Histograms
Column
“Meta-Analysis Proportional”
Column
proportion 95%-CI %W(fixed) %W(random)
Kumar et al 2019 1.0000 [0.9465; 1.0000] 1.0 3.8
Jarquin et al 2012 0.9348 [0.8250; 0.9776] 0.7 3.8
Villalpando-Guzmán et al 2013 0.2824 [0.2534; 0.3133] 12.8 4.0
Molina et al 2019 0.5846 [0.4634; 0.6964] 1.0 3.8
Castellanos et al 2018 0.6462 [0.5863; 0.7018] 3.8 3.9
Monte et al 2019 0.5455 [0.4852; 0.6044] 3.9 3.9
Rau et al 2021 0.9088 [0.8735; 0.9350] 5.0 3.9
Mogollón et al 2016 0.0293 [0.0160; 0.0531] 5.0 3.9
Khan et al 2022 0.4178 [0.3409; 0.4989] 2.2 3.9
Mascitti et al 2021 0.3077 [0.1991; 0.4427] 0.8 3.8
Puig-Peña et al 2020 0.1667 [0.1334; 0.2062] 5.9 3.9
Cunha-Neto et al 2018 0.0365 [0.0258; 0.0513] 12.6 4.0
Yamatogi et al 2016 0.7206 [0.6645; 0.7706] 4.0 3.9
Alikhan et al 2022 0.8145 [0.7681; 0.8533] 4.7 3.9
Santos et al 2023 0.4609 [0.3725; 0.5518] 1.7 3.9
Castellanos et al 2020 1.0000 [0.8668; 1.0000] 0.4 3.7
Castellanos et al 2020 0.8000 [0.3755; 0.9638] 0.1 2.9
Dos Santos et al 2021 0.6667 [0.6018; 0.7258] 3.2 3.9
Castro et al 2021 1.0000 [0.7961; 1.0000] 0.2 3.5
Perin et al 2020 0.3167 [0.2666; 0.3713] 4.4 3.9
Vaz et al 2010 0.5521 [0.4525; 0.6476] 1.4 3.9
Khan et al 2021 0.2702 [0.2288; 0.3160] 5.9 3.9
Kipper et al 2021 1.0000 [0.9465; 1.0000] 1.0 3.8
Piña-Iturbe et al 2021 0.2205 [0.1842; 0.2615] 6.5 3.9
Saidenberg et al 2022 0.1150 [0.0873; 0.1500] 5.9 3.9
Vinueza-Burgos et al 2016 0.1598 [0.1267; 0.1996] 5.7 3.9
Number of studies: k = 26
Number of observations: o = 6755
Number of events: e = 2425
proportion 95%-CI
Fixed effect model 0.3379 [0.3264; 0.3494]
Random effects model 0.5495 [0.4099; 0.6853]
Quantifying heterogeneity (with 95%-CIs):
tau^2 = 0.1246 [0.0837; 0.2928]; tau = 0.3530 [0.2892; 0.5411]
I^2 = 99.2% [99.1%; 99.3%]; H = 11.27 [10.56; 12.03]
Test of heterogeneity:
Q d.f. p-value
3174.58 25 0
Details of meta-analysis methods:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
- Jackson method for confidence interval of tau^2 and tau
- Calculation of I^2 based on Q
- Freeman-Tukey double arcsine transformation
- Wilson Score confidence interval for individual studies“Prevalence of Salmonella spp. Forest Plot”
Column
Forest Plot
“Sample size and Prevalence Estimate”
Column
Column
“Meta-Analysis Proportional_Subgroup Region”
Column
proportion 95%-CI %W(fixed) %W(random)
Kumar et al 2019 1.0000 [0.9465; 1.0000] 1.0 3.8
Jarquin et al 2012 0.9348 [0.8250; 0.9776] 0.7 3.8
Villalpando-Guzmán et al 2013 0.2824 [0.2534; 0.3133] 12.8 4.0
Molina et al 2019 0.5846 [0.4634; 0.6964] 1.0 3.8
Castellanos et al 2018 0.6462 [0.5863; 0.7018] 3.8 3.9
Monte et al 2019 0.5455 [0.4852; 0.6044] 3.9 3.9
Rau et al 2021 0.9088 [0.8735; 0.9350] 5.0 3.9
Mogollón et al 2016 0.0293 [0.0160; 0.0531] 5.0 3.9
Khan et al 2022 0.4178 [0.3409; 0.4989] 2.2 3.9
Mascitti et al 2021 0.3077 [0.1991; 0.4427] 0.8 3.8
Puig-Peña et al 2020 0.1667 [0.1334; 0.2062] 5.9 3.9
Cunha-Neto et al 2018 0.0365 [0.0258; 0.0513] 12.6 4.0
Yamatogi et al 2016 0.7206 [0.6645; 0.7706] 4.0 3.9
Alikhan et al 2022 0.8145 [0.7681; 0.8533] 4.7 3.9
Santos et al 2023 0.4609 [0.3725; 0.5518] 1.7 3.9
Castellanos et al 2020 1.0000 [0.8668; 1.0000] 0.4 3.7
Castellanos et al 2020 0.8000 [0.3755; 0.9638] 0.1 2.9
Dos Santos et al 2021 0.6667 [0.6018; 0.7258] 3.2 3.9
Castro et al 2021 1.0000 [0.7961; 1.0000] 0.2 3.5
Perin et al 2020 0.3167 [0.2666; 0.3713] 4.4 3.9
Vaz et al 2010 0.5521 [0.4525; 0.6476] 1.4 3.9
Khan et al 2021 0.2702 [0.2288; 0.3160] 5.9 3.9
Kipper et al 2021 1.0000 [0.9465; 1.0000] 1.0 3.8
Piña-Iturbe et al 2021 0.2205 [0.1842; 0.2615] 6.5 3.9
Saidenberg et al 2022 0.1150 [0.0873; 0.1500] 5.9 3.9
