RV infection and HBGAs
K Phohlo
2022-09-21
Tables
Table 1: Rotavirus detection by enzyme immunoassay (EIA) according to …(n = 342)
Table 2: Distribution of RV status and RV genotypes according to sex (n = 342)
Table 3: Distribution of RV genotypes, Lewis profiles and ABO phenotypes according to RV status (n = 342)
Table 4: Association between ABO blood group, secretor status (secretor, non-secretor), and Lewis phenotypes with the infecting G/P rotavirus genotypes.
Contingency Tables:
RV vs Gender
RV vs Age_Interval_mo
RV vs Vaccine
RV vs fever
RV vs refusal_to_feed
RV vs ABO_pheno
RV vs H1_secretor
RV vs Le_ab_pheno
RV vs Combined
RV vs cough
RV vs source_of_water
RV vs toilet
RV vs heating
RV vs nursery
RV vs Site
Univariate models:
Association between fever and rotavirus (RV) infection among children
| RV | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.12 | 0.08 – 0.18 | <0.001 |
| fever [Yes] | 2.43 | 1.27 – 4.60 | 0.006 |
| Observations | 334 | ||
| R2 Tjur | 0.023 | ||
## We fitted a logistic model (estimated using ML) to predict RV with fever
## (formula: RV ~ fever). The model's explanatory power is weak (Tjur's R2 =
## 0.02). The model's intercept, corresponding to fever = None, is at -2.10 (95%
## CI [-2.52, -1.72], p < .001). Within this model:
##
## - The effect of fever [Yes] is statistically significant and positive (beta =
## 0.89, 95% CI [0.24, 1.53], p = 0.006; Std. beta = 0.89, 95% CI [0.24, 1.53])
##
## Standardized parameters were obtained by fitting the model on a standardized
## version of the dataset. 95% Confidence Intervals (CIs) and p-values were
## computed using a Wald z-distribution approximation.##
## Logistic regression predicting RV : Positive vs Negative
##
## OR(95%CI) P(Wald's test) P(LR-test)
## fever: Yes vs None 2.43 (1.28,4.61) 0.006 0.008
##
## Log-likelihood = -132.1382
## No. of observations = 334
## AIC value = 268.2764Association between ABO phenotypes and rotavirus (RV) infection among children
| RV | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.26 | 0.15 – 0.43 | <0.001 |
| ABO pheno [AB] | 3.79 | 0.83 – 17.42 | 0.077 |
| ABO pheno [B] | 0.52 | 0.14 – 1.54 | 0.273 |
| ABO pheno [O] | 0.44 | 0.23 – 0.87 | 0.018 |
| Observations | 342 | ||
| R2 Tjur | 0.041 | ||
## We fitted a logistic model (estimated using ML) to predict RV with ABO_pheno
## (formula: RV ~ ABO_pheno). The model's explanatory power is weak (Tjur's R2 =
## 0.04). The model's intercept, corresponding to ABO_pheno = A, is at -1.33 (95%
## CI [-1.87, -0.85], p < .001). Within this model:
##
## - The effect of ABO pheno [AB] is statistically non-significant and positive
## (beta = 1.33, 95% CI [-0.19, 2.86], p = 0.077; Std. beta = 1.33, 95% CI [-0.19,
## 2.86])
## - The effect of ABO pheno [B] is statistically non-significant and negative
## (beta = -0.65, 95% CI [-1.95, 0.43], p = 0.273; Std. beta = -0.65, 95% CI
## [-1.95, 0.43])
## - The effect of ABO pheno [O] is statistically significant and negative (beta =
## -0.81, 95% CI [-1.49, -0.14], p = 0.018; Std. beta = -0.81, 95% CI [-1.49,
## -0.14])
##
## Standardized parameters were obtained by fitting the model on a standardized
## version of the dataset. 