Association Between Dental Expenditure and Socioeconomic Status in Iran.

BACKGROUND: Health care spending by households can be a great indicator of a society's commitment to good health stewardship and the efficiency of institutions responsible for managing health costs. Equitable and appropriate distribution of dental services is a challenging issue for realising universal health coverage. This study aimed to evaluate Iranian households' per capita dental expenditure (DE) according to their socioeconomic status (SES). METHODS: In this cross-sectional study, the income and expenditure of 18,701 urban and 19,261 rural households in Iran were scrutinised according to the data provided by the Statistics Center of Iran (2017-2018). After model creation, the SES index was determined using principal component analysis and weighting based on the analytical hierarchy process. The dependent variable was the share of per capita household's expenditure spent on dental health. The zero-inflated gamma regression model was applied to confirm the presumed association between per capita DE and SES. Analyses were performed using PROC NLMIXED in SAS software (version 4.9). RESULTS: The results revealed that approximately 9% of urban and 4% of rural households paid for dental treatments in the past month. The DE to total health expenditure (HE) ratios were 8.5% and 14.8% for rural and urban households, respectively. Also, with each level increase in SES, the average per capita DE increased by 23% and 16% in rural and urban households, respectively. CONCLUSIONS: The study confirms association between per capita DE and SES in Iran. This implies targeted strategies to facilitate the utilisation of dental care especially for lower SES groups according to their needs.

Cytokine associated with severity of depressive symptoms in female nurses in Korea.

Background: Depression has been associated with the risk of developing physical illnesses and diseases. Inflammatory hypotheses of immunoactive and dysregulated cytokine production have been proposed to describe this association; however, data pertaining to the high prevalence of depression among nurses are limited. Objective: This study aimed to use a comprehensive immune-profiling approach to determine whether an abnormal profile of circulating cytokines could be identified in nurses with self-reported depression and whether this profile is associated with the severity of depression. Methods: We investigated a cohort of 157 female nurses in Korea. The self-report Patient Health Questionnaire was used to measure the depression levels of nurses. In addition, peripheral blood samples were collected and used to measure the cytokine profile using the Luminex multiplexing system. Generalized gamma regression analyses were conducted to evaluate the association between cytokine and depressive symptoms. Results: Regarding severity of depressive symptoms, 28.0% of nurses had moderately severe depression while 9.6% had severe depression. Moderately-severe depressive symptoms in nurses were associated with elevated levels of interleukin-6 (B = 0.460, p = 0.003), interleukin-8 (B = 0.273, p = 0.001), and interleukin-18 (B = 0.236, p = 0.023), whereas interferon-gamma levels (B = -0.585, p = 0.003) showed the opposite profile. Participants with severe depressive symptoms presented decreased interferon-gamma levels (B = -1.254, p < 0.001). Conclusion: This study demonstrated that proinflammatory cytokines were associated with depression among nurses. This calls for early detection and intervention, considering the mechanisms linking depression to physical illness and disease.

Bayesian hierarchical models for the prediction of the driver flow and passenger waiting times in a stochastic carpooling service.

Carpooling is an integral component in smart carbon-neutral cities, in particular to facilitate home-work commuting. We study an innovative carpooling service which offers stochastic passenger-driver matching. Stochastic matching is when a passenger makes a carpooling request, and then waits for the first driver from a population of drivers who are already en route. Crucially a designated driver is not assigned as in a traditional carpooling service. For this new form of stochastic carpooling, we propose a two-stage Bayesian hierarchical model to predict the driver flow and the passenger waiting times. The first stage focuses on prediction of the aggregated daily driver flows, and the second stage processes these daily driver flow into hourly predictions of the passenger waiting times. We demonstrate, for an operational carpooling service, that the predictions from our Bayesian hierarchical model outperform the predictions from a frequentist model and a Bayesian non-hierarchical model. The inferences from our proposed model provide insights for the service operator in their evidence-based decision making.

The Lomax regression model with residual analysis: an application to insurance data.

In this paper, we introduce a new regression model, called Lomax regression model, as an alternative to the gamma regression model. The maximum-likelihood method is used to estimate the unknown parameters of the proposed model, and the finite sample performance of the maximum-likelihood estimation method is evaluated by means of the Monte-Carlo simulation study. The randomized quantile residuals are used to check the adequacy of the fitted model. The insurance data are analyzed to demonstrate the usefulness of the proposed regression model against the gamma regression model.

Modified likelihood ratio tests for unit gamma regressions.

Regression analyses are commonly performed with doubly limited continuous dependent variables; for instance, when modeling the behavior of rates, proportions and income concentration indices. Several models are available in the literature for use with such variables, one of them being the unit gamma regression model. In all such models, parameter estimation is typically performed using the maximum likelihood method and testing inferences on the model's parameters are usually based on the likelihood ratio test. Such a test can, however, deliver quite imprecise inferences when the sample size is small. In this paper, we propose two modified likelihood ratio test statistics for use with the unit gamma regressions that deliver much more accurate inferences when the number of data points in small. Numerical (i.e. simulation) evidence is presented for both fixed dispersion and varying dispersion models, and also for tests that involve nonnested models. We also present and discuss two empirical applications.

Stein-type shrinkage estimators in gamma regression model with application to prostate cancer data.

Gamma regression is applied in several areas such as life testing, forecasting cancer incidences, genomics, rainfall prediction, experimental designs, and quality control. Gamma regression models allow for a monotone and no constant hazard in survival models. Owing to the broad applicability of gamma regression, we propose some novel and improved methods to estimate the coefficients of gamma regression model. We combine the unrestricted maximum likelihood (ML) estimators and the estimators that are restricted by linear hypothesis, and we present Stein-type shrinkage estimators (SEs). We then develop an asymptotic theory for SEs and obtain their asymptotic quadratic risks. In addition, we conduct Monte Carlo simulations to study the performance of the estimators in terms of their simulated relative efficiencies. It is evident from our studies that the proposed SEs outperform the usual ML estimators. Furthermore, some tabular and graphical representations are given as proofs of our assertions. This study is finally ended by appraising the performance of our estimators for a real prostate cancer data.

