scholarly journals Time of Day is Associated with Opioid Prescribing for Low Back Pain in Primary Care

2018 ◽  
Vol 33 (11) ◽  
pp. 1828-1830 ◽  
Author(s):  
Lindsey M. Philpot ◽  
Bushra A. Khokhar ◽  
Daniel L. Roellinger ◽  
Priya Ramar ◽  
Jon O. Ebbert
2021 ◽  
Vol 10 (16) ◽  
pp. 3699
Author(s):  
Simona Cammarota ◽  
Valeria Conti ◽  
Graziamaria Corbi ◽  
Luigi Di Gregorio ◽  
Pasquale Dolce ◽  
...  

This study explores which patient characteristics could affect the likelihood of starting low back pain (LBP) treatment with opioid analgesics vs. Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) in an Italian primary care setting. Through the computerized medical records of 65 General Practitioners, non-malignant LBP subjects who received the first pain intensity measurement and an NSAID or opioid prescription, during 2015–2016, were identified. Patients with an opioid prescription 1-year before the first pain intensity measurement were excluded. A multivariable logistic regression model was used to determine predictive factors of opioid prescribing. Results were reported as Odds Ratios (ORs) with a 95% confidence interval (CI), with p < 0.05 indicating statistical significance. A total of 505 individuals with LBP were included: of those, 72.7% received an NSAID prescription and 27.3% an opioid one (64% of subjects started with strong opioid). Compared to patients receiving an NSAID, those with opioid prescriptions were younger, reported the highest pain intensity (moderate pain OR = 2.42; 95% CI 1.48–3.96 and severe pain OR = 2.01; 95% CI 1.04–3.88) and were more likely to have asthma (OR 3.95; 95% CI 1.99–7.84). Despite clinical guidelines, a large proportion of LBP patients started with strong opioid therapy. Asthma, younger age and pain intensity were predictors of opioid prescribing when compared to NSAIDs for LBP treatment.


2013 ◽  
Vol 71 (Suppl 3) ◽  
pp. 724.4-725
Author(s):  
L. Myasoutova ◽  
S. Lapshina ◽  
M. Protopopov ◽  
S. Erdes

Rheumatology ◽  
2021 ◽  
Author(s):  
Dahai Yu ◽  
George Peat ◽  
Kelvin P Jordan ◽  
James Bailey ◽  
Daniel Prieto-Alhambra ◽  
...  

Abstract Objectives Better indicators from affordable, sustainable data sources are needed to monitor population burden of musculoskeletal conditions. We propose five indicators of musculoskeletal health and assessed if routinely available primary care electronic health records (EHR) can estimate population levels in musculoskeletal consulters. Methods We collected validated patient-reported measures of pain experience, function and health status through a local survey of adults (≥35 years) presenting to English general practices over 12 months for low back pain, shoulder pain, osteoarthritis and other regional musculoskeletal disorders. Using EHR data we derived and validated models for estimating population levels of five self-reported indicators: prevalence of high impact chronic pain, overall musculoskeletal health (based on Musculoskeletal Health Questionnaire), quality of life (based on EuroQoL health utility measure), and prevalence of moderate-to-severe low back pain and moderate-to-severe shoulder pain. We applied models to a national EHR database (Clinical Practice Research Datalink) to obtain national estimates of each indicator for three successive years. Results The optimal models included recorded demographics, deprivation, consultation frequency, analgesic and antidepressant prescriptions, and multimorbidity. Applying models to national EHR, we estimated that 31.9% of adults (≥35 years) presenting with non-inflammatory musculoskeletal disorders in England in 2016/17 experienced high impact chronic pain. Estimated population health levels were worse in women, older aged and those in the most deprived neighbourhoods, and changed little over 3 years. Conclusion National and subnational estimates for a range of subjective indicators of non-inflammatory musculoskeletal health conditions can be obtained using information from routine electronic health records.


2003 ◽  
Vol 28 (8) ◽  
pp. 26-31 ◽  
Author(s):  
Kelly Phillips ◽  
Anne P.Y. Ch’ien ◽  
Barbara R. Norwood ◽  
Chris Smith

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046446
Author(s):  
Monica Unsgaard-Tøndel ◽  
Ottar Vasseljen ◽  
Tom Ivar Lund Nilsen ◽  
Gard Myhre ◽  
Hilde Stendal Robinson ◽  
...  

ObjectivePrimary care screening tools for patients with low back pain may improve outcome by identifying modifiable obstacles for recovery. The STarT Back Screening Tool (SBST) consists of nine biological and psychological items, with less focus on work-related factors. We aimed at testing the prognostic ability of SBST and the effect of adding items for future and present work ability.MethodsProspective observational study in patients (n=158) attending primary care physical therapy for low back pain. The prognostic ability of SBST and the added prognostic value of two work items; expectation for future work ability and current work ability, were calculated for disability, pain and quality of life outcome at 3 months follow-up. The medium and high-risk group in the SBST were collapsed in the analyses due to few patients in the high-risk group. The prognostic ability was assessed using the explained variance (R2) of the outcomes from univariable and multivariable linear regression and beta values with 95% CIs were used to assess the prognostic value of individual items.ResultsThe SBST classified 107 (67.7%) patients as low risk and 51 (32.3%) patients as medium/high risk. SBST provided prognostic ability for disability (R2=0.35), pain (R2=0.25) and quality of life (R2=0.28). Expectation for return to work predicted outcome in univariable analyses but provided limited additional prognostic ability when added to the SBST. Present work ability provided additional prognostic ability for disability (β=−2.5; 95% CI=−3.6 to −1.4), pain (β=−0.2; 95% CI=−0.5 to −0.002) and quality of life (β=0.02; 95% CI=0.001 to 0.04) in the multivariable analyses. The explained variance (R2) when work ability was added to the SBST was 0.60, 0.49 and 0.47 for disability, pain and quality of life, respectively.ConclusionsAdding one work ability item to the SBST gives additional prognostic information across core outcomes.Clinical trial number:NCT03626389


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