95% Confidence Intervals (CIs) and p-values were
## computed using a Wald z-distribution approximation.##
## Logistic regression predicting RV : Positive vs Negative
##
## OR(95%CI) P(Wald's test) P(LR-test)
## ABO_pheno: ref.=A 0.01
## AB 3.79 (0.87,16.57) 0.077
## B 0.52 (0.16,1.67) 0.273
## O 0.44 (0.23,0.87) 0.018
##
## Log-likelihood = -134.7956
## No. of observations = 342
## AIC value = 277.5913Association between cough and rotavirus (RV) infection among children
| RV | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.13 | 0.08 – 0.19 | <0.001 |
| cough [Yes] | 1.97 | 1.04 – 3.68 | 0.035 |
| Observations | 332 | ||
| R2 Tjur | 0.014 | ||
## We fitted a logistic model (estimated using ML) to predict RV with cough
## (formula: RV ~ cough). The model's explanatory power is very weak (Tjur's R2 =
## 0.01). The model's intercept, corresponding to cough = None, is at -2.05 (95%
## CI [-2.48, -1.66], p < .001). Within this model:
##
## - The effect of cough [Yes] is statistically significant and positive (beta =
## 0.68, 95% CI [0.04, 1.30], p = 0.035; Std. beta = 0.68, 95% CI [0.04, 1.30])
##
## Standardized parameters were obtained by fitting the model on a standardized
## version of the dataset. 95% Confidence Intervals (CIs) and p-values were
## computed using a Wald z-distribution approximation.##
## Logistic regression predicting RV : Positive vs Negative
##
## OR(95%CI) P(Wald's test) P(LR-test)
## cough: Yes vs None 1.97 (1.05,3.69) 0.035 0.038
##
## Log-likelihood = -133.2281
## No. of observations = 332
## AIC value = 270.4563Association between heating and rotavirus (RV) infection among children
| RV | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.14 | 0.10 – 0.19 | <0.001 |
| heating [paraffin] | 3.24 | 1.32 – 7.47 | 0.007 |
| heating [wood] | 2.40 | 0.12 – 19.27 | 0.454 |
| Observations | 336 | ||
| R2 Tjur | 0.024 | ||
## We fitted a logistic model (estimated using ML) to predict RV with heating
## (formula: RV ~ heating). The model's explanatory power is weak (Tjur's R2 =
## 0.02). The model's intercept, corresponding to heating = electricity, is at
## -1.97 (95% CI [-2.33, -1.64], p < .001). Within this model:
##
## - The effect of heating [paraffin] is statistically significant and positive
## (beta = 1.17, 95% CI [0.27, 2.01], p = 0.007; Std. beta = 1.17, 95% CI [0.27,
## 2.01])
## - The effect of heating [wood] is statistically non-significant and positive
## (beta = 0.87, 95% CI [-2.15, 2.96], p = 0.454; Std. beta = 0.87, 95% CI [-2.15,
## 2.96])
##
## Standardized parameters were obtained by fitting the model on a standardized
## version of the dataset. 95% Confidence Intervals (CIs) and p-values were
## computed using a Wald z-distribution approximation.##
## Logistic regression predicting RV : Positive vs Negative
##
## OR(95%CI) P(Wald's test) P(LR-test)
## heating: ref.=electricity 0.036
## paraffin 3.24 (1.37,7.63) 0.007
## wood 2.4 (0.24,23.64) 0.454
##
## Log-likelihood = -132.6583
## No. of observations = 336
## AIC value = 271.3166Association between study site and rotavirus (RV) infection among children
| RV | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.40 | 0.26 – 0.61 | <0.001 |
