Historical view and future demand for knee arthroplasty in Sweden

Szilárd Nemes, Ola Rolfson, Annette W-Dahl, Göran Garellick, Martin Sundberg, Johan Kärrholm, Otto Robertsson, Szilárd Nemes, Ola Rolfson, Annette W-Dahl, Göran Garellick, Martin Sundberg, Johan Kärrholm, Otto Robertsson

Abstract

Background and purpose: The incidence of knee osteoarthritis will most likely increase. We analyzed historical trends in the incidence of knee arthroplasty in Sweden between 1975 and 2013, in order to be able to provide projections of future demand.

Patients and methods: We obtained information on all knee arthroplasties in Sweden in the period 1975-2013 from the Swedish Knee Arthroplasty Register, and used public domain data from Statistics Sweden on the evolution of and forecasts for the Swedish population. We forecast the incidence, presuming the existence of a maximum incidence.

Results: We found that the incidence of knee arthroplasty will continue to increase until a projected upper incidence level of about 469 total knee replacements per 10(5) Swedish residents aged 40 years and older is reached around the year 2130. In 2020, the estimated incidence of total knee arthroplasties per 10(5) Swedish residents aged 40 years and older will be 334 (95% prediction interval (PI): 281-374) and in 2030 it will be 382 (PI: 308-441). Using officially forecast population growth data, around 17,500 operations would be expected to be performed in 2020 and around 21,700 would be expected to be performed in 2030.

Interpretation: Today's levels of knee arthroplasty are well below the expected maximum incidence, and we expect a continued annual increase in the total number of knee arthroplasties performed.

Figures

Figure 1.
Figure 1.
Incidence of primary knee arthroplasty in Swedish residents aged 40 years or more, from the introduction of the procedure until the end of 2013, and the estimated time point (1988) and associated 95% confidence intervals when the growth rate of the incidence changed. The black diamonds represent the observed incidence and the red line is the fitted regression line.
Figure 2.
Figure 2.
Compound annual growth rate for primary knee arthroplasty and the comparative values for hip arthroplasty in Sweden.
Figure 3.
Figure 3.
The recorded and projected incidence of knee arthroplasty per 105 Swedish residents aged 40 years or more. The gray horizontal line represents the highest primary knee arthroplasty incidence estimated. The red line represents the projected number of knee arthroplasties with associated 95% prediction intervals. The black diamonds represent the incidence observed and the blue line is the fitted regression line.
Figure 4.
Figure 4.
Proportion of female patients who underwent knee arthroplasty and the associated 95% confidence intervals from 1975 until 2013. There was a significant decreasing trend, but females still predominate. Trend test: χ2 = 1722.02, p < 0.0001

