Oligoarticular Versus Polyarticular Psoriatic Arthritis: A Longitudinal Study Showing Similar Characteristics

Objective The objectives of this study were to determine whether patients with oligoarticular presentation differ from those with polyarticular presentation and identify potential predictors for evolution of oligoarthritis to polyarthritis in patients with PsA. Methods Patients who entered the University of Toronto PsA clinic between 1978 and 2018 within 12 months of diagnosis were identified. Only patients with ≥ 2 clinic visits were included. Patients were followed at 6 to 12-month intervals according to standard protocol, which included demographics, clinical history, detailed clinical examination, laboratory information and patient questionnaires. Radiographs were done at 2-year intervals. Oligoarthritis was defined by the presence of ≤4 inflamed joints and progression as an increase to ≥5 joints. Statistical analyses included logistic regression models as well as Weibull regression models adjusted for age, disease duration and sex. Results 192 of 407 (47%) patients presented with oligoarthritis. While demographic features were similar to those with polyarthritis, more patients with polyarthritis presented with dactylitis and enthesitis. Similar joint distribution was observed, with small joints of the hands and feet being most commonly affected. Patients with polyarthritis had higher HAQ and lower SF-36 scores. 117 of 192 oligoarticular patients (61%) remained oligoarticular and 75 (39%) progressed to polyarthritis. Lower SF-36 mental component summary score was the predictor for progressing to polyarthritis. Conclusion Oligoarticular PsA occurs in 39% of patients with PsA and is similar to polyarticular disease, with most patients having small joint involvement. The only predictor for progression to polyarthritis was a lower SF-36 mental health component.

Differential in infant, childhood and under-five death clustering among the empowered and non-empowered action group regions in India

Background With 8,82,000 deaths in the under-five period, India observed varied intra-state and inter-regional differences across infant and child mortality in 2018. However, scarce literature is present to capture this unusual concentration of mortality in certain families by examining the association of the mortality risks among the siblings of those families along with various unobserved characteristics of the mother. Looking towards the regional and age differential in mortality, this paper attempts to provide evidence for the differential in mortality clustering among infants (aged 0–11 months), children (12–59 months) and under-five (0–59 months) period among mothers from the Empowered Action Group (EAG) and non-EAG regions of India. Methods The study used data from the National Family Health Survey (2015–16) which includes all the birth histories of 475,457 women aged 15–49 years. Bivariate and multivariate analyses were used to fulfil the objectives of the study. A two-level random intercept Weibull regression model was used to account for the unexplained mother (family) level heterogeneity. Results About 3.3% and 5.9% of infant deaths and 0.8% and 1.6% of childhood deaths were observed in non-EAG and EAG regions respectively. Among them, a higher percentage of infant and child death was observed due to the death of a previous sibling. There were 1.67 times [95% CI: 1.55–1.80] and 1.46 times [CI: 1.37–1.56] higher odds of infant and under-five mortality of index child respectively when the previous sibling at the time of conception of the index child was dead in the non-EAG regions. In contrast, the odds of death scarring (death of previous sibling scars the survival of index child) were 1.38 times [CI: 1.32–1.44] and 1.24 times [CI: 1.20–1.29] higher for infant and under-five mortality respectively in the EAG regions. Conclusion The extent of infant and child mortality clustering and unobserved heterogeneity was higher among mothers in the non-EAG regions in comparison to their EAG region counterparts. With the growing situation of under-five mortality clustering in non-EAG states, region-wise interventions are recommended. Additionally, proper care is needed to ameliorate the inter-family variation in mortality risk among the children of both EAG and non-EAG regions throughout their childhood.

Bayesian using Importance Sampling Technique of Weibull Regression with Type II Censored Data

Keeping in view the Bayesian approach, the study aims to develop methods through the utilization of Jeffreys prior and modified Jeffreys prior to the covariate obtained by using the Importance sampling technique. For maximum likelihood estimator, covariate parameters, and the shape parameter of Weibull regression distribution with the censored data of Type II will be estimated by the study. It is shown that the obtained estimators in closed forms are not available, but through the usage of appropriate numerical methods, they can be solved. The mean square error is the criterion of comparison. With the use of simulation, performances of these three estimates are assessed, bearing in mind different censored percentages, and various sizes of the sample.

A BAYESIAN WEIBULL ANALYSIS OF BREAST CANCER DATA WITH LONG-TERM SURVIVORS IN PARANA STATE, BRAZIL

Breast cancer is one of the most common diseases among women worldwide with about 25% of new cases each year. In Brazil, 59,700 new cases of breast cancer were expected in 2019, according to the Brazilian National Cancer Institute (INCA). Survival analysis has been an useful tool for the identifying the risk and prognostic factors for cancer patients. This work aims to characterize the prognostic value of demographic, clinical and pathological variables in relation to the survival time of 2,092 patients diagnosed with breast cancer in Parana State, Brazil, from 2004 to 2016. In this sense, we propose a Bayesian analysis of survival data with long-term survivors by using Weibull regression models through integrated nested Laplace approximations (INLA). The results point to a proportion of long-term survivors around 57:6% in the population under study. In regard to potential risk factors, we namely concluded that 40-50 year age group has superior survival than younger and older age groups, white women have higher breast cancer risk than other races, and marital status decreases that risk. Caution on the general use of these results is nevertheless advised, since we have analyzed population-based breast cancer data without proper monitoring by a healthprofessional.

ANALISIS SURVIVAL MENGGUNAKAN REGRESI WEIBULL PADA LAJU KESEMBUHAN PASIEN TUBERKULOSIS PARU DI RSUD ALOEI SABOE KOTA GORONTALO

This study was aimed at discussing survival analysis in Pulmonary Tuberculosis patients in Aloei Saboe using Weibull regression to find out the factors that influence the patient’s recovery rate. To analyze the survival time, the Kaplan-Meier curve is used then the process continues into Log-Rank Test to see the differences between groups in a curve. Weibull Regression is used to determine the significant factors based on a log-rank test in the rate of recovery of Pulmonary Tuberculosis patients. The results of the study concluded age, shortness of breath, fever, cough, history of illness and smoking habits are factors that significantly influence the rate of recovery of Pulmonary Tuberculosis patients.