| Site [OPHC] | 0.22 | 0.12 – 0.42 | <0.001 |
| Observations | 342 | ||
| R2 Tjur | 0.071 | ||
## We fitted a logistic model (estimated using ML) to predict RV with Site
## (formula: RV ~ Site). The model's explanatory power is weak (Tjur's R2 = 0.07).
## The model's intercept, corresponding to Site = DGMAH, is at -0.91 (95% CI
## [-1.36, -0.49], p < .001). Within this model:
##
## - The effect of Site [OPHC] is statistically significant and negative (beta =
## -1.49, 95% CI [-2.13, -0.87], p < .001; Std. beta = -1.49, 95% CI [-2.13,
## -0.87])
##
## Standardized parameters were obtained by fitting the model on a standardized
## version of the dataset. 95% Confidence Intervals (CIs) and p-values were
## computed using a Wald z-distribution approximation.##
## Logistic regression predicting RV : Positive vs Negative
##
## OR(95%CI) P(Wald's test) P(LR-test)
## Site: OPHC vs DGMAH 0.22 (0.12,0.42) < 0.001 < 0.001
##
## Log-likelihood = -129.4831
## No. of observations = 342
## AIC value = 262.9662Association between Combined_amend and rotavirus (RV) infection among children
| RV | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.64 | 0.23 – 1.62 | 0.350 |
|
Combined amend [nonsec Lea+b-] |
0.00 | 0.00 – 4247518100.04 | 0.985 |
|
Combined amend [Sec Lea-b-] |
0.31 | 0.06 – 1.41 | 0.146 |
|
Combined amend [Sec Lea+b-] |
0.00 | 0.00 – 27248842238187563646976.00 | 0.991 |
| Combined amend [Sec Leb+] | 0.30 | 0.11 – 0.86 | 0.020 |
| Observations | 342 | ||
| R2 Tjur | 0.059 | ||
## We fitted a logistic model (estimated using ML) to predict RV with
## Combined_amend (formula: RV ~ Combined_amend). The model's explanatory power is
## weak (Tjur's R2 = 0.06). The model's intercept, corresponding to Combined_amend
## = nonsec Lea-b-, is at -0.45 (95% CI [-1.45, 0.48], p = 0.350). Within this
## model:
##
## - The effect of Combined amend [nonsec Lea+b-] is statistically non-significant
## and negative (beta = -18.11, 95% CI [-328.00, 22.17], p = 0.985; Std. beta =
## -18.11, 95% CI [-328.00, 22.17])
## - The effect of Combined amend [Sec Lea-b-] is statistically non-significant
## and negative (beta = -1.16, 95% CI [-2.86, 0.34], p = 0.146; Std. beta = -1.16,
## 95% CI [-2.86, 0.34])
## - The effect of Combined amend [Sec Lea+b-] is statistically non-significant
## and negative (beta = -18.11, 95% CI [-554.85, 51.66], p = 0.991; Std. beta =
## -18.11, 95% CI [-554.85, 51.66])
## - The effect of Combined amend [Sec Leb+] is statistically significant and
## negative (beta = -1.20, 95% CI [-2.19, -0.15], p = 0.020; Std. beta = -1.20,
## 95% CI [-2.19, -0.15])
##
## Standardized parameters were obtained by fitting the model on a standardized
## version of the dataset. 95% Confidence Intervals (CIs) and p-values were
## computed using a Wald z-distribution approximation.##
## Logistic regression predicting RV : Positive vs Negative
##
## OR(95%CI) P(Wald's test) P(LR-test)
## Combined_amend: ref.=nonsec Lea-b- < 0.001
## nonsec Lea+b- 0 (0,Inf) 0.985
## Sec Lea-b- 0.31 (0.07,1.5) 0.146
## Sec Lea+b- 0 (0,Inf) 0.991
## Sec Leb+ 0.3 (0.11,0.83) 0.02
##
## Log-likelihood = -127.0018
## No. of observations = 342
## AIC value = 264.0036Showing bivariate models
| RV | RV | RV | RV | RV | RV | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Odds Ratios | CI | p | Odds Ratios | CI | p | Odds Ratios | CI | p | Odds Ratios | CI | p | Odds Ratios | CI | p | Odds Ratios | CI | p |
