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Daily monitoring of diabetic treatment amongst TB-DM patients under NTEP: Does it improve the treatment outcomes?

Open AccessPublished:July 31, 2022DOI:https://doi.org/10.1016/j.cegh.2022.101118

      Abstract

      Problem considered

      Directly Observed Treatment (DOT) has been the cornerstone for favorable outcomes in Tuberculosis treatment. Hitherto, the role of Diabetic DOT for TB-DM patients has not been explored in the present times when TB-DM dual epidemic has grown by significant proportions. The study aims to know the treatment outcomes in TB-DM patients under programmatic settings with or without DM treatment supervision and also to assess the concurrent glycemic control during the course of TB treatment.

      Methods

      In this cohort study with nested case control design, total 102 TB-DM patients registered under National TB Elimination Programme of District Dakshina Kannada from July 2017 to March 2019 were enrolled. Systematic Diabetic Treatment monitoring was done in one geographical area whereas in other areas the monitoring was not done. Socio-clinico-demographic variables including glycemic control were analyzed.

      Results

      The results showed that the treatment success rate was 92% in both intervention and non-intervention geographical areas. There was no association between the favorable TB treatment outcome amongst the TB DM patients and their demographic variables. A two-way repeated measure ANOVA with a Greenhouse Giesser correction determined that the mean value of HbA1c was statistically significant between assessment stages during the course of treatment and the interaction HbA1c and supervision arm had a significant effect.

      Conclusion

      Diabetic DOT led to relatively better glycemic control (HbA1c) in TB-DM patients although in programmatic management of TB-DM patients, it did not have any significant effect on the TB treatment outcomes.

      Keywords

      1. Introduction

      Globally, tuberculosis (TB) is one of the major public health problems; in 2019, the estimated TB cases were 10 million (8.9–11 million).

      World Health Organization-Global Tuberculosis Report 2020-eng.pdf [Internet]. [cited 2020 Dec 30]. Available from:: https://apps.who.int/iris/bitstream/handle/10665/336069/9789240013131-eng.pdf.

      India continues to be the major contributor to the global TB cases with an estimated TB incidence of 2.69 million cases in 2019.

      India TB Report 2020.pdf [Internet]. [cited 2020 Dec 30]. Available from: https://tbcindia.gov.in/WriteReadData/l892s/India%20TB%20Report%202020.pdf.

      Globally, diabetes is on a rise and about 463 million people were having diabetes in 2019,

      International Diabetes Federation - Facts & figures [Internet]. [cited 2020 Dec 30]. Available from: https://www.idf.org/aboutdiabetes/what-is-diabetes/facts-figures.html.

      of which the majority were residing in middle and low-income countries. India is deemed to be the global capital for diabetes with 77 million (17%) cases in 2019 alone.

      Members [Internet]. [cited 2020 Dec 30]. Available from: https://idf.org/our-network/regions-members/south-east-asia/members/94-india.html.

      ,
      • Pandey S.K.
      • Sharma V.
      World diabetes day 2018: battling the emerging epidemic of diabetic retinopathy.
      About 95% of patients with tuberculosis (TB) and 70% of patients with diabetes mellitus (DM) live in low and middle-income countries.
      • Harries A.D.
      • Murray M.B.
      • Jeon C.Y.
      • et al.
      Defining the research agenda to reduce the joint burden of disease from Diabetes Mellitus and Tuberculosis.
      ,
      • Sen T.
      • Joshi S.R.
      • Udwadia Z.F.
      Tuberculosis and diabetes mellitus: merging epidemics.
      The epidemic growth of DM is witnessed in the many developing countries where TB is high. Currently, India is facing the dual problem of being the highest TB burden country with a large number of people with diabetes posing a serious challenge for the public health system.

      Annual Reports :: Central TB Division [Internet]. [cited 2020 Dec 9]. Available from: https://tbcindia.gov.in/index1.php?lang=1&level=1&sublinkid=4160&lid=2807.

