Chapter 6 Discussion
6.5 Limitations
While this study benefits from the large sample size and longitudinal time trend decomposition analysis and Bayesian dynamic linear model, there are some limitations that are noteworthy. First, the database was obtained from the electronic hospital registration and infection control system. The initial electronic database lack individual antibiotics dosing documentation and disease severity index for non-intensive care unit patients, which might affect the exact relative risk estimates.
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Secondly, this study was a metropolitan teaching hospital-based study, the generalization of specific results such as relative risk or intervention efficacy to other levels of healthcare institutions may be limited. Further study was needed for the external validation. Third, the HAI definition was modified in 2008 during the study period of twenty years, and the inclusion criteria was changed accordingly by the hospital central infection committee. The absolute incidence and the absolute interventions efficacy might be affected, especially in urinary tract infection and bacteremia. Their monthly representation was still useful for evaluating trends. Fourth, we did not include covariates such as patient comorbidities into the model. The most related covariates was departments of admission and infection sites, which represented part of major comorbidities of the patients in this study. In the time series study, the autoregressive order was the proxy for the history information unavailable.
Fifth, the follow-up time for the intervention evaluation may be short, especially for Bundle care intervention. Finally, the model fitting diagnostics were not done, further studies may be needed.
In conclusion, my thesis here developed a novel Bayesian generalized linear mixed ARMA model to monitor and evaluate the long-term time series on monthly
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frequencies of HAIs in association with the impact of a set of interventions, the effects of time trend, seasonal variation, autoregressive order and innovation (moving average), and personal characteristics (age and gender) taking in account hierarchical correlated data property. This approach can be easily applied to forecasting the outcome of long-term time-series data and can be used for evaluation of the efficacy of intervention programs in the absence of randomized controlled trial design.
120 TABLES
Table 5.1. 1 Incidence rate by calendar year Year patient-days
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Table 5.1. 2 The incidence rate of HAI by different factors and microbes Variable Classification Incidence rate (95% CI)
Age 0-9 1.27(1.14, 1.41)
Gender Female 4.22(4.13, 4.30)
Male 4.35(4.26, 4.43)
Department CV 3.61(3.38, 3.84)
Chest 7.52(7.21, 7.83)
Season Spring 4.46(4.33, 4.58)
Summer 4.57(4.44, 4.70)
Autumn 4.22(4.10, 4.34)
Winter 3.91(3.79, 4.02)
Infection site Bacteremia 1.12(1.09, 1.15)
RTI 0.72(0.69, 0.74)
