TY - JOUR
T1 - Development of prediction models for upper and lower respiratory and gastrointestinal tract infections using social network parameters in middle-aged and older persons -The Maastricht Study-
AU - BRINKHUES, S.
AU - VAN KUIJK, S. M.J.
AU - HOEBE, C. J.P.A.
AU - SAVELKOUL, P. H.M.
AU - KRETZSCHMAR, M. E.E.
AU - JANSEN, M. W.J.
AU - DE VRIES, N.
AU - SEP, S. J.S.
AU - DAGNELIE, P. C.
AU - SCHAPER, N. C.
AU - VERHEY, F. R.J.
AU - BOSMA, H.
AU - MAES, J.
AU - SCHRAM, M. T.
AU - DUKERS-MUIJRERS, N. H.T.M.
PY - 2018/4/1
Y1 - 2018/4/1
N2 - The ability to predict upper respiratory infections (URI), lower respiratory infections (LRI), and gastrointestinal tract infections (GI) in independently living older persons would greatly benefit population and individual health. Social network parameters have so far not been included in prediction models. Data were obtained from The Maastricht Study, a population-based cohort study (N = 3074, mean age (±s.d.) 59·8 ± 8·3, 48·8% women). We used multivariable logistic regression analysis to develop prediction models for self-reported symptomatic URI, LRI, and GI (past 2 months). We determined performance of the models by quantifying measures of discriminative ability and calibration. Overall, 953 individuals (31·0%) reported URI, 349 (11·4%) LRI, and 380 (12·4%) GI. The area under the curve was 64·7% (95% confidence interval (CI) 62·6–66·8%) for URI, 71·1% (95% CI 68·4–73·8) for LRI, and 64·2% (95% CI 61·3–67·1%) for GI. All models had good calibration (based on visual inspection of calibration plot, and Hosmer–Lemeshow goodness-of-fit test). Social network parameters were strong predictors for URI, LRI, and GI. Using social network parameters in prediction models for URI, LRI, and GI seems highly promising. Such parameters may be used as potential determinants that can be addressed in a practical intervention in older persons, or in a predictive tool to compute an individual's probability of infections.
AB - The ability to predict upper respiratory infections (URI), lower respiratory infections (LRI), and gastrointestinal tract infections (GI) in independently living older persons would greatly benefit population and individual health. Social network parameters have so far not been included in prediction models. Data were obtained from The Maastricht Study, a population-based cohort study (N = 3074, mean age (±s.d.) 59·8 ± 8·3, 48·8% women). We used multivariable logistic regression analysis to develop prediction models for self-reported symptomatic URI, LRI, and GI (past 2 months). We determined performance of the models by quantifying measures of discriminative ability and calibration. Overall, 953 individuals (31·0%) reported URI, 349 (11·4%) LRI, and 380 (12·4%) GI. The area under the curve was 64·7% (95% confidence interval (CI) 62·6–66·8%) for URI, 71·1% (95% CI 68·4–73·8) for LRI, and 64·2% (95% CI 61·3–67·1%) for GI. All models had good calibration (based on visual inspection of calibration plot, and Hosmer–Lemeshow goodness-of-fit test). Social network parameters were strong predictors for URI, LRI, and GI. Using social network parameters in prediction models for URI, LRI, and GI seems highly promising. Such parameters may be used as potential determinants that can be addressed in a practical intervention in older persons, or in a predictive tool to compute an individual's probability of infections.
KW - Respiratory tract infections
KW - gastrointestinal tract infections
KW - prediction
KW - social networks
UR - http://www.scopus.com/inward/record.url?scp=85030849428&partnerID=8YFLogxK
U2 - https://doi.org/10.1017/S0950268817002187
DO - https://doi.org/10.1017/S0950268817002187
M3 - Article
C2 - 28946936
SN - 0950-2688
VL - 146
SP - 533
EP - 543
JO - Epidemiology and Infection
JF - Epidemiology and Infection
IS - 5
ER -