Abstract：
To explore the impact of various measurements of long-term health conditions (LTCs) on the resulting prevalence estimates using data from a nationally representative dataset.
Children and young people in the Millennium Cohort Study were followed at ages 3, 5, 7, 11, and 14 years (N = 15,631). We estimated the weighted prevalence of LTCs at each time point and examined the degree to which estimates agreed with alternate health indicators (special educational needs and disability [SEND], specific chronic conditions, and common chronicity criteria) using descriptive analyses, Cohen\'s kappa statistic, and percentage agreement.
The estimated weighted prevalence of LTCs peaked at 5 years old (20%). Despite high percentage agreement, we observed at best moderate chance-corrected agreement between the type of LTC and reasons for SEND (kappas from 0.02 to 0.56, percentage agreement from 97% to 99%) or specified chronic conditions (kappas from 0.002 to 0.02, percentage agreement from 73% to 97%). Applying chronicity criteria decreased the estimated weighted prevalence of LTCs (3%).
How long-term conditions are defined drastically alters the estimated weighted prevalence of LTCs. Improved clarity and consistency in the definition and measurement of LTCs is urgently needed to underpin policy and commissioning of services.
Children and young people in the Millennium Cohort Study were followed at ages 3, 5, 7, 11, and 14 years (N = 15,631). We estimated the weighted prevalence of LTCs at each time point and examined the degree to which estimates agreed with alternate health indicators (special educational needs and disability [SEND], specific chronic conditions, and common chronicity criteria) using descriptive analyses, Cohen\'s kappa statistic, and percentage agreement.
The estimated weighted prevalence of LTCs peaked at 5 years old (20%). Despite high percentage agreement, we observed at best moderate chance-corrected agreement between the type of LTC and reasons for SEND (kappas from 0.02 to 0.56, percentage agreement from 97% to 99%) or specified chronic conditions (kappas from 0.002 to 0.02, percentage agreement from 73% to 97%). Applying chronicity criteria decreased the estimated weighted prevalence of LTCs (3%).
How long-term conditions are defined drastically alters the estimated weighted prevalence of LTCs. Improved clarity and consistency in the definition and measurement of LTCs is urgently needed to underpin policy and commissioning of services.
摘要：
使用来自全国代表性数据集的数据，探讨长期健康状况（LTC）的各种测量对所得患病率估计的影响。
千年队列研究中的儿童和年轻人在3、5、7、11和14岁时被随访（N=15,631）。我们估计了每个时间点LTC的加权患病率，并检查了估计与替代健康指标（特殊教育需求和残疾[SEND]，特定的慢性病，和常见的慢性标准)使用描述性分析，科恩的卡帕统计，和百分比协议。
LTC的估计加权患病率在5岁时达到峰值（20％）。尽管达成了很高的协议，我们最多观察到LTC类型与SEND原因(kappas为0.02~0.56,百分比一致性为97%~99%)或特定慢性疾病(kappas为0.002~0.02,百分比一致性为73%~97%)之间的中度机会校正一致性.应用慢性标准降低了LTC的估计加权患病率(3%)。
如何定义长期条件会极大地改变LTC的估计加权患病率。迫切需要在LTC的定义和测量方面提高清晰度和一致性，以支持服务的政策和调试。
千年队列研究中的儿童和年轻人在3、5、7、11和14岁时被随访（N=15,631）。我们估计了每个时间点LTC的加权患病率，并检查了估计与替代健康指标（特殊教育需求和残疾[SEND]，特定的慢性病，和常见的慢性标准)使用描述性分析，科恩的卡帕统计，和百分比协议。
LTC的估计加权患病率在5岁时达到峰值（20％）。尽管达成了很高的协议，我们最多观察到LTC类型与SEND原因(kappas为0.02~0.56,百分比一致性为97%~99%)或特定慢性疾病(kappas为0.002~0.02,百分比一致性为73%~97%)之间的中度机会校正一致性.应用慢性标准降低了LTC的估计加权患病率(3%)。
如何定义长期条件会极大地改变LTC的估计加权患病率。迫切需要在LTC的定义和测量方面提高清晰度和一致性，以支持服务的政策和调试。