Vinueza-Burgos et al 2016 0.1598 [0.1267; 0.1996] 5.7 3.9
Region
Kumar et al 2019 México, C.America & Caribe
Jarquin et al 2012 México, C.America & Caribe
Villalpando-Guzmán et al 2013 México, C.America & Caribe
Molina et al 2019 México, C.America & Caribe
Castellanos et al 2018 South America
Monte et al 2019 South America
Rau et al 2021 South America
Mogollón et al 2016 South America
Khan et al 2022 México, C.America & Caribe
Mascitti et al 2021 South America
Puig-Peña et al 2020 México, C.America & Caribe
Cunha-Neto et al 2018 South America
Yamatogi et al 2016 South America
Alikhan et al 2022 South America
Santos et al 2023 South America
Castellanos et al 2020 South America
Castellanos et al 2020 México, C.America & Caribe
Dos Santos et al 2021 South America
Castro et al 2021 South America
Perin et al 2020 South America
Vaz et al 2010 South America
Khan et al 2021 México, C.America & Caribe
Kipper et al 2021 South America
Piña-Iturbe et al 2021 South America
Saidenberg et al 2022 South America
Vinueza-Burgos et al 2016 South America
Number of studies: k = 26
Number of observations: o = 6755
Number of events: e = 2425
proportion 95%-CI
Fixed effect model 0.3379 [0.3264; 0.3494]
Random effects model 0.5495 [0.4099; 0.6853]
Quantifying heterogeneity (with 95%-CIs):
tau^2 = 0.1246 [0.0837; 0.2928]; tau = 0.3530 [0.2892; 0.5411]
I^2 = 99.2% [99.1%; 99.3%]; H = 11.27 [10.56; 12.03]
Test of heterogeneity:
Q d.f. p-value
3174.58 25 0
Results for subgroups (fixed effect model):
k proportion 95%-CI Q
Region = México, C.America & Caribe 8 0.3145 [0.2935; 0.3358] 407.73
Region = South America 18 0.3476 [0.3340; 0.3614] 2761.55
I^2
Region = México, C.America & Caribe 98.3%
Region = South America 99.4%
Test for subgroup differences (fixed effect model):
Q d.f. p-value
Between groups 5.30 1 0.0213
Within groups 3169.28 24 0
Results for subgroups (random effects model):
k proportion 95%-CI tau^2
Region = México, C.America & Caribe 8 0.5700 [0.3754; 0.7546] 0.0693
Region = South America 18 0.5393 [0.3547; 0.7186] 0.1572
tau
Region = México, C.America & Caribe 0.2633
Region = South America 0.3965
Test for subgroup differences (random effects model):
Q d.f. p-value
Between groups 0.05 1 0.8296
Details of meta-analysis methods:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
- Jackson method for confidence interval of tau^2 and tau
- Calculation of I^2 based on Q
- Freeman-Tukey double arcsine transformation
- Wilson Score confidence interval for individual studies“Prevalence of Salmonella spp. Forest Plot Subgroup Region”
Column
Forest Plot
“Regions_Subgroups”
Column
AB Resistance
Column
proportion 95%-CI %W(fixed) %W(random)
Kumar et al 2019 0.7222 [0.4913; 0.8750] 1.1 3.9
Jarquin et al 2012 0.6667 [0.4782; 0.8136] 1.6 4.1
Villalpando-Guzmán et al 2013 1.0000 [0.8318; 1.0000] 1.1 3.9
Molina et al 2019 0.9211 [0.7920; 0.9728] 2.2 4.1
Castellanos et al 2018 0.9038 [0.8619; 0.9340] 15.0 4.4
Monte et al 2019 0.2986 [0.2299; 0.3778] 8.3 4.3
Rau et al 2021 0.6472 [0.5925; 0.6984] 17.9 4.4
Mogollón et al 2016 NA 0.0 0.0
Khan et al 2022 1.0000 [0.7009; 1.0000] 0.5 3.5
Mascitti et al 2021 0.1250 [0.0350; 0.3602] 1.0 3.9
Puig-Peña et al 2020 0.5522 [0.4336; 0.6652] 3.9 4.3
Cunha-Neto et al 2018 1.0000 [0.8897; 1.0000] 1.8 4.1
Yamatogi et al 2016 0.3107 [0.2472; 0.3823] 10.2 4.3
Alikhan et al 2022 0.7778 [0.4526; 0.9368] 0.5 3.5
Santos et al 2023 0.8333 [0.7354; 0.8999] 4.5 4.3
Castellanos et al 2020 0.7600 [0.5657; 0.8850] 1.5 4.0
Castellanos et al 2020 1.0000 [0.5101; 1.0000] 0.3 2.9
Dos Santos et al 2021 0.9760 [0.9318; 0.9918] 7.2 4.3
