Bayesian estimation of the effect of health inequality in disease detection.

BACKGROUND: Measuring health inequality is essential to ensure that everyone has equal accessibility to health care. Studies in the past have continuously presented and showed areas or groups of people affected by various inequality in accessing the health resources and services to help improve this matter. Alongside, disease prevention is as important to minimise the disease burden and improve health and quality of life. These aspects are interlinked and greatly contributes to one's health. METHOD: In this study, the Gini coefficient and Lorenz curve are used to give an indication of the overall health inequality. The impact of this inequality in granular level is demonstrated using Bayesian estimation for disease detection. The Bayesian estimation used a two-component modelling approach that separates the case detection process and incidence rate using a mixed Poisson distribution while capturing underlying spatio-temporal characteristics. Bayesian model averaging is used in conjunction with the two-component modelling approach to improve the accuracy of estimates by incorporating many candidate models into the analysis instead of using fixed component models. This method is applied to an infectious disease, influenza, in Victoria, Australia between 2013 and 2016 and the corresponding primary health care of the state. RESULT: There is a relatively equal distribution of health resources and services pertaining to general practitioners (GP) and GP clinics in Victoria, Australia. Roughly 80 percent of the population shares 70 percent of the number of GPs and GP clinics. The Bayesian estimation with model averaging revealed that access difficulty to health services impacts both case detection probability and incidence rate. Minimal differences are recorded in the observed and estimated incidence of influenza cases considering social deprivation factors. In most years, areas in Victoria's southwest and eastern parts have potential under-reported cases consistent with their relatively lower number of GP or GP clinics. CONCLUSION: The Bayesian model estimated a slight discrepancy between the estimated incidence and the observed cases of influenza in Victoria, Australia in 2013-2016 period. This is consistent with the relatively equal health resources and services in the state. This finding is beneficial in determining areas with potential under-reported cases and under-served health care. The proposed approach in this study provides insight into the impact of health inequality in disease detection without requiring costly and time-extensive surveys and relying mainly on the data at hand. Furthermore, the application of Bayesian model averaging provided a flexible modelling framework that allows covariates to move between case detection and incidence models.

Bayesian approaches to the weighted kappa-like inter-rater agreement measures.

Inter-rater agreement measures are used to estimate the degree of agreement between two or more assessors. When the agreement table is ordinal, different weight functions that incorporate row and column scores are used along with the agreement measures. The selection of row and column scores is effectual on the estimated degree of agreement. The weighted measures are prone to the anomalies frequently seen in agreement tables such as unbalanced table structures or grey zones due to the assessment behaviour of the raters. In this study, Bayesian approaches for the estimation of inter-rater agreement measures are proposed. The Bayesian approaches make it possible to include prior information on the assessment behaviour of the raters in the analysis and impose order restrictions on the row and column scores. In this way, we improve the accuracy of the agreement measures and mitigate the impact of the anomalies in the estimation of the strength of agreement between the raters. The elicitation of prior distributions is described theoretically and practically for the Bayesian estimation of five agreement measures with three different weights using an agreement table having two grey zones. A Monte Carlo simulation study is conducted to assess the classification accuracy of the Bayesian and classical approaches for the considered agreement measures for a given level of agreement. Recommendations for the selection of the highest performing agreement measure and weight combination are made in the breakdown of the table structure and sample size.

The impact of grey zones on the accuracy of agreement measures for ordinal tables.

BACKGROUND: In an inter-rater agreement study, if two raters tend to rate considering different aspects of the subject of interest or have different experience levels, a grey zone occurs among the levels of a square contingency table showing the inter-rater agreement. These grey zones distort the degree of agreement between raters and negatively impact the decisions based on the inter-rater agreement tables. In this sense, it is important to know how the existence of a grey zone impacts the inter-rater agreement coefficients to choose the most reliable agreement coefficient against the grey zones to reach out with more reliable decisions. METHODS: In this article, we propose two approaches to create grey zones in simulations setting and conduct an extensive Monte Carlo simulation study to figure out the impact of having grey zones on the weighted inter-rater agreement measures for ordinal tables over a comprehensive simulation space. RESULTS: The weighted inter-rater agreement coefficients are not reliable against the existence of grey zones. Increasing sample size and the number of categories in the agreement table decreases the accuracy of weighted inter-rater agreement measures when there is a grey zone. When the degree of agreement between the raters is high, the agreement measures are not significantly impacted by the existence of grey zones. However, if there is a medium to low degree of inter-rater agreement, all the weighted coefficients are more or less impacted. CONCLUSIONS: It is observed in this study that the existence of grey zones has a significant negative impact on the accuracy of agreement measures especially for a low degree of true agreement and high sample and tables sizes. In general, Gwet's AC2 and Brennan-Prediger's κ with quadratic or ordinal weights are reliable against the grey zones.

Anterior inferior iliac spine morphology: quantitative and qualitative assessment in an asymptomatic population.

