PubMed Health⌕ Search

PubMed · 15520486

Multidrug-resistant tuberculosis.

Abstract

Multidrug-resistant tuberculosis (MDR-TB) caused by Mycobacterium tuberculosis resistant to both isoniazid and rifampicin with or without resistance to other drugs is among the most worrisome elements of the pandemic of antibiotic resistance. Globally, about three per cent of all newly diagnosed patients have MDR-TB. The proportion is higher in patients who have previously received antituberculosis treatment reflecting the failure of programmes designed to ensure complete cure of patients with tuberculosis. While host genetic factors may probably contribute, incomplete and inadequate treatment is the most important factor leading to the development of MDR-TB. The definitive diagnosis of MDR-TB is difficult in resource poor low income countries because of non-availability of reliable laboratory facilities. Efficiently run tuberculosis control programmes based on directly observed treatment, short-course (DOTS) policy is essential for preventing the emergence of MDR-TB. Management of MDR-TB is a challenge which should be undertaken by experienced clinicians at centres equipped with reliable laboratory service for mycobacterial culture and in vitro sensitivity testing as it requires prolonged use of expensive second-line drugs with a significant potential for toxicity. Judicious use of drugs, supervised individualised treatment, focussed clinical, radiological and bacteriological follow up, use of surgery at the appropriate juncture are key factors in the successful management of these patients. In certain areas, currently available programme approach may not be adequate and innovative approaches such as DOTS-plus may have to be employed to effectively control MDR-TB.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

S K Sharma, A Mohan. 2004. Multidrug-resistant tuberculosis.. https://pubmed.ncbi.nlm.nih.gov/15520486/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

The causal relationship between multiple cardiovascular diseases and glioblastoma: A Mendelian randomization study.

Observational studies suggest an association between glioblastoma (GBM) and cardiovascular diseases (CVDs), but a causal relationship remains unestablished. This study aimed to investigate the causal link between multiple CVDs and GBM risk. The inverse variance weighted method indicated that all 18 CVDs had significant causal associations with GBM (P&#x2005;<&#x2005;.05). Genetically predicted CVDs were uniformly associated with a lower risk of GBM (odds ratio&#x2005;<&#x2005;1), identifying them as potential protective factors. Sensitivity analyses confirmed the absence of significant heterogeneity or horizontal pleiotropy, and the MR-Steiger test validated the correct causal direction. This Mendelian randomization (MR) study provides evidence that a range of CVDs are causally associated with a decreased risk of developing GBM. These findings suggest shared biological pathways and offer new insights for understanding GBM etiology. We conducted a 2-sample MR analysis using publicly available genome-wide association study data. GBM was the outcome, and 18 cardiovascular-related traits (including coronary artery disease, myocardial infarction, and venous thromboembolism) were exposures. Instrumental variables were single-nucleotide polymorphisms significantly associated with exposures (P&#x2005;<&#x2005;5&#x2005;&#xd7;&#x2005;10-8). The primary analysis used the inverse variance weighted method, supplemented with MