## REFERENCES

1.Klevens, R, et al. Invasive methicillin-resistant *Staphylococcus aureus* infections in the United States. Journal of the American Medical Association 2007 298: 1763–1771.

2.Ellingson, K, et al. Sustained reduction in the clinical incidence of methicillin-resistant *Staphylococcus aureus* colonization or infection associated with a multifaceted infection control intervention. Infection Control and Hospital Epidemiology 2011 32: 1–8.

3.Brown, C, Lilford, R. The stepped wedge trial design: a systematic review. BMC Medical Research Methodology 2006; 6: 54.

4.Smith, P. Splines as a useful and convenient statistical tool. American Statistician 1979; 33: 57–62.

5.Kim, H, et al. Comparability of segmented line regression models. Biometrics 2004; 60: 1005–1014.

6.Kim, H, et al. Permutation tests for joinpoint regression with application to cancer rates. Statistics in Medicine 2000; 19: 335–351.

7.Cleveland, W, Devlin, S. Locally weighted regression: an approach to regression analysis by local fitting. American Statistician 1988; 83: 596–610.

8.Wagner, AK, et al. Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics 2002; 7: 299–309.

9.Matowe, L, et al. Interrupted time series analysis in clinical research. Annals of Pharmacotherapy 2003; 37: 1110–1116.

10.Shardell, M, et al. Statistical analysis and application of quasi experiments to antimicrobial resistance intervention studies. Clinical Infectious Diseases 2007; 45: 901–907.

11.Gillings, D, Makuc, D, Sigel, E. Analysis of interrupted time series mortality trends: an example to evaluate regionalized perinatal care. American Journal of Public Health 1981; 71: 38–46.

12.Madden, J, et al. Effects of a law against early postpartum discharge on newborn follow-up, adverse events and HMO expenditures. New England Journal of Medicine 2002; 347: 2031–2038.

13.Ross-Degnan, D, et al. Examining product risk in context. Market withdrawal of zomepirac as a case study. Journal of the American Medical Association 1993; 270: 1937–1942.

14.Soumerai, SB, et al. Payment restrictions for prescription drugs under Medicaid: effects on therapy, cost, and equity. New England Journal of Medicine 1987; 317: 550–556.

15.Mol, P, et al. Improving compliance with hospital antibiotic guidelines: a time-series intervention analysis. Journal of Antimicrobial Chemotherapy 2005; 55: 550–557.

16.Haung, S, et al. Impact of routine intensive care unit surveillance cultures and resultant barrier precautions on hospital-wide methicillin-resistant *Staphylococcus aureus* bacteremia. Clinical Infectious Diseases 2006; 43: 971–978.

17.Bosso, J, Mauldin, P. Using interrupted time series to assess associations of fluoroquinolone formulary changes with susceptibility of gram-negative pathogens and isolation rates of methicillin-resistant *Staphylococcus aureus*. Antimicrobial Agents and Chemotherapy 2006; 50: 2106–2112.

18.Biglan, A, Ary, D, Wagenaar, A. The value of interrupted time-series experiments for community intervention research. Prevention Science 2000; 1: 31–49.

19.Fernández-Pérez, C, Tejada, J, Carrasco, M. Multivariate time series analysis in nosocomial infection surveillance: a case study. International Journal of Epidemiology 1998; 27: 282–288.

20.Feng, PJ, et al. Clinical incidence of methicillin-resistant *Staphylococcus aureus* (MRSA) colonization or infection as a proxy measure for MRSA transmission in acute care hospitals. Infection Control and Hospital Epidemiology 2011; 32: 20–25.

21.DerSimonian, R, Laird, N. Meta-analysis in clinical trials. Controlled Clinical Trials 1986; 7: 177–188.

22.Egger, M, Davey Smith, G, Altman, D. Systematic Reviews in Health Care. Meta-analysis in Context. London: BMJ Books, 2001.

23.Bhargava, A, Franzini, L, Narendranathan, W. Serial correlation and the fixed effects models. Review of Economic Studies 1982; 49: 533–549.

24.Hardin, J, Hilbe, J.Generalized Estimating Equations. London: Chapman and Hall/CRC London, 2003.

25.Marschner, I, Gillett, A. Relative risk regression: reliable and flexible methods for log-binomial models. Biostatistics 2012; 13; 179–192.