Humma Nawaz  ( Department of Statistics, GC Women University, Sialkot, Pakistan )
Sahar Bibi  ( Department of Statistics, GC Women University, Sialkot, Pakistan )
Mahwish Rabia  ( Department of Statistics, GC Women University, Sialkot, Pakistan )

Objective: To determine the prevalence rate of diabetes mellitus among active tuberculosis patients, and to assess the impact of age in this regard.

Method: The meta-analysis comprised studies conducted in Asian and African countries from 2012 to 2018. Data was extracted from the selected studies and was analyzed using the Meta extension of Excel. Comprised studies conducted in Asian and African countries from 2012 to 2018. Data was extracted from the selected studies and was analyzed using the Meta extension of Excel.

Results: Of the 200 studies reviewed, 15(7.5%) were selected for further analyses. The selected studies involved a total of 28,055 patients. Of the selected studies, 8(53%) were from Asia and 7(47%) were from Africa. The overall pooled prevalence of diabetes among tuberculosis patients was 26% (95% confidence interval: 14.62 to 35.34). Age had a significant negative effect on the prevalence rate (95% confidence interval: -0.634 to 4.179).

Conclusion: Diabetes was found to be widely spreading among Asian and African people, and age was found to be a significant negative factor.

Keywords: Meta-analysis, Tuberculosis, Diabetes mellitus, Forest plot, Moderator analysis. (JPMA 71: 1200; 2021)

DOI: https://doi.org/10.47391/JPMA.846

 

Introduction

 

A statistical analysis to combine data of multiple scientific studies is called meta-analysis.¹ Data in such a scenario is extracted from published or unpublished multiple studies in order to explore general trends or to evaluate overall effect.^2,3 It provides a consistent method for inspecting the existing study's literature on any specific field and the results from such analysis can provide proof for or against expert opinion or popular belief. While conducting meta-analysis, the methods and interpretations need to be revisited to re-evaluate the results of previous research to derive conclusions on the topic concerned. Systematic review methodology is at the heart of meta-analysis.^4-7

According to a report of World Health Organisation (WHO), 10.4 million new tuberculosis (TB) cases and 1.7 million deaths were registered in 2017.⁸ Moreover, 415 million cases and 5 million deaths due to diabetes mellitus (DM) were also registered.⁹ Unfortunately, 95% people living in middle-rated countries, like those in Africa and southeast Asia, were affected by TB and DM cases were 75%.¹⁰

TB disease is caused by a bacteria called mycobacterium tuberculosis which affects all parts of the body and spreads by air when a person with TB coughs, sneezes or talks. People are infected by TB complex by one of three routes: inhalation, ingestion and inoculation.¹¹ TB bacteria mostly increases in the lungs, and identification of TB can be done by bad cough that lasts 3 weeks or longer, pain in the chest and coughing up blood or saliva with mucus deep inside the lungs.¹²

TB bacteria affects all ages and is active in people who are smokers, or working close to chemicals, or in areas with more dust and perfume particles.¹³ The most common investigative test for TB, purified protein derivative (PPD) tuberculin, is the originator of TB bacteria. Tactlessly, the skin test is not 100% good and has been known to give false positive (FP) and false negative (FN) readings.¹⁴

DM is a recorded disorder due to high or low blood sugar levels for a long period. If the identification of diabetes is not done in time, it can create more complexity for the human body because it can badly attack other parts of the body. Insulin and some oral medication can lower blood sugar level.^15,16

A systematic review about the prevalence of TB among diabetic patients included 15 studies for meta-analysis in order to find the pooled overall prevalence rate of TB in DM cases.¹⁷

A systematic review conducted on the management of TB healthcare practitioners in Pakistan observed that patients' compliance was low <50% for the use of sputum microscopy in the diagnosis of TB in Pakistan, and 50% practitioners correctly identified cough as the main TB symptom.¹⁸

It was screened for DM during TB diagnosis and highlighted the association between genetic diversity and DM in Ghana and other countries.^19,20 Consistent results showed the increased risk of TB among DM patients and association between TB and DM.²¹

DM among TB patients in sub-Saharan Africa was significantly high. The pooled prevalence of DM among TB patients was 9% (95% confidence interval [CI]: 6-12%), while the highest prevalence of DM in TB patients was 15% in Nigeria.²² The study found that there was a high prevalence of DM among human immunodeficiency virus (HIV)-infected compared to those not infected. Besides, the prevalence of DM among HIV-infected TB patients was 8.9% which is slightly higher than HIV-uninfected 7.7% TB patients. It was strongly recommended by the authors to screen TB patients for DM, and special emphasis should be given for early screening of DM among TB/HIV co-infected patients.²³

The current meta-analysis was planned to determine the prevalence rate of DM among active TB patients, and to assess the impact of age in this regard.