| (Intercept) | 0.12 | 0.08 – 0.18 | <0.001 | 0.26 | 0.15 – 0.43 | <0.001 | 0.13 | 0.08 – 0.19 | <0.001 | 0.14 | 0.10 – 0.19 | <0.001 | 0.40 | 0.26 – 0.61 | <0.001 | 0.64 | 0.23 – 1.62 | 0.350 |
| fever [Yes] | 2.43 | 1.27 – 4.60 | 0.006 | |||||||||||||||
| ABO pheno [AB] | 3.79 | 0.83 – 17.42 | 0.077 | |||||||||||||||
| ABO pheno [B] | 0.52 | 0.14 – 1.54 | 0.273 | |||||||||||||||
| ABO pheno [O] | 0.44 | 0.23 – 0.87 | 0.018 | |||||||||||||||
| cough [Yes] | 1.97 | 1.04 – 3.68 | 0.035 | |||||||||||||||
| heating [paraffin] | 3.24 | 1.32 – 7.47 | 0.007 | |||||||||||||||
| heating [wood] | 2.40 | 0.12 – 19.27 | 0.454 | |||||||||||||||
| Site [OPHC] | 0.22 | 0.12 – 0.42 | <0.001 | |||||||||||||||
|
Combined amend [nonsec Lea+b-] |
0.00 | 0.00 – 4247518100.04 | 0.985 | |||||||||||||||
|
Combined amend [Sec Lea-b-] |
0.31 | 0.06 – 1.41 | 0.146 | |||||||||||||||
|
Combined amend [Sec Lea+b-] |
0.00 | 0.00 – 27248842238187563646976.00 | 0.991 | |||||||||||||||
| Combined amend [Sec Leb+] | 0.30 | 0.11 – 0.86 | 0.020 | |||||||||||||||
| Observations | 334 | 342 | 332 | 336 | 342 | 342 | ||||||||||||
| R2 Tjur | 0.023 | 0.041 | 0.014 | 0.024 | 0.071 | 0.059 | ||||||||||||
Multivariate models:
Model A
modelA <- glm(RV ~ fever + ABO_pheno + heating + Combined_amend,
family = "binomial",
data = African_infants)
tab_model(modelA)| RV | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.74 | 0.23 – 2.28 | 0.607 |
| fever [Yes] | 2.03 | 1.01 – 4.06 | 0.045 |
| ABO pheno [AB] | 7.10 | 1.16 – 57.83 | 0.039 |
| ABO pheno [B] | 0.50 | 0.13 – 1.59 | 0.275 |
| ABO pheno [O] | 0.50 | 0.24 – 1.05 | 0.066 |
| heating [paraffin] | 3.94 | 1.43 – 10.47 | 0.006 |
| heating [wood] | 2.01 | 0.09 – 18.20 | 0.570 |
|
Combined amend [nonsec Lea+b-] |
0.00 | 0.00 – 4174784215.02 | 0.984 |
|
Combined amend [Sec Lea-b-] |
0.29 | 0.05 – 1.41 | 0.138 |
|
Combined amend [Sec Lea+b-] |
0.00 | 0.00 – 25059605166731082858496.00 | 0.991 |
| Combined amend [Sec Leb+] | 0.23 | 0.08 – 0.69 | 0.007 |
| Observations | 333 | ||
| R2 Tjur | 0.157 | ||
epiDisplay::logistic.display(modelA) ##
## Logistic regression predicting RV : Positive vs Negative
##
## crude OR(95%CI) adj. OR(95%CI)
## fever: Yes vs None 2.42 (1.28,4.59) 2.03 (1.02,4.06)
##
## ABO_pheno: ref.=A
## AB 4.98 (1.03,24.2) 7.1 (1.1,45.82)
## B 0.55 (0.17,1.78) 0.5 (0.15,1.73)
## O 0.4 (0.2,0.8) 0.5 (0.24,1.05)
##
## heating: ref.=electricity
## paraffin 3.38 (1.42,8.02) 3.94 (1.47,10.57)
## wood 2.38 (0.24,23.47) 2.01 (0.18,22.12)
##
## Combined_amend: ref.