Castro et al 2021 1.0000 [0.7961; 1.0000] 0.9 3.8
Perin et al 2020 0.8571 [0.7744; 0.9130] 5.7 4.3
Vaz et al 2010 0.4340 [0.3095; 0.5673] 3.1 4.2
Khan et al 2021 0.6000 [0.3575; 0.8018] 0.9 3.8
Kipper et al 2021 1.0000 [0.9465; 1.0000] 4.0 4.3
Piña-Iturbe et al 2021 1.0000 [0.9619; 1.0000] 5.6 4.3
Saidenberg et al 2022 0.5000 [0.2366; 0.7634] 0.6 3.6
Vinueza-Burgos et al 2016 0.2222 [0.0632; 0.5474] 0.5 3.5
Number of studies: k = 25
Number of observations: o = 1730
Number of events: e = 1217
proportion 95%-CI
Fixed effect model 0.7616 [0.7399; 0.7827]
Random effects model 0.7812 [0.6538; 0.8881]
Quantifying heterogeneity (with 95%-CIs):
tau^2 = 0.1062 [0.0590; 0.2291]; tau = 0.3259 [0.2428; 0.4787]
I^2 = 96.5% [95.7%; 97.2%]; H = 5.37 [4.82; 5.99]
Test of heterogeneity:
Q d.f. p-value
692.87 24 < 0.0001
Details of meta-analysis methods:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
- Jackson method for confidence interval of tau^2 and tau
- Calculation of I^2 based on Q
- Freeman-Tukey double arcsine transformation
- Wilson Score confidence interval for individual studies“Proportion of AB Resistance (Forest Plot)”
Column
Forest Plot
“Sample size and Prevalence of AB.Resistance Estimate”
Column
Column
S.enteritidis
Column
proportion 95%-CI %W(fixed) %W(random)
Kumar et al 2019 0.0000 [0.0000; 0.1103] 2.6 5.6
Villalpando-Guzmán et al 2013 0.0000 [0.0000; 0.0583] 5.2 5.7
Molina et al 2019 0.0000 [0.0000; 0.0941] 3.1 5.6
Castellanos et al 2018 0.0294 [0.0052; 0.1492] 2.9 5.6
Monte et al 2019 0.0000 [0.0000; 0.1103] 2.6 5.6
Mogollón et al 2016 0.8000 [0.4902; 0.9433] 0.9 5.1
Khan et al 2022 0.2083 [0.1305; 0.3157] 6.0 5.7
Cunha-Neto et al 2018 0.0000 [0.0000; 0.1103] 2.6 5.6
Yamatogi et al 2016 0.1412 [0.0975; 0.2002] 14.7 5.8
Alikhan et al 2022 0.0219 [0.0085; 0.0548] 15.2 5.8
Duarte et al 2024 0.0000 [0.0000; 0.4899] 0.4 4.4
Castellanos et al 2020 0.0278 [0.0049; 0.1417] 3.0 5.6
Dos Santos et al 2021 0.0000 [0.0000; 0.0256] 12.2 5.8
Perin et al 2020 0.0000 [0.0000; 0.0377] 8.2 5.8
Vaz et al 2010 1.0000 [0.9615; 1.0000] 8.0 5.7
Khan et al 2021 0.2083 [0.1305; 0.3157] 6.0 5.7
Saidenberg et al 2022 0.0000 [0.0000; 0.2153] 1.2 5.3
Vinueza-Burgos et al 2016 0.1452 [0.0783; 0.2534] 5.2 5.7
Number of studies: k = 18
Number of observations: o = 1196
Number of events: e = 174
proportion 95%-CI
Fixed effect model 0.0883 [0.0713; 0.1067]
Random effects model 0.0838 [0.0027; 0.2304]
Quantifying heterogeneity (with 95%-CIs):
tau^2 = 0.1658 [0.0829; 0.3791]; tau = 0.4072 [0.2879; 0.6157]
I^2 = 97.7% [97.1%; 98.2%]; H = 6.62 [5.91; 7.41]
Test of heterogeneity:
Q d.f. p-value
744.12 17 < 0.0001
Details of meta-analysis methods:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
- Jackson method for confidence interval of tau^2 and tau
- Calculation of I^2 based on Q
- Freeman-Tukey double arcsine transformation
- Wilson Score confidence interval for individual studiesProportion of Senteritidis (Forest Plot)
Column
Forest Plot
“Sample size and Prevalence of S. enteritidis”
Column
Column
AB Resistance (Q,C,A)
Hoja electrónica de Google Link
Tabla ( )
Column
Quinolonas
proportion 95%-CI %W(fixed) %W(random)
Kumar et al 2020 0.7407 [0.5566; 0.8914] 2.3 6.3
Molina et al 2019 0.9155 [0.8377; 0.9709] 6.0 6.4
Castellanos et al 2018 0.0000 [0.0000; 0.0073] 19.6 6.4
Monte et al 2019 1.0000 [0.9469; 1.0000] 2.7 6.3
Khan et al 2022 0.0000 [0.0000; 0.0767] 1.9 6.2
Mascitti et al 2021 0.0000 [0.0000; 0.0328] 4.4 6.4
Alikhan et al 2022 0.0000 [0.0000; 0.0267] 5.4 6.4
Duarte et al 2024 0.0000 [0.0000; 0.5003] 0.3 5.3
Santos et al 2023 0.0000 [0.0000; 0.0219] 6.5 6.4
Castellanos et al 2020 0.9011 [0.8302; 0.9552] 7.6 6.4
