The influence of population characteristics on variation in general practice based morbidity estimations

BMC Public Health

GP Westert³  FG Schellevis¹  MWM de Waal⁶  RA Verheij⁴  HJ Brouwer²  K van Boven⁸  MCJ Biermans⁷  M van den Akker⁵  HC Boshuizen⁹  N Hoeymans⁹  C van den Dungen³ 

[1] Department of General Practice/EMGO Institute for health and care research, VU University Medical Centre, Amsterdam, the Netherlands;Department of General Practice, Academic Medical Centre/University of Amsterdam, Amsterdam, the Netherlands;Department Tranzo, Faculty of Social and Behavioural Sciences, Tilburg University, Tilburg, the Netherlands;Netherlands Institute for Health Services Research (NIVEL), Utrecht, the Netherlands;Department of General Practice, School for Public Health and Primary Care (Caphri), Maastricht University, Maastricht, the Netherlands;Department of Public Health and Primary Care, Leiden University Medical Centre, Leiden, the Netherlands;Department of Primary and Community Care, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands;General Practitioner, Franeker, the Netherlands;National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA, Bilthoven, the Netherlands

关键词: Prevalence;    Public health;    Population characteristics;    Medical records;    Incidence;    Family practice;   

Others  :  1164049
DOI  :  10.1186/1471-2458-11-887

 received in 2011-05-04, accepted in 2011-11-24,  发布年份 2011

【 摘 要 】

Background

General practice based registration networks (GPRNs) provide information on morbidity rates in the population. Morbidity rate estimates from different GPRNs, however, reveal considerable, unexplained differences. We studied the range and variation in morbidity estimates, as well as the extent to which the differences in morbidity rates between general practices and networks change if socio-demographic characteristics of the listed patient populations are taken into account.

Methods

The variation in incidence and prevalence rates of thirteen diseases among six Dutch GPRNs and the influence of age, gender, socio economic status (SES), urbanization level, and ethnicity are analyzed using multilevel logistic regression analysis. Results are expressed in median odds ratios (MOR).

Results

We observed large differences in morbidity rate estimates both on the level of general practices as on the level of networks. The differences in SES, urbanization level and ethnicity distribution among the networks' practice populations are substantial. The variation in morbidity rate estimates among networks did not decrease after adjusting for these socio-demographic characteristics.

Conclusion

Socio-demographic characteristics of populations do not explain the differences in morbidity estimations among GPRNs.

【 授权许可】

   
2011 van den Dungen et al.; licensee BioMed Central Ltd.

【 参考文献 】

• [1]Lucht vd F, Polder JJ: Towards better health. The 2010 Dutch Public Health Status and Frorecasts report. Bilthoven: The National Institute for Public Health and the Environment (RIVM); 2011.
• [2]Hollander d AEM, Hoeymans N: Care for Health. The 2006 Dutch Public Health Status and Forecasts Report. Bilthoven: The National Institute for Public Health and the Environment; 2007. Report No.: 270061004
• [3]Gijsen R, Poos R: Using registries in general practice to estimate countrywide morbidity in The Netherlands. Public Health 2006, 120:923-936.
• [4]Dungen vd C, Hoeymans N, Gijsen R, Akker vd M, Boesten J, Brouwer H, et al.: What factors explain the differences in morbidity estimations among general practice registration networks in the Netherlands? A first analysis. Eur J Gen Pract 2008, 14(suppl 1):53-62.
• [5]Bakker d DH, Groenewegen PP: Primary care in the Netherlands: current situation and trends. Ital J Public Health 2009, 6(2):128-134.
• [6]Mulder M: Non western immigrants 2009 [Dutch]. [http:/ / www.zorgatlas.nl/ beinvloedende-factoren/ demografie/ etniciteit/ niet-westerse-allochtonen-2009] webciteBilthoven: RIVM; 2009. [updated 2009 9 December 2010; cited 2009 4 December 2009];
• [7]Marinus AMF: Inter-doctorvariation in the Transition Project [Dutch]. Huisarts Wet 1990, 33(1):4-8.
• [8]Jordan K, Clarke AM, Symmons DP, Fleming D, Porcheret M, Kadam UT, et al.: Measuring disease prevalence: a comparison of musculoskeletal disease using four general practice consultation databases. Br J Gen Pract 2007, 57(534):7-14.
• [9]Nightingale AL, Farmer RD, de Vries CS: Systemic lupus erythematosus prevalence in the UK: methodological issues when using the General Practice Research Database to estimate frequency of chronic relapsing-remitting disease. Pharmacoepidemiol Drug Saf 2007, 16(2):144-151.
• [10]WONCA (classification Committee), Bentzen N: An international glossary for general/family practice. Fam Pract 1995, 12(3):341-369.
• [11]Bot SD, van der Waal JM, Terwee CB, van der Windt DA, Schellevis FG, Bouter LM, et al.: Incidence and prevalence of complaints of the neck and upper extremity in general practice. Ann Rheum Dis 2005, 64(1):118-123.
• [12]Fleming DM, Bartelds A, Chapman RS, Cross KW: The consistency of shingles and its significance for health monitoring. Eur J Epidemiol 2004, 19(12):1113-1118.
• [13]Lamberts H, Woods M: International Classification of Primary Care (ICPC). Oxford: Oxford University Press; 1987.
• [14]The Research Committee of the Council of the College of General Practitioners: A Classification of Disease. J Coll Gen Pract Res Newsl (Br J Gen Pract) 1959, 2(2):140-159.
• [15]Oliemans AP: Morbidity in General Practice [Dutch]. Leiden; 1969:1-257.
• [16]Statistics Netherlands (CBS Centraal Bureau voor de Statistiek)[http://www.cbs.nl/nl-NL/menu/cijfers/default.htm] webcite 2010.
• [17]Knol F: Statusscore. [http://www.scp.nl/content.jsp?objectid=default:20133] webciteThe netherlands Institute for Social Science (SCP Sociaal en Cultureel Planbureau); 2006.
• [18]Larsen K, Merlo J: Appropriate assessment of Neighborhood Effects on Individual Health: Integrating Random and Fixed Effects in Multilevel Logistic Regression. Am J Epidemiol 2005, 161(1):81-88.
• [19]Merlo J, Chaix B, Ohlsson H, Beckman A, Johnell K, Hjerpe P, et al.: A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena. J Epidemiol Community Health 2006, 60:290-297.
• [20]Connolly V, Unwin N, Sherriff P, Bilous R, Kelly W: Diabetes prevalence and socioeconomic status: a population based study showing increased prevalence of type 2 diabetes mellitus in deprived areas. J Epidemiol Community Health 2000, 54(3):173-177.