Division of Population Health, Health Services Research and Primary Care, NIHR School for Primary Care Research, Centre for Primary Care, University of Manchester, Manchester, UK.
Manchester Academic Health Sciences Centre (MAHSC), Manchester, UK.
Psychol Med. 2020 Jan;50(2):293-302. doi: 10.1017/S0033291719000023. Epub 2019 Feb 12.
We aimed to spatially describe mental illness prevalence in England at small-area geographical level, as measured by prevalence of depression, severe mental illness (SMI) and antidepressant prescription volume in primary care records, and how much of their variation was explained by deprivation, social fragmentation and sociodemographic characteristics.
Information on prevalence of depression and SMI was obtained from the Quality and Outcomes Framework (QOF) administrative dataset for 2015/16 and the national dispensing dataset for 2015/16. Linear regression models were fitted to examine ecological associations between deprivation, social fragmentation, other sociodemographic characteristics and mental illness prevalence.
Mental illness prevalence varied within and between regions, with clusters of high prevalence identified across England. Our models explained 33.4-68.2% of variability in prevalence, but substantial variability between regions remained after adjusting for covariates. People in socially cohesive and socially deprived areas were more likely to be diagnosed with depression, while people in more socially fragmented and more socially deprived areas were more likely to be diagnosed with SMI.
Our findings suggest that to tackle mental health inequalities, attention needs to be targeted at more socially deprived localities. The role of social fragmentation warrants further investigation, and it is possible that depression remains undiagnosed in more socially fragmented areas. The wealth of routinely collected data can provide robust evidence to aid optimal resource allocation. If comparable data are available in other countries, similar methods could be deployed to identify high prevalence clusters and target funding to areas of greater need.
本研究旨在从空间角度描述英格兰的精神疾病流行情况，使用初级保健记录中的抑郁、严重精神疾病（SMI）和抗抑郁药处方量来衡量，同时还评估了社会剥夺、社会碎片化和社会人口学特征对其变化的解释程度。
使用 2015/16 年质量和结果框架（QOF）行政数据集及 2015/16 年全国配药数据集获取抑郁和 SMI 的流行率信息。采用线性回归模型检验社会剥夺、社会碎片化和其他社会人口学特征与精神疾病流行率之间的生态关联。
精神疾病的流行率在地区内和地区间存在差异，在英格兰各地都发现了高流行率的聚集区。我们的模型解释了流行率 33.4%-68.2%的变异性，但在调整了协变量后，地区间仍存在大量的变异性。在社会凝聚力强和社会贫困地区的人更有可能被诊断为抑郁，而在社会碎片化程度更高和社会贫困程度更高的地区的人更有可能被诊断为 SMI。
本研究结果表明，要解决精神健康不平等问题，需要将注意力集中在社会贫困程度更高的地方。社会碎片化的作用值得进一步研究，在社会碎片化程度更高的地区，抑郁可能仍未被诊断出来。常规收集的数据量丰富，可以提供有力的证据来帮助优化资源配置。如果其他国家也有类似的数据，那么可以采用类似的方法来识别高流行率聚集区，并将资金投向需求更大的地区。
