Electronic Health Use in the European Union and the Effect of Multimorbidity: Cross-Sectional Survey

Francisco Lupiáñez-Villanueva, Dimitra Anastasiadou, Cristiano Codagnone, Roberto Nuño-Solinís, Maria Begona Garcia-Zapirain Soto, Francisco Lupiáñez-Villanueva, Dimitra Anastasiadou, Cristiano Codagnone, Roberto Nuño-Solinís, Maria Begona Garcia-Zapirain Soto

Abstract

Background: Multimorbidity is becoming increasingly common and is a leading challenge currently faced by societies with aging populations. The presence of multimorbidity requires patients to coordinate, understand, and use the information obtained from different health care professionals, while simultaneously striving to distinguish the symptoms of different diseases and self-manage their sometimes conflicting health problems. Electronic health (eHealth) tools provide a means to disseminate health information and education for both patients and health professionals and hold promise for more efficient and cost-effective care processes.

Objective: The aim of this study was to analyze the use of eHealth tools, taking into account the citizens' sociodemographic and clinical characteristics, and above all, the presence of multimorbidity.

Methods: Cross-sectional and exploratory research was conducted using online survey data from July 2011 to August 2011. Participants included a total of 14,000 citizens from 14 European countries aged 16 to 74 years, who had used an eHealth tool in the past 3 months. The variables studied were sociodemographic variables of the participants, the questionnaire items assessing the frequency of using eHealth tools, the degree of morbidity, and the eHealth adoption gradient. Chi-square tests were conducted to examine the relationship between the sociodemographic and clinical variables of participants and the group the participants were assigned to according to their frequency of eHealth use (eHealth user group). A one-way analysis of variance (ANOVA) allowed for assessing the differences in the eHealth adoption gradient average between different groups of individuals according to their morbidity level. A two-way between-groups ANOVA was performed to explore the effects of multimorbidity and age group on the eHealth adoption gradient.

Results: According to the eHealth adoption gradient, most participants (68.15%, 9541/14,000) were labeled as rare users, with the majority of them (55.1%, 508/921) being in the age range of 25 to 54 years, with upper secondary education (50.3%, 464/921), currently employed (49.3%, 454/921), and living in medium-sized cities (40.7%, 375/921). Results of the one-way ANOVA showed that the number of health problems significantly affected the use of eHealth tools (F2,13996=11.584; P<.001). The two-way ANOVA demonstrated that there was a statistically significant interaction between the effects of age and number of health problems on the eHealth adoption gradient (F4,11991=7.936; P<.001).

Conclusions: The eHealth adoption gradient has proven to be a reliable way to measure different aspects of eHealth use. Multimorbidity is associated with a more intense use of eHealth, with younger Internet users using new technologies for health purposes more frequently than older groups with the same level of morbidity. These findings suggest the need to consider different strategies aimed at making eHealth tools more sensitive to the characteristics of older populations to reduce digital disadvantages.

Keywords: Europe; adoption; eHealth; multimorbidity.

Conflict of interest statement

Conflicts of Interest: None declared.

©Francisco Lupiáñez-Villanueva, Dimitra Anastasiadou, Cristiano Codagnone, Roberto Nuño-Solinís, Maria Begona Garcia-Zapirain Soto. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.05.2018.

