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James X. Zhang

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James X. Zhang
NationalityAmerican
Academic career
FieldHealth economy
InstitutionUniversity of Chicago

James X. Zhang is an American health economist and health services researcher at the University of Chicago known for his innovative approaches in exploring complex data to measure a range of factors influencing healthcare delivery and outcomes.

Zhang initially worked with Nicholas Christakis, and the products included a novel methodology for identifying married couples in the Medicare claims to study mortality, morbidity, and health care use among the married elderly,[1] and a novel claims-based dataset exploiting substantial cross-set linkages to study end-of-life care.[2]

Zhang's research addressed the significance of comorbidity in clinical setting, and was among the most frequently cited papers in the field.[3] His contributions have also included some other influential studies[4] in the field of Medicare Part D program, and generic drug use.[5][6][7] His more recent contributions with David O. Meltzer includes a novel method identifying patient with cost-related medication non-adherence using a big-data approach.[8] His most recent contribution aims to advance the understanding of gender's role in healthcare behaviors and outcomes and the role of age advancement in health-related behavioral changes.[9][10][11][12][13]

Zhang has also contributed to the advancement of understanding regarding patterns of concentration in healthcare spending and in drug utilizations. He showed that the concentration of healthcare spending is present even in patient populations with the same high-cost condition, such as heart failure, and that varying comorbidities are one substantive contributor to such concentration.[14] He has also shown that, regarding the relationship between market mechanisms and drug prices, the observed positive relationship between the decreasing utilization of brand-name drugs and their increased prices can be explained in part by increases in market concentration of the brand-name drugs, despite the competition from generic drugs.[15]

In addition, Zhang has made contributions that advance the understanding of the role of health insurance with respect to quality of and access to care among older patients with diabetes (a high-cost, high-resource-utilization patient population). His research demonstrated that insurance plays a more variable and nuanced role than commonly thought. He showed that while those without insurance are the least likely to meet quality-of-care measures, provision of health insurance such as Medicaid alone is not necessarily sufficient for the delivery of high-quality care.[16]

Beyond econometric and statistical approaches, Zhang has contributed to the health sciences by introducing and applying machine-learning techniques to prognostic modeling for patients with lung cancer. His research showed that, while the traditional statistical approach and machine-learning approach have similar performance in identifying the most important predictive variables, the order of variable importance is more robust in the machine-learning model than in traditional statistical models regarding the differential functional forms of the variables.[17]

