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Objective: In India, about one third of tuberculosis (TB) patients diagnosed at tertiary hospitals are missed during a referral to peripheral health institutes for treatment. To address this, we assessed whether mobile voice call reminders to TB patients after diagnosis at a tertiary hospital decrease the proportion of "pretreatment loss to follow-up" (PTLFU), compared with the conventional paper-based referral. Design: A two-group parallel-arm randomized controlled trial was conducted. Setting: The study was conducted in a tertiary care hospital at Puducherry, South India. Participants: All newly diagnosed TB patients, both pulmonary and extrapulmonary, who were referred for treatment from the selected tertiary care hospital and possessed a mobile phone were eligible to participate. The participants were enrolled between March 2015 and June 2016 and were randomized to study groups using the block randomization with allocation concealment. Intervention: The participants in the intervention arm received standardized mobile voice calls reminding them to register for anti-TB treatment on the second and seventh day after referral in addition to the conventional paper-based referral received by the control group. Primary outcomes: Patients not started on anti-TB treatment within 14 days of referral were considered as PTLFU. The outcome of PTLFU was ascertained through phone calls made on the 14th day after referral. The intention-to-treat analysis was used, and the proportion of PTLFU in the study groups and the risk difference with 95% confidence interval (CI) were calculated. Results: Of the 393 patients assessed for eligibility, 310 were randomized to the intervention (n = 155) and control (n = 155) arms. In the intervention arm, 14 (9%) out of 155 were PTLFU compared with 28 (18%) of the 155 patients in the control arm. The absolute risk difference was 9% (95% CI [1.5, 16.6], P = 0.01). Conclusion: Mobile voice call reminder to patients is a feasible intervention and can reduce PTLFU among referred TB patients. [ABSTRACT FROM AUTHOR]

Background and Objective: In the analysis of dichotomous type response variable, logistic regression is usually used. However, the performance of logistic regression in the presence of sparse data is questionable. In such a situation, a common problem is the presence of high odds ratios (ORs) with very wide 95% confidence interval (CI) (OR: >999.999, 95% CI: <0.001, >999.999). In this paper, we addressed this issue by using penalized logistic regression (PLR) method. Materials and Methods: Data from case-control study on hyponatremia and hiccups conducted in Christian Medical College, Vellore, Tamil Nadu, India was used. The outcome variable was the presence/absence of hiccups and the main exposure variable was the status of hyponatremia. Simulation dataset was created with different sample sizes and with a different number of covariates. Results: A total of 23 cases and 50 controls were used for the analysis of ordinary and PLR methods. The main exposure variable hyponatremia was present in nine (39.13%) of the cases and in four (8.0%) of the controls. Of the 23 hiccup cases, all were males and among the controls, 46 (92.0%) were males. Thus, the complete separation between gender and the disease group led into an infinite OR with 95% CI (OR: >999.999, 95% CI: <0.001, >999.999) whereas there was a finite and consistent regression coefficient for gender (OR: 5.35; 95% CI: 0.42, 816.48) using PLR. After adjusting for all the confounding variables, hyponatremia entailed 7.9 (95% CI: 2.06, 38.86) times higher risk for the development of hiccups as was found using PLR whereas there was an overestimation of risk OR: 10.76 (95% CI: 2.17, 53.41) using the conventional method. Simulation experiment shows that the estimated coverage probability of this method is near the nominal level of 95% even for small sample sizes and for a large number of covariates. Conclusions: PLR is almost equal to the ordinary logistic regression when the sample size is large and is superior in small cell values. [ABSTRACT FROM AUTHOR]