Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, Ontario, Canada.
Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada.
Pediatr Pulmonol. 2019 Oct;54(10):1547-1556. doi: 10.1002/ppul.24432. Epub 2019 Jul 22.
Asthma prevalence is commonly measured in national surveys by questionnaire. The Ontario Asthma Surveillance Information System (OASIS) developed a validated health claims diagnosis algorithm to estimate asthma prevalence. The primary objective was to assess the agreement between two approaches of measuring asthma in young children. Secondary objectives were to identify concordant and discordant pairs, and to identify factors associated with disagreement.
A measurement study to evaluate the agreement between the OASIS algorithm and parent-reported asthma (criterion standard). Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. Multivariable logistic regression was used to determine factors associated with disagreement.
Healthy children aged 1 to 5 years (n =3642) participating in the TARGet Kids! practice based research network 2008-2013 in Toronto, Canada were included. Prevalence of asthma was 14% and 6% by the OASIS algorithm and parent-reported asthma, respectively. The Kappa statistic was 0.43, sensitivity 81%, specificity 90%, PPV 34%, and NPV 99%. There were 3249 concordant and 393 discordant pairs. Statistically significant factors associated with asthma identified by OASIS but not parent report included: male sex, higher zBMI, and parent history of asthma. Males were less likely to have asthma identified by parent report but not OASIS.
The OASIS algorithm identified more asthma cases in young children than parent-reported asthma. The OASIS algorithm had high sensitivity, specificity, and NPV but low PPV relative to parent-reported asthma. These findings need replication in other populations.
哮喘患病率通常通过问卷调查在全国性调查中进行测量。安大略省哮喘监测信息系统（OASIS）开发了一种经过验证的健康索赔诊断算法，以估计哮喘的患病率。主要目的是评估两种测量幼儿哮喘方法的一致性。次要目标是确定一致和不一致的对，并确定与不一致相关的因素。
一项旨在评估 OASIS 算法与父母报告的哮喘（标准）之间测量一致性的测量研究。计算了敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）。使用多变量逻辑回归确定与不一致相关的因素。
2008 年至 2013 年，加拿大多伦多 TARGet Kids！实践基础研究网络中的 1 至 5 岁健康儿童（n=3642）参与了该研究。哮喘的患病率分别为 OASIS 算法和父母报告的哮喘的 14%和 6%。Kappa 统计量为 0.43，敏感性为 81%，特异性为 90%，PPV 为 34%，NPV 为 99%。有 3249 对一致和 393 对不一致的对。通过 OASIS 但未通过父母报告确定的与哮喘相关的统计学显著因素包括：男性、更高的 zBMI 和父母的哮喘史。男性通过父母报告而不是 OASIS 报告哮喘的可能性较小。
OASIS 算法在幼儿中识别出的哮喘病例多于父母报告的哮喘病例。与父母报告的哮喘相比，OASIS 算法具有较高的敏感性、特异性和 NPV，但 PPV 较低。这些发现需要在其他人群中复制。
