Advertisement
Mayo Clinic Proceedings Home

Why Patients Visit Their Doctors: Assessing the Most Prevalent Conditions in a Defined American Population

      Abstract

      Objective

      To describe the prevalence of nonacute conditions among patients seeking health care in a defined US population, emphasizing age, sex, and ethnic differences.

      Patients and Methods

      The Rochester Epidemiology Project (REP) medical records linkage system was used to identify all residents of Olmsted County, Minnesota, on April 1, 2009, who had consented to review of their medical records for research (142,377 patients). We then electronically extracted all International Classification of Diseases, Ninth Revision codes noted in the records of these patients by any health care institution between January 1, 2005, and December 31, 2009. We grouped International Classification of Diseases, Ninth Revision codes into clinical classification codes and then into 47 broader disease groups associated with health-related quality of life. Age- and sex-specific prevalence was estimated by dividing the number of individuals within each group by the corresponding age- and sex-specific population. Patients within a group who had multiple codes were counted only once.

      Results

      We included a total of 142,377 patients, 75,512 (53%) of whom were female. Skin disorders (42.7%), osteoarthritis and joint disorders (33.6%), back problems (23.9%), disorders of lipid metabolism (22.4%), and upper respiratory tract disease (22.1%, excluding asthma) were the most prevalent disease groups in this population. Ten of the 15 most prevalent disease groups were more common in women in almost all age groups, whereas disorders of lipid metabolism, hypertension, and diabetes were more common in men. Additionally, the prevalence of 7 of the 10 most common groups increased with advancing age. Prevalence also varied across ethnic groups (whites, blacks, and Asians).

      Conclusion

      Our findings suggest areas for focused research that may lead to better health care delivery and improved population health.

      Abbreviations and Acronyms:

      CCC (clinical classification code), ICD-9 (International Classification of Diseases, Ninth Revision), REP (Rochester Epidemiology Project)
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Mayo Clinic Proceedings
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Hwang W.
        • Weller W.
        • Ireys H.
        • Anderson G.
        Out-of-pocket medical spending for care of chronic conditions.
        Health Aff (Millwood). 2001; 20: 267-278
        • Naessens J.M.
        • Stroebel R.J.
        • Finnie D.M.
        • et al.
        Effect of multiple chronic conditions among working-age adults.
        Am J Manag Care. 2011; 17: 118-122
        • Vogeli C.
        • Shields A.E.
        • Lee T.A.
        • et al.
        Multiple chronic conditions: prevalence, health consequences, and implications for quality, care management, and costs.
        J Gen Intern Med. 2007; 22: 391-395
        • Katon W.
        • Russo J.
        • Lin E.H.
        • et al.
        Cost-effectiveness of a multicondition collaborative care intervention: a randomized controlled trial.
        Arch Gen Psychiatry. 2012; 69: 506-514
        • Hussey P.S.
        • Ridgely M.S.
        • Rosenthal M.B.
        The PROMETHEUS bundled payment experiment: slow start shows problems in implementing new payment models.
        Health Aff (Millwood). 2011; 30: 2116-2124
        • Bates D.W.
        • Bitton A.
        The future of health information technology in the patient-centered medical home.
        Health Aff (Millwood). 2010; 29: 614-621
        • St Sauver J.L.
        • Grossardt B.R.
        • Leibson C.L.
        • Yawn B.P.
        • Melton III, L.J.
        • Rocca W.A.
        Generalizability of epidemiological findings and public health decisions: an illustration from the Rochester Epidemiology Project.
        Mayo Clin Proc. 2012; 87: 151-160
        • St Sauver J.L.
        • Grossardt B.R.
        • Yawn B.P.
        • Melton III, L.J.
        • Rocca W.A.
        Use of a medical records linkage system to enumerate a dynamic population over time: the Rochester Epidemiology Project.
        Am J Epidemiol. 2011; 173: 1059-1068
        • Melton III, L.J.
        History of the Rochester Epidemiology Project.
        Mayo Clin Proc. 1996; 71: 266-274
      1. Agency for Healthcare Research and Quality. Medical Expenditure Panel Survey HC-120, Appendix 3: Clinical Classification Code to ICD-9-CM Code Crosswalk. MEPS website. http://meps.ahrq.gov/mepsweb/data_stats/download_data/pufs/h120/h120_icd9codes.shtml. Accessed February 7, 2011.

      2. Agency for Healthcare Research and Quality. Healthcare Cost and Utilization Project: Clinical Classifications Software (CCS) for ICD-9-CM. HCUP website. www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Updated August 30, 2012. Accessed February 1, 2012.

