近年来,与公共卫生相关的公开数据的可用性显着增加。这些数据具有制定公共卫生政策的巨大潜力；然而，这需要有意义和深刻的分析。我们的目的是演示如何使用数据分析技术来解决数据减少的问题，使用在线可用的公共卫生数据进行预测和解释，以便为公共卫生政策提供良好的依据。
从现有的在线英国国家公共卫生数据库中分析了观察性自杀预防数据。多重共线性分析和主成分分析用于减少相关数据，其次是预测和解释自杀的回归分析。
多重共线性分析有效地将预测因子的指标集减少了30％，而主成分分析进一步将指标集减少了86％。用于预测的回归确定了自杀行为的四个重要指标预测因子（因故意自残而紧急住院，离开照顾的孩子，法定无家可归和自我报告的幸福感/低幸福感)和两个主要成分预测因子(相关度功能障碍，以及行为问题和精神疾病)。对解释的回归确定了社会因素（独自生活）对自杀行为的幸福感（低幸福感）的显着调节作用，从而支持现有的理论，并提供超越回归预测结果的洞察力。还确定了两个独立的预测因子，这些预测因子捕获了社会护理服务提供中的相关性需求。
我们证明了回归技术在在线公共卫生数据分析中的有效性。预测和解释的回归分析都适用于公共卫生数据分析，以便更好地了解公共卫生结果。因此，必须明确分析的目的（预测准确性或理论发展），作为选择最合适模型的基础。我们将这些技术应用于自杀数据分析；然而，我们认为,本研究中提出的分析应应用于整个公共卫生领域的数据集,以提高卫生政策建议的质量.
In recent years, the availability of publicly available data related to public health has significantly increased. These data have substantial potential to develop public health policy; however, this requires meaningful and insightful analysis. Our aim is to demonstrate how data analysis techniques can be used to address the issues of data reduction, prediction and explanation using online available public health data, in order to provide a sound basis for informing public health policy.
Observational suicide prevention data were analysed from an existing online United Kingdom national public health database. Multi-collinearity analysis and principal-component analysis were used to reduce correlated data, followed by regression analyses for prediction and explanation of suicide.
Multi-collinearity analysis was effective in reducing the indicator set of predictors by 30% and principal component analysis further reduced the set by 86%. Regression for prediction identified four significant indicator predictors of suicide behaviour (emergency hospital admissions for intentional self-harm, children leaving care, statutory homelessness and self-reported well-being/low happiness) and two main component predictors (relatedness dysfunction, and behavioural problems and mental illness). Regression for explanation identified significant moderation of a well-being predictor (low happiness) of suicide behaviour by a social factor (living alone), thereby supporting existing theory and providing insight beyond the results of regression for prediction. Two independent predictors capturing relatedness needs in social care service delivery were also identified.
We demonstrate the effectiveness of regression techniques in the analysis of online public health data. Regression analysis for prediction and explanation can both be appropriate for public health data analysis for a better understanding of public health outcomes. It is therefore essential to clarify the aim of the analysis (prediction accuracy or theory development) as a basis for choosing the most appropriate model. We apply these techniques to the analysis of suicide data; however, we argue that the analysis presented in this study should be applied to datasets across public health in order to improve the quality of health policy recommendations.
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