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 - Apr 2018

Keywords

  • Respiratory tract infections
  • gastrointestinal tract infections
  • prediction
  • social networks
  • PROGNOSTIC MODEL
  • SUPPORT
  • HEALTH
  • INTERVENTIONS
  • VALIDATION
  • DEPRESSION
  • STATE
  • MINI
  • Prospective Studies
  • Humans
  • Middle Aged
  • Male
  • Incidence
  • Adult
  • Female
  • Netherlands/epidemiology
  • Models, Theoretical
  • Cross-Sectional Studies
  • Social Networking
  • Respiratory Tract Infections/epidemiology
  • Gastrointestinal Diseases/epidemiology
  • Aged

Cite this

@article{6c26965eaf8445a0952364293bfe7377,
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 = "Respiratory tract infections, gastrointestinal tract infections, prediction, social networks, PROGNOSTIC MODEL, SUPPORT, HEALTH, INTERVENTIONS, VALIDATION, DEPRESSION, STATE, MINI, Prospective Studies, Humans, Middle Aged, Male, Incidence, Adult, Female, Netherlands/epidemiology, Models, Theoretical, Cross-Sectional Studies, Social Networking, Respiratory Tract Infections/epidemiology, Gastrointestinal Diseases/epidemiology, Aged",
author = "Stephanie Brinkhues and {van Kuijk}, Sander and Christian Hoebe and Paul Savelkoul and M.E.E. Kretzschmar and Maria Jansen and {de Vries}, Nanne and Simone Sep and Pieter Dagnelie and Nicolaas Schaper and Frans Verhey and Hans Bosma and J. Maes and Miranda Schram and Nicole Dukers",
year = "2018",
month = "4",
doi = "10.1017/S0950268817002187",
language = "English",
volume = "146",
pages = "533--543",
journal = "Epidemiology and Infection",
issn = "0950-2688",
publisher = "Cambridge University Press",
number = "5",

}

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

T2 - The Maastricht Study

AU - Brinkhues, Stephanie

AU - van Kuijk, Sander

AU - Hoebe, Christian

AU - Savelkoul, Paul

AU - Kretzschmar, M.E.E.

AU - Jansen, Maria

AU - de Vries, Nanne

AU - Sep, Simone

AU - Dagnelie, Pieter

AU - Schaper, Nicolaas

AU - Verhey, Frans

AU - Bosma, Hans

AU - Maes, J.

AU - Schram, Miranda

AU - Dukers, Nicole

PY - 2018/4

Y1 - 2018/4

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

KW - PROGNOSTIC MODEL

KW - SUPPORT

KW - HEALTH

KW - INTERVENTIONS

KW - VALIDATION

KW - DEPRESSION

KW - STATE

KW - MINI

KW - Prospective Studies

KW - Humans

KW - Middle Aged

KW - Male

KW - Incidence

KW - Adult

KW - Female

KW - Netherlands/epidemiology

KW - Models, Theoretical

KW - Cross-Sectional Studies

KW - Social Networking

KW - Respiratory Tract Infections/epidemiology

KW - Gastrointestinal Diseases/epidemiology

KW - Aged

U2 - 10.1017/S0950268817002187

DO - 10.1017/S0950268817002187

M3 - Article

VL - 146

SP - 533

EP - 543

JO - Epidemiology and Infection

JF - Epidemiology and Infection

SN - 0950-2688

IS - 5

ER -