        • Mukherjee B.
        • Ou H.T.
        • Wang F.
        • Erickson S.R.
        A new comorbidity index: the health-related quality of life comorbidity index.
        J Clin Epidemiol. 2011; 64: 309-319
        • Porta M.S.
        • International Epidemiological Association
        A Dictionary of Epidemiology.
        5th ed. Oxford University Press, Oxford, England2008
        • Anderson D.W.
        • Mantel N.
        On epidemiologic surveys.
        Am J Epidemiol. 1983; 118: 613-619
        • Deming W.E.
        Boundaries of statistical inference.
        in: Johnson N.L. Smith H. New Developments in Survey Sampling. Wiley-Interscience, New York1969: 652-670
        • Rocca W.A.
        • Cha R.H.
        • Waring S.C.
        • Kokmen E.
        Incidence of dementia and Alzheimer's disease: a reanalysis of data from Rochester, Minnesota, 1975-1984.
        Am J Epidemiol. 1998; 148: 51-62
      3. Small area income and poverty estimates: state and county estimates for 2009. United States Census Bureau website. http://www.census.gov/did/www/saipe/data/statecounty/data/2009.html. Updated November 29, 2011. Accessed August 1, 2012.

      4. US Census Bureau. 2010 Demographic Profile Data, Olmsted County, MN. http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=bkmk. Accessed November 21, 2012.

        • Keenan N.L.
        • Rosendorf K.A.
        Centers for Disease Control and Prevention (CDC). Prevalence of hypertension and controlled hypertension—United States, 2005-2008.
        MMWR Surveill Summ. 2011; 60: 94-97
        • Lawrence R.C.
        • Felson D.T.
        • Helmick C.G.
        • et al.
        • National Arthritis Data Workgroup
        Estimates of the prevalence of arthritis and other rheumatic conditions in the United States: Part II.
        Arthritis Rheum. 2008; 58: 26-35
        • Ko C.J.
        Actinic keratosis: facts and controversies.
        Clin Dermatol. 2010; 28: 249-253
        • van der Heijden J.P.
        • de Keizer N.F.
        • Bos J.D.
        • Spuls P.I.
        • Witkamp L.
        Teledermatology applied following patient selection by general practitioners in daily practice improves efficiency and quality of care at lower cost.
        Br J Dermatol. 2011; 165: 1058-1065
        • Zhang Y.
        • Jordan J.M.
        Epidemiology of osteoarthritis.
        Clin Geriatr Med. 2010; 26: 355-369
        • Sowers M.R.
        • Karvonen-Gutierrez C.A.
        The evolving role of obesity in knee osteoarthritis.
        Curr Opin Rheumatol. 2010; 22: 533-537
        • Deyo R.A.
        • Mirza S.K.
        • Martin B.I.
        Back pain prevalence and visit rates: estimates from U.S. national surveys, 2002.
        Spine (Phila PA 1976). 2006; 31: 2724-2727
        • Carey T.S.
        • Garrett J.
        • Jackman A.
        • McLaughlin C.
        • Fryer J.
        • Smucker D.R.
        The outcomes and costs of care for acute low back pain among patients seen by primary care practitioners, chiropractors, and orthopedic surgeons: the North Carolina Back Pain Project.
        N Engl J Med. 1995; 333: 913-917
        • Hill J.C.
        • Whitehurst D.G.
        • Lewis M.
        • et al.
        Comparison of stratified primary care management for low back pain with current best practice (STarT Back): a randomised controlled trial.
        Lancet. 2011; 378: 1560-1571
        • Chou R.
        • Qaseem A.
        • Snow V.
        • et al.
        Clinical Efficacy Assessment Subcommittee of the American College of Physicians; American College of Physicians; American Pain Society Low Back Pain Guidelines Panel. Diagnosis and treatment of low back pain: a joint clinical practice guideline from the American College of Physicians and the American Pain Society [published correction appears in Ann Intern Med. 2008;148(3):247-248].
        Ann Intern Med. 2007; 147: 478-491
        • Centers for Disease Control and Prevention (CDC)
        Vital signs: prevalence, treatment, and control of high levels of low-density lipoprotein cholesterol—United States, 1999-2002 and 2005-2008.
        MMWR Morb Mortal Wkly Rep. 2011; 60: 109-114
        • Grundy S.M.
        • Cleeman J.I.
        • Merz C.N.
        • National Heart, Lung, and Blood Institute
        • et al.
        American College of Cardiology Foundation; American Heart Association. Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines.
        Circulation. 2004; 110: 227-239
        • Fischer H.H.
        • Eisert S.L.
        • Everhart R.M.
        • et al.
        Nurse-run, telephone-based outreach to improve lipids in people with diabetes.
        Am J Manag Care. 2012; 18: 77-84
        • Nathan R.A.
        The burden of allergic rhinitis.
        Allergy Asthma Proc. 2007; 28: 3-9
        • Blaiss M.S.
        Allergic rhinitis: direct and indirect costs.
        Allergy Asthma Proc. 2010; 31: 375-380
        • Ker J.
        • Hartert T.V.
        The atopic march: what's the evidence?.
        Ann Allergy Asthma Immunol. 2009; 103: 282-289
        • Leibson C.L.
        • Brown A.W.
        • Ransom J.E.
        • et al.
        Incidence of traumatic brain injury across the full disease spectrum: a population-based medical record review study.
        Epidemiology. 2011; 22: 836-844
        • Leibson C.L.
        • Naessens J.M.
        • Brown R.D.
        • Whisnant J.P.
        Accuracy of hospital discharge abstracts for identifying stroke.
        Stroke. 1994; 25: 2348-2355
        • Leibson C.L.
        • Needleman J.
        • Buerhaus P.
        • et al.
        Identifying in-hospital venous thromboembolism (VTE): a comparison of claims-based approaches with the Rochester Epidemiology Project VTE cohort.
        Med Care. 2008; 46: 127-132
        • Roger V.L.
        • Killian J.
        • Henkel M.
        • et al.
        Coronary disease surveillance in Olmsted County objectives and methodology.
        J Clin Epidemiol. 2002; 55: 593-601
        • Yawn B.P.
        • Wollan P.
        • St Sauver J.
        Comparing shingles incidence and complication rates from medical record review and administrative database estimates: how close are they?.
        Am J Epidemiol. 2011; 174: 1054-1061
        • Kessler R.C.
        • Berglund P.
        • Demler O.
        • Jin R.
        • Merikangas K.R.
        • Walters E.E.
        Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication [published correction appears in Arch Gen Psychiatry. 2005;62(7):768].
        Arch Gen Psychiatry. 2005; 62: 593-602
      5. 2011 National Diabetes Fact Sheet. Centers for Disease Control and Prevention website. http://www.cdc.gov/diabetes/pubs/estimates11.htm#1. Updated May 23, 2011. Accessed February 8, 2012.