 

Materials and Methods

 

The meta-analysis study was conducted at Sialkot, Pakistan from 2018 to 2019 and comprised studies conducted in Asian and African countries from 2012 to 2018. The study was conducted in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.²⁴

Inclusion criteria was to include studies that had ethnical approval, related to TB and DM and were carried out among diverse age groups under different conditions in African and Asian countries. The included studies could be be either cross-sectional or case studies and involved primary outcomes of interest.

Studies excluded were those that only reported incidence, anti-drug resistance mainly focussing on TB without reporting prevalence rate of DM among TB patients.

Data extracted included country, area and the year of publication. The number of DM and TB cases were also recorded to estimate an overall CI for the prevalence rate.

The data was analysed using a systematic review of the selected studies. Meta-analysis was conducted for the collected data to assess pooled prevalence of DM in TB patients using random effect model through Meta-Excel software. Subgroup analysis was carried out using Asia and Africa as the regions.^25-39 The role of age was analysed using meta-regression.

 

Results

 

After screening 1110 research papers, 200(%) were fully reviewed. After applying the inclusion/exclusion criteria, 15(7.5%) of them were included which comprised 28,055 participants (Figure-1).

Of the 15 selected studies, 8(53%) were from Asia, and 7(47%) were from Africa (Table-1).

The overall pooled prevalence of DM among TB patients was 26% (95% CI: 14.62-35.34 overall; 27.472-32.528 Asia; 12.992-16.008 Africa).

Forest plot suggested that within 95% CI the combined effect size for all the included studies were significant except one (Figure-2).

Subgroup analysis showed the significance of studies done in Asia (Figure-3) and Africa (Figure-4) separately

Subgroup analysis also extracted different parameters, like CI, standard weights and Q statistics (Table-2).

Data for moderate analysis included standard error (SE) and weights for prevalence rate, and used age as the moderator variable which was extracted from all the included studies (Table-3).

Coefficient of meta-regression indicated that A 1.7 unit change in moderator variable resulted in 1.7 unit change in the prevalence rate (p<0.05) (Table-4).

The CI for intercept ranged from -156.672 to 52.601, while for moderator it was between -0.634 and 4.179 (Table-5).

There existed a significant negative relationship between the prevalence rate and age (Figure-5).

 

Discussion

 

Evidence based on the 15 included studies with 28,055 participants revealed 14% prevalence of DM among TB patients in countries where the studies were conducted. This rate also justifies other studies.⁴⁰ Forest plot indicated that there was no non-significant studies in data except one,³⁹ and that was significant in subgroup analyses

There was almost no heterogeneity in the included studies. To examine heterogeneity and significance of the results within groups, subgroup analysis was also conducted because it is more reliable to draw conclusions on the basis of subgroups. Meta-regression analysis revealed that age was playing a significant role in the analysis. Likewise, prevalence of DM among TB patients could also be affected by the age factor which is in line with past studies.⁴¹

 

Conclusion

 

DM was found to be widely spreading among Asian and African people, and age was found to be a significant negative factor. The findings are expected to be helpful in examining the rate of DM among TB patients.

 

Disclaimer: None.

Conflict of Interest: None.

Source of Funding: None.

 

References

 

1.      Rothman KJ, Greenland S, Lash TL. Modern epidemiology. New York: Lippincott Williams & Wilkins, 2012.

2.      Cooper H, Hedges LV, Valentine JC. The handbook of research synthesis and meta-analysis. New York: Russell Sege Foundation, 2019; pp-632.

3.      Hedges LV, Olkin I. Statistical methods for meta-analysis. USA: Academic press, 1985.

4.      Antman EM, Lau J, Kupelnick B, Mosteller F, Chalmers TC. A comparison of results of meta-analyses of randomized control trials and recommendations of clinical experts: treatments for myocardial infarction. JAMA. 1992; 268:240-8.

