• Latest study: Early death or readmission not linked to socio-economic status

    The recent study by van Walraven, Wong, and Forster finds that “after accounting for known risk factors, early death or [hospital] readmission is not more common in people from lower-income neighborhoods.” Below are a few quotes from the paper and related comments.

    Nine studies have examined the association between SES and readmissions. [3–11]

    This is an undercount of the studies that have done so. Not mentioned, for example, is the work of Arbaje, et al., Lindenauer, et al., and Joynt, et al. See also the studies mentioned in the TIE interview with Joynt.

    Of these 9 studies, 5 found no independent association between their SES measure and readmission, [5,6,8–10] and 2 included SES in their final regression model but did not present the model—making it impossible to determine if SES significantly influenced outcomes. [3,15*] One study found that the risk of hospital readmission independently increased as a composite measure of area-level social and economic indicators decreased. [4] A Canadian study [11] measured neighborhood income quintile and showed, after adjusting for patient sex, comorbidities, LOS variance, and previous admissions, that the odds of acute, nonpsychiatric readmission within 30 days of discharge were approximately 10% higher in the lowest versus the highest SES quintile. The ability of this model to adjust for important confounders when associating SES and risk of readmission is uncertain because the model fit was not reported.

    In contrast to many prior studies, van Walraven and colleagues do not investigate readmissions per se, but instead 30-day death or urgent readmissions, excluding psychiatric readmissions.

    In analyses having readmission as the sole outcome, the categorization of early deaths that occur prior to readmission as nonevents could minimize the importance of factors (such as severe comorbidities or patient age) that are associated with both early death and readmission.

    This is nicely phrased.

    Note also that,

    Our unit of analysis was the patient, whereas in [11] it was the hospitalization. A recent analysis by our group found that this distinction can change the results on analyses in early postdischarge outcomes. In the present analysis, different results could occur if patients with multiple readmissions were disproportionately prevalent in low-income neighborhood

    The investigators risk adjust using the LACE+ index, which accounts for “length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency-department use (E).” The SES measure is neighborhood income quintile. Other choices for either risk adjustment or SES are possible, as have been explored by others.

    * It’s odd to find [15] here since it wasn’t in the list of nine studies. Also, [7] is not cited in this summary.

    UPDATE: Karen Joynt’s two tweets are worth reading.


    3. Bottle A, Aylin P, Majeed A. Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. J R Soc Med. 2006;99(8):406–414.

    4. Howell S, Coory M, Martin J, Duckett S. Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Serv Res. 2009;9:96.

    5. Silverstein MD, Qin H, Mercer SQ, Fong J, Haydar Z. Risk factors for 30-day hospital readmission in patients 65 years of age. Proc (Bayl Univ Med Cent). 2008;21(4):363–372.

    6. Amarasingham R, Moore BJ, Tabak YP, et al. An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. Med Care. 2010;48(11):981–988.

    7. Billings J, Mijanovich T. Improving the management of care for high cost Medicaid patients. Health Aff (Millwood). 2007;26(6):1643– 1654.

    8. Burns R, Nichols LO. Factors predicting readmission of older general medicine patients.J Gen Intern Med. 1991;6(5):389–393.

    9. Hasan O, Meltzer DO, Shaykevich SA, et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211–219.

    10. Boult C, Dowd B, McCaffrey D, Boult L, Hernandez R, Krulewitch H. Screening elders for risk of hospital admission. J Am Geriatr Soc. 1993;41(8):811–817.

    11. Canadian Institute for Health Information. All-Cause Readmission to Acute Care and Return to the Emergency Department. Ottawa, ON: Canadian Institute for Health Information; 2012:1–64.


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