      ,
      • Mohan V.
      • Sandeep S.
      • Deepa R.
      • Shah B.
      • Varghese C.
      Epidemiology of type 2 diabetes: Indian scenario.
      Human Immunodeficiency Virus (HIV) infection is one of the important risk factors to develop TB; similarly, DM is one of the important risk factors to develop TB. The prevalence of DM is more common than HIV, making DM one of the important risk factors for TB.
      • Niazi A.K.
      • Kalra S.
      Diabetes and tuberculosis: a review of the role of optimal glycemic control.
      Diabetics are three times more likely to develop active TB than nondiabetics. The number of TB patients with concomitant diabetes is now more than TB with HIV infection.
      • Ruslami R.
      • Aarnoutse R.E.
      • Alisjahbana B.
      • van der Ven Andre J.A. M.
      • Crevel R.V.
      Implications of the global increase of diabetes for tuberculosis control and patient care.
      DM may affect TB disease presentation and antitubercular treatment response, thereby affecting the TB treatment outcomes. This convergence of DM and TB epidemiology can have a serious impact on the control of TB.
      Several studies have shown increased time to sputum conversion
      • Chang J.T.
      • Dou H.Y.
      • Yen C.L.
      • et al.
      Effect of type 2 diabetes mellitus on the clinical severity and treatment outcome in patients with pulmonary tuberculosis: a potential role in the emergence of multidrug-resistance.
      • Dooley K.E.
      • Chaisson R.E.
      Tuberculosis and diabetes mellitus: convergence of two epidemics.
      • Dooley K.E.
      • Tang T.
      • Golub J.E.
      • Dorman S.E.
      • Cronin W.
      Impact of diabetes mellitus on treatment outcomes of patients with active tuberculosis.
      • Heysell S.K.
      • Moore J.L.
      • Keller S.J.
      • Houpt E.R.
      Therapeutic drug monitoring for slow response to tuberculosis treatment in a state control program, Virginia, USA.
      and some of them have illustrated that there is no relation between DM and sputum conversion rate at the end of 2nd month.
      • Dooley K.E.
      • Chaisson R.E.
      Tuberculosis and diabetes mellitus: convergence of two epidemics.
      ,
      • Dooley K.E.
      • Tang T.
      • Golub J.E.
      • Dorman S.E.
      • Cronin W.
      Impact of diabetes mellitus on treatment outcomes of patients with active tuberculosis.
      ,
      • Alisjahbana B.
      • Sahiratmadja E.
      • Nelwan E.J.
      • et al.
      The effect of type 2 diabetes mellitus on the presentation and treatment response of pulmonary tuberculosis.
      ,

      Singla R, Khan N, Al-Sharif N, Al-Sayegh MO, Shaikh MA, Osman MM. Influence of Diabetes on Manifestations and Treatment Outcome of Pulmonary TB Patients: 6.

      Park et al. in their study found that uncontrolled DM (HbA1c ≥ 7%) was a significant risk factor for positive sputum culture after two months.
      • Park S.W.
      • Shin J.W.
      • Kim J.Y.
      • et al.
      The effect of diabetic control status on the clinical features of pulmonary tuberculosis.
      In all these studies the TB treatment was supervised and monitored, and the diabetic treatment was not monitored. We hypothesized that the TB treatment outcome would be still better if the diabetic treatment was systematically monitored along the lines of TB treatment through a DOT provider. The feasibility of monitoring diabetic patients at the field level by the programme is also not known. We did not come across any studies from India that have seen the effect of supervision of DM treatment on the TB treatment outcomes among TB and diabetes patients.
      We conducted the study among the TB-DM patients on treatment under the programmatic settings in Dakshina Kannada district of Karnataka, India with the following objectives: (a) to compare the TB treatment outcomes with and without DM treatment supervision (b) to compare the HbA1c levels at the baseline, 3 months from baseline and at the end of TB treatment (c) to assess the concurrent control of diabetes through FBS and PPBS at the baseline and at the end of TB treatment (d) to find the association between the control of diabetes and TB treatment outcome.