Castro et al 2021 0.0000 [0.0000; 0.1116] 1.3 6.1
Perin et al 2020 0.0000 [0.0000; 0.1282] 1.1 6.1
Kipper et al 2021 0.0364 [0.0079; 0.0812] 9.2 6.4
Piña-Iturbe et al 2021 0.7337 [0.6840; 0.7806] 26.9 6.4
Saidenberg et al 2022 1.0000 [0.8807; 1.0000] 1.2 6.1
Vinueza-Burgos et al 2016 0.0000 [0.0000; 0.0387] 3.7 6.3
Number of studies: k = 16
Number of observations: o = 1193
Number of events: e = 453.25
proportion 95%-CI
Fixed effect model 0.2822 [0.2556; 0.3094]
Random effects model 0.2540 [0.0448; 0.5426]
Quantifying heterogeneity (with 95%-CIs):
tau^2 = 0.3285 [0.1642; 0.8592]; tau = 0.5732 [0.4052; 0.9269]
I^2 = 98.9% [98.7%; 99.1%]; H = 9.56 [8.70; 10.50]
Test of heterogeneity:
Q d.f. p-value
1371.00 15 < 0.0001
Details of meta-analysis methods:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
- Jackson method for confidence interval of tau^2 and tau
- Calculation of I^2 based on Q
- Freeman-Tukey double arcsine transformation
- Normal approximation confidence interval for individual studiesColumn
Cefalosporinas
proportion 95%-CI %W(fixed) %W(random)
Kumar et al 2020 0.1111 [0.0151; 0.2626] 2.3 6.3
Molina et al 2019 0.6197 [0.5033; 0.7297] 6.0 6.5
Castellanos et al 2018 1.0000 [0.9927; 1.0000] 19.6 6.6
Monte et al 2019 0.8125 [0.6562; 0.9319] 2.7 6.3
Khan et al 2022 0.4545 [0.2495; 0.6673] 1.9 6.2
Mascitti et al 2021 0.0000 [0.0000; 0.0328] 4.4 6.5
Alikhan et al 2022 0.8125 [0.7066; 0.8999] 5.4 6.5
Duarte et al 2024 1.0000 [0.4997; 1.0000] 0.3 4.4
Santos et al 2023 0.5000 [0.3889; 0.6111] 6.5 6.5
Castellanos et al 2020 0.9670 [0.9181; 0.9959] 7.6 6.6
Castro et al 2021 1.0000 [0.8884; 1.0000] 1.3 6.0
Perin et al 2020 0.6154 [0.3327; 0.8653] 1.1 5.9
Kipper et al 2021 0.7455 [0.6595; 0.8229] 9.2 6.6
Piña-Iturbe et al 2021 0.6755 [0.6232; 0.7256] 26.9 6.7
Saidenberg et al 2022 1.0000 [0.8807; 1.0000] 1.2 5.9
Vinueza-Burgos et al 2016 0.7500 [0.6102; 0.8683] 3.7 6.4
Number of studies: k = 16
Number of observations: o = 1193
Number of events: e = 869.5
proportion 95%-CI
Fixed effect model 0.7819 [0.7565; 0.8063]
Random effects model 0.7192 [0.5289; 0.8783]
Quantifying heterogeneity (with 95%-CIs):
tau^2 = 0.1379 [0.0718; 0.3852]; tau = 0.3714 [0.2680; 0.6206]
I^2 = 97.4% [96.7%; 98.0%]; H = 6.24 [5.51; 7.07]
Test of heterogeneity:
Q d.f. p-value
584.28 15 < 0.0001
Details of meta-analysis methods:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
- Jackson method for confidence interval of tau^2 and tau
- Calculation of I^2 based on Q
- Freeman-Tukey double arcsine transformation
- Normal approximation confidence interval for individual studiesCOlumn
Aminoglucosidos
proportion 95%-CI %W(fixed) %W(random)
Kumar et al 2020 0.1111 [0.0151; 0.2626] 2.3 6.3
Molina et al 2019 0.6197 [0.5033; 0.7297] 6.0 6.5
Castellanos et al 2018 1.0000 [0.9927; 1.0000] 19.6 6.6
Monte et al 2019 0.8125 [0.6562; 0.9319] 2.7 6.3
Khan et al 2022 0.4545 [0.2495; 0.6673] 1.9 6.2
Mascitti et al 2021 0.0000 [0.0000; 0.0328] 4.4 6.5
Alikhan et al 2022 0.8125 [0.7066; 0.8999] 5.4 6.5
Duarte et al 2024 1.0000 [0.4997; 1.0000] 0.3 4.4
Santos et al 2023 0.5000 [0.3889; 0.6111] 6.5 6.5
Castellanos et al 2020 0.9670 [0.9181; 0.9959] 7.6 6.6
Castro et al 2021 1.0000 [0.8884; 1.0000] 1.3 6.0
Perin et al 2020 0.6154 [0.3327; 0.8653] 1.1 5.9
Kipper et al 2021 0.7455 [0.6595; 0.8229] 9.2 6.6
Piña-Iturbe et al 2021 0.6755 [0.6232; 0.7256] 26.9 6.7
Saidenberg et al 2022 1.0000 [0.8807; 1.0000] 1.2 5.9
Vinueza-Burgos et al 2016 0.7500 [0.6102; 0.8683] 3.7 6.4
Number of studies: k = 16
Number of observations: o = 1193
Number of events: e = 869.5
proportion 95%-CI