References

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  1. ^ Iwashyna TJ, Zhang JX, Lauderdale DS, Christakis NA (November 1998). "A methodology for identifying married couples in Medicare data: mortality, morbidity, and health care use among the married elderly". Demography. 35 (4): 413–9. doi:10.2307/3004010. hdl:2027.42/61405. JSTOR 3004010. PMID 9850466. S2CID 12464825.
  2. ^ Christakis NA, Iwashyna TJ, Zhang JX (August 2002). "Care after the onset of serious illness: a novel claims-based dataset exploiting substantial cross-set linkages to study end-of-life care". Journal of Palliative Medicine. 5 (4): 515–29. doi:10.1089/109662102760269751. PMID 12243676.
  3. ^ Zhang JX, Iwashyna TJ, Christakis NA (November 1999). "The performance of different lookback periods and sources of information for Charlson comorbidity adjustment in Medicare claims". Medical Care. 37 (11): 1128–39. doi:10.1097/00005650-199911000-00005. JSTOR 3767066. PMID 10549615.
  4. ^ "University of Chicago News Office | First rigorous analysis defines impact of Medicare Part D". www-news.uchicago.edu. Retrieved 2021-01-12.
  5. ^ "Lipitor Among Top Drugs Coming Off Patent". ABC News. Retrieved 2021-01-12.
  6. ^ Yin W, Basu A, Zhang JX, Rabbani A, Meltzer DO, Alexander GC (February 2008). "The effect of the Medicare Part D prescription benefit on drug utilization and expenditures". Annals of Internal Medicine. 148 (3): 169–77. doi:10.7326/0003-4819-148-3-200802050-00200. PMID 18180465. S2CID 41129746.
  7. ^ Zhang JX, Yin W, Sun SX, Alexander GC (October 2008). "The impact of the Medicare Part D prescription benefit on generic drug use". Journal of General Internal Medicine. 23 (10): 1673–8. doi:10.1007/s11606-008-0742-6. PMC 2533371. PMID 18661190.
  8. ^ Zhang JX, Meltzer DO (August 2016). "Identifying patients with cost-related medication non-adherence: a big-data approach". Journal of Medical Economics. 19 (8): 806–11. doi:10.1080/13696998.2016.1176031. PMC 5538308. PMID 27052465.
  9. ^ "Gender and Cost-related Medication Non-adherence". Cancer Therapy Advisor. 27 December 2016.
  10. ^ Zhang JX, Crowe JM, Meltzer DO (July 2017). "The differential rates in cost-related non-adherence to medical care by gender in the US adult population". Journal of Medical Economics. 20 (7): 752–759. doi:10.1080/13696998.2017.1326383. PMID 28466689. S2CID 4798300.
  11. ^ De Avila JL, Meltzer DO, Zhang JX (March 2021). "Prevalence and Persistence of Cost-Related Medication Nonadherence Among Medicare Beneficiaries at High Risk of Hospitalization". JAMA Network Open. 4 (3): e210498. doi:10.1001/jamanetworkopen.2021.0498. PMC 7930921. PMID 33656528.
  12. ^ Zhang, James X.; Meltzer, David O. (2021-09-06). "Association Between the Modalities of Complementary and Alternative Medicine Use and Cost-Related Nonadherence to Medical Care Among Older Americans: A Cohort Study". The Journal of Alternative and Complementary Medicine. 27 (12): 1131–1135. doi:10.1089/acm.2021.0225. ISSN 1075-5535. PMC 8713274. PMID 34491838.
  13. ^ Zhang, JX; Bhaumik, D; Meltzer, D (2022). "Decreasing rates of cost-related medication non-adherence by age advancement among American generational cohorts 2004–2014: a longitudinal study". BMJ Open. 12 (5): e051480. doi:10.1136/bmjopen-2021-051480. ISSN 2044-6055. PMC 9083426. PMID 35523499. S2CID 248554192.
  14. ^ Zhang JX, Rathouz PJ, Chin MH (April 2003). "Comorbidity and the concentration of healthcare expenditures in older patients with heart failure". Journal of the American Geriatrics Society. 51 (4): 476–82. doi:10.1046/j.1532-5415.2003.51155.x. PMID 12657066. S2CID 27649478.
  15. ^ Zhang JX (2020-06-11). Böckerman P (ed.). "Decreasing utilization and increasing prices of brand-name oral contraceptive pills: Implications to societal costs and market competition". PLOS ONE. 15 (6): e0234463. Bibcode:2020PLoSO..1534463Z. doi:10.1371/journal.pone.0234463. PMC 7289391. PMID 32525965.
  16. ^ Zhang, James X.; Huang, Elbert S.; Drum, Melinda L.; Kirchhoff, Anne C.; Schlichting, Jennifer A.; Schaefer, Cynthia T.; Heuer, Loretta J.; Chin, Marshall H. (April 2009). "Insurance status and quality of diabetes care in community health centers". American Journal of Public Health. 99 (4): 742–747. doi:10.2105/AJPH.2007.125534. ISSN 1541-0048. PMC 2661469. PMID 18799773.
  17. ^ He J, Zhang JX, Chen CT, Ma Y, De Guzman R, Meng J, Pu Y (May 2020). "The Relative Importance of Clinical and Socio-demographic Variables in Prognostic Prediction in Non-Small Cell Lung Cancer: A Variable Importance Approach". Medical Care. 58 (5): 461–467. doi:10.1097/MLR.0000000000001288. PMID 31985586. S2CID 210922993.
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