      2. Methods

      2.1 Study design

      It is a cohort study with a nested case-control study design. All the TB-DM patients registered under the National Tuberculosis Elimination Programme (NTEP) during 2017–19 formed the cohort. The TB-DM patients registered in the select geographical region were provided with systematic diabetic treatment monitoring as an intervention while the TB-DM patients registered in other geographical areas were not monitored. The selection of geographical regions for intervention and non-intervention was based on feasibility and convenience.

      2.2 Settings

      The study was conducted in Dakshina Kannada district, which is situated on the coastal border of Karnataka state. The District has a population of about 22.15 lakhs.

      Dakshina Kannada-District Profile.pdf [Internet]. [cited 2020 Dec 30]. Available from: https://kum.karnataka.gov.in/KUM/PDFS/DistrictProfile/DakshinaKannada.pdf.

      The district has a high literacy rate among males and females. Under the National Tuberculosis Elimination Programme, the district Dakshina Kannada has five tuberculosis units (TUs) which are aligned with the sub-district administrative blocks and have a population of approximately 4–4.5 lakhs each. The tuberculosis units were namely Mangalore, Moodabidri, Puttur, Belthangady, Bantwal, Each of the TUs has a designated Medical Officer Tuberculosis Control identified from one of the Primary health centres in the area; and, Senior Treatment Supervisor (STS) and Senior TB Laboratory Supervisor (STLS) to supervise and implement the TB services in the area.

      India TB Report 2020.pdf [Internet]. [cited 2020 Dec 30]. Available from: https://tbcindia.gov.in/WriteReadData/l892s/India%20TB%20Report%202020.pdf.

      In the year 2018, there were 2373 TB cases notified of which 1407 cases knew their diabetic status and 246 (10.3%) were found to be diabetic.

      2.3 Intervention and non-intervention areas

      The five TUs were divided into intervention areas (two TUs) and non-intervention areas (three TUs) based on the feasibility and convenience of the intervention. The intervention TUs were namely Mangalore and Moodabidri and the non-intervention TUs were Belthangady, Bantwal and Puttur TUs. The TUs in intervention areas contributed to nearly fifty percent of the notified TB cases in Dakshina Kannada district [Fig. 1].
      Fig. 1
      Fig. 1Diagram showing the details of intervention and non-intervention tuberculosis units.

      2.4 Study period

      All the adult TB-DM patients registered under the NTEP from July 2017 to March 2019 were included in the study. The recruited cohort was observed till December 2019 for their treatment outcome.

      2.5 Sample size

      Based on the previous literature, the success rate among the TB-DM patients was found to be 81%.
      • Ranganath T.
      • Shivaraj B.
      A study on tuberculosis treatment outcome in known diabetic patients treated under Revised National Tuberculosis Control Program in Bengaluru.
      The sample size was calculated with the power of 80%, confidence interval of 80%, the assumed risk difference of 15% and the ratio of unexposed to exposed in the sample as 1:1. Considering the non-response rate of 10% and the lost to follow-up of 5%, the sample size for the intervention and non-intervention areas was estimated to be 47 each.

      2.6 Study population

      All the adult TB-DM patients routinely diagnosed and consecutively registered in the area under the programme were included in the study. Any patient on immunosuppressive therapy, or coinfected with HIV, pregnant and lactating mothers and lost to follow-up were excluded from the study.

      2.7 Intervention strategy

      In the TUs selected for intervention, the diabetic patients who were put on treatment for Tuberculosis (Secondary sampling units) were supervised for Diabetic Management through antidiabetic medications card in addition to TB treatment [Fig. 1]. The card contained basic demographic details about the patient and diabetic treatment details such as drugs, dosages, and frequency. It also had dates of all the months to tick after the patient took diabetic treatment. Anti-diabetes medication card was given to the DOT providers, who also supervised the TB treatment. The card was filled by the DOT provider every week.
      The investigators trained the NTEP programme staff (STS, STLS and TBHV) during the routine monthly review meetings and in turn, they trained the treatment supporters (DOT Providers) in their respective areas. Subsequently, the DOT providers were supervised directly by STS and STLS and external validation of the supervision data was done by the investigators randomly.
      The cohort of TB patients with diabetes was followed up till the completion of TB treatment. The nursing staff at the Designated Microscopy Centres (DMCs) or PHCs did the screening of the TB patients for diabetes by conducting random blood sugar (RBS) examination using a glucometer through finger prick method. If RBS reading was found to be greater than 140 mg/dl, fasting blood sugar (FBS) and postprandial blood sugar (PPBS) were conducted at the level of primary health centre. If the FBS was found to be greater than 126 mg/dl, the patients were labelled as diabetic.

