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EBM analysis
Promise and peril of claims-based dementia ascertainment in causal inference
  1. Natalia Festa1,2,3,
  2. Lidia MVR Moura3,4,
  3. Deborah Blacker5,6,
  4. Joseph P Newhouse7,8,9,10,
  5. John Hsu3,7,11
  1. 1 National Clinician Scholars Program at Yale University, New Haven, Connecticut, USA
  2. 2 Section of Geriatrics, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
  3. 3 Mongan Institute, Massachusetts General Hospital, Boston, Massachusetts, USA
  4. 4 Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
  5. 5 Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts, USA
  6. 6 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
  7. 7 Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
  8. 8 Harvard Kennedy School, Cambridge, Massachusetts, USA
  9. 9 National Bureau of Economic Research, Cambridge, Massachusetts, USA
  10. 10 Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
  11. 11 Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
  1. Correspondence to Dr Natalia Festa, National Clinician Scholars Program at Yale University, New Haven, CT 06510, USA; natalia.festa{at}yale.edu

https://doi.org/10.1136/bmjebm-2022-112134

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    Festa and colleagues highlight under-recognised factors that may bias research, policy and population health strategies predicated on claims-based ascertainment of Alzheimer’s disease and related dementias within the USA

    Absent effective disease-modifying therapies for Alzheimer’s disease and related dementias (ADRD), measures to prevent incident disease, delay symptom onset and prolong functional independence are paramount. Identifying effective preventive measures and care delivery models, in turn, requires improved information regarding modifiable risk factors and corresponding population-level interventions that may alter the trajectory of ADRD. In many countries, including the USA, administrative data offer a singular means of studying these relationships in large and representative populations.

    Researchers in the USA have increasingly drawn on administrative diagnostic information from the Centers for Medicare & Medicaid Services (CMS)—the major public payer for healthcare of the elderly—to ascertain ADRD status. For example, the often used CMS Chronic Conditions Warehouse (CCW) definition of ADRD has underpinned recent public health research identifying air pollution as a potentially modifiable contributor to neurodegenerative disease.1 The CMS-CCW definition has also been used in health services research to examine the effects of local healthcare access on ADRD-related outcomes.2 Moreover, clinical research has applied this definition to ascertain ADRD among persons for whom this diagnosis has important prognostic and treatment-related implications for comorbid conditions, such as cancer.3 Emerging applications of claims-based ADRD ascertainment now include drug-repurposing and pharmacogenomics.4

    Some limitations of ADRD ascertainment using claims have been enumerated, including selection bias due to underdiagnosis within administrative data.5 However, we argue that significant issues remain unaddressed, which may bias research, policy and population health strategies predicated on administrative data within the US Medicare population. Below, we discuss potential sources of bias that researchers should consider when using diagnostic claims to ascertain ADRD status. Our illustrative examples are focused within the US Medicare programme due to the proliferation of research measuring ADRD within this population. Many of the considerations that we outline, however, are universally applicable to research predicated …

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    Footnotes

    • Contributors In this submission, we articulate several under-recognised factors that may bias research, policy and population health strategies predicated upon claims-based ascertainment of Alzheimer’s disease and related dementias (ADRD). The conceptual contributions in this manuscript build on core insights from the authors’ prior research focused on ADRD ascertainment and population risk-adjustment using electronic health record and administrative diagnostic information. NF is a geriatrician and postdoctoral fellow with expertise in claims-based measurement of ADRD. LMVRM is an associate professor of Neurology at Harvard Medical School with expertise in the claims-based measurement of ageing outcomes. DB is a professor of psychiatry at Harvard Medical School and in Epidemiology at the Harvard T.H. Chan School of Public Health, with expertise in assessment methods and epidemiology of neurodegenerative diseases. JPN is the JDM research professor of Health Policy and Management at Harvard University with expertise in health economics and health services research. JH (guarantor of this article) is an associate professor of Medicine at Harvard Medical School and director of the Massachusetts General Hospital Program for Clinical Economics and Policy Analysis, with expertise in health policy and alternative payment models.

    • Funding National Institute on Aging T32AG019134 (NF). National Institutes of Health, National Center for Advancing Translational Science TL1TR001864 (NF). National Institutes of Health—National Institute on Aging K08AG053380 (LMVRM). National Institutes of Health R01AG062282 (JH). National Institutes of Health P01AG032952 (JPN).

    • Competing interests We have read and understood BMJ policy on declaration of interests and have the following interests to declare: JH has consulted for Cambridge Health Alliance, Columbia University, Community Servings, Delta Health Alliance, the Robert Wood Johnson Foundation and the University of Southern California. The authors otherwise declare no conflicts of interest.

    • Provenance and peer review Not commissioned; externally peer reviewed.