Prediction of temporary disability due to of circulatory system diseases in housing and communal services employees

G. Y. Bendyuk, Бендюк Григорий Яковлевич, N. Grishina, Гришина Наталья Константиновна, M. A. Dokhov, Дохов Михаил Александрович, A. A. Sidorov, Сидоров Александр Анатольевич
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Abstract

Aim. To develop a prognostic model of incidence of temporary (short-term) disability due to cardiovascular diseases in a housing and communal services employees using the example of the State Unitary Enterprise (SUE) “Vodokanal of St. Petersburg”. Methods. The study included 4634 people, observed in the departmental outpatient clinic of SUE “Vodokanal of St. Petersburg”, between 2015 and 2017 years. We calculated extensive indicators determining the disease patterns and intensive indicators characterizing the prevalence of the pathological process in the study of morbidity by the findings of periodic health examinations and incidence of short-term disability. The incidence of short-term disability was predicted by using the method of sequential analysis A. Wald modified by E.V. Gubler and the neural network. Results. We found that based on the operational and statistical data of the medical and sanitary unit, using a neural network, relying on linear and nonlinear dependence of comorbidity, in particular, respiratory diseases, it is possible to predict (in 92.4% of cases) the incidence of temporary disability due to cardiovascular diseases in employees of the housing and communal services SUE “Vodokanal of St. Petersburg”. The high sensitivity (97.7%) and specificity (90.7%) of the method allows it to predict the need for treatment and prophylaxis measures in the contingent attached to the medical and sanitary unit. Conclusion. The developed model allows to determine the main directions of preventive work to reduce the incidence of circulatory system diseases with temporary disability: prevention of high blood pressure and respiratory diseases.
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住房和公共服务人员因循环系统疾病而暂时残疾的预测
的目标。以国家统一企业"圣彼得堡Vodokanal "为例,建立住房和公共服务雇员因心血管疾病而暂时(短期)残疾发生率的预测模型。方法。该研究包括2015年至2017年间在圣彼得堡Vodokanal医院的部门门诊观察到的4634人。我们通过定期健康检查和短期残疾发生率的结果,计算了确定疾病模式的广泛指标和表征病理过程流行程度的密集指标。采用经E.V. Gubler修正的序列分析法A. Wald和神经网络预测短期残疾的发生率。结果。我们发现,基于医疗和卫生单位的操作和统计数据,使用神经网络,依赖于合并症的线性和非线性依赖,特别是呼吸系统疾病,可以预测(92.4%的情况下)住房和公共服务SUE“圣彼得堡Vodokanal”的雇员因心血管疾病而暂时残疾的发生率。该方法的高灵敏度(97.7%)和高特异性(90.7%)使其能够预测医疗卫生单位所属特遣队的治疗和预防措施需求。结论。开发的模型可以确定预防工作的主要方向,以减少循环系统疾病的发病率,暂时残疾:预防高血压和呼吸系统疾病。
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