5.      Oxman AD, Guyatt GH. The Science of Reviewing Research a. Ann N Y Acad Sci. 1993; 703:125-34.

6.      Stewart LA, Tierney JF. To IPD or not to IPD? Advantages and disadvantages of systematic reviews using individual patient data. Eval Health Prof. 2002; 25:76-97.

7.      Ioannidis JPA, Vlachoyiannopoulos PG, Haidich AB, Medsger TA Jr, Lucas M, Michet CJ, et al. Mortality in systemic sclerosis: an international meta-analysis of individual patient data. Am J Med. 2005; 118: 2-10.

8.      World Health Organization. Global tuberculosis report. Geneva: World Health Organization, 2017.

9.      Atlas D. International diabetes federation. IDF Diabetes Atlas, 7th ed. Brussels, Belgium: International Diabetes Federation, 2015.

10.    Jeon CY, Harries AD, Baker MA, Hart JE, Kapur A, Lönnroth K, et al. Bi‐directional screening for tuberculosis and diabetes: a systematic review. Trop Med Int Health. 2010; 15:1300-14.

11.    Pratt RJ, Grange JM, Williams VG. Tuberculosis: a foundation for nursing and healthcare practice. Intensive Care Med. 2020; 46:10-67.

12.    Zaman K. Tuberculosis: a global health problem. J Health Popul Nutr. 2010; 28:111.

13.    Leung CC, Lam TH, Ho KS, Yew WW, Tam CM, Chan WM, et al. Passive smoking and tuberculosis. Arch Intern Med. 2010; 170:287-92.

14.    Kurup SK, Buggage RR, Clarke GL, Ursea R, Lim WK, Nussenblatt RB. Gamma interferon assay as an alternative to PPD skin testing in selected patients with granulomatous intraocular inflammatory disease. Can J Ophthalmol. 2006; 41:737-40.

15.    Bryden KS, Dunger DB, Mayou RA, Peveler RC, Neil HA. Poor prognosis of young adults with type 1 diabetes: a longitudinal study. Diabetes care. 2003; 26:1052-7.

16.    Barnard K, Parkin C, Young A, Ashraf M. Use of an automated bolus calculator reduces fear of hypoglycemia and improves confidence in dosage accuracy in patients with type 1 diabetes mellitus treated with multiple daily insulin injections. J Diabetes Sci Technol. 2012 ; 6:144-9.

17.    Kerner W, Brückel J. Definition, classification and diagnosis of diabetes mellitus. Exp Clin Endocrinol Diabetes. 2019; 127:S1-7.

18.    Wagnew F, Eshetie S, Alebel A, Dessie G, Tesema C, Abajobir AA. Meta-analysis of the prevalence of tuberculosis in diabetic patients and its association with cigarette smoking in African and Asian countries. BMC Res Notes. 2018; 11:298.

19.    Braham CA, White PJ, Arinaminpathy N. Management of tuberculosis by healthcare practitioners in Pakistan: A systematic review. PLoS One. 2018; 13:e0199413.

20.    Rao S, Rahim M, Iqbal K, Haroon F, Hasan Z. Impact of diabetes on mechanisms of immunity against Mycobacterium tuberculosis. J Pak Med Assoc. 2019; 69:94-8.

21.    Poku AA, Asare P, Baddoo NA, Forson A, Klevor P, Otchere ID, et al. TB-diabetes co-morbidity in Ghana: The importance of Mycobacterium africanum infection. PLoS One. 2019; 14:e0211822.

22.    Basir MS, Habib SS, Zaidi SM, Khowaja S, Hussain H, Ferrand RA, et al. Operationalization of bi-directional screening for tuberculosis and diabetes in private sector healthcare clinics in Karachi, Pakistan. BMC Health Serv Res. 2019; 19:147.

23.    Alebel A, Wondemagegn AT, Tesema C, Kibret GD, Wagnew F, Petrucka P, et al. Prevalence of diabetes mellitus among tuberculosis patients in Sub-Saharan Africa: a systematic review and meta-analysis of observational studies. BMC Infect Dis. 2019; 19:254.

24.    Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Med. 2009;6:e1000100.