=nonsec Lea-b-
## nonsec Lea+b- 0 (0,Inf) 0 (0,Inf)
## Sec Lea-b- 0.31 (0.07,1.5) 0.29 (0.05,1.5)
## Sec Lea+b- 0 (0,Inf) 0 (0,Inf)
## Sec Leb+ 0.29 (0.11,0.81) 0.23 (0.08,0.67)
##
## P(Wald's test) P(LR-test)
## fever: Yes vs None 0.045 0.048
##
## ABO_pheno: ref.=A 0.013
## AB 0.039
## B 0.275
## O 0.066
##
## heating: ref.=electricity 0.031
## paraffin 0.006
## wood 0.57
##
## Combined_amend: ref.=nonsec Lea-b- < 0.001
## nonsec Lea+b- 0.984
## Sec Lea-b- 0.138
## Sec Lea+b- 0.991
## Sec Leb+ 0.007
##
## Log-likelihood = -110.8197
## No. of observations = 333
## AIC value = 243.6394Model B
modelB <- glm(RV ~ fever + ABO_pheno + heating + Gender + refusal_to_feed,
family = "binomial",
data = African_infants)
epiDisplay::logistic.display(modelB)##
## Logistic regression predicting RV : Positive vs Negative
##
## crude OR(95%CI) adj. OR(95%CI)
## fever: Yes vs None 2.4 (1.27,4.55) 2.18 (1.09,4.37)
##
## ABO_pheno: ref.=A
## AB 4.98 (1.03,24.2) 5.51 (1.02,29.75)
## B 0.55 (0.17,1.78) 0.48 (0.14,1.64)
## O 0.41 (0.21,0.81) 0.43 (0.21,0.88)
##
## heating: ref.=electricity
## paraffin 3.35 (1.41,7.96) 3.51 (1.36,9.05)
## wood 2.36 (0.24,23.29) 2.07 (0.18,24.27)
##
## Gender: Male vs Female 1.66 (0.88,3.12) 1.78 (0.9,3.5)
##
## refusal_to_feed: Yes vs None 1.65 (0.88,3.1) 1.4 (0.7,2.82)
##
## P(Wald's test) P(LR-test)
## fever: Yes vs None 0.028 0.03
##
## ABO_pheno: ref.=A 0.005
## AB 0.047
## B 0.244
## O 0.02
##
## heating: ref.=electricity 0.042
## paraffin 0.01
## wood 0.561
##
## Gender: Male vs Female 0.095 0.091
##
## refusal_to_feed: Yes vs None 0.346 0.349
##
## Log-likelihood = -120.3031
## No. of observations = 331
## AIC value = 258.6063plot_model(modelB,
show.values = T,
width = 0.1)
Model C
modelC <- glm(RV ~ fever + ABO_pheno + heating + Gender + refusal_to_feed + Le_ab_pheno,
family = "binomial",
data = African_infants)
epiDisplay::logistic.display(modelC)##
## Logistic regression predicting RV : Positive vs Negative
##
## crude OR(95%CI) adj. OR(95%CI)
## fever: Yes vs None 2.4 (1.27,4.55) 1.94 (0.95,3.94)
##
## ABO_pheno: ref.=A
## AB 4.98 (1.03,24.2) 7.08 (1.08,46.58)
## B 0.55 (0.17,1.78) 0.43 (0.12,1.53)
## O 0.41 (0.21,0.81) 0.53 (0.26,1.11)
##
## heating: ref.=electricity
## paraffin 3.35 (1.41,7.96) 4.2 (1.54,11.5)
## wood 2.36 (0.24,23.29) 1.47 (0.11,18.81)
##
## Gender: Male vs Female 1.66 (0.88,3.12) 1.55 (0.78,3.1)
##
## refusal_to_feed: Yes vs None 1.65 (0.88,3.1) 1.36 (0.66,2.78)
##
## Le_ab_pheno: ref.=a-b-
## a-b+ 0.6 (0.23,1.57) 0.47 (0.16,1.33)
## a+b- 0 (0,Inf) 0 (0,Inf)
## a+b+ 0.45 (0.19,1.04) 0.36 (0.15,0.89)
##
## P(Wald's test) P(LR-test)
## fever: Yes vs None 0.067 0.069
##
## ABO_pheno: ref.=A 0.016
## AB 0.042
## B 0.195
## O 0.093
##
## heating: ref.=electricity 0.027
## paraffin 0.005
## wood 0.767
##
## Gender: Male vs Female 0.214 0.21
##
## refusal_to_feed: Yes vs None 0.405 0.407
##
## Le_ab_pheno: ref.=a-b- < 0.001
## a-b+ 0.152
## a+b- 0.983
## a+b+ 0.027
##
## Log-likelihood = -110.5603
## No. of observations = 331
## AIC value = 245.1205Model D
modelD <- glm(RV ~ fever + ABO_pheno + heating + Gender + refusal_to_feed + Le_ab_pheno + Site,
