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-

S. BRINKHUES, S. M.J. VAN KUIJK, C. J.P.A. HOEBE, P. H.M. SAVELKOUL, M. E.E. KRETZSCHMAR, M. W.J. JANSEN, N. DE VRIES, S. J.S. SEP, P. C. DAGNELIE, N. C. SCHAPER, F. R.J. VERHEY, H. BOSMA, J. MAES, M. T. SCHRAM, N. H.T.M. DUKERS-MUIJRERS

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)533-543
Number of pages11
JournalEpidemiology and Infection
Volume146
Issue number5
DOIs
Publication statusPublished - 1 Apr 2018

Cite this

BRINKHUES, S. ; VAN KUIJK, S. M.J. ; HOEBE, C. J.P.A. ; SAVELKOUL, P. H.M. ; KRETZSCHMAR, M. E.E. ; JANSEN, M. W.J. ; DE VRIES, N. ; SEP, S. J.S. ; DAGNELIE, P. C. ; SCHAPER, N. C. ; VERHEY, F. R.J. ; BOSMA, H. ; MAES, J. ; SCHRAM, M. T. ; DUKERS-MUIJRERS, N. H.T.M. / 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-. In: Epidemiology and Infection. 2018 ; Vol. 146, No. 5. pp. 533-543.
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title = "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-",
abstract = "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.",
keywords = "gastrointestinal tract infections, prediction, Respiratory tract infections, social networks",
author = "S. BRINKHUES and {VAN KUIJK}, {S. M.J.} and HOEBE, {C. J.P.A.} and SAVELKOUL, {P. H.M.} and KRETZSCHMAR, {M. E.E.} and JANSEN, {M. W.J.} and {DE VRIES}, N. and SEP, {S. J.S.} and DAGNELIE, {P. C.} and SCHAPER, {N. C.} and VERHEY, {F. R.J.} and H. BOSMA and J. MAES and SCHRAM, {M. T.} and DUKERS-MUIJRERS, {N. H.T.M.}",
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BRINKHUES, S, VAN KUIJK, SMJ, HOEBE, CJPA, SAVELKOUL, PHM, KRETZSCHMAR, MEE, JANSEN, MWJ, DE VRIES, N, SEP, SJS, DAGNELIE, PC, SCHAPER, NC, VERHEY, FRJ, BOSMA, H, MAES, J, SCHRAM, MT & DUKERS-MUIJRERS, NHTM 2018, '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-' Epidemiology and Infection, vol. 146, no. 5, pp. 533-543. https://doi.org/10.1017/S0950268817002187

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-. / BRINKHUES, S.; VAN KUIJK, S. M.J.; HOEBE, C. J.P.A.; SAVELKOUL, P. H.M.; KRETZSCHMAR, M. E.E.; JANSEN, M. W.J.; DE VRIES, N.; SEP, S. J.S.; DAGNELIE, P. C.; SCHAPER, N. C.; VERHEY, F. R.J.; BOSMA, H.; MAES, J.; SCHRAM, M. T.; DUKERS-MUIJRERS, N. H.T.M.

In: Epidemiology and Infection, Vol. 146, No. 5, 01.04.2018, p. 533-543.

Research output: Contribution to journalArticleAcademicpeer-review

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 - gastrointestinal tract infections

KW - prediction

KW - Respiratory tract infections

KW - social networks

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U2 - 10.1017/S0950268817002187

DO - 10.1017/S0950268817002187

M3 - Article

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