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Background: The National Tuberculosis Control Program (NTP) guidelines emphasize that all presumptive Drug Resistant Tuberculosis (DRTB) with history of previous tuberculosis (TB) treatment and with Multi-Drug Resistant Tuberculosis (MDRTB) contacts should be screened for MDR. Due to lack of tools to identify the referral outcome of presumptive DRTB, the RJPI developed an MDRTB Presumptive Masterlist to account referrals from health center toMDRtreatment center. The study aims to understand gaps in the referral pathway experienced by Local Government Units (LGUs) and Non-Government Organizations (NGOs) from initial consultation until initiation of treatment. Methods & Materials: A retrospective descriptive study of patients' data registered on MDRTB Presumptive Masterlist of eighteen Directly Observed Treatment Short-Course (DOTS) facilities in District 1 Tondo, Manila and Payatas, Quezon City from October 2012 to September 2013, were reviewed and analyzed using structured questionnaire. Unpaired t-test used in comparing the turnaround time between LGUs and NGOs including time between Direct Sputum Smear Microscopy (DSSM) results presented to patient and referred to MDR treatment center. A p-value < 0.05 was considered statistically significant. All analysis performed using EZR with graphical user interface for R. Results: A total of 378 Presumptive DRTB was identified and listed in the masterlist. Among them, 97% (368/378) referred and 90% (333/368) screened at MDR Treatemnt center. Among screened, 85% (283/333) completed the process of MDR screening and provided with an appropriate treatment based on NTP guidelines. 9.5% (35/368) were not screened mainly due to lost to follow up. The duration of time between sample collected and examined at laboratory of NGOs was significantly longer than LGUs (n = 283; p < 0.001). The time duration between the release of DSSM results and presentation of patient at NGOs was significantly shorter than LGUs (p = 0.009). Conclusion: Development of MDRTB Presumptive Masterlist has facilitated tracking of patients due for diagnosis and treatment. Referral system between health center and MDR treatment centers should be strengthened for proper patient endorsement and provided with an appropriate action. The NGOs should lessen diagnosis delays and LGUs should follow up patient for early start of TB treatment. [ABSTRACT FROM AUTHOR]

Introduction: High levels of treatment adherence are critical for achieving optimal treatment outcomes among patients with tuberculosis (TB), especially for drug-resistant TB (DR TB). Current tools for identifying high-risk non-adherence are insufficient. Here, we apply trajectory analysis to characterize adherence behavior early in DR TB treatment and assess whether these patterns predict treatment outcomes., Methods: We conducted a retrospective analysis of Philippines DR TB patients treated between 2013 and 2016. To identify unique patterns of adherence, we performed group-based trajectory modelling on adherence to the first 12 weeks of treatment. We estimated the association of adherence trajectory group with six-month and final treatment outcomes using univariable and multivariable logistic regression. We also estimated and compared the predictive accuracy of adherence trajectory group and a binary adherence threshold for treatment outcomes., Results: Of 596 patients, 302 (50.7%) had multidrug resistant TB, 11 (1.8%) extremely drug-resistant (XDR) TB, and 283 (47.5%) pre-XDR TB. We identified three distinct adherence trajectories during the first 12 weeks of treatment: a high adherence group (n = 483), a moderate adherence group (n = 93) and a low adherence group (n = 20). Similar patterns were identified at 4 and 8 weeks. Being in the 12-week moderate or low adherence group was associated with unfavorable six-month (adjusted OR [aOR] 3.42, 95% CI 1.90-6.12) and final (aOR 2.71, 95% 1.73-4.30) treatment outcomes. Adherence trajectory group performed similarly to a binary threshold classification for the prediction of final treatment outcomes (65.9% vs. 65.4% correctly classified), but was more accurate for prediction of six-month treatment outcomes (79.4% vs. 60.0% correctly classified)., Conclusions: Adherence patterns are strongly predictive of DR TB treatment outcomes. Trajectory-based analyses represent an exciting avenue of research into TB patient adherence behavior seeking to inform interventions which rapidly identify and support patients with high-risk adherence patterns., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Huddart et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

A cross-sectional study was conducted between October 2011 and March 2012 in two major pig producing provinces in the Philippines. Four hundred and seventy one pig farms slaughtering finisher pigs at government operated abattoirs participated in this study. The objectives of this study were to group: (a) smallholder (S) and commercial (C) production systems into patterns according to their herd health providers (HHPs), and obtain descriptive information about the grouped S and C production systems; and (b) identify key HHPs within each production system using social network analysis. On-farm veterinarians, private consultants, pharmaceutical company representatives, government veterinarians, livestock and agricultural technicians, and agricultural supply stores were found to be actively interacting with pig farmers. Four clusters were identified based on production system and their choice of HHPs. Differences in management and biosecurity practices were found between S and C clusters. Private HHPs provided a service to larger C and some larger S farms, and have little or no interaction with the other HHPs. Government HHPs provided herd health service mainly to S farms and small C farms. Agricultural supply stores were identified as a dominant solitary HHP and provided herd health services to the majority of farmers. Increased knowledge of the routine management and biosecurity practices of S and C farmers and the key HHPs that are likely to be associated with those practices would be of value as this information could be used to inform a risk-based approach to disease surveillance and control., (Copyright © 2014 Elsevier B.V. All rights reserved.)