family = "binomial",
data = African_infants)
epiDisplay::logistic.display(modelD)##
## Logistic regression predicting RV : Positive vs Negative
##
## crude OR(95%CI) adj. OR(95%CI)
## fever: Yes vs None 2.4 (1.27,4.55) 0.97 (0.41,2.31)
##
## ABO_pheno: ref.=A
## AB 4.98 (1.03,24.2) 6.42 (0.91,45.25)
## B 0.55 (0.17,1.78) 0.54 (0.15,1.94)
## O 0.41 (0.21,0.81) 0.5 (0.24,1.06)
##
## heating: ref.=electricity
## paraffin 3.35 (1.41,7.96) 3.56 (1.26,10.01)
## wood 2.36 (0.24,23.29) 2.32 (0.18,29.64)
##
## Gender: Male vs Female 1.66 (0.88,3.12) 1.36 (0.67,2.76)
##
## refusal_to_feed: Yes vs None 1.65 (0.88,3.1) 1.33 (0.65,2.76)
##
## Le_ab_pheno: ref.=a-b-
## a-b+ 0.6 (0.23,1.57) 0.44 (0.15,1.27)
## a+b- 0 (0,Inf) 0 (0,Inf)
## a+b+ 0.45 (0.19,1.04) 0.37 (0.14,0.93)
##
## Site: OPHC vs DGMAH 0.22 (0.12,0.42) 0.3 (0.13,0.71)
##
## P(Wald's test) P(LR-test)
## fever: Yes vs None 0.953 0.953
##
## ABO_pheno: ref.=A 0.024
## AB 0.062
## B 0.346
## O 0.072
##
## heating: ref.=electricity 0.056
## paraffin 0.016
## wood 0.516
##
## Gender: Male vs Female 0.396 0.394
##
## refusal_to_feed: Yes vs None 0.435 0.437
##
## Le_ab_pheno: ref.=a-b- 0.001
## a-b+ 0.127
## a+b- 0.983
## a+b+ 0.035
##
## Site: OPHC vs DGMAH 0.006 0.006
##
## Log-likelihood = -106.79
## No. of observations = 331
## AIC value = 239.58Showing Multivariate models
| RV | RV | RV | RV | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Odds Ratios | CI | p | Odds Ratios | CI | p | Odds Ratios | CI | p | Odds Ratios | CI | p |
| (Intercept) | 0.74 | 0.23 – 2.28 | 0.607 | 0.11 | 0.05 – 0.23 | <0.001 | 0.30 | 0.10 – 0.83 | 0.024 | 0.84 | 0.23 – 3.11 | 0.796 |
| fever [Yes] | 2.03 | 1.01 – 4.06 | 0.045 | 2.18 | 1.08 – 4.37 | 0.028 | 1.94 | 0.95 – 3.94 | 0.067 | 0.97 | 0.41 – 2.30 | 0.953 |
| ABO pheno [AB] | 7.10 | 1.16 – 57.83 | 0.039 | 5.51 | 1.02 – 32.98 | 0.047 | 7.08 | 1.13 – 58.59 | 0.042 | 6.42 | 0.96 – 56.35 | 0.062 |
| ABO pheno [B] | 0.50 | 0.13 – 1.59 | 0.275 | 0.48 | 0.12 – 1.52 | 0.244 | 0.43 | 0.11 – 1.41 | 0.195 | 0.54 | 0.13 – 1.79 | 0.346 |
| ABO pheno [O] | 0.50 | 0.24 – 1.05 | 0.066 | 0.43 | 0.21 – 0.88 | 0.020 | 0.53 | 0.26 – 1.12 | 0.093 | 0.50 | 0.24 – 1.07 | 0.072 |
| heating [paraffin] | 3.94 | 1.43 – 10.47 | 0.006 | 3.51 | 1.31 – 8.90 | 0.010 | 4.20 | 1.49 – 11.42 | 0.005 | 3.56 | 1.23 – 9.94 | 0.016 |
| heating [wood] | 2.01 | 0.09 – 18.20 | 0.570 | 2.07 | 0.09 – 20.47 | 0.561 | 1.47 | 0.06 – 15.96 | 0.767 | 2.32 | 0.10 – 24.30 | 0.516 |
|
Combined amend [nonsec Lea+b-] |
0.00 | 0.00 – 4174784215.02 | 0.984 | |||||||||
|
Combined amend [Sec Lea-b-] |
0.29 | 0.05 – 1.41 | 0.138 | |||||||||
|
Combined amend [Sec Lea+b-] |
0.00 | 0.00 – 25059605166731082858496.00 | 0.991 | |||||||||
| Combined amend [Sec Leb+] | 0.23 | 0.08 – 0.69 | 0.007 | |||||||||
| Gender [Male] | 1.78 | 0.91 – 3.56 | 0.095 | 1.55 | 0.78 – 3.15 | 0.214 | 1.36 | 0.67 – 2.80 | 0.396 | |||
| refusal to feed [Yes] | 1.40 | 0.69 – 2.81 | 0.346 | 1.36 | 0.66 – 2.78 | 0.405 | 1.33 | 0.64 – 2.75 | 0.435 | |||
