Method: We developed a dynamic transmission microsimulation model that follows India’s population stratified by age, sex, TB, drug resistance, and treatment status. We calibrate the model to Indian demographic, epidemiologic, and TB healthcare patterns in the public and private sectors. Control interventions include: 1) improving treatment effectiveness in the public sector only; 2) improving the accuracy and rapidity of TB diagnosis and drug sensitivity testing in the public and/or the private sector; 3) increasing referrals from the private sector to the public sector through PPM; 4) reducing inappropriate medication use to prevent MDR in the private sector; 5) combinations of these efforts. Outcomes include incidence and prevalence of active non-MDR and MDR TB in 2023 relative to 2013 levels.
Result: Without interventions, the model projects declines in non-MDR TB incidence (12%) and prevalence (12%) and increases in MDR incidence (15%) and prevalence (19%). For non-MDR TB, increasing referrals from the private to the public sector (through PPM) alone or in combination with improved diagnostics yields 15-17% lower incidence and 34-47% lower prevalence. Synergies provided by combined public and private sector interventions are evident for MDR outcomes. Exclusively private sector interventions result in MDR incidence and prevalence increases of 13-16%, whereas exclusively public sector interventions result in 2-7% declines. Combinations of PPM and increases in non-MDR TB treatment effectiveness to avoid generating MDR reduce incidence by 13-19%. Likewise, although MDR prevalence increases 14-18% with PPM alone, PPM combined with rapid, accurate diagnostics results in MDR prevalence declines of 55-58%.
Conclusion: Combining public and private sector interventions to improve and link TB care and rapid, accurate diagnostics is a promising approach for reducing non-MDR and MDR TB in India and similar Asian countries.
Method: Currently, many challenges in segmentation, feature design and modelling make medical image mining a labour intensive process that requires medical expertise. Consequently, much of the information in medical image databases is currently not effectively used to support diagnosis, research and education. We propose to sidestep segmentation and feature design by automatically extracting general purpose, localized visual features using Gabor filters. We then sidestep model construction and model based classification by applying an ensemble of of case based image reconstruction methods that yield a sparse presentation of the new image. This combination of techniques offers an easy to deploy system for retrieving useful old images that are similar to the new image from image databases.
The symbolic information in the old images can then be used to automatically suggest annotations to the new image. In our work we have used the pathology class information attached to the computer tomography (CT) images of the traumatic brain injury (TBI) to suggest classification of the new images. However, the method is not specific to CT scans and it scales well to large image databases.
Results: We applied our method to 847 CT images of TBI obtained from the database of the Neuroradiology Department in a tertiary referral hospital specializing in neurological diseases in Singapore. Our stratified cross-validation results demonstrate the capability of our method to automatically classify the types of traumatic brain injuries into subdural hematoma, extradural hematoma, and intracerebral hemorrage. This functionality allows searching for medical images by their diagnosis based on the image content only. We also demonstrate a tool that shows the relevant images used in these automatic classifications.
Conclusions: Our method offers an easy way to use information in medical image databases. The tool based on the methodology can be used to support diagnosis, and possibly in future, prognosis in medical decision making process.
Method: We conducted a qualitative study to capture the experiences of HCPs who have used the PDA in primary care consultations. In-depth interviews and focus group discussions were conducted after the consultations at university-based primary care clinics, public healthcare clinics and private general practices in 2012-2013. Participants included general practitioners (n=2), medical officers (n=7), diabetes nurses (n=3), and pharmacists (n=1). The interviews were audio-recorded, transcribed verbatim, checked and managed using Nvivo 9 software. A thematic analysis was used.
The challenges faced by HCPs include patient barriers (e.g. patient’s unwillingness to read the PDA, visual impairment); system barriers (e.g. lack of time to use the PDA in consultations, lack of reading space for patients); and potential medico-legal risks in using the PDA (e.g. negative health outcomes).