SSI: surgical site infection; UTI: urinary tract infection; GI: gastrointestinal system;
SST: skin and soft tissue; EENT: eye, ear, nose, throat, or mouth infection
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Table 5.1. 3 Univariate and multi-variable analysis for HAIs incidence
Variable Classification Univariate analysis Multi-variable analysis RR(95%CI) p-value aRR(95%CI) p-value
Admission type Emergency/ OPD 2.17(2.11, 2.23) <0.0001 1.60(1.55, 1.65) <0.0001
Infection site Bacteremia / SSI 2.40(2.28, 2.52) <0.0001 2.40(2.28, 2.52) <0.0001 PNEU / SSI 1.57(1.49, 1.66) 1.57(1.49, 1.66) CV: cardiovascular, GI: Gastrointestinal unit, PNEU: pneumonia, SSI: surgical site
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infection, UTI: urinary tract infection, SST: skin and soft tissue, EENT: eye, ear, nose, throat, or mouth
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Table 5.2. 1 The seasonal effect on HAIs incidence (univariate analysis)
Season RR 95% CI p value
Spring /Winter 1.13 1.09 1.18 <.0001
Summer /Winter 1.16 1.11 1.21
Autumn /Winter 1.07 1.03 1.12
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Table 5.2. 2 Time trend analysis with de-seasonalized HAIs
Method Model Trend Estimate SD R2 p value
Original Model a Linear 2.790E-03 1.660E-03 0.0362 0.0941 Quadratic -2.341E-05 1.239E-05 0.0602
Cubic -5.777E-07 2.183E-07 0.0087
Model b Linear -1.210E-03 6.868E-04 0.0091 0.0785 Quadratic -1.475E-05 1.212E-05 0.2250
Model c Linear -1.070E-03 6.767E-04 0.0069 0.1165 Excluding Model a Linear 2.610E-03 1.690E-03 0.0365 0.1241
Outliers* Quadratic -2.287E-05 1.259E-05 0.0707
Cubic -5.660E-07 2.221E-07 0.0115
Model b Linear -1.320E-03 7.007E-04 0.0111 0.0605 Quadratic -1.466E-05 1.233E-05 0.2357
Model c Linear -1.180E-03 6.916E-04 0.0091 0.0884 Model a: time trend analysis using residual for linear, quadratic, and cubic
Model b: time trend analysis using residual for linear, and quadratic Model c: time trend analysis using residual for linear
*excluding outliers : May-95, Jun-96, Feb-97, Dec-99, Nov-00
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Table 5.2. 3 De-seasonalization and de-trend time series of HAI incidence Autoregressive Coefficient SD p value
AR(1) -0.0982 0.0688 0.1532
AR(2) 0.0775 0.0683 0.2565
AR(3) 0.1068 0.0682 0.1175
AR(4) 0.0307 0.0678 0.6506
AR(1) -0.0846 0.0686 0.2174
AR(2) 0.0819 0.0686 0.2321
AR(3) 0.1254 0.0678 0.0644
AR(1) -0.0735 0.0689 0.2860
AR(2) 0.0833 0.0681 0.2214
AR(1) -0.0794 0.0679 0.2426
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Table 5.2. 4 The seasonal effect on bacteremia HAIs incidence (univariate analysis)
Season RR 95% CI p value
Spring /Winter 1.16 1.07 1.25 <.0001
Summer /Winter 1.19 1.10 1.29
Autumn /Winter 1.08 1.00 1.17
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Table 5.2. 5 Time trend analysis with de-seasonalized residual for bacteremia HAIs
Method Model Trend Estimate SD R2 p value
Original Model a Linear 3.920E-03 2.160E-03 0.0397 0.0710 Quadratic -3.485E-05 1.618E-05 0.0324
Cubic -7.838E-07 2.850E-07 0.0065
