Complex sociodemographic inequalities in consultations for low back pain: lessons from multilevel intersectional analysis. Kiadaliri, A., Merlo, J., & Englund, M. Pain, September, 2020.
Complex sociodemographic inequalities in consultations for low back pain: lessons from multilevel intersectional analysis [link]Paper  doi  abstract   bibtex   
Sociodemographic inequalities in the occurrence of low back pain (LBP) are well-studied. This study aimed to examine complex sociodemographic inequalities in the risk LBP consultation in the population from a socioeconomical intersectional perspective. Using register data, we identified 458,852 individuals aged 35-75 years residing in Skåne in 2013, with no previous LBP consultation since 2006. We created 108 strata using categories of age, sex, education, income, and nativity. With individuals nested within strata, we modelled the absolute risk (AR) of LBP consultation during 2014 in a series of multilevel logistic regression models. We quantified discriminatory accuracy (DA) of these variables by computing the variance partition coefficient (VPC) and area under the receiver operating characteristic curve (AUC). We identified 13,657 (3.0%) people with a LBP consultation. The AR ranged from 2.1% (95% credible interval: 1.9%, 2.3%) among young native men with high education and high income to 4.8% (4.3%, 5.5%) among young foreign-born women with medium education and low income (2.3-fold relative difference). DA of intersectional strata was very low (VPC 1.1%, (0.7, 1.6); and AUC 0.56, (0.55, 0.56)). Sex (35.6%) and nativity (19.2%) had the largest contributions in explaining the initially small between-strata variation in risk of LBP. The low DA of the intersectional strata indicates the existence of limited intersectional inequalities in LBP consultation. Therefore, interventions to reduce LBP risk should be universal rather than targeted to specific socioeconomic groups with a higher average risk. Before planning targeted intervention, other risk factors with higher DA needs to be identified.
@article{kiadaliri_complex_2020,
	title = {Complex sociodemographic inequalities in consultations for low back pain: lessons from multilevel intersectional analysis},
	volume = {In Press},
	issn = {1872-6623},
	shorttitle = {Complex sociodemographic inequalities in consultations for low back pain},
	url = {https://doi.org/10.1097/j.pain.0000000000002081},
	doi = {10.1097/j.pain.0000000000002081},
	abstract = {Sociodemographic inequalities in the occurrence of low back pain (LBP) are well-studied. This study aimed to examine complex sociodemographic inequalities in the risk LBP consultation in the population from a socioeconomical intersectional perspective. Using register data, we identified 458,852 individuals aged 35-75 years residing in Skåne in 2013, with no previous LBP consultation since 2006. We created 108 strata using categories of age, sex, education, income, and nativity. With individuals nested within strata, we modelled the absolute risk (AR) of LBP consultation during 2014 in a series of multilevel logistic regression models. We quantified discriminatory accuracy (DA) of these variables by computing the variance partition coefficient (VPC) and area under the receiver operating characteristic curve (AUC). We identified 13,657 (3.0\%) people with a LBP consultation. The AR ranged from 2.1\% (95\% credible interval: 1.9\%, 2.3\%) among young native men with high education and high income to 4.8\% (4.3\%, 5.5\%) among young foreign-born women with medium education and low income (2.3-fold relative difference). DA of intersectional strata was very low (VPC 1.1\%, (0.7, 1.6); and AUC 0.56, (0.55, 0.56)). Sex (35.6\%) and nativity (19.2\%) had the largest contributions in explaining the initially small between-strata variation in risk of LBP. The low DA of the intersectional strata indicates the existence of limited intersectional inequalities in LBP consultation. Therefore, interventions to reduce LBP risk should be universal rather than targeted to specific socioeconomic groups with a higher average risk. Before planning targeted intervention, other risk factors with higher DA needs to be identified.},
	language = {eng},
	journal = {Pain},
	author = {Kiadaliri, Ali and Merlo, Juan and Englund, Martin},
	month = sep,
	year = {2020},
	pmid = {32947540},
}

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