HCPs identified opportunities to use PDA at two levels: the health system and individual consultations. At the system level, HCPs suggested incorporating the PDA use into the existing patient care pathway and individualising the timing of introducing the PDA to patients (e.g. before, during or take home after consultations).When selecting patients to use the PDA, the HCPs considered patients’ literacy, the decision maker (patient or significant others), patient preparedness to decide, and knowledge of insulin.
At the individual consultation level, the use of the PDA by the HCPs was influenced by the following factors: being aware of different ways of using the PDA (e.g. cover to cover, focusing only on patient concerns, using the PDA over multiple consultations), being willing to modify their consultation style to use the PDA, giving more guidance to patients who had difficulty in understanding the PDA, and being able to use different language versions of the PDA. Some HCPs would avoid discussing the PDA at the initial consultation as they perceived that this might influence the patient’s decision to start insulin.
Conclusion: HCPs identified patient and system barriers in implementing the PDA. The implementation of the PDA would depend on integrating the PDA into existing clinic pathway and being flexible when using the PDA with individual patients.
Method: All hospital admissions and ED visits for COPD during the period of 2008 to 2011 were identified from the Blue Cross Blue Shield of Texas claims data. Patients were included in the study if they: were enrolled in PPO, PPO+, RPO and POS plan; had drug benefits with BCBS of TX plan; were 40 years of age and over and resided in a Texas Hospital Referral Region (HRR). Patients were identified as a recipient of guideline recommended care if within 30 days of discharge, they had at least one claim of prescription fills for any long-acting bronchodilators either beta2-agonists and/or anticholinergics with or without inhaled corticosteroids AND had at least one follow up visit with a primary care physician or pulmonologist. The adherence to guideline recommended care rates for each HRR were calculated by dividing number of discharges that received guideline recommended care by number of COPD-related hospitalizations/ED visit for each HRR. Index of variation (each HRR guideline compliance rate compare to overall Texas means) and coefficients of variation (CV; standard deviations from the Texas means) were calculated to examine the variation in guideline compliance rate.
Result: Of the 2,326 COPD-related hospitalizations/ED visit (1,100 ED visits and 1,226 admissions), 23.99% (29.85% of ED visits and 17.45% of admissions) had at least one prescription filled for maintenance medication and at least one follow up visit with primary care physician or pulmonologist within 30 days of discharge. Guideline compliance rates ranges from 15.38% in Waco HRR to 33.33% in Longview HRR with Texas coefficient of variation equal to 0.12.
Conclusion: Variations in guideline compliance rates were found among HRRs in Texas indicating inefficiencies in the treatment of COPD patients. Further investigation on factors contribute to this variation will provide insights for better policies and program interventions that may increase guideline compliance rates and reduce preventable COPD readmission.
Method: In Phase 1 (feasibility) adults aged ≥18 years were passively recruited over 5 months by downloading the Study app via the app stores. Participants were invited to enter data about demographics, smoking behavior, stage of change and use of health-related apps. In Phase 2 (effectiveness,) the RCT app will be released in the app stores. When the app is opened for the first time, participants are asked to answer the baseline questionnaire. The app then randomizes them in blocks to the decision aid or information only sub-apps. Participants will be followed up at 4 time-points (10 days, 1 month, 3 months, and six months) to measure their smoking behavior. In addition, comparing groups in terms of informed choice, on our multidimensional measure of informed choice for smoking, at the first follow up (10 days).
Result: The total number of app downloads (after 5 months) was 451 (31% Australia and 69% Singapore), with 84% being Apple users. 140 participants of the 451 completed the questionnaire (55% Australia and 44% Singapore). There were no significant differences between countries in terms of education, operation system used, quitting attempts last year, and stage of change. Majority of participants 73% in Australia, and 61% in Singapore were ready to quit within the next 30 days. Participants that never seek professional quitting help (e.g. Quitline) were about 70% in both countries.
(Phase 2) will be commenced September 2013, results of the first follow up will be presented.
Conclusion: The study results show that the smartphone app effectively reaches smokers across a both countries who are most ready to quit and eschewing professional help. In addition to comparing the interventions effects on quitting decision and period of quitting, this project provides a new method of conducting an automated global RCT with no human intervention utilizing smartphone capabilities.