Model b Linear -1.500E-03 8.977E-04 0.0101 0.0953 Quadratic -2.310E-05 1.584E-05 0.1464 Model c Linear -1.270E-03 8.859E-04 0.0049 0.1521 Excluding Model a Linear 2.430E-03 2.180E-03 0.0341 0.2650 Outliers* Quadratic -1.448E-05 1.617E-05 0.3713
Cubic 2.134E-08 2.878E-07 0.9410
Model b Linear 2.580E-03 8.851E-04 0.0388 0.0040 Quadratic -1.476E-05 1.568E-05 0.3476 Model c Linear 2.700E-03 8.749E-04 0.0393 0.0023 Model a: time trend analysis using residual for linear, quadratic, and cubic
Model b: time trend analysis using residual for linear, and quadratic Model c: time trend analysis using residual for linear
*excluding outliers : May-95, Jun-96, Feb-97, Dec-99, Nov-00
Table 5.2. 6 Bacteremia HAI incidence time series after seasonalization and
de-129 trend
Autoregressive Coefficient SD p value
AR(1) 0.0582 0.0693 0.4012
AR(2) 0.0478 0.0688 0.4870
AR(3) 0.1122 0.0687 0.1023
AR(4) -0.0736 0.0688 0.2850
AR(1) 0.0461 0.0687 0.5025
AR(2) 0.0465 0.0687 0.4987
AR(3) 0.1057 0.0685 0.1229
AR(1) 0.0482 0.0690 0.4851
AR(2) 0.0582 0.0689 0.3977
AR(1) 0.0470 0.0688 0.4941
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Table 5.2. 7 The seasonal effect on pneumonia HAIs incidence (univariate analysis)
Season RR 95% CI p value
Spring /Winter 1.03 0.93 1.13 0.0897
Summer /Winter 1.12 1.02 1.24
Autumn /Winter 1.09 0.98 1.20
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Table 5.2. 8 Time trend analysis with de-seasonalized residual for pneumonia HAIs
Method Model Trend Estimate SD R2 p value
Original Model a Linear 2.590E-03 2.130E-03 0.0369 0.2240 Quadratic -1.413E-05 1.589E-05 0.3747
Cubic 4.453E-09 2.825E-07 0.9874
Model b Linear 2.620E-03 8.654E-04 0.0414 0.0027 Quadratic -1.420E-05 1.538E-05 0.3570 Model c Linear 2.750E-03 8.538E-04 0.0421 0.0015 Excluding Model a Linear 3.070E-03 2.140E-03 0.0421 0.1526 Outliers* Quadratic -1.377E-05 1.590E-05 0.3875
Cubic -3.984E-08 2.829E-07 0.8882
Model b Linear 2.790E-03 8.683E-04 0.0466 0.0015 Quadratic -1.323E-05 1.539E-05 0.3910 Model c Linear 2.910E-03 8.564E-04 0.0478 0.0008 Model a: time trend analysis using residual for linear, quadratic, and cubic
Model b: time trend analysis using residual for linear, and quadratic Model c: time trend analysis using residual for linear
*excluding outliers: Jul-03, Apr-08, Nov-08
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Table 5.2. 9 Pneumonia HAI incidence time series after seasonalization and de-trend
Autoregressive Coefficient SD p value
AR(1) 0.1174 0.0691 0.0895
AR(2) 0.1671 0.0693 0.0158
AR(3) 0.1099 0.0692 0.1125
AR(4) -0.0701 0.0694 0.3124
AR(1) 0.1108 0.0688 0.1072
AR(2) 0.1561 0.0684 0.0225
AR(3) 0.1025 0.0688 0.1367
AR(1) 0.1284 0.068 0.0591
AR(2) 0.1695 0.0681 0.0127
AR(1) 0.1546 0.068 0.023
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Table 5.2. 10 The seasonal effect on SSI HAIs incidence (univariate analysis)
Season RR 95% CI p value
Spring /Winter 1.24 1.10 1.40 0.0029
Summer /Winter 1.20 1.06 1.36
Autumn /Winter 1.10 0.97 1.25
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Table 5.2. 11 Time trend analysis with de-seasonalized residual for SSI HAIs