| Le ab pheno [a-b+] | 0.47 | 0.16 – 1.34 | 0.152 | 0.44 | 0.15 – 1.27 | 0.127 | ||||||
| Le ab pheno [a+b-] | 0.00 | 0.00 – 10299545.78 | 0.983 | 0.00 | 0.00 – 6851241.20 | 0.983 | ||||||
| Le ab pheno [a+b+] | 0.36 | 0.15 – 0.91 | 0.027 | 0.37 | 0.15 – 0.95 | 0.035 | ||||||
| Site [OPHC] | 0.30 | 0.13 – 0.71 | 0.006 | |||||||||
| Observations | 333 | 331 | 331 | 331 | ||||||||
| R2 Tjur | 0.157 | 0.103 | 0.150 | 0.182 | ||||||||
Measures of Association by epiR:
Fever
## Outcome
## Predictor Positive Negative
## Yes 20 67
## None 27 220
##
## Outcome + Outcome - Total Inc risk * Odds
## Exposed + 20 67 87 23.0 0.299
## Exposed - 27 220 247 10.9 0.123
## Total 47 287 334 14.1 0.164
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.10 (1.25, 3.55)
## Odds ratio 2.43 (1.28, 4.61)
## Attrib risk in the exposed * 12.06 (2.40, 21.72)
## Attrib fraction in the exposed (%) 52.45 (19.69, 71.84)
## Attrib risk in the population * 3.14 (-2.25, 8.53)
## Attrib fraction in the population (%) 22.32 (2.56, 38.07)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 7.735 Pr>chi2 = 0.005
## Fisher exact test that OR = 1: Pr>chi2 = 0.007
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population units
##
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: dat
## X-squared = 6.7704, df = 1, p-value = 0.009268By cough
## Outcome
## Predictor Positive Negative
## Yes 21 83
## None 26 202
##
## Outcome + Outcome - Total Inc risk * Odds
## Exposed + 21 83 104 20.2 0.253
## Exposed - 26 202 228 11.4 0.129
## Total 47 285 332 14.2 0.165
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.77 (1.05, 3.00)
## Odds ratio 1.97 (1.05, 3.69)
## Attrib risk in the exposed * 8.79 (0.04, 17.54)
## Attrib fraction in the exposed (%) 43.53 (4.42, 66.63)
## Attrib risk in the population * 2.75 (-2.82, 8.33)
## Attrib fraction in the population (%) 19.45 (-2.11, 36.45)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 4.540 Pr>chi2 = 0.033
## Fisher exact test that OR = 1: Pr>chi2 = 0.041
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population units
##
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: dat
## X-squared = 3.8452, df = 1, p-value = 0.04989By Site
## Outcome
## Predictor Positive Negative
## OPHC 20 221
## DGMAH 29 72
##
## Outcome + Outcome - Total Inc risk * Odds
## Exposed + 20 221 241 8.3 0.0905
## Exposed - 29 72 101 28.7 0.4028
## Total 49 293 342 14.3 0.1672
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.29 (0.17, 0.49)
## Odds ratio 0.22 (0.12, 0.42)
## Attrib risk in the exposed * -20.41 (-29.90, -10.93)
## Attrib fraction in the exposed (%) -245.99 (-482.06, -105.67)
## Attrib risk in the population * -14.39 (-23.96, -4.81)
## Attrib fraction in the population (%) -100.40 (-150.52, -60.31)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 24.164 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = <0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population units
##
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: dat
## X-squared = 22.529, df = 1, p-value = 2.07e-06