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  • The details of study methods have been published previously [19]. Briefly, the DITTO study was a prospective cohort study undertaken in October 2013 at Gulab Devi Chest Hospital (GDH), a tertiary care hospital in Lahore, Pakistan. GDH is one of the oldest and biggest cardiothoracic hospitals in South-Asia, which provides free anti-tuberculosis treatment to patients from all over the country, both from the rural and urban areas and all socio economic strata [20]. The DITTO cohort comprised of new adult (15 years and above) cases of pulmonary tuberculosis, both sputum smear positive and sputum smear negative that were registered with GDH for ATT. New case was a PTB patient who had never taken TB drugs in the past, or had taken TB drugs for less than 4 weeks in the past but was not registered with National Tuberculosis Control Program, Pakistan (NTP) [21]. The diagnosis of PTB was made in line with definitions provided by NTP and World Health Organization (WHO) [21,22]. The treatment regimens adhered to in this study were in accordance with those recommended by WHO and NTP. The recruitment of 614 cases was completed in March 2014. Ethical approval was obtained from the Institutional Ethical Review Committee of Health Services Academy, Islamabad (F. No. 107/2013-IERC/HSA). Permission was also taken from the administration of the Gulab Devi Chest Hospital, Lahore, where data collection was undertaken. All patients gave written informed consent before recruitment in the study. At baseline cohort members’ detailed contact information was obtained, which was refreshed at every follow up visit. Anthropometric data were collected. Respondents completed an interviewer-administered questionnaire, which collated data on socio-demographics, co-morbidities, lifestyle and behavioural characteristics, clinical presentation of TB, family history of diabetes, adherence to DOTS and glyceamic control among diabetics. At recruitment, PTB patients’ diabetic status was ascertained. Patients who gave a self-report of diabetes were labeled as diabetic and all others were screened with a random blood glucose (RBG) test. Among the known diabetic patients, those below 30 years of age who were on insulin monotherapy and had never used any other anti-diabetic medication were labeled as Type 1 diabetic and all others as Type 2 diabetic [23]. PTB patients having a RBG <110mg/dl (<6.1mM) were labeled as non-diabetic [24]. Patients with RBG ≥110mg/dl (≥ 6.1mM), were made to undergo a fasting blood glucose test (FBG) on their next visit which was scheduled at 2 months of follow up to confirm their diabetic status. Fasting was defined as no caloric intake for at least 8 hours. A fasting plasma glucose value ≥ 126mg/dl (7.0mmol/l) was considered diagnostic of diabetes. The cut-off thresholds used were those laid down by WHO [25,26]. At this first follow up visit, contact details were also reviewed and sputum smear examination was performed. At the second follow up visit scheduled at fifth month, while on ATT in addition to the above, blood sample was drawn to determine glycosylated haemoglobin of diabetic patients. PTB cohort was followed up prospectively at second, fifth and sixth month while on ATT and also at six months after ATT completion to determine treatment outcomes. Standardized treatment outcome definitions of NTP and WHO were adhered to in the study (before the 2013 revision) [21,22]. We assessed diabetic status of PTB patients with treatment outcomes, which was coded as a dichotomous variable into favourable treatment outcome (patients who were cured and who completed treatment) and unfavourable treatment outcome (patients who defaulted or died, were transferred out, who had treatment failure and who relapsed). In addition to our main exposure variable i.e. diabetic status, the co-variates that were studied included socio-demographic characteristics such as age, gender, education, occupation and income. Lifestyle and behavioural characteristics included smoking status, alcohol consumption status and drug abuse. BMI and history of co-morbidities were also included. Comparisons were made between socio-demographics, lifestyle and behavioural patterns, clinical presentation and co-morbidities in patients with and without diabetes using the Chi-square or Fisher’s exact tests. We performed logistic regression analysis to determine association between diabetic status, other independent variables and treatment outcome. Odds Ratios (OR) and 95% Confidence Intervals (CI) were calculated. Variables with p≤0.20 in the univariate analysis and biological plausibility were included in multivariate model. Biologically meaningful interactions were assessed. Goodness of fit for the final model was evaluated by using Hosmer and Lemeshow goodness of fit test, with a p-value of > 0.5 considered to be a good fit. Statistical package for Social Sciences version 16 was used for data analysis.
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