Method Model Trend Estimate SD R2 p value
Original Model a Linear -2.920E-03 2.280E-03 0.1214 0.2018 Quadratic -6.460E-06 1.698E-05 0.7042
Cubic -3.195E-07 2.997E-07 0.2877
Model b Linear -5.140E-03 9.299E-04 0.1209 <.0001 Quadratic -1.600E-06 1.636E-05 0.9221 Model c Linear -5.120E-03 9.138E-04 0.1250 <.0001 Excluding Model a Linear -3.400E-03 2.320E-03 0.1312 0.1433
Outliers* Quadratic -4.780E-06 1.715E-05 0.7808
Cubic -2.863E-07 3.033E-07 0.3463
Model b Linear -5.400E-03 9.439E-04 0.1317 <.0001 Quadratic -5.668E-07 1.655E-05 0.9727 Model c Linear -5.390E-03 9.292E-04 0.1359 <.0001 SSI: surgical site infection
Model a: time trend analysis using residual for linear, quadratic, and cubic Model b: time trend analysis using residual for linear, and quadratic Model c: time trend analysis using residual for linear
*excluding outliers:
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Table 5.2. 12 SSI HAIs incidence time series after de-seasonalization and de-trend Autoregressive Coefficient SD p value
AR(1) 0.125 0.0696 0.0728
AR(2) 0.0286 0.0701 0.6837
AR(3) -0.0352 0.0701 0.6160
AR(4) -0.0778 0.0696 0.2637
AR(1) 0.1287 0.0696 0.0646
AR(2) 0.0263 0.0702 0.7080
AR(3) -0.0452 0.0696 0.5164
AR(1) 0.1275 0.0695 0.0666
AR(2) 0.0205 0.0695 0.7678
AR(1) 0.1302 0.0688 0.0583
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Table 5.2. 13 The seasonal effect on urinary tract HAIs incidence (univariate analysis)
Season RR 95% CI p value
Spring /Winter 1.11 1.04 1.18 <0.0001
Summer /Winter 1.16 1.09 1.24
Autumn /Winter 1.04 0.97 1.11
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Table 5.2. 14 Time trend analysis with de-seasonalized residual for UTI HAIs
Method Model Trend Estimate SD R2 p value
Original Model a Linear 6.330E-03 1.850E-03 0.0517 0.0007 Quadratic -2.600E-05 1.384E-05 0.0617
Cubic -9.053E-07 2.438E-07 0.0003
Model b Linear 6.705E-05 7.787E-04 -0.0052 0.9315 Quadratic -1.242E-05 1.374E-05 0.3672 Model c Linear 1.913E-04 7.662E-04 -0.0044 0.8031
Excluding Model a Linear 6.260E-03 1.880E-03 0.0502 0.001
Outliers* Quadratic -2.530E-05 1.403E-05 0.0727
Cubic -9.036E-07 2.473E-07 0.0003
Model b Linear -1.418E-05 7.933E-04 -0.0059 0.9858 Quadratic -1.201E-05 1.394E-05 0.3901 Model c Linear 9.803E-05 7.820E-04 -0.0047 0.9004 Model a: time trend analysis using residual for linear, quadratic, and cubic
Model b: time trend analysis using residual for linear, and quadratic Model c: time trend analysis using residual for linear
*excluding outliers : May-95, Jun-96, Feb-97, Dec-99
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Table 5.2. 15 UTI HAI incidence time series after de-seasonalization and de-trend Autoregressive Coefficient SD p value
AR(1) 0.0748 0.0686 0.2755
AR(2) 0.1033 0.0683 0.1304
AR(3) 0.0416 0.0683 0.5424
AR(4) -0.1408 0.0682 0.0389
AR(1) 0.0652 0.0688 0.3431
AR(2) 0.0922 0.0686 0.1793
AR(3) 0.0347 0.0687 0.6138
AR(1) 0.066 0.0688 0.3370
AR(2) 0.0903 0.0687 0.1886
AR(1) 0.0735 0.0687 0.2842