      ICMR_GuidelinesType2diabetes2018_0.pdf.

      The medical officer of PHC either initiated the treatment or referred the patients to higher centres for further management. The HbA1C was tested within two weeks of the initiation of treatment if the patient was found to be diabetic, either by test or by self-reporting, followed by one at the completion of 12 weeks and the last one at the end of TB treatment respectively. The patient's blood samples were collected at the DMCs for HbA1C. The laboratory investigations like FBS and PPBS were done at the peripheral health institutions. The samples were sent to the nearest laboratory for the HBA1c test and the cost of the test along with the transportation charge was refunded through the project to the DOT provider on submission of receipt or the bill to the investigators monthly. All the laboratories were using either High-Performance liquid Chromatography with Bio-rad Variant II Turbo HBA1c kit or Turbidimetric inhibition immunoassay. The patients were asked to return empty blisters of oral hypoglycemic drugs or empty insulin bottles to DOT providers on completion of treatment.

      2.7.1 Operational definitions for TB treatment outcomes

      Cured- Microbiologically confirmed TB patient at the beginning of treatment who was smear or culture negative at the end of the complete treatment.
      Treatment completed- A TB patient who completed treatment without evidence of failure or clinical deterioration but with no record to show that the smear or culture results of biological specimens in the last month of treatment were negative, either because the test was not done or because results are unavailable.
      Treatment success- TB patients whether cured or treatment completed are accounted in treatment success.
      Failure- A TB patient whose biological specimen is positive by smear or culture at the end of treatment.
      Treatment regimen changed-A TB patient who is on first line regimen and has been diagnosed as having DRTB and switched to DRTB regimen prior to being declared as failed.
      Died- A TB patient who died during the course of anti TB treatment.
      Unfavorable outcome- Failure, treatment regimen changed and died were considered together as negative results for statistical analysis.
      Lost to follow up- A TB patient whose treatment was interrupted for 1 consecutive month or more.

      2.7.2 Operational definitions for diabetic treatment adherence

      Patients were categorized as adherent to diabetic treatment if they had consumed anti-diabetic medications consecutively seven days in a week. Number of days of missed treatment was assessed and the adherence percentage was calculated as the number of days of missed diabetes treatment per 100 days of diabetes treatment.

      2.7.3 Sources of data and date variables

      Semi-structured questionnaire data collection tool was used. The data from both groups was extracted from the TB treatment cards, diabetic treatment cards, laboratory reports. The data variables included socio-demographic profile, clinical features, diabetic status, treatment adherence and treatment outcomes.

      2.8 Data analysis

      The data was entered and analyzed using SPSS software version 24.
      The results are interpreted as proportions and percentages. Fisher exact tests was used to find out the association of supervision of DM treatment and control of diabetes and Sociodemographic-clinico variables with treatment outcome of tuberculosis separately. The mean differences of HbA1c were calculated among the intervention and non-intervention groups using two-way repeated measures of ANOVA and the ‘p'value of less than 0.05 was considered statistically significant.

      2.9 Institutional ethics

      The study was approved by Father Muller Charitable Institution ethics committee at Mangalore, Karnataka vide no. FMMC/FMIEC/4281/2017. The official permission to conduct the study was also obtained from the State and District TB office. The patients’ data were kept completely confidential and the personal identifiers were never revealed in any form while reporting the results.