References

    1. Fortin M, Hudon C, Haggerty J, Akker MV, Almirall J. Prevalence estimates of multimorbidity: a comparative study of two sources. BMC Health Serv Res. 2010 May 06;10:111. doi: 10.1186/1472-6963-10-111.
    1. Loza E, Jover JA, Rodriguez L, Carmona L, EPISER Study Group Multimorbidity: prevalence, effect on quality of life and daily functioning, and variation of this effect when one condition is a rheumatic disease. Semin Arthritis Rheum. 2009 Feb;38(4):312–9. doi: 10.1016/j.semarthrit.2008.01.004.
    1. Salive ME. Multimorbidity in older adults. Epidemiol Rev. 2013;35:75–83. doi: 10.1093/epirev/mxs009.
    1. Fried LP, Ferrucci L, Darer J, Williamson JD, Anderson G. Untangling the concepts of disability, frailty, and comorbidity: implications for improved targeting and care. J Gerontol A Biol Sci Med Sci. 2004 Mar;59(3):255–63.
    1. Gijsen R, Hoeymans N, Schellevis F, Ruwaard D, Satariano WA, van den Bos GA. Causes and consequences of comorbidity: a review. J Clin Epidemiol. 2001 Jul;54(7):661–74.
    1. Honka A, Kaipainen K, Hietala H, Saranummi N. Rethinking health: ICT-enabled services to empower people to manage their health. IEEE Rev Biomed Eng. 2011;4:119–39. doi: 10.1109/RBME.2011.2174217.
    1. McMurdo ME, Witham MD, Gillespie ND. Including older people in clinical research. BMJ. 2005 Nov 05;331(7524):1036–7. doi: 10.1136/bmj.331.7524.1036.
    1. Hughes LD, McMurdo ME, Guthrie B. Guidelines for people not for diseases: the challenges of applying UK clinical guidelines to people with multimorbidity. Age Ageing. 2013 Jan;42(1):62–69. doi: 10.1093/ageing/afs100.
    1. Zulman DM, Jenchura EC, Cohen DM, Lewis ET, Houston TK, Asch SM. How Can eHealth Technology Address Challenges Related to Multimorbidity? Perspectives from Patients with Multiple Chronic Conditions. J Gen Intern Med. 2015 Aug;30(8):1063–1070. doi: 10.1007/s11606-015-3222-9.
    1. Orueta JF, García-Álvarez A, García-Goñi M, Paolucci F, Nuño-Solinís R. Prevalence and costs of multimorbidity by deprivation levels in the basque country: a population based study using health administrative databases. PLoS One. 2014 Feb 27;9(2):e89787. doi: 10.1371/journal.pone.0089787.
    1. Barbabella F, Melchiorre MG, Quattrini S, Papa R, Lamura G. How can eHealth improve care for people with multimorbidity in Europe? Health Systems and Policy Analysis: Policy Brief 25. 2017:1–31.
    1. World Health Organization. 2016. [2018-03-09]. From innovation to implementation – eHealth in the WHO European Region .
    1. Jimison H, Gorman P, Woods S, Nygren P, Walker M, Norris S, Hersh W. Barriers and drivers of health information technology use for the elderly, chronically ill, and underserved. Evid Rep Technol Assess (Full Rep) 2008 Nov;(175):1–1422.
    1. Finkelstein J, Knight A, Marinopoulos S, Gibbons MC, Berger Z, Aboumatar H, Wilson RF, Lau BD, Sharma R, Bass EB. Enabling patient-centered care through health information technology. Evid Rep Technol Assess (Full Rep) 2012 Jun;(206):1–1531.
    1. Ham C. The ten characteristics of the high-performing chronic care system. Health Econ Policy Law. 2010 Jan;5(Pt 1):71–90. doi: 10.1017/S1744133109990120.
    1. Wagner TH, Baker LC, Bundorf MK, Singer S. Use of the Internet for health information by the chronically ill. Prev Chronic Dis. 2004 Oct;1(4):A13.
    1. World Health Organization. Geneva, Switzerland: World Health Organization; 2002. The World Health Report 2002: Reducing Risks, Promoting Healthy Life .
    1. de Jong CC, Ros WJ, Schrijvers G. The effects on health behavior and health outcomes of Internet-based asynchronous communication between health providers and patients with a chronic condition: a systematic review. J Med Internet Res. 2014 Jan 16;16(1):e19. doi: 10.2196/jmir.3000.
    1. Bower P, Cartwright M, Hirani SP, Barlow J, Hendy J, Knapp M, Henderson C, Rogers A, Sanders C, Bardsley M, Steventon A, Fitzpatrick R, Doll H, Newman S. A comprehensive evaluation of the impact of telemonitoring in patients with long-term conditions and social care needs: protocol for the whole systems demonstrator cluster randomised trial. BMC Health Serv Res. 2011 Aug 05;11:184. doi: 10.1186/1472-6963-11-184.