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Table 5.2. 16 The seasonal effect on E.coli HAIs incidence (univariate analysis)
Season RR 95% CI p value
Spring /Winter 1.10 0.85 A1.43 0.7962 Summer /Winter 1.08 0.83 1.39
Autumn /Winter 1.14 0.88 1.47
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Table 5.2. 17 Time trend analysis with de-seasonalized residual for E.coli HAIs
Method Model Trend Estimate SD R2 p value
Original Model a Linear 1.099E+00 8.453E-01 0.0760 0.9958 Quadratic 1.078E+00 8.335E-01 <.0001
Cubic 1.136E+00 8.796E-01 0.6073 Model b Linear -1.610E-03 1.530E-03 0.0799 0.2943 Quadratic -1.088E-04 2.606E-05 <.0001 Model c Linear 8.710E-05 1.540E-03 -0.0056 0.9550
Excluding Model a Linear -4.585E-05 3.590E-03 0.0746 0.9898 Outliers* Quadratic -1.139E-04 2.835E-05 <.0001
Cubic -2.418E-07 4.936E-07 0.6249
Model b Linear -1.630E-03 1.560E-03 0.0787 0.2973 Quadratic -1.091E-04 2.655E-05 <.0001 Model c Linear 1.339E-05 1.570E-03 -0.0058 0.9932 Model a: time trend analysis using residual for linear, quadratic, and cubic
Model b: time trend analysis using residual for linear, and quadratic Model c: time trend analysis using residual for linear
*excluding outliers: May-95, Feb-97, Dec-99, Apr-00
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Table 5.2. 18 E. coli bacteremia HAI incidence time series after de-seasonalization and de-trend
Autoregressive Coefficient SD p value
AR(1) -0.0266 0.0755 0.7242
AR(2) -0.1758 0.0753 0.0196
AR(3) 0.0791 0.0755 0.2948
AR(4) -0.0536 0.0723 0.4582
AR(1) -0.0385 0.076 0.6126
AR(2) -0.1713 0.0751 0.0225
AR(3) 0.0376 0.0729 0.6065
AR(1) -0.0431 0.075 0.5660
AR(2) -0.1547 0.072 0.0315
AR(1) -0.0302 0.0727 0.6778
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Table 5.2. 19 The seasonal effect on Pseudomonas aeruginosa HAIs incidence
Season RR 95% CI p value
Spring /Winter 0.94 0.67 1.31 0.9171
Summer /Winter 1.05 0.76 1.47
Autumn /Winter 1.01 0.73 1.40
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Table 5.2. 20 Time trend analysis with de-seasonalized Pseudomonas aeruginosa bacteremia HAIs
Method Model Trend Estimate SD R2 p value
Original Model a Linear -1.650E-03 4.210E-03 -0.0033 0.696 Quadratic -2.140E-05 3.358E-05 0.5249
Cubic -1.793E-07 5.796E-07 0.7575
Model b Linear -2.820E-03 1.830E-03 0.0025 0.1255 Quadratic -1.815E-05 3.180E-05 0.5690 Model c Linear -2.540E-03 1.760E-03 0.0069 0.1509
Excluding Model a Linear -1.770E-03 4.310E-03 -0.0021 0.6811 Outliers* Quadratic -1.877E-05 3.410E-05 0.5828
Cubic -1.873E-07 5.911E-07 0.7517
Model b Linear -3.000E-03 1.870E-03 0.0038 0.11 Quadratic -1.553E-05 3.243E-05 0.6328 Model c Linear -2.770E-03 1.800E-03 0.0089 0.1256
Model a: time trend analysis using residual for linear, quadratic, and cubic Model b: time trend analysis using residual for linear, and quadratic Model c: time trend analysis using residual for linear
*excluding outliers: May-95, Sep-96, Dec-99, Jan-00
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Table 5.2. 21 P. aeruginosa HAI incidence time series after de-seasonalization and de-trend