Fixed effect model 0.7819 [0.7565; 0.8063]
Random effects model 0.7192 [0.5289; 0.8783]
Quantifying heterogeneity (with 95%-CIs):
tau^2 = 0.1379 [0.0718; 0.3852]; tau = 0.3714 [0.2680; 0.6206]
I^2 = 97.4% [96.7%; 98.0%]; H = 6.24 [5.51; 7.07]
Test of heterogeneity:
Q d.f. p-value
584.28 15 < 0.0001
Details of meta-analysis methods:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
- Jackson method for confidence interval of tau^2 and tau
- Calculation of I^2 based on Q
- Freeman-Tukey double arcsine transformation
- Normal approximation confidence interval for individual studiesProportion of samples with AB resistance (Q,C,A) (Forest Plot)
Column
Forest Plot Quinolonas
“Sample size and Pr. of AB resistance (Q,C,A)”
Column
Quinolonas
Cefalosporinas
Column
# WORKING
AB Resistance - Subgroup Method (Q,C,A)
Tabla ( )
Column
Quinolonas
proportion 95%-CI %W(fixed) %W(random)
Kumar et al 2020 0.7407 [0.5566; 0.8914] 2.3 6.3
Molina et al 2019 0.9155 [0.8377; 0.9709] 6.0 6.4
Castellanos et al 2018 0.0000 [0.0000; 0.0073] 19.6 6.4
Monte et al 2019 1.0000 [0.9469; 1.0000] 2.7 6.3
Khan et al 2022 0.0000 [0.0000; 0.0767] 1.9 6.2
Mascitti et al 2021 0.0000 [0.0000; 0.0328] 4.4 6.4
Alikhan et al 2022 0.0000 [0.0000; 0.0267] 5.4 6.4
Duarte et al 2024 0.0000 [0.0000; 0.5003] 0.3 5.3
Santos et al 2023 0.0000 [0.0000; 0.0219] 6.5 6.4
Castellanos et al 2020 0.9011 [0.8302; 0.9552] 7.6 6.4
Castro et al 2021 0.0000 [0.0000; 0.1116] 1.3 6.1
Perin et al 2020 0.0000 [0.0000; 0.1282] 1.1 6.1
Kipper et al 2021 0.0364 [0.0079; 0.0812] 9.2 6.4
Piña-Iturbe et al 2021 0.7337 [0.6840; 0.7806] 26.9 6.4
Saidenberg et al 2022 1.0000 [0.8807; 1.0000] 1.2 6.1
Vinueza-Burgos et al 2016 0.0000 [0.0000; 0.0387] 3.7 6.3
Método
Kumar et al 2020 PCR
Molina et al 2019 EgenCompl
Castellanos et al 2018 EgenCompl
Monte et al 2019 EgenCompl
Khan et al 2022 EgenCompl
Mascitti et al 2021 EgenCompl
Alikhan et al 2022 EgenCompl
Duarte et al 2024 EgenCompl
Santos et al 2023 PCR
Castellanos et al 2020 PCR y Egencompl
Castro et al 2021 PCR
Perin et al 2020 PCR y Egencompl
Kipper et al 2021 PCR y Egencompl
Piña-Iturbe et al 2021 EgenCompl
Saidenberg et al 2022 EgenCompl
Vinueza-Burgos et al 2016 PCR
Number of studies: k = 16
Number of observations: o = 1193
Number of events: e = 453.25
proportion 95%-CI
Fixed effect model 0.2822 [0.2556; 0.3094]
Random effects model 0.2540 [0.0448; 0.5426]
Quantifying heterogeneity (with 95%-CIs):
tau^2 = 0.3285 [0.1642; 0.8592]; tau = 0.5732 [0.4052; 0.9269]
I^2 = 98.9% [98.7%; 99.1%]; H = 9.56 [8.70; 10.50]
Test of heterogeneity:
Q d.f. p-value
1371.00 15 < 0.0001
Results for subgroups (fixed effect model):
k proportion 95%-CI Q I^2
Método = EgenCompl 9 0.3301 [0.2962; 0.3647] 964.45 99.2%
Método = PCR 4 0.0371 [0.0096; 0.0765] 84.30 96.4%
Método = PCR y Egencompl 3 0.3524 [0.2880; 0.4196] 231.80 99.1%
Test for subgroup differences (fixed effect model):
Q d.f. p-value
Between groups 90.45 2 < 0.0001
Within groups 1280.54 13 < 0.0001
Results for subgroups (random effects model):
k proportion 95%-CI tau^2 tau
Método = EgenCompl 9 0.3505 [0.0273; 0.7744] 0.3932 0.6271
Método = PCR 4 0.0854 [0.0000; 0.4619] 0.1832 0.4280
Método = PCR y Egencompl 3 0.2489 [0.0000; 0.9532] 0.4822 0.6944
Test for subgroup differences (random effects model):
Q d.f. p-value
Between groups 1.13 2 0.5685
Details of meta-analysis methods:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
- Jackson method for confidence interval of tau^2 and tau
- Calculation of I^2 based on Q
- Freeman-Tukey double arcsine transformation
- Normal approximation confidence interval for individual studiesColumn
Cefalosporinas