    1. Hsu J, Huang J, Kinsman J, Fireman B, Miller R, Selby J, Ortiz E. Use of e-Health services between 1999 and 2002: a growing digital divide. J Am Med Inform Assoc. 2005;12(2):164–71. doi: 10.1197/jamia.M1672.
    1. Flynn KE, Smith MA, Freese J. When do older adults turn to the internet for health information? Findings from the Wisconsin Longitudinal Study. J Gen Intern Med. 2006 Dec;21(12):1295–301. doi: 10.1111/j.1525-1497.2006.00622.x.
    1. Houston TK, Allison JJ. Users of Internet health information: differences by health status. J Med Internet Res. 2002;4(2):E7. doi: 10.2196/jmir.4.2.e7.
    1. Yamin CK, Emani S, Williams DH, Lipsitz SR, Karson AS, Wald JS, Bates DW. The digital divide in adoption and use of a personal health record. Arch Intern Med. 2011 Mar 28;171(6):568–74. doi: 10.1001/archinternmed.2011.34.
    1. Newman L, Lupiáñez-Villanueva F. A framework to research the social determinants of ICTs for E-Health. The Journal of Community Informatics. 2015;11(3):--.
    1. McInnes DK, Gifford AL, Kazis LE, Wagner TH. Disparities in health-related internet use by US veterans: results from a national survey. Inform Prim Care. 2010;18(1):59–68.
    1. Nazi KM. Veterans' voices: use of the American Customer Satisfaction Index (ACSI) Survey to identify My HealtheVet personal health record users' characteristics, needs, and preferences. J Am Med Inform Assoc. 2010;17(2):203–11. doi: 10.1136/jamia.2009.000240.
    1. Fox S. Pew Research Center [Internet] 2011. Feb 28, Peer-to-peer Health Care
    1. Bundorf MK, Wagner TH, Singer SJ, Baker LC. Who searches the internet for health information? Health Serv Res. 2006 Jun;41(3 Pt 1):819–36. doi: 10.1111/j.1475-6773.2006.00510.x.
    1. Lupiañez F, Maghiros I, Abadie F. Citizens and ICT for Health in 14 European Countries: Results from an Online Panel. JRC Scientific and Policy Reports. 2013:1–216. doi: 10.2791/84062.
    1. Baker L, Wagner TH, Singer S, Bundorf MK. Use of the Internet and e-mail for health care information: results from a national survey. JAMA. 2003 May 14;289(18):2400–6. doi: 10.1001/jama.289.18.2400.
    1. Grant RW, Cagliero E, Chueh HC, Meigs JB. Internet use among primary care patients with type 2 diabetes: the generation and education gap. J Gen Intern Med. 2005 May;20(5):470–3. doi: 10.1111/j.1525-1497.2005.04239.x.
    1. Löffler C, Kaduszkiewicz H, Stolzenbach CO, Streich W, Fuchs A, van den Bussche H, Stolper F, Altiner A. Coping with multimorbidity in old age--a qualitative study. BMC Fam Pract. 2012 May 29;13:45. doi: 10.1186/1471-2296-13-45.
    1. European Commission. 2007. Dec, Health and long-term care in the European Union .
    1. Handbook on Constructing Composite Indicators: Methodology and User Guide. Italy: OECD; 2008.
    1. Eysenbach G. What is e-health? J Med Internet Res. 2001;3(2):E20. doi: 10.2196/jmir.3.2.e20.
    1. Pagliari C, Sloan D, Gregor P, Sullivan F, Detmer D, Kahan JP, Oortwijn W, MacGillivray S. What is eHealth (4): a scoping exercise to map the field. J Med Internet Res. 2005 Mar 31;7(1):e9. doi: 10.2196/jmir.7.1.e9.
    1. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp; 2013.
    1. O'Neill B, Ziebland S, Valderas J, Lupiáñez-Villanueva F. User-generated online health content: a survey of Internet users in the United Kingdom. J Med Internet Res. 2014 Apr 30;16(4):e118. doi: 10.2196/jmir.3187.
    1. Atkinson NL, Saperstein SL, Pleis J. Using the internet for health-related activities: findings from a national probability sample. J Med Internet Res. 2009 Feb 20;11(1):e4. doi: 10.2196/jmir.1035.
    1. Fortin M, Stewart M, Poitras ME, Almirall J, Maddocks H. A systematic review of prevalence studies on multimorbidity: toward a more uniform methodology. Ann Fam Med Internet. 2012;10(2):142–151. doi: 10.1370/afm.1337.
    1. Mercer SW, Smith SM, Wyke S, O'Dowd T, Watt GC. Multimorbidity in primary care: developing the research agenda. Fam Pract. 2009 Apr;26(2):79–80. doi: 10.1093/fampra/cmp020.

Source: PubMed

3
Se inscrever