Autoregressive Coefficient SD p value
AR(1) 0.1270 0.0685 0.0635
AR(2) -0.0036 0.0684 0.9578
AR(3) -0.1402 0.0686 0.0409
AR(4) 0.1186 0.0687 0.0842
AR(1) 0.1192 0.0687 0.0827
AR(2) -0.0050 0.0692 0.9428
AR(3) -0.1201 0.0688 0.0809
AR(1) 0.1202 0.0691 0.0819
AR(2) -0.0162 0.069 0.8144
AR(1) 0.1179 0.0683 0.0846
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Table 5.2. 22 ARMA model for the HAI infection sites
Outcome Model Effect Estimate S.E. p-value
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Table 5.2. 23 ARMA model for the bacteremia species
Outcome Model Effect Estimate S.E. p-value
E. coli ARMA(1,1) Mean 4.24E-08 5.38E-05 0.9994
MA(1) -0.5227 0.5101 0.3066
AR(1) -0.4318 0.5393 0.4243
P. aeruginosa ARMA(3,2) Mean 3.81E-06 3.88E-05 0.9219
MA(1) -1.2203 0.0094 <.0001
MA(2) -0.9983 0.0102 <.0001
AR(1) -1.0610 0.0705 <.0001
AR(2) -0.8077 0.0855 <.0001
AR(3) 0.0442 0.0711 0.5347
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Table 5.3. 1 The adjusted time series model for HAIs incidence (normal distribution)
Parameter Level Estimate S.E. Chisq p-value
Season Spring/Winter 0.0101 0.0168 1.16 0.7619
Summer/Winter 0.0174 0.0168
Autumn/Winter 0.0131 0.0170
Time trend Linear 2.6649E-05 0.0002 0.01 0.9104 Quadratic -5.1747E-06 1.7619E-06 8.62 0.0033 Cubic -1.1630E-07 3.1100E-08 13.93 0.0002 Autocorrelations AR (1) -0.0347 0.0055 39.40 <.0001
Age 0-9 / >=80 -0.9433 0.0257 2639.75 <.0001 10-19 / >=80 -0.9245 0.0257
20-29 / >=80 -0.9346 0.0257 30-39 / >=80 -0.8989 0.0256 40-49 / >=80 -0.7309 0.0255 50-59 / >=80 -0.6117 0.0254 60-69 / >=80 -0.4370 0.0253 70-79 / >=80 -0.2422 0.0252
Gender Male / Female 0.0250 0.0119 4.43 0.0354
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Table 5.3. 2 The adjusted Poisson model for HAIs incidence
Parameter Level Estimate S.E. Chisq p-value Season Spring/Winter 0.0381 0.0212 11.4 0.0097
Summer/Winter 0.0710 0.0213 Autumn/Winter 0.0477 0.0218
Time trend Linear 7.4737E-04 0.000296757 6.34 0.0118 Quadratic -1.6703E-05 2.3646E-06 50.61 <.0001 Cubic -3.4250E-07 4.05E-08 71.48 <.0001 Autocorrelations AR (1) 8.7558E-04 0.0004 6.05 0.0139 Age 0-9 / >=80 -2.0160 0.0591 6508.2 <.0001
10-19 / >=80 -1.7863 0.0783 20-29 / >=80 -2.1405 0.0531 30-39 / >=80 -1.8330 0.0420 40-49 / >=80 -1.1082 0.0309 50-59 / >=80 -0.8066 0.0262 60-69 / >=80 -0.5360 0.0229 70-79 / >=80 -0.2868 0.0214
Gender Male / Female 0.0301 0.0149 4.1 0.043
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Table 5.3. 3 The adjusted negative binominal model for HAIs incidence
Parameter Level Estimate S.E. Chisq p-value
Season Spring/Winter 0.0446 0.0319 4.23 0.2379 Summer/Winter 0.0628 0.0318
Autumn/Winter 0.0480 0.0324
Time trend Linear 6.3281E-04 4.4455E-04 2.02 0.1548 Quadratic -1.5758E-05 3.5092E-06 20.01 <.0001 Cubic -3.5340E-07 6.07E-08 33.68 <.0001 Autocorrelations AR (1) 7.5456E-04 0.0005 1.95 0.1629 Age 0-9 / >=80 -2.0345 0.0658 2481.32 <.0001
10-19 / >=80 -1.8159 0.0842 20-29 / >=80 -2.1219 0.061 30-39 / >=80 -1.8335 0.0510 40-49 / >=80 -1.1325 0.0421 50-59 / >=80 -0.8336 0.0387 60-69 / >=80 -0.5421 0.0364 70-79 / >=80 -0.2803 0.0355