proportion 95%-CI %W(fixed) %W(random)
Kumar et al 2020 0.1111 [0.0151; 0.2626] 2.3 6.3
Molina et al 2019 0.6197 [0.5033; 0.7297] 6.0 6.5
Castellanos et al 2018 1.0000 [0.9927; 1.0000] 19.6 6.6
Monte et al 2019 0.8125 [0.6562; 0.9319] 2.7 6.3
Khan et al 2022 0.4545 [0.2495; 0.6673] 1.9 6.2
Mascitti et al 2021 0.0000 [0.0000; 0.0328] 4.4 6.5
Alikhan et al 2022 0.8125 [0.7066; 0.8999] 5.4 6.5
Duarte et al 2024 1.0000 [0.4997; 1.0000] 0.3 4.4
Santos et al 2023 0.5000 [0.3889; 0.6111] 6.5 6.5
Castellanos et al 2020 0.9670 [0.9181; 0.9959] 7.6 6.6
Castro et al 2021 1.0000 [0.8884; 1.0000] 1.3 6.0
Perin et al 2020 0.6154 [0.3327; 0.8653] 1.1 5.9
Kipper et al 2021 0.7455 [0.6595; 0.8229] 9.2 6.6
Piña-Iturbe et al 2021 0.6755 [0.6232; 0.7256] 26.9 6.7
Saidenberg et al 2022 1.0000 [0.8807; 1.0000] 1.2 5.9
Vinueza-Burgos et al 2016 0.7500 [0.6102; 0.8683] 3.7 6.4
Método
Kumar et al 2020 PCR
Molina et al 2019 EgenCompl
Castellanos et al 2018 EgenCompl
Monte et al 2019 EgenCompl
Khan et al 2022 EgenCompl
Mascitti et al 2021 EgenCompl
Alikhan et al 2022 EgenCompl
Duarte et al 2024 EgenCompl
Santos et al 2023 PCR
Castellanos et al 2020 PCR y Egencompl
Castro et al 2021 PCR
Perin et al 2020 PCR y Egencompl
Kipper et al 2021 PCR y Egencompl
Piña-Iturbe et al 2021 EgenCompl
Saidenberg et al 2022 EgenCompl
Vinueza-Burgos et al 2016 PCR
Number of studies: k = 16
Number of observations: o = 1193
Number of events: e = 869.5
proportion 95%-CI
Fixed effect model 0.7819 [0.7565; 0.8063]
Random effects model 0.7192 [0.5289; 0.8783]
Quantifying heterogeneity (with 95%-CIs):
tau^2 = 0.1379 [0.0718; 0.3852]; tau = 0.3714 [0.2680; 0.6206]
I^2 = 97.4% [96.7%; 98.0%]; H = 6.24 [5.51; 7.07]
Test of heterogeneity:
Q d.f. p-value
584.28 15 < 0.0001
Results for subgroups (fixed effect model):
k proportion 95%-CI Q I^2
Método = EgenCompl 9 0.8009 [0.7705; 0.8299] 457.42 98.3%
Método = PCR 4 0.5615 [0.4830; 0.6386] 56.04 94.6%
Método = PCR y Egencompl 3 0.8607 [0.8085; 0.9064] 27.03 92.6%
Test for subgroup differences (fixed effect model):
Q d.f. p-value
Between groups 43.79 2 < 0.0001
Within groups 540.49 13 < 0.0001
Results for subgroups (random effects model):
k proportion 95%-CI tau^2 tau
Método = EgenCompl 9 0.7315 [0.4335; 0.9521] 0.1848 0.4299
Método = PCR 4 0.6178 [0.2654; 0.9143] 0.1195 0.3457
Método = PCR y Egencompl 3 0.8193 [0.5595; 0.9826] 0.0525 0.2292
Test for subgroup differences (random effects model):
Q d.f. p-value
Between groups 0.94 2 0.6248
Details of meta-analysis methods:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
- Jackson method for confidence interval of tau^2 and tau
- Calculation of I^2 based on Q
- Freeman-Tukey double arcsine transformation
- Normal approximation confidence interval for individual studiesColumn
Aminoglucosidos
proportion 95%-CI %W(fixed) %W(random)
Kumar et al 2020 0.1111 [0.0151; 0.2626] 2.3 6.3
Molina et al 2019 0.6197 [0.5033; 0.7297] 6.0 6.5
Castellanos et al 2018 1.0000 [0.9927; 1.0000] 19.6 6.6
Monte et al 2019 0.8125 [0.6562; 0.9319] 2.7 6.3
Khan et al 2022 0.4545 [0.2495; 0.6673] 1.9 6.2
Mascitti et al 2021 0.0000 [0.0000; 0.0328] 4.4 6.5
Alikhan et al 2022 0.8125 [0.7066; 0.8999] 5.4 6.5
Duarte et al 2024 1.0000 [0.4997; 1.0000] 0.3 4.4
Santos et al 2023 0.5000 [0.3889; 0.6111] 6.5 6.5
Castellanos et al 2020 0.9670 [0.9181; 0.9959] 7.6 6.6
Castro et al 2021 1.0000 [0.8884; 1.0000] 1.3 6.0
Perin et al 2020 0.6154 [0.3327; 0.8653] 1.1 5.9
Kipper et al 2021 0.7455 [0.6595; 0.8229] 9.2 6.6
Piña-Iturbe et al 2021 0.6755 [0.6232; 0.7256] 26.9 6.7
Saidenberg et al 2022 1.0000 [0.8807; 1.0000] 1.2 5.9