      3. Results

      3.1 Comparison of the TB treatment outcomes

      The mean age of the TB-DM patients was found to be 54 ± 11 years. The mean age among the intervention and non-intervention TUs were 55 ± 11 and 53 ± 11 years respectively.
      Nearly 75% of patients were males among both the groups. Nearly 84% and 44% of patients were from rural settings among the non-intervention and intervention groups. About 80% of TB patients had pulmonary TB among both the groups.
      There was no association between the favorable TB treatment outcomes amongst the TB DM patients and their socio-clinico-demographic variables [Table 2].
      Table 1Sociodemographic characteristics, diabetes management and treatment outcome among TB-DM patients in non-intervention and intervention group (N = 94).
      Non-intervention TUs (n = 50)Intervention TUs (n = 52)‘p’ value
      N (%)
      Column percentages.
      N (%)
      Age group65 years11(22)14(27)0.563
      <65 years39(78)38(73)
      SexFemale11(22)9(17)0.480
      Male38(76)43(83)
      Transgender1(2)0(0)
      SettingsUrban8(16)29(56)<.001
      Rural42(84)23(44)
      Type of TBEPTB4(8)11(21)0.061
      Pulmonary46(92)41(79)
      Diabetic treatmentTablets only11(22)21(40)0.005
      Tablets and Insulin29(58)30(58)
      Insulin only10(20)1(2)
      TB treatment outcomeNegative results4(8)2(4)0.263
      Treatment Success46(92)48(92)
      Lost to follow up0(0)2(4)
      a Column percentages.
      Table 2Distribution of TB treatment outcome with socio-clinico-demographic variables.
      TB Treatment Outcome
      Unfavorable OutcomeFavorable outcome
      NN
      Row percentages.
      %
      NN %p value
      Age≥65 years312.50%2187.50%0.148
      <65 years33.95%7396.05%
      SexFemale315.79%1684.21%0.138
      Male33.75%7796.25%
      Transgender00.00%1100.00%
      AddressUrban410.81%3389.19%0.190
      Rural23.17%6196.83%
      Site of TuberculosisEPTB00.00%14100.00%0.591
      Pulmonary66.98%8093.02%
      Treatment DetailsTablets only00.00%32100.00%0.187
      Tablets and Insulin58.77%5291.23%
      Insulin only19.09%1090.91%
      SupervisionYes24.00%4896.00%0.678
      No48.00%4692.00%
      FBS at initiation of treatmentDiabetic Range44.88%7895.12%0.294
      Normal211.11%1688.89%
      FBS at the end of treatmentDiabetic Range55.56%8594.44%0.478
      Normal110.00%990.00%
      PPBS at initiation of treatmentDiabetic Range67.06%7992.94%0.587
      Normal00.00%15100.00%
      PPBS at the end of treatmentDiabetic Range67.06%7992.94%0.587
      Normal00.00%15100.00%
      HbA1C at initiation of treatmentDiabetic Range55.60%8596.40%0.575
      Normal110.00%990.00%
      HbA1C after 3months of initiation of treatmentDiabetic Range66.60%8593.40%0.427
      Normal00.00%9100.00%
      HbA1C at the end of treatmentDiabetic Range66.80%8293.20%0.351
      Normal00.00%12100.00%
      a Row percentages.

      3.2 HbA1c levels at the baseline, 3 months from baseline and at the end of TB treatment

      A two-way repeated measure ANOVA with a Greenhouse Giesser correction determined that the mean value of HbA1c was statistically significant between assessment stages (Initiation of Treatment, middle to treatment and Completion of treatment) (F (1.340, 125.933) = 4.029, p 0.035) for the study subjects and interaction HbA1c and supervision arm also had a significant effect. (F (1.340, 125.933) = 8.012, p 0.002) [Fig. 2].
      Fig. 2
      Fig. 2Clustered Box plot showing the distribution of HbA1c values around the mean at different points of follow up in Supervised and Non Supervised TUs.