Gender Male / Female 0.1063 0.0225 22.68 <.0001
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Table 5.3. 4 The adjusted time series model for HAIs bacteremia incidence (normal distribution)
Parameter Level Estimate S.E. Chisq p-value
Season Spring/Winter 0.0989 0.0675 4.91 0.1785 Summer/Winter 0.1175 0.067
Autumn/Winter 0.0084 0.068
Time trend Linear -1.30E-03 0.0004 11.18 0.0008
Autocorrelations AR (1) -1.28E-02 0.0203 0.4 0.5263
Age 0-9 / >=80 -1.7992 0.1054 546.83 <.0001 10-19 / >=80 -1.9445 0.1059
20-29 / >=80 -1.8778 0.1067 30-39 / >=80 -1.7759 0.1057 40-49 / >=80 -1.3476 0.1031 50-59 / >=80 -1.0596 0.1017 60-69 / >=80 -0.7646 0.101 70-79 / >=80 -0.4505 0.1007
Gender Male / Female 0.249 0.0474 27.43 <.0001
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Table 5.3. 5 The adjusted Poisson model for bacteremia HAIs incidence Parameter Level Estimate S.E. Chisq p-value Season Spring/Winter 0.0561 0.0415 3.9 0.2726
Summer/Winter 0.0744 0.0417 Autumn/Winter 0.0226 0.0428
Time trend Linear -1.1876E-03 0.0002 23.22 <.0001 Autocorrelations AR (1) 9.2969E-03 0.0019 22.73 <.0001 Age 0-9 / >=80 -1.5271 0.0994 1346.41 <.0001
10-19 / >=80 -1.7659 0.1619 20-29 / >=80 -2.0728 0.1099 30-39 / >=80 -1.5756 0.0801 40-49 / >=80 -0.8296 0.059 50-59 / >=80 -0.5143 0.0506 60-69 / >=80 -0.3509 0.0461 70-79 / >=80 -0.1959 0.0442
Gender Male / Female 0.1724 0.0292 35.04 <.0001
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Table 5.3. 6 The adjusted negative binominal model for bacteremia HAIs incidence Parameter Level Estimate S.E. Chisq p-value
Season Spring/Winter 0.0681 0.0499 2.79 0.4255 Summer/Winter 0.0745 0.0499
Autumn/Winter 0.0368 0.0511
Time trend Linear -1.3145E-03 0.0003 19.36 <.0001 Autocorrelations AR (1) 9.3852E-03 0.0024 15.57 <.0001 Age 0-9 / >=80 -1.5427 0.1048 976.92 <.0001
10-19 / >=80 -1.7821 0.1658 20-29 / >=80 -2.0857 0.1153 30-39 / >=80 -1.5854 0.0866 40-49 / >=80 -0.8448 0.0675 50-59 / >=80 -0.5323 0.0603 60-69 / >=80 -0.3626 0.0565 70-79 / >=80 -0.2019 0.055
Gender Male / Female 0.1940 0.0351 30.76 <.0001
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Table 5.3. 7 The adjusted time series model for HAIs pneumonia incidence (normal distribution)
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Table 5.3. 8 The adjusted Poisson model for pneumonia HAIs incidence Parameter Level Estimate S.E. Chisq p-value Season Spring/Winter -0.0156 0.0515 10.19 0.0170
Summer/Winter 0.1110 0.0511 Autumn/Winter 0.1022 0.0508
Time trend Linear 1.4807E-03 0.0003 6.34 0.0118 Autocorrelations AR (1) 2.2691E-02 0.0031 53.11 <.0001
AR (2) 0.0003 0.0029 0.01 0.9296
Age 0-9 / >=80 -3.0450 0.2269 1316.81 <.0001 10-19 / >=80 -1.6101 0.1713
20-29 / >=80 -2.3704 0.1395 30-39 / >=80 -1.9751 0.1102 40-49 / >=80 -1.1961 0.0757 50-59 / >=80 -1.0075 0.0634 60-69 / >=80 -0.6025 0.0546 70-79 / >=80 -0.3327 0.0511
Gender Male / Female 0.4891 0.0374 177.03 <.0001
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Table 5.3. 9 The adjusted negative binominal model for pneumonia HAIs incidence Parameter Level Estimate S.E. Chisq p-value