Vinueza-Burgos et al 2016 0.7500 [0.6102; 0.8683] 3.7 6.4
Método
Kumar et al 2020 PCR
Molina et al 2019 EgenCompl
Castellanos et al 2018 EgenCompl
Monte et al 2019 EgenCompl
Khan et al 2022 EgenCompl
Mascitti et al 2021 EgenCompl
Alikhan et al 2022 EgenCompl
Duarte et al 2024 EgenCompl
Santos et al 2023 PCR
Castellanos et al 2020 PCR y Egencompl
Castro et al 2021 PCR
Perin et al 2020 PCR y Egencompl
Kipper et al 2021 PCR y Egencompl
Piña-Iturbe et al 2021 EgenCompl
Saidenberg et al 2022 EgenCompl
Vinueza-Burgos et al 2016 PCR
Number of studies: k = 16
Number of observations: o = 1193
Number of events: e = 869.5
proportion 95%-CI
Fixed effect model 0.7819 [0.7565; 0.8063]
Random effects model 0.7192 [0.5289; 0.8783]
Quantifying heterogeneity (with 95%-CIs):
tau^2 = 0.1379 [0.0718; 0.3852]; tau = 0.3714 [0.2680; 0.6206]
I^2 = 97.4% [96.7%; 98.0%]; H = 6.24 [5.51; 7.07]
Test of heterogeneity:
Q d.f. p-value
584.28 15 < 0.0001
Results for subgroups (fixed effect model):
k proportion 95%-CI Q I^2
Método = EgenCompl 9 0.8009 [0.7705; 0.8299] 457.42 98.3%
Método = PCR 4 0.5615 [0.4830; 0.6386] 56.04 94.6%
Método = PCR y Egencompl 3 0.8607 [0.8085; 0.9064] 27.03 92.6%
Test for subgroup differences (fixed effect model):
Q d.f. p-value
Between groups 43.79 2 < 0.0001
Within groups 540.49 13 < 0.0001
Results for subgroups (random effects model):
k proportion 95%-CI tau^2 tau
Método = EgenCompl 9 0.7315 [0.4335; 0.9521] 0.1848 0.4299
Método = PCR 4 0.6178 [0.2654; 0.9143] 0.1195 0.3457
Método = PCR y Egencompl 3 0.8193 [0.5595; 0.9826] 0.0525 0.2292
Test for subgroup differences (random effects model):
Q d.f. p-value
Between groups 0.94 2 0.6248
Details of meta-analysis methods:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
- Jackson method for confidence interval of tau^2 and tau
- Calculation of I^2 based on Q
- Freeman-Tukey double arcsine transformation
- Normal approximation confidence interval for individual studiesProportion of samples with AB resistance - Subgroup Method (Q,C,A) (Forest Plot)
Column
Forest Plot Quinolonas
“Funnels AB resistance - Subgroup Method (Q,C,A)”
Column
Column
Column
AB Resistencia Fenotípica (Q,C,A)
Hoja electrónica de Google Link
Tabla ( )
[1] 27 8Column
Quinolonas
proportion 95%-CI %W(fixed) %W(random)
Kumar et al 2019 0.1176 [0.0608; 0.2153] 4.1 6.3
Jarquin et al 2012 0.5243 [0.4287; 0.6181] 6.2 6.4
Villalpando-Guzmán et al 2013 0.5891 [0.5029; 0.6703] 7.7 6.4
Molina et al 2024 0.9296 [0.8455; 0.9695] 4.3 6.3
Rau et al 2021 0.3657 [0.3139; 0.4207] 18.5 6.5
Puig-Peña et al 2020 0.3051 [0.2499; 0.3666] 14.1 6.5
Cunha-Neto et al 2018 0.0000 [0.0000; 0.1103] 1.9 6.0
Yamatogi et al 2016 0.3622 [0.2982; 0.4316] 11.7 6.5
Alikhan et al 2022 0.0000 [0.0000; 0.2775] 0.6 5.3
Santos et al 2023 0.1667 [0.1001; 0.2646] 4.7 6.3
Castellanos et al 2020 0.9275 [0.8413; 0.9687] 4.1 6.3
Dos Santos et al 2021 0.5120 [0.4253; 0.5980] 7.5 6.4
Perin et al 2020 0.9388 [0.8728; 0.9716] 5.9 6.4
Khan et al 2021 0.0833 [0.0361; 0.1807] 3.6 6.3
Piña-Iturbe et al 2021 0.0870 [0.0242; 0.2680] 1.4 5.9
Vinueza-Burgos et al 2016 0.8689 [0.7620; 0.9320] 3.7 6.3
Number of studies: k = 16
Number of observations: o = 1667
Number of events: e = 753
proportion 95%-CI
Fixed effect model 0.4521 [0.4278; 0.4764]
Random effects model 0.4166 [0.2661; 0.5751]
Quantifying heterogeneity (with 95%-CIs):
tau^2 = 0.0971 [0.0662; 0.3257]; tau = 0.3115 [0.2574; 0.5707]
I^2 = 97.5% [96.8%; 98.1%]; H = 6.33 [5.60; 7.17]
Test of heterogeneity:
Q d.f. p-value
601.94 15 < 0.0001
Details of meta-analysis methods:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
- Jackson method for confidence interval of tau^2 and tau