References

    1. Bates JM, Granger CWJ. The combination of forecasts. OR. 1969;20(4):451–68.
    1. Bini SA, Sidney S, Sorel M. . J Arthroplasty. 2011;26(6):124–8.
    1. Bourne R, Chesworth B, Davis A, Mahomed N, Charron KJ. . Clin Orthop Relat Res. 2010;468(1):57–63.
    1. Bozdogan H. Model selection and Akaike Information Criterion (AIC)—The general-theory and its analytical extensions. Psychometrika. 1987;52(3):345–70.
    1. Cram P, Lu X, Kates SL, Singh JA, Li Y, Wolf BR. . JAMA. 2012;308(12):1227–36.
    1. Cross M, Smith E, Hoy D, Nolte S, Ackerman I, Fransen M, et al. . Ann Rheum Dis. 2014;73:1323–30.
    1. Fehring TK, Odum SM, Troyer JL, Iorio R, Kurtz SM, Lau EC. . J Arthroplasty. 2010;25:1175–81.
    1. Holmgren M, Lindgren A, de Munter J, Rasmussen F, Ahlstrom G. . BMC Public Health. 2014;14:10.
    1. Iorio R, Robb WJ, Healy WL, Berry DJ, Hozack WJ, Kyle RF, et al. . J Bone Joint Surg (Am) 2008;90-A(7):1598–605.
    1. Johnson VL, Hunter DJ. . Best Pract Res Clin Rheumatol. 2014;28(1):5–15.
    1. Knutson K, Robertsson O. . Acta Orthop. 2010;81:5–7.
    1. Koh IJ, Kim TK, Chang CB, Cho HJ, In Y. . Clin Orthop Rel Res. 2013;471(5):1441–50.
    1. Kotlarz H, Gunnarsson CL, Fang H, Rizzo JA. . Arthritis Rheum. 2009;60(12):3546–53.
    1. Kurtz S, Mowat F, Ong K, Chan N, Lau E, Halpern M. . J Bone Joint Surg (Am) 2005;87A(7):1487–97.
    1. Kurtz S, Ong K, Lau E, Mowat F, Halpern M. . J Bone Joint Surg (Am) 2007a;89-A(4):780–5.
    1. Kurtz SM, Ong KL, Schmier J, Mowat F, Saleh K, Dybvik E, et al. . J Bone Joint Surg (Am) 2007b;89-A:144–51.
    1. Kurtz SM, Ong KL, Lau E, Bozic KJ. . J Bone Joint Surg (Am) 2014;96-A:624–30.
    1. Lukacs PM, Burnham KP, Anderson DR. Model selection bias and Freedman’s paradox. Ann Inst Stat Math. 2010;62(1):117–25.
    1. Moral-Benito E. Model averaging in economics: and overview. Journal of Economic Surveys. 2013 DOI: 10.1111/joes.12044.
    1. Moré J. The Levenberg-Marquardt algorithm: Implementation and theory. In: Numerical analysis. (Ed. Watson GA) Springer Berlin Heidelberg. 1978;630:105–16.
    1. Muggeo V MR. . Statistics in Medicine. 2003;22(19):3055–71.
    1. Murray CJL, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, et al. . The Lancet. 2012;380(9859):2197–223.
    1. Nemes S, Gordon M, Rogmark C, Rolfson O. . Acta Orthop. 2014;85(3):238–43.
    1. Neovius K, Johansson K, Kark M, Tynelius P, Rasmussen F. . Eur J Public Health. 2013;23(2):312–5.
    1. Pabinger C, Geissler A. . Osteoarthritis Cartilage. 2014;22(6):734–41.
    1. Park EW, Lim SM. Empirical estimation of the asymptotes of disease progress curves and the use of the Richards generalized rate parameters for describing disease progress. Phytopathology. 1985;75(1):786.
    1. R Foundation for Statistical Computing 2014; Vienna, Austria: R Core Team. R: A Language and environment for statistical computing.
    1. Ranawat CS. History of total knee replacement. J South Orthop Assoc. 2002;11(4):218–26.
    1. Robertsson O, Dunbar MJ, Knutson K, Lidgren L. . Acta Orthop Scand. 2000;71:376–80.
    1. SKAR. Swedish Knee Arthroplasty Register Annual Report 2014. 2014. ISBN 978-91-979924-8-0.
    1. Symonds M RE, Moussalli A. A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behav Ecol Sociobiol. 2011;65(1):13–21.
    1. Thorstensson CA, Garellick G, Rystedt H, Dahlberg LE. . Musculoskeletal Care. 2014 doi: 10.1002/msc.1085. [Epub ahead of print]
    1. Turner ME, Monroe RJ, Lucas HL., Jr Generalized asymptotic regression and non-linear path analysis. Biometrics. 1961;17(1):120–43.
    1. Turner ME, Jr, Blumenstein BA, Sebaugh JL. . Biometrics. 1969;25(3):577–80.
    1. Wagenmakers EJ, Farrell S. . Psychon Bull Rev. 2004;11(1):192–6.
    1. Wilson NA, Schneller ES, Montgomery K, Bozic KJ. . Health Affairs. 2008;27:1587–98.
    1. Zhang Y, Jordan JM. . Clin Geriatr Med. 2010;26:355–69.

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