Season Spring/Winter -0.0441 0.0692 1.04 0.7909 Summer/Winter 0.0101 0.0683
Autumn/Winter 0.0210 0.0679
Time trend Linear 5.8614E-04 0.0004 2.07 0.1499 Autocorrelations AR (1) 1.9548E-02 0.0041 22.21 <.0001
AR (2) 0.0112 0.0042 7.07 0.0078
Age 0-9 / >=80 -3.0691 0.2332 890.61 <.0001 10-19 / >=80 -1.6525 0.1804
20-29 / >=80 -2.4019 0.1562 30-39 / >=80 -2.0246 0.122 40-49 / >=80 -1.2525 0.0924 50-59 / >=80 -0.9384 0.0826 60-69 / >=80 -0.5987 0.0756 70-79 / >=80 -0.2694 0.0727
Gender Male / Female 0.5422 0.0491 121.82 <.0001
156
Table 5.3. 10 The adjusted time series model for SSI HAIs incidence (normal distribution)
157
Table 5.3. 11 The adjusted Poisson model for SSI HAIs incidence
Parameter Level Estimate S.E. Chisq p-value Season Spring/Winter 0.0904 0.0652 2.11 0.5508
Summer/Winter 0.0321 0.0662 Autumn/Winter 0.0332 0.0669
Time trend Linear -3.3693E-03 0.0004 65.21 <.0001 Autocorrelations AR (1) 6.6174E-03 0.0048 1.92 0.1659 Age 0-9 / >=80 -0.5651 0.1625 172.00 <.0001
10-19 / >=80 -0.1660 0.1893 20-29 / >=80 -0.1050 0.1275 30-39 / >=80 0.0023 0.1165 40-49 / >=80 0.5188 0.1031 50-59 / >=80 0.4849 0.1006 60-69 / >=80 0.5461 0.0968 70-79 / >=80 0.4720 0.0975
Gender Male / Female 0.3457 0.046 57.48 <.0001
158
Table 5.3. 12 The adjusted negative binominal model for SSI HAIs incidence Parameter Level Estimate S.E. Chisq p-value Season Spring/Winter 0.1171 0.0947 1.91 0.5913
Summer/Winter 0.0163 0.0945 Autumn/Winter 0.0439 0.0956
Time trend Linear -4.1034E-03 0.0006 46.35 <.0001 Autocorrelations AR (1) 4.0409E-03 0.0072 0.31 0.5752 Age 0-9 / >=80 -0.6135 0.1861 104.71 <.0001
10-19 / >=80 -0.2015 0.2125 20-29 / >=80 -0.0956 0.156 30-39 / >=80 -0.0522 0.1467 40-49 / >=80 0.4787 0.1357 50-59 / >=80 0.4467 0.1336 60-69 / >=80 0.5307 0.1306 70-79 / >=80 0.5239 0.1309
Gender Male / Female 0.3664 0.0656 31.1 <.0001
159
Table 5.3. 13 The adjusted time series model for UTI HAIs incidence (normal distribution)
160
Table 5.3. 14 The adjusted Poisson model for UTI HAIs incidence
Parameter Level Estimate S.E. Chisq p-value Season Spring/Winter 0.0459 0.035 6.73 0.0810
Summer/Winter 0.0800 0.0347 Autumn/Winter 0.0114 0.0353
Time trend Linear 3.0828E-03 0.0005 33.8 <.0001 Quadratic -9.1979E-06 4E-06 5.6 0.0182 Cubic -5.4700E-07 7E-08 53.1 <.0001 Autocorrelations AR (1) 1.5313E-03 0.0012 1.59 0.2068
AR (2) 6.8500E-03 0.0013 27.54 <.0001
161
Table 5.3. 15 The adjusted negative binominal model for UTI HAIs incidence Parameter Level Estimate S.E. Chisq p-value Season Spring/Winter 0.0431 0.0448 4.89 0.1801
Summer/Winter 0.0891 0.0441 Autumn/Winter 0.0154 0.0447
Time trend Linear 2.5617E-03 0.0007 14.4 0.0001 Quadratic -7.4606E-06 5E-06 2.3 0.1321 Cubic -4.7140E-07 9E-08 24.4 <.0001 Autocorrelations AR (1) 2.2398E-03 0.0015 2.13 0.1447
AR (2) 6.5026E-03 0.0017 14.91 0.0001
162
Table 5.3. 16 The adjusted time series model for E. coli bacteremia incidence (normal distribution) Quadratic -2.95E-07 1.822E-07 2.63 0.1052 Autocorrelations AR (1) -0.0037 0.0057 0.42 0.5161