- Calculation of I^2 based on Q
- Freeman-Tukey double arcsine transformation
- Wilson Score confidence interval for individual studiesCefalosporinas
proportion 95%-CI %W(fixed) %W(random)
Kumar et al 2019 0.1765 [0.0939; 0.2771] 4.1 6.3
Jarquin et al 2012 0.0194 [0.0003; 0.0576] 6.2 6.4
Villalpando-Guzmán et al 2013 0.4597 [0.3742; 0.5464] 7.7 6.4
Molina et al 2024 0.6761 [0.5621; 0.7806] 4.3 6.3
Rau et al 2021 0.3204 [0.2694; 0.3736] 18.5 6.5
Puig-Peña et al 2020 0.1568 [0.1130; 0.2061] 14.1 6.5
Cunha-Neto et al 2018 0.1613 [0.0492; 0.3147] 1.9 6.0
Yamatogi et al 2016 0.0969 [0.0591; 0.1427] 11.7 6.5
Alikhan et al 2022 0.5000 [0.1892; 0.8108] 0.6 5.1
Santos et al 2023 0.5128 [0.4014; 0.6236] 4.7 6.3
Castellanos et al 2020 0.0000 [0.0000; 0.0248] 4.1 6.3
Dos Santos et al 2021 0.5680 [0.4800; 0.6539] 7.5 6.4
Perin et al 2020 0.8571 [0.7801; 0.9202] 5.9 6.4
Khan et al 2021 0.0833 [0.0243; 0.1689] 3.6 6.3
Piña-Iturbe et al 2021 0.1739 [0.0415; 0.3600] 1.4 5.8
Vinueza-Burgos et al 2016 0.3934 [0.2739; 0.5196] 3.7 6.3
Number of studies: k = 16
Number of observations: o = 1667
Number of events: e = 514.3
proportion 95%-CI
Fixed effect model 0.2790 [0.2573; 0.3012]
Random effects model 0.2886 [0.1656; 0.4292]
Quantifying heterogeneity (with 95%-CIs):
tau^2 = 0.0820 [0.0453; 0.2255]; tau = 0.2864 [0.2128; 0.4749]
I^2 = 97.1% [96.2%; 97.7%]; H = 5.84 [5.12; 6.65]
Test of heterogeneity:
Q d.f. p-value
510.94 15 < 0.0001
Details of meta-analysis methods:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
- Jackson method for confidence interval of tau^2 and tau
- Calculation of I^2 based on Q
- Freeman-Tukey double arcsine transformation
- Normal approximation confidence interval for individual studiesAminoglucosidos
proportion 95%-CI %W(fixed) %W(random)
Kumar et al 2019 0.1765 [0.0939; 0.2771] 4.1 6.3
Jarquin et al 2012 0.0194 [0.0003; 0.0576] 6.2 6.4
Villalpando-Guzmán et al 2013 0.4597 [0.3742; 0.5464] 7.7 6.4
Molina et al 2024 0.6761 [0.5621; 0.7806] 4.3 6.3
Rau et al 2021 0.3204 [0.2694; 0.3736] 18.5 6.5
Puig-Peña et al 2020 0.1568 [0.1130; 0.2061] 14.1 6.5
Cunha-Neto et al 2018 0.1613 [0.0492; 0.3147] 1.9 6.0
Yamatogi et al 2016 0.0969 [0.0591; 0.1427] 11.7 6.5
Alikhan et al 2022 0.5000 [0.1892; 0.8108] 0.6 5.1
Santos et al 2023 0.5128 [0.4014; 0.6236] 4.7 6.3
Castellanos et al 2020 0.0000 [0.0000; 0.0248] 4.1 6.3
Dos Santos et al 2021 0.5680 [0.4800; 0.6539] 7.5 6.4
Perin et al 2020 0.8571 [0.7801; 0.9202] 5.9 6.4
Khan et al 2021 0.0833 [0.0243; 0.1689] 3.6 6.3
Piña-Iturbe et al 2021 0.1739 [0.0415; 0.3600] 1.4 5.8
Vinueza-Burgos et al 2016 0.3934 [0.2739; 0.5196] 3.7 6.3
Number of studies: k = 16
Number of observations: o = 1667
Number of events: e = 514.3
proportion 95%-CI
Fixed effect model 0.2790 [0.2573; 0.3012]
Random effects model 0.2886 [0.1656; 0.4292]
Quantifying heterogeneity (with 95%-CIs):
tau^2 = 0.0820 [0.0453; 0.2255]; tau = 0.2864 [0.2128; 0.4749]
I^2 = 97.1% [96.2%; 97.7%]; H = 5.84 [5.12; 6.65]
Test of heterogeneity:
Q d.f. p-value
510.94 15 < 0.0001
Details of meta-analysis methods:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
- Jackson method for confidence interval of tau^2 and tau
- Calculation of I^2 based on Q
- Freeman-Tukey double arcsine transformation
- Normal approximation confidence interval for individual studiesProporciónn de muestras con resistencia fenotípica (Q,C,A) (Forest Plot)
Column
Forest Plot Quinolonas
“Tamaño de muestra y Pr. of resistencia AB Fenotípica (Q,C,A)”
Column
Quinolonas
Cefalosporinas
Column
Serovars & Country
Column
Serovars & Region
Column
Serovars Similitud
Column