PURPOSE: To assess the prevalence of anterior inferior iliac spine (AIIS) types, and to investigate the quantitative measurements that characterize AIIS morphology in an asymptomatic adult population. METHODS: In this prospective study, 358 hips from 179 consecutive subjects (age range, 19-82 years; 91 males, 88 females), who underwent CT examination for reasons other than hip problems and were negative for hip impingement test, were analyzed. AIIS types were determined (1, flat wall of the ilium between distal end of AIIS and acetebular rim; 2, bony eminence between distal end of AIIS and acetebular rim; and 3, extension of AIIS to the anterior superior acetebular rim) and AIIS tip angle (TA), direct distance (DD) of the anterior acetabular rim to AIIS as well as projectional distances in vertical (VD) and horizontal (HD) planes were measured. Age- and gender-related factors were searched using two-way ANOVA test under three age groups (18-39, 40-59, and ≥ 60 years). RESULTS: There were 238 (66.5%) type 1, 118 (33.0%) type 2, and two (0.5%) type 3 AIISs, with significant difference between AIIS types among age groups and genders (P < 0.001). VD and DD showed age- and gender-related (P < 0.001, P < 0.001), and TA demonstrated gender-related differences (P < 0.001). Inter-observer agreement was good for TA and moderate to poor for other measurements. CONCLUSIONS: Type 1 AIIS is the most common shape across all age groups in adult females and in young and middle-aged adult males. TA, DD, and VD might be reliably used for the evaluation of AIIS morphology.

Validity and reliability of the International Cooperative Ataxia Rating Scale (ICARS) and the Scale for the Assessment and Rating of Ataxia (SARA) in multiple sclerosis patients with ataxia.

BACKGROUND: Ataxia is an extremely common problem in multiple sclerosis (MS) patients. Thus, appropriate scales are required for detailed assessment of this issue. The aim of our study was to investigate the reliability and validity of the Turkish version of the International Cooperative Ataxia Rating Scale (ICARS) and Scale for the Assessment and Rating of Ataxia (SARA), which are widely used in ataxia evaluation in the context of other cerebellar diseases. METHOD: This cross-sectional study included 80 MS patients with Kurtzke cerebellar functional system score (C-FSS) greater than zero and slight pyramidal involvement. The Expanded Disability Status Scale (EDSS), C-FSS, and Berg Balance Scale (BBS) were administered. SARA and ICARS were assessed on first admission by two physical therapists. Seven days later, second assessments were repeated in same way for reliability. RESULTS: Intra-rater and inter-rater reliability were found to be high for both ICARS and SARA (p< 0.001) The Cronbach's α coefficients were 0.922 and 0.921 for SARA (reviewer 1 and reviewer 2 respectively) and 0.952 and 0.952 for ICARS (reviewer 1 and reviewer 2, respectively). There were no floor or ceiling effects determined for either scale except for item 17 of ICARS (p= 0.055). The EDSS total score had significant correlations with both SARA and ICARS (rho: 0.557 and 0.707, respectively). C-FSS had moderate correlation with SARA and high correlation with ICARS (rho: 0.469 and 0.653, respectively). BBS had no significant correlation with SARA and ICARS. (rho: -0.048 and -0.008 respectively). According to the area under the curve (AUC) value, ICARS is the best scale to discriminate mild and moderate ataxia. (AUC: 0.875). Factor analyses of ICARS showed that the rating results were determined by five different factors that did not coincide with the ICARS sub-scales. CONCLUSION: Our study demonstrated that ICARS and SARA are both reliable in MS patients with ataxia. Although ICARS has some structural problems, it seems to be more valid given its high correlations with EDSS and C-FSS. SARA also can be preferred as a brief assessment.

CT assessment of asymptomatic hip joints for the background of femoroacetabular impingement morphology.

PURPOSE: The purposes of this study were to assess the presence of cam and pincer morphology in asymptomatic individuals with a negative femoroacetabular impingement test, and to determine and compare the ranges of alpha angle using two measurement methods. MATERIALS AND METHODS: In total, 68 consecutive patients who underwent abdominopelvic computed tomography (CT) for reasons other than hip problems were the patient population. Patients who had a positive femoroacetabular impingement test were excluded. Alpha angle measurements from axial oblique (AN) and radial reformat-based images (AR) from the anterior through the superior portion of the femoral head-neck junction, as well as femoral head-neck offset, center-edge angle, acetabular version angle measurements, and acetabular crossover sign assessment, were made. RESULTS: Overall prevalences of cam (increased alpha angle, decreased femoral head-neck offset) and pincer morphology (increased center-edge angle, decreased acetabular version) were 20.0%, 26.8%, 25.8%, and 10.2% of the hips, respectively. The mean AR ranged from 41.64° ± 4.23° to 48.13° ± 4.63°, whereas AN was 41.10° ± 4.44°. The values of AR were higher than AN, and the difference was statistically significant (P <0.001). The highest AR values were measured on images from the anterosuperior section of femoral head-neck junction. CONCLUSION: In asymptomatic subjects, higher alpha angle values were obtained from radial reformatted images, specifically from the anterosuperior portion of the femoral head-neck junction compared with the axial oblique CT images. Other measurements used for the assessment of cam and pincer morphology can also be beyond the ranges that are considered normal in the general population.