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Table 5 Summary of logistic regression coefficients for the original dataset containing missing observations (n = 116,721) and the Complete Cases dataset (n = 44,285) using Bayesian models

From: Filling gaps in notification data: a model-based approach applied to travel related campylobacteriosis cases in New Zealand

Coefficients

Original dataseta

Complete Casesb

Mean

95 % CI2

Mean

95 % CI

Intercept

−6.503

−6.965

−6.041

−6.522

−6.978

−6.070

Urbanc

0.804

0.231

1.377

0.834

0.297

1.414

DepIndexd

0.091

0.063

0.119

0.091

0.063

0.120

Travel Ratee

0.045

0.040

0.051

0.045

0.039

0.050

Age (5–19)

0.473

0.262

0.683

0.476

0.270

0.680

Age (20–59)

1.273

1.095

1.452

1.278

1.105

1.449

Age (60+)

0.885

0.688

1.082

0.889

0.697

1.080

Summer

−0.393

−0.491

−0.294

−0.393

−0.491

−0.297

Autumn

−0.254

−0.364

−0.143

−0.255

−0.367

−0.145

Winter

0.128

0.027

0.230

0.128

0.026

0.229

Male

0.015

−0.060

0.090

0.015

−0.059

0.089

Interventionf

0.288

0.200

0.377

0.287

0.199

0.377

  1. aAll campylobacteriosis notifications available for analysis (n = 116,271); bcampylobacteriosis notifications containing information on overseas travel status (n = 44,285). c Proportion of DHB population under urban influence; dDeprivation index (scale 0–10, 0 = least deprived and 10 = most deprived DHB); eShort term international travel per 100 residents of a DHB; fA binary indicator variable to identify pre and post 2006 intervention. Age (<5), Spring, and Female sex are reference categories
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BMC Infectious Diseases

ISSN: 1471-2334

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