The Prevalence and Predictors of Hypertension and the Metabolic Syndrome in Police Personnel.
James D YatesJeffrey William Frederick AldousDaniel P BaileyAngel Marie ChaterAndrew C S MitchellJoanna C RichardsPublished in: International journal of environmental research and public health (2021)
Hypertension and metabolic syndrome (METSYN) are reportedly high in police forces. This may contribute to health deterioration and absenteeism in police personnel. Police forces comprise of staff in 'operational' and 'non-operational' job types but it is not known if job type is associated to hypertension and METSYN prevalence. This study aimed to explore the prevalence of hypertension and METSYN, the factors associated with the risk of hypertension and METSYN, and compare physiological, psychological, and behavioural factors between operational and non-operational police personnel. Cross-sectional data was collected from 77 operational and 60 non-operational police workers. Hypertension and METSYN were prevalent in 60.5% and 20% of operational and 60.0% and 13.6% of non-operational police personnel, respectively (p > 0.05). Operational job type, moderate organisational stress (compared with low stress) and lower high-density lipoprotein cholesterol were associated with lower odds of hypertension, whereas increasing body mass index was associated with increased odds of hypertension (p < 0.05). None of the independent variables were significantly associated with the odds of METSYN. Operational police had several increased cardiometabolic risk markers compared with non-operational police. Given the high prevalence of hypertension and METSYN in operational and non-operational personnel, occupational health interventions are needed for the police and could be informed by the findings of this study.
Keyphrases
- blood pressure
- metabolic syndrome
- body mass index
- public health
- healthcare
- cross sectional
- risk factors
- mental health
- type diabetes
- cardiovascular disease
- skeletal muscle
- adipose tissue
- insulin resistance
- risk assessment
- high intensity
- uric acid
- social media
- big data
- climate change
- human health
- deep learning
- health information
- weight loss
- artificial intelligence