Tuberculosis Infection Among People With Diabetes: United States Population Differences by Race/Ethnicity

Estimated rates of progression to tuberculosis disease for persons infected with Mycobacterium tuberculosis in the United States

BACKGROUND

In the United States, over 80% of tuberculosis (TB) disease cases are estimated to result from reactivation of latent TB infection (LTBI) acquired more than 2 years previously ("reactivation TB"). We estimated reactivation TB rates for the US population with LTBI, overall, by age, sex, race-ethnicity, and US-born status, and for selected comorbidities (diabetes, end-stage renal disease, and HIV).

METHODS

We collated nationally representative data for 2011-2012. Reactivation TB incidence was based on TB cases reported to the National TB Surveillance System that were attributed to LTBI reactivation. Person-years at risk of reactivation TB were calculated using interferon-gamma release assay (IGRA) positivity from the National Health and Nutrition Examination Survey, published values for interferon-gamma release assay sensitivity and specificity, and population estimates from the American Community Survey.

RESULTS

For persons aged ≥6 years with LTBI, the overall reactivation rate was estimated as 0.072 (95% uncertainty interval: 0.047, 0.12) per 100 person-years. Estimated reactivation rates declined with age. Compared to the overall population, estimated reactivation rates were higher for persons with diabetes (adjusted rate ratio [aRR] = 1.6 [1.5, 1.7]), end-stage renal disease (aRR = 9.8 [5.4, 19]), and HIV (aRR = 12 [10, 13]).

CONCLUSIONS

In our study, individuals with LTBI faced small, non-negligible risks of reactivation TB. Risks were elevated for individuals with medical comorbidities that weaken immune function.

Impact of diabetes itself and glycemic control status on tuberculosis

Objectives

To explore the impact of diabetes itself and glycemic control status on tuberculosis (TB).

Methods

A total of 3393 patients with TB and diabetes mellitus (DM) who were hospitalized in the Public Health Clinical Center of Chengdu from January 1, 2019, to December 31, 2021, were retrospectively included and divided into three groups according to baseline glycemic control status: two groups according to glycemic status at discharge, two groups according to cavity occurrence, three groups according to sputum results, and three groups according to lesion location. The influencing factors and the differences in cavity occurrence, sputum positivity and lesion location among different glycemic control groups or between different glycemic status groups were analyzed.

Results

In this TB with DM cohort, most of the subjects were male, with a male to female ratio of 4.54:1, most of them were 45-59 years old, with an average age of 57.44 ± 13.22 years old. Among them, 16.8% (569/3393) had cavities, 52.2% (1770/3393) were sputum positive, 30.4% (1030/3393) had simple intrapulmonary lesions, 68.1% (2311/3393) had both intra and extrapulmonary lesions, only 15.8% (537/3393) had good glycemic control,16.0% (542/3393) and 68.2% (2314/3393) had fair and poor glycemic control, respectively. Compared with the non-cavity group, the sputum-negative group and the extrapulmonary lesion group, the cavity group, sputum-positive group, intrapulmonary lesion group and the intra and extrapulmonary lesion group all had higher fasting plasma glucose (FPG) and glycosylated hemoglobin A 1c (HbA1c) and lower good glycemic control rates at admission (all P<0.001). Another aspect, compared with the good glycemic control group, the poor glycemic control group had a higher cavity occurrence rate, sputum positive rate, and greater proportion of intrapulmonary lesions. Moreover, FPG and HbA1c levels and poor glycemic control were significantly positively correlated with cavity occurrence, sputum positivity, and intrapulmonary lesions and were the main risk factors for TB disease progression. On the other hand, cavity occurrence, sputum positivity, and intrapulmonary lesions were also main risk factors for hyperglycemia and poor glycemic control.

Conclusion

Diabetes itself and glycemic control status could impact TB disease. Good glycemic control throughout the whole process is necessary for patients with TB and DM to reduce cavity occurrence and promote sputum negative conversion and lesion absorption.

Latent tuberculosis infection among patients with and without type-2 diabetes mellitus: results from a hospital case-control study in Atlanta

OBJECTIVE

The purpose of this study is to compare the prevalence of latent TB infection (LTBI) among patients with type-2 diabetes mellitus (T2DM) to healthy controls without T2DM. To achieve this objective, we conducted a case-control study in a large hospital in Atlanta from 2016 to 2019.

RESULTS

We enrolled 98 cases; 119 potential controls were screened, 84 of which had HbA1c  ≥  5.7% and one did not have QFT result, leaving 34 (28.6%) individuals enrolled as controls. LTBI prevalence was 9.2% among cases and 14.7% among controls (crude odds ratio 0.59, 95% CI 0.19-2.04). After adjusting for age and sex, the adjusted odds of LTBI among patients with T2DM was 0.45 (95% CI 0.13, 1.71) times the controls. We did not observe a statistically significant association between LTBI and T2DM. However, we reported a positive correlation between HbA1c level and nil count among individuals with LTBI (R²  =  0.55, p  <  0.01). In addition, we reported a high prevalence of LTBI among adults with T2DM and family members without T2DM.

Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia

Tuberculosis (TB) cases have increased drastically over the last two decades and it remains as one of the deadliest infectious diseases in Malaysia. This cross-sectional study aimed to establish the spatial distribution of TB cases and its association with the sociodemographic and environmental factors in the Gombak district. The sociodemographic data of 3325 TB cases such as age, gender, race, nationality, country of origin, educational level, employment status, health care worker status, income status, residency, and smoking status from 1st January 2013 to 31st December 2017 in Gombak district were collected from the MyTB web and Tuberculosis Information System (TBIS) database at the Gombak District Health Office and Rawang Health Clinic. Environmental data consisting of air pollution such as air quality index (AQI), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter 10 (PM10,) were obtained from the Department of Environment Malaysia from 1st July 2012 to 31st December 2017; whereas weather data such as rainfall were obtained from the Department of Irrigation and Drainage Malaysia and relative humidity, temperature, wind speed, and atmospheric pressure were obtained from the Malaysia Meteorological Department in the same period. Global Moran's I, kernel density estimation, Getis-Ord Gi* statistics, and heat maps were applied to identify the spatial pattern of TB cases. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to determine the spatial association of sociodemographic and environmental factors with the TB cases. Spatial autocorrelation analysis indicated that the cases was clustered (p<0.05) over the five-year period and year 2016 and 2017 while random pattern (p>0.05) was observed from year 2013 to 2015. Kernel density estimation identified the high-density regions while Getis-Ord Gi* statistics observed hotspot locations, whereby consistently located in the southwestern part of the study area. This could be attributed to the overcrowding of inmates in the Sungai Buloh prison located there. Sociodemographic factors such as gender, nationality, employment status, health care worker status, income status, residency, and smoking status as well as; environmental factors such as AQI (lag 1), CO (lag 2), NO2 (lag 2), SO2 (lag 1), PM10 (lag 5), rainfall (lag 2), relative humidity (lag 4), temperature (lag 2), wind speed (lag 4), and atmospheric pressure (lag 6) were associated with TB cases (p<0.05). The GWR model based on the environmental factors i.e. GWR2 was the best model to determine the spatial distribution of TB cases based on the highest R2 value i.e. 0.98. The maps of estimated local coefficients in GWR models confirmed that the effects of sociodemographic and environmental factors on TB cases spatially varied. This study highlighted the importance of spatial analysis to identify areas with a high TB burden based on its associated factors, which further helps in improving targeted surveillance.