This study measured geographic variation in depression hospitalizations and identified community-level risk factors. Depression hospitalizations were identified from the Statewide Inpatient Database. The dependent variable was specified as the indirectly standardized hospitalization rate. County-level data for 14 states were collected from federal agencies. The Bayesian spatial regression model included socio-demographic, economic, and health system characteristics as independent variables. There were 8.5 depression hospitalizations per 1,000 residents. 8.8% of counties had hospitalization rates 33% greater than the standardized rate. Significant risk factors included unemployment, poverty, physician supply, and hospital bed supply. Significant protective factors included rurality, economic dependence, and housing stress.
As noted by Fremer and Olson, analysis of answer changes is often used to investigate testing irregularities because the analysis is readily performed and has proven its value in practice. Researchers such as Belov, Sinharay and Johnson, van der Linden and Jeon, van der Linden and Lewis, and Wollack, Cohen, and Eckerly have suggested several statistics for detection of aberrant answer changes. This article suggests a new statistic that is based on the likelihood ratio test. An advantage of the new statistic is that it follows the standard normal distribution under the null hypothesis of no aberrant answer changes. It is demonstrated in a detailed simulation study that the Type I error rate of the new statistic is very close to the nominal level and the power of the new statistic is satisfactory in comparison to those of several existing statistics for detecting aberrant answer changes. The new statistic and several existing statistics were shown to provide useful information for a real data set. Given the increasing interest in analysis of answer changes, the new statistic promises to be useful to measurement practitioners.
Clinical High Risk for Psychosis (CHR-P) services have been primarily developed to support young people with attenuated symptoms (indicated prevention). No evidence-based appraisal has systematically investigated to what extent these clinics may implement other preventive approaches. PRISMA 2020-compliant systematic review of Web of Science, Cochrane Central Register of Reviews, and Ovid/PsychINFO, from inception until 14th June 2021, identifying original studies describing public health strategies: (a) service characteristics (configuration of mental health service, outreach, pathways to care); (b) universal interventions (general population); (c) selective interventions targeting CHR-P service-users or family/carers. Public health preventive initiatives were systematically stratified according to core social determinants of mental disorders associated with the 2030 Sustainable Development Goals promoted by the United Nations Member States (UN 2030 SDG) and good mental health outcomes. A total of 66 publications were included, providing data on 13 standalone, 40 integrated, three networks, and six regional or international surveys of CHR-P services across Europe, Asia, Oceania, Africa, North and South America, providing care to >28 M people. CHR-P services implement numerous public health initiatives targeting social and cultural (16 initiatives), economic (seven initiatives), demographic (six initiatives), environmental events (four initiatives) and neighbourhood (three initiatives) UN 2030 SGD determinants of mental disorders. There is additional evidence for CHR-P services promoting good mental health. The main barriers were the lack of resources for expanding public health prevention at a large scale. CHR-P services implement numerous public health prevention initiatives and promotion of good mental health beyond indicated prevention of psychosis.
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