Risk Factors Associated with Survival of Pulmonary Tuberculosis.

BACKGROUND: We conducted this study among adults with pulmonary tuberculosis (TB) who received treatment, in order to determine the risk factors associated with survival of during treatments. METHODS: A retrospective cohort study was conducted from 2005-2015 with newly registered TB patients in the Hospital of Masih Daneshvari Doctor, Tehran, Iran. Overall, 5313 patients met our study's cohort definition, but the analysis was performed on 2299 patients (43.2%) who had a correct address and they could be traced-out by the Medical - registry. Time in days was used in survival model and patients who were still alive (until last follow-up date) considered as censored. To study the effect of risk factors on patients' survival, the generalized gamma regression model was used. RESULTS: Based on the results of univariate analysis, gender (RR=2 (95% CI: 1.1-3.7), high school education (Relative Risk: RR=0.3 (95% CI: 0.2-0.7), higher education (RR=0.3 (95% CI: 0.1-0.9), smoker (RR=2.5 (95% CI: 1.4-4.2), drug user (RR=2.4 (95% CI: 1.4-4), TB contact (RR=0.5 (95% CI: 0.3-0.8) and HIV positive (RR=4 (95% CI: 1.7-9.2) affected patients' survival. Moreover, the results of multivariate analysis showed that, gender (RR=5.5 (95% CI: 2.2-13.5), age (RR=1.1 (95% CI: 1-1.1), adverse drug effect (RR=2.5 (95% CI: 1.2-5.4), smoker (RR=3.3 (95% CI: 1.2-9.4), TB contact (RR=0.2 (95% CI: 0.1-0.5), diabetic mellitus (RR=3 (95% CI: 1-8.3), HIV positive (RR=26 (95% CI: 4.6-145.9) and comorbidities (RR=4.9 (95% CI: 2-11.6) were identified as factors affecting patients' survival. CONCLUSION: Our data indicated associated risk factors in TB mortality and could suggest way to progressing national tuberculosis program (NTP) for predicating and plan for effective interventional strategies.

Bayesian regression models for the estimation of net cost of disease using aggregate data.

Estimation of net costs attributed to a disease or other health condition is very important for health economists and policy makers. Skewness and heteroscedasticity are well-known characteristics for cost data, making linear models generally inappropriate and dictating the use of other types of models, such as gamma regression. Additional hurdles emerge when individual level data are not available. In this paper, we consider the latter case were data are only available at the aggregate level, containing means and standard deviations for different strata defined by a number of demographic and clinical factors. We summarize a number of methods that can be used for this estimation, and we propose a Bayesian approach that utilizes the sample stratum specific standard deviations as stochastic. We investigate the performance of two linear mixed models, comparing them with two proposed gamma regression mixed models, to analyze simulated data generated by gamma and log-normal distributions. Our proposed Bayesian approach seems to have significant advantages for net cost estimation when only aggregate data are available. The implemented gamma models do not seem to offer the expected benefits over the linear models; however, further investigation and refinement is needed.

The Effects of the Metabolic Syndrome on the Total Medical Charge / 보건행정학회지

BACKGROUND: This study purposed to analyze the effects of metabolic syndrome on the total medical charge of patients. METHODS: 2013 National Health Insurance Service sample research database (eligibility database, medical database, and health examination database) was used for this study. Gamma regression was applied to analyze the effects of metabolic syndrome on the total medical charge and logistic regression was used to determine the probability of medical charge which was higher than the third quartile. Sociodemographic characteristics (age and household income), health behavior factors (smoking, drinking, exercise, and body mass index), and disease related factors (family history and metabolic syndrome) were included as the independent variables. RESULTS: people who had metabolic syndrome spent more medical expenses than those without metabolic syndrome both in man and woman group. The standard regression coefficient was 0.09 (p<0.001) in man with metabolic syndrome and 0.16 (p<0.001) in woman. In addition, woman with metabolic syndrome spent more than the third quartile of medical charge. The odds ratios was 1.04 (p=0.16) for man with metabolic syndrome and 1.18 (p=0.013) for woman. CONCLUSION: people with metabolic syndrome spent more medical charge, so it will need to consider policy interventions for preventing the incidence and management of metabolic syndrome in Korean people.

A statistical model for the analysis of beta values in DNA methylation studies.

BACKGROUND: The analysis of DNA methylation is a key component in the development of personalized treatment approaches. A common way to measure DNA methylation is the calculation of beta values, which are bounded variables of the form M/(M+U) that are generated by Illumina's 450k BeadChip array. The statistical analysis of beta values is considered to be challenging, as traditional methods for the analysis of bounded variables, such as M-value regression and beta regression, are based on regularity assumptions that are often too strong to adequately describe the distribution of beta values. RESULTS: We develop a statistical model for the analysis of beta values that is derived from a bivariate gamma distribution for the signal intensities M and U. By allowing for possible correlations between M and U, the proposed model explicitly takes into account the data-generating process underlying the calculation of beta values. Using simulated data and a real sample of DNA methylation data from the Heinz Nixdorf Recall cohort study, we demonstrate that the proposed model fits our data significantly better than beta regression and M-value regression. CONCLUSION: The proposed model contributes to an improved identification of associations between beta values and covariates such as clinical variables and lifestyle factors in epigenome-wide association studies. It is as easy to apply to a sample of beta values as beta regression and M-value regression.