      3.3 The concurrent control of diabetes through FBS and PPBS at the baseline and at the end of TB treatment

      A two-way repeated measure ANOVA was conducted for the mean FBS and PPBS value at two stages (initiation of treatment and completion of treatment) for the intervention and non-intervention groups. However, it was found to be statistically non-significant [Table 3, Table 4].
      Table 3Concurrent control of diabetes through FBS at the baseline and at the end of treatment in Supervised and Non-Supervised TUs.
      Supervision ArmFBSMeanStd. Error95% Confidence Interval
      Lower BoundUpper Bound
      Non Supervised TUsFBS at initiation of treatment182.6608.296166.187199.132
      FBS at end of treatment195.8518.641178.694213.008
      Supervised TUsFBS at initiation of treatment182.9398.125166.806199.071
      FBS at end of treatment184.9808.463168.177201.783
      Table 4Concurrent control of diabetes through PPBS at the baseline and at the end of treatment in Supervised and Non-Supervised TUs.
      Supervision ArmPPBSMeanStd. Error95% Confidence Interval
      Lower BoundUpper Bound
      Non Supervised TUsPPBS at initiation of treatment275.89113.425249.236302.547
      PPBS at end of treatment295.34012.118271.280319.401
      Supervised TUsPPBS at initiation of treatment282.17113.148256.066308.277
      PPBS at end of treatment263.40811.868239.844286.973

      3.4 Association between the control of diabetes and TB treatment outcome

      The diabetes management among the non-intervention group included tablets (22%), tablets and insulin (58%) and insulin alone (20%). While the therapeutic management of diabetes among the intervention group included tablets (40%), tablets and insulin (58%) and insulin alone (2%) [Table 1].
      The mean treatment adherence for antidiabetic medication in the intervention group as verified by the empty blisters and vials was found to be 87%. There was no association between control of diabetes and TB Treatment outcomes [Table 2].

