JAMA Netw Open
January 2025
Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts.
Importance: Nearly all Medicare Advantage (MA) plans offer dental, vision, and hearing benefits not covered by traditional Medicare (TM). However, little is known about MA enrollees' use of those benefits or how much they cost MA insurers or enrollees.
Objective: To estimate use, out-of-pocket (OOP) spending, and insurer payments for dental, hearing, and vision services among Medicare beneficiaries.
J Gen Intern Med
January 2025
Department of Neurology, University of Michigan Medical School, Ann Arbor, MI, USA.
Background: Previous reports suggest patient and caregiver lack of awareness of dementia. Little is known about how this varies by ethnicity and how informal (family) caregiver burden is associated with knowing a dementia diagnosis.
Objective: To investigate whether participants with probable dementia were aware of a diagnosis provided by a physician and how this differed among Mexican American and non-Hispanic White participants; whether having a primary care physician was associated with dementia diagnosis unawareness; and the association of dementia diagnosis unawareness with caregiver burden.
J Med Syst
January 2025
Department of Computing, University of North Florida, 1 UNF Dr., Jacksonville, 32246, FL, USA.
The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions.
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