Improving Public Health Surveillance Using a Dual-Frame Survey of Landline and Cell Phone Numbers
Improving Public Health Surveillance Using a Dual-Frame Survey of Landline and Cell Phone Numbers
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DOI：
10.1093/aje/kwq442
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发表时间：
2011-03-15
影响因子：
5
通讯作者：
Frankel, Martin R.
中科院分区：
文献类型：
--
作者：
Hu, S. Sean;Balluz, Lina;Frankel, Martin R.
由于仅使用手机的家庭，美国固定电话样本的覆盖率不断上升，为了应对这一挑战，行为风险因素监测系统 (BRFSS) 将传统的基于固定电话的随机数字拨号调查扩展到固定电话和手机号码的双框架调查。 2008 年，一项针对仅使用手机的成年人的调查与 18 个州正在进行的固定电话健康调查同时进行。作者使用最佳方法将样本分配到仅限固定电话和仅使用手机的层中，并使用一种新方法对州级固定电话和手机样本进行加权。他们为 16 项健康指标中的每一项开发了逻辑模型，以检验排除拥有手机的成年人是否只影响调整人口特征后的估计值。对排除手机的固定电话调查中潜在偏差的程度进行了估计。 16 项健康指标中的 9 项发现，由于仅将拥有手机的成年人排除在固定电话调查之外而导致偏差。由于拥有手机的成年人的固定电话覆盖率只会持续增加，因此这些偏见可能会增加。使用固定电话和手机号码的双框架调查有助于 BRFSS 获得有效、可靠和有代表性的数据。
To meet challenges arising from increasing rates of noncoverage in US landline-based telephone samples due to cell-phone-only households, the Behavioral Risk Factor Surveillance System (BRFSS) expanded a traditional landline-based random digit dialing survey to a dual-frame survey of landline and cell phone numbers. In 2008, a survey of adults with cell phones only was conducted in parallel with an ongoing landline-based health survey in 18 states. The authors used the optimal approach to allocate samples into landline and cell-phone-only strata and used a new approach to weighting state-level landline and cell phone samples. They developed logistic models for each of 16 health indicators to examine whether exclusion of adults with cell phones only affected estimates after adjustment for demographic characteristics. The extents of the potential biases in landline telephone surveys that exclude cell phones were estimated. Biases resulting from exclusion of adults with cell phones only from the landline-based survey were found for 9 out of the 16 health indicators. Because landline noncoverage rates for adults with cell phones only continue to increase, these biases are likely to increase. Use of a dual-frame survey of landline and cell phone numbers assisted the BRFSS efforts in obtaining valid, reliable, and representative data.