      4. Discussion

      This is a novel study to assess the effect of supervision of Diabetes treatment along with TB treatment supervision under programmatic settings in TB DM patients; the outcome variable being the comparison of TB treatment outcomes among TB DM patients.
      Diabetes was associated with poor treatment outcomes in TB DM patients, especially, in pulmonary TB cases.
      • Dooley K.E.
      • Tang T.
      • Golub J.E.
      • Dorman S.E.
      • Cronin W.
      Impact of diabetes mellitus on treatment outcomes of patients with active tuberculosis.
      ,
      • Wang C.S.
      • Yang C.J.
      • Chen H.C.
      • et al.
      Impact of type 2 diabetes on manifestations and treatment outcome of pulmonary tuberculosis.
      • Oursler K.K.
      • Moore R.D.
      • Bishai W.R.
      • Harrington S.M.
      • Pope D.S.
      • Chaisson R.E.
      Survival of patients with pulmonary tuberculosis: clinical and molecular epidemiologic factors.
      • Workneh M.H.
      • Bjune G.A.
      • Yimer S.A.
      Diabetes mellitus is associated with increased mortality during tuberculosis treatment: a prospective cohort study among tuberculosis patients in South-Eastern Amahra Region, Ethiopia.
      • Baker M.A.
      • Harries A.D.
      • Jeon C.Y.
      • et al.
      The impact of diabetes on tuberculosis treatment outcomes: a systematic review.
      • Alisjahbana B.
      • Sahiratmadja E.
      • Nelwan E.J.
      • et al.
      The effect of type 2 diabetes mellitus on the presentation and treatment response of pulmonary tuberculosis.
      but the DOT strategy which was adopted in Diabetic treatment supervision in our study in TB DM patients over and above the Supervision of TB treatment did not yield any significant difference over the unsupervised group in terms of TB treatment outcomes. Singla A et al. and Satung J et al. also observed that there was no effect on treatment outcomes of Tuberculosis in TB DM patients.
      • Singla R.
      • Khan N.
      • Al-Sharif N.
      • Al-Sayegh M.O.
      • Shaikh M.A.
      • Osman M.M.
      Influence of diabetes on manifestations and treatment outcome of pulmonary TB patients.
      ,
      • Satung J.
      • Kaewkungwal J.
      • Silachamroon U.
      • et al.
      Treatment outcomes among diabetic patients with tuberculosis in Thailand.
      This might be attributed to the high level of literacy and better health indicators and access to better health care in the coastal district of Dakshina Kannada. One key factor can be the age of the patients. The majority of the patients were below the age of 65 years. The mean age of the study participants was 54.37 years. Though the mean age of the study participants in the supervised group was less than the mean age in the unsupervised group but that was not found to have any significant effect on the TB treatment outcomes.
      Diabetic DOT led to relative improvement in diabetic control over the period of time from the baseline to the last measurement, especially for Postprandial Blood sugar and HbA1c. Undoubtedly, diabetic screening and follow-up are a must in TB patients. Though many point of care HbA1c tests have been tested, their sensitivity is always questionable. Political commitment, the first pillar of DOTS strategy in Tuberculosis management needs to be also focusing on overall glycemic control since we are dealing with dual epidemics of Diabetes and Tuberculosis.
      Most of the patients could not attain glycemic control at the end of treatment and glycemic control did not appear to translate into favorable outcomes significantly. Nandakumar et al.
      • Kv N.
      • Duraisamy K.
      • Balakrishnan S.
      • et al.
      Outcome of tuberculosis treatment in patients with diabetes mellitus treated in the revised national tuberculosis control programme in malappuram district, Kerala, India.
      also did not observe any association between the diabetic control with treatment response or outcome. On the contrary. Mahisale et al. observed the negative effect of poor glycemic control on the treatment outcomes in patients with pulmonary tuberculosis.
      • Mahishale V.
      • Avuthu S.
      • Patil B.
      • Lolly M.
      • Eti A.
      • Khan S.
      Effect of poor glycemic control in newly diagnosed patients with smear-positive pulmonary tuberculosis and type-2 diabetes mellitus.
      Chiang CY also analyzed the effect of glycemic control on TB treatment outcomes. The treatment success proportion was related inversely to HbA1C, but they considered mainly the pretreatment HbA1C values.
      • Chiang C.Y.
      • Bai K.J.
      • Lin H.H.
      • et al.
      The influence of diabetes, glycemic control, and diabetes-related comorbidities on pulmonary tuberculosis.
      There are a few programmatic implications of this operational research. First, linking treatment support of TB with the supervision of diabetes treatment may help in the long term Glycemic control during the course of TB treatment in TB-DM patients although supervision of diabetes treatment through diabetic DOT did not have any bearing on the Tuberculosis treatment outcome.
      Secondly, regular FBS and PPBS monitoring in TB-DM patients may not give the true picture of glycemic control. Hence, routine monitoring of FBS and PPBS in patients with higher Random blood sugar should be replaced with HbA1c monitoring.
      The study was unique as it involved the Diabetic treatment supervision along with the TB treatment supervision under the Programmatic settings. The supervision was done by treatment supporters at the level of Primary Health Centres. The study had a few limitations, bigger sample size is required to determine the role of Glycemic control in TB DM patients. Standardized Diabetic treatment could not be ensured due to different physicians involved in the treatment in various TUs. Further, in the field settings, the HbA1c testing was done in different laboratories which were in the vicinity of the patient's residential area. The different laboratories might have used different process/principle for HbA1c, FBS and PPBS testing. Standardized Glycemic profile testing could not be achieved in the programmatic settings. There were challenges also observed in the study viz, nonavailability of HbA1c testing in the Primary Health Care and the varying cost of the HbA1c in the private health sector.

      5. Conclusion

      Under programmatic settings, Diabetic Treatment supervision for TB DM patients over and above the TB treatment supervision by the Treatment Supporters helps in relatively better glycemic control as compared to that in non-supervised TB-DM patients. There was no effect of Diabetic Treatment supervision for TB DM patients on the TB treatment outcomes.

      Funding

      The study was undertaken as a part of the ‘Operational Research for Medical College Faculty under National TB Control Programme (NTEP)’ conducted by NTEP State Task Force Operational Research Committee, Karnataka with the funding support from the State Tuberculosis Office (Government of Karnataka), Bengaluru vide grant number- LWSTC/RNTCP/ACC/92/2016-17 . The project and manuscript were developed during the ‘protocol development’ and ‘scientific paper writing’ workshops respectively.

      Declaration of competing interest

      None Declared.

      Acknowledgements

      We express our heartfelt gratitude to all the STS and STLS for their unconditional support during the conduct of the study. We also thank Dr Ghansham Sharma (Posthumously) who guided us in the protocol development.

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