25.    Kapoor D, Bhardwaj AK, Kumar D, Raina SK. Prevalence of diabetes mellitus and its risk factors among permanently settled tribal individuals in tribal and urban areas in northern state of sub-Himalayan region of India. Int J Chronic Dis. 2014; 2014:380597

26.    Chagas AC, Hans Filho G, Oliveira SM, Ivo ML, Corrêa Filho RA, Donatti MI. Prevalence of latent tuberculosis and treatment adherence among patients with chronic kidney disease in Campo Grande, State of Mato Grosso do Sul. Rev Soc Bras Med Trop. 2014; 47:204-11.

27.    Pavlovic JM, Pavlovic AD, Bulajic MV, Pešut DP. Prevalence of diabetes mellitus (DM) in tuberculosis (TB) patients: clinical and radiologic features in the TB-DM association based on a five-year hospital study. Infez Med. 2018; 26:22-27.

28.    Pande T, Huddart S, Xavier W, Kulavalli S, Chen T, Pai M, et al. Prevalence of diabetes mellitus amongst hospitalized tuberculosis patients at an Indian tertiary care center: A descriptive analysis. PLoS One. 2018; 13:e0200838.

29.    Ekeke N, Ukwaja KN, Chukwu JN, Nwafor CC, Meka AO, Egbagbe EE, et al. Screening for diabetes mellitus among tuberculosis patients in Southern Nigeria: a multi-centre implementation study under programme settings. Sci Rep. 2017; 7:44205.

30.    Balakrishnan S, Vijayan S, Nair S, Subramoniapillai J, Mrithyunjayan S, Wilson N, et al. High diabetes prevalence among tuberculosis cases in Kerala, India. PLoS One 2012; 7:e46502.

31.    Gadallah M, Amin W, Fawzy M, Mokhtar A, Mohsen A. Screening for diabetes among tuberculosis patients: a nationwide population-based study in Egypt. Afr Health Sci. 2018; 18:884-90.

32.    Viswanathan V, Kumpatla S, Aravindalochanan V, Rajan R, Chinnasamy C, Srinivasan R, et al. Prevalence of diabetes and pre-diabetes and associated risk factors among tuberculosis patients in India. PLoS One. 2012; 7:e41367.

33.    Ogbera AO, Kapur A, Abdur-Razzaq H, Harries AD, Ramaiya K, Adeleye O, et al. Clinical profile of diabetes mellitus in tuberculosis. BMJ Open Diabetes Res Care. 2015; 3:e000112.

34.    Workneh MH, Bjune GA, Yimer SA. Diabetes mellitus is associated with increased mortality during tuberculosis treatment: a prospective cohort study among tuberculosis patients in South-Eastern Amahra Region, Ethiopia. Infect Dis Poverty. 2016; 5:22.

35.    Kornfeld H, West K, Kane K, Kumpatla S, Zacharias RR, Martinez-Balzano C, et al. High prevalence and heterogeneity of diabetes in patients with TB in South India: a report from the effects of diabetes on tuberculosis severity (EDOTS) study. Chest. 2016; 149:1501-8.

36.    Grint D, Alisjhabana B, Ugarte-Gil C, Riza AL, Walzl G, Pearson F, et al. Accuracy of diabetes screening methods used for people with tuberculosis, Indonesia, Peru, Romania, South Africa. Bull World Health Organ. 2018; 96:738-49.

37.    Atif M, Anwar Z, Fatima RK, Malik I, Asghar S,  Scahill S. Analysis of tuberculosis treatment outcomes among pulmonary tuberculosis patients in Bahawalpur, Pakistan. BMC Res Notes 2018; 11:370.

38.    Dobler CC, Flack JR, Marks GB. Risk of tuberculosis among people with diabetes mellitus: an Australian nationwide cohort study. BMJ Open. 2012; 2:e000666.

39.    Tahir Z, Akhtar AM, Yaqub T, Mushtaq MH, Javed H. Diabetes mellitus among tuberculosis patients: a cross sectional study from Pakistan. Afr Health Sci. 2016; 16:671-6.

40.    Ji LN, Lu JM, Guo XH, Yang WY, Weng JP, Jia WP, et al. Glycemic control among patients in China with type 2 diabetes mellitus receiving oral drugs or injectables. BMC Public Health. 2013; 13:602.

41.    Kibirige D, Ssekitoleko R, Mutebi E, Worodria W. Overt diabetes mellitus among newly diagnosed Ugandan tuberculosis patients: a cross sectional study. Gastroenterol Rep (Oxf). 2015; 3:86-9.