An algorithmic approach to reducing unexplained pain disparities in underserved populations

Abstract

Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients’ pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients’ experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3–16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7× more (95% CI, 3.2–11.8×), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients’ pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm’s ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients’ pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty.

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Fig. 1: Heatmap of a representative X-ray image.

Data availability

Anonymized imaging and clinical data to reproduce results of this study are available online at https://nda.nih.gov/oai/.

Code availability

Code to reproduce the results of this study is available online at https://github.com/epierson9/pain-disparities.

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Acknowledgements

We thank K. Blumer, J. Duryea, J. Irvin, P.W. Koh, S. Lamb, G. Lester, S. Li, K. Lin, B. McCann, A. Miller, L. Pierson, C. Olah, M. Raghu, P. Rajpurkar, N. Roth, C. Ruiz, C. Sabatti and participants at several seminars and meetings for helpful comments. We acknowledge financial support from Hertz and NDSEG graduate fellowships and the US Social Security Administration (SSA) grant RDR18000003, funded as part of the Retirement and Disability Research Consortium.

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E.P., D.M.C., J.L., S.M. and Z.O. jointly analyzed the results and wrote the paper.

Corresponding author

Correspondence to Sendhil Mullainathan.

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Competing interests

E.P. is employed by Microsoft Research. S.M. and Z.O. have equity interests in LookDeep Health (healthcare services), Dandelion (healthcare services) and Spur Labs (healthcare services). Z.O. has equity interests in Berkeley Data Ventures (consulting).

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Peer review information Jennifer Sargent was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 The effect of dataset diversity on model performance.

Each row of plots shows the effect of removing one minority group from the training set: from top, Black, lower-income, and lower-education patients. Each column of plots shows one metric: from left, R2 in predicting KOOS pain score, and the reductions in the education, income, and racial pain disparities (relative to KLG). In each subplot, the blue dot shows, as a baseline, the performance of KLG. The red dot shows the performance of a neural network trained on a non-diverse training set, with all minority patients removed. The five black dots show the performance of neural networks trained on five diverse training sets of equal size, with five random subsets of non-minority patients removed; in all cases, the diverse training sets yield superior performance to non-diverse training sets of equal size.

Extended Data Fig. 2 Analysis pipeline.

Prior to conducting any analysis, 1,295 patients (red box) were reserved as a held-out validation set to assess final results. In the exploratory phase, the remaining patients were analyzed as follows: a training set was used to optimize model weights, and a development set to select model hyperparameters and conduct early stopping to avoid overfitting. The main analyses to run on the held-out validation set were determined prior to examining it, and the hyperparameters were finalized. In the final analysis, all models were retrained using the hyperparameters chosen in the exploratory phase, and model predictions were assessed on the 1,295 patients in the held-out validation set.

Extended Data Fig. 3 Pain levels among overlapping racial and socioeconomic subgroups.

Race and socioeconomic status are correlated: among Black patients, 61% were lower-education and 63% were lower-income, while among non-Black patients, 34% were lower-education and 34% were lower-income.

Extended Data Fig. 4 Predictive performance for pain.

95% CIs are computed by cluster bootstrapping at the patient level.

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Pierson, E., Cutler, D.M., Leskovec, J. et al. An algorithmic approach to reducing unexplained pain disparities in underserved populations. Nat Med 27, 136–140 (2021). https://doi.org/10.1038/s41591-020-01192-7

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