Estimating local prevalence of mental health problems

Stern, Steven

doi:10.1007/s10742-014-0120-2

Cited by 6 publications

(4 citation statements)

References 66 publications

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“…However, there are some surprising correlations in terms of being small: worthless and hopeless; too much effort and hopeless. Stern (2014) finds much smaller correlation terms across DSM-IV diagnoses. There is one large eigenvalue of Ω (4.791), three moderately small eigenvalues (0.173, 0.428, 0.608), and two very small eigenvalues (0.0004, 0.0002).…”

Section: Correlated Ordered Probit Resultsmentioning

confidence: 85%

“…It increases with being a veteran (0.055) and being employed (0.051). It decreases with family income along withStern (2014). It increases for those with Medicare (0.146) and decreases for those with private insurance (-0.142…”

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confidence: 98%

“…Probit Estimates and SignificanceMarried people are less likely to have a mental illness (-0.173), andStrine et al (2009) and Stern (2014) find the same. As inStern (2014), the prevalence of mental health problems declines with improving health. It increases with being a veteran (0.055) and being employed (0.051).…”

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confidence: 99%

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Statistical Properties of K6

arvapally,

stern

2024

Preprint

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We consider five different ways to estimate the effects of demographic and other variables on mental illness using data from the NSDUH. The first four methods use an aggregated K6 score, and the last uses the six individual K6 questions together. We identify many problems with using the K6 questions and their sum as a measure of mental illness. We think that the correlated ordered probit model are the best for fitting and understanding the data. But we also think that this method makes it clearer about the problems with K6 in general.

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Section: Correlated Ordered Probit Resultsmentioning

confidence: 85%

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confidence: 98%

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Statistical Properties of K6

arvapally,

stern

2024

Preprint

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“…Regional prevalence estimation is an essential element of modern epidemiologic research (Branscum et al 2008;Stern 2014;Burgard et al 2019). Policymakers and health care providers need reliable information on regional disease distributions to plan comprehensive health programs.…”

Section: Introductionmentioning

confidence: 99%

$$\ell _2$$-penalized approximate likelihood inference in logit mixed models for regional prevalence estimation under covariate rank-deficiency

Krause

Burgard

Morales

2021

Metrika

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Regional prevalence estimation requires the use of suitable statistical methods on epidemiologic data with substantial local detail. Small area estimation with medical treatment records as covariates marks a promising combination for this purpose. However, medical routine data often has strong internal correlation due to diagnosis-related grouping in the records. Depending on the strength of the correlation, the space spanned by the covariates can become rank-deficient. In this case, prevalence estimates suffer from unacceptable uncertainty as the individual contributions of the covariates to the model cannot be identified properly. We propose an area-level logit mixed model for regional prevalence estimation with a new fitting algorithm to solve this problem. We extend the Laplace approximation to the log-likelihood by an $$\ell _2$$ ℓ 2 -penalty in order to stabilize the estimation process in the presence of covariate rank-deficiency. Empirical best predictors under the model and a parametric bootstrap for mean squared error estimation are presented. A Monte Carlo simulation study is conducted to evaluate the properties of our methodology in a controlled environment. We further provide an empirical application where the district-level prevalence of multiple sclerosis in Germany is estimated using health insurance records.

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