Forecasting Participants in the All Women Count! Mammography Program.
Forecasting Participants in the All Women Count! Mammography Program.
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DOI：
10.5888/pcd15.180177
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发表时间：
2018-10-25
影响因子：
5.5
通讯作者：
Michael S
中科院分区：
文献类型：
--
作者：
Holzhauser C;Da Rosa P;Michael S
所有女人都算数！(AWC!)该计划是一个免费的乳腺癌和宫颈癌筛查计划，为合格的妇女在南达科他州。我们的研究旨在确定具有相似社会经济特征的县，并估计未来5年将使用该计划的妇女人数。我们用了AWC！数据和社会人口预测变量（例如，贫困水平[年收入等于或低于联邦贫困水平200%的人口百分比]，中位收入）以及高斯回归时间序列模型的混合物，以同时执行聚类和预测。采用贝叶斯信息准则（BIC）进行模型选择。预测变量的预测是通过使用自回归综合移动平均模型。通过使用BIC，我们确定了5个集群，显示了南达科他州县的群体在预测变量和参与者数量方面具有相似的特征。混合模型确定了参与率呈上升或下降趋势的县组，并预测了每个组的平均数。本研究所使用的混合回归时间序列模型，可以识别相似的县，并提供了一个预测模型，为未来几年。虽然有几个预测因素有助于项目的参与，但我们相信，我们按县进行的预测分析可能会提供有用的信息，以改善AWC的实施！通过告知项目经理未来5年的预期参与人数，反过来，这将有助于数据驱动的资源分配。
The All Women Count! (AWC!) program is a no-cost breast and cervical cancer screening program for qualifying women in South Dakota. Our study aimed to identify counties with similar socioeconomic characteristics and to estimate the number of women who will use the program for the next 5 years. We used AWC! data and sociodemographic predictor variables (eg, poverty level [percentage of the population with an annual income at or below 200% of the Federal Poverty Level], median income) and a mixture of Gaussian regression time series models to perform clustering and forecasting simultaneously. Model selection was performed by using Bayesian information criterion (BIC). Forecasting of the predictor variables was done by using an autoregressive integrated moving average model. By using BIC, we identified 5 clusters showing the groups of South Dakota counties with similar characteristics in terms of predictor variables and the number of participants. The mixture model identified groups of counties with increasing or decreasing trends in participation and forecast averages per cluster. The mixture of regression time series model used in this study allowed for the identification of similar counties and provided a forecasting model for future years. Although several predictors contributed to program participation, we believe our forecasting analysis by county may provide useful information to improve the implementation of the AWC! program by informing program managers on the expected number of participants in the next 5 years. This, in turn, will help in data-driven resource allocation.
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影响因子：
254.7
作者：
DeSantis, Carol;Ma, Jiemin;Jemal, Ahmedin
通讯作者：
Jemal, Ahmedin
影响因子：
5.5
作者：
Wang B;He M;Wang L;Engelgau MM;Zhao W;Wang L
通讯作者：
Wang L
影响因子：
9.7
作者：
Lissenden, Brett;Yao, Nengliang Aaron
通讯作者：
Yao, Nengliang Aaron
影响因子：
5.8
作者：
Hyndman, Rob J.;Khandakar, Yeasmin
通讯作者：
Khandakar, Yeasmin
DOI：
10.1111/j.2517-6161.1977.tb01600.x
发表时间：
1977-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL
影响因子：
--
作者：
DEMPSTER, AP;LAIRD, NM;RUBIN, DB
通讯作者：
RUBIN, DB