        • Akinbami L.J.
        • Moorman J.E.
        • Liu X.
        Asthma prevalence, health care use, and mortality: United States, 2005-2009.
        Natl Health Stat Report. 2011; 32: 1-14
        • Cheng H.
        • Gary L.C.
        • Curtis J.R.
        • et al.
        Estimated prevalence and patterns of presumed osteoporosis among older Americans based on Medicare data.
        Osteoporos Int. 2009; 20: 1507-1515
      6. Cancer prevalence: how many people have cancer? American Cancer Society website. http://www.cancer.org/cancer/cancerbasics/cancer-prevalence. Updated October 18, 2011. Accessed February 2, 2012.

      7. HIV/AIDS prevalence and mortality tables - 2010. Minnesota Department of Health website. http://www.health.state.mn.us/divs/idepc/diseases/hiv/stats/pmtables2010.html#table1. Updated April 30, 2012. Accessed February 8, 2012.

      8. Occupational Employment Statistics: May 2011 metropolitan and nonmetropolitan area occupational employment and wage estimates; Rochester, MN. US Bureau of Labor Statistics website. http://www.bls.gov/oes/current/oes_40340.htm#29-0000. Updated March 27, 2012. Accessed August 1, 2012.

      9. Occupational Employment Statistics: May 2011 national occupational employment and wage estimates by ownership; cross-industry, private ownership only. US Bureau of Labor Statistics website. http://www.bls.gov/oes/current/000001.htm. Updated March 29, 2012. Accessed August 1, 2012.

      Linked Article

      • Prevalence of Skin Disorders in Patients Seeking Health Care
        Mayo Clinic ProceedingsVol. 88Issue 7
        • Preview
          In their article published in the January 2013 issue of Mayo Clinic Proceedings, St. Sauver et al1 reported the prevalence of skin disorders in patients seeking health care, noting that almost half of the observed population (42.7%) had at least one International Classification of Diseases, Ninth Revision code for skin conditions within 5 years. The authors stated that “skin disorders are not typically major drivers of disability” and that perhaps teledermatology should be investigated as a way to increase health care efficiency and reduce health care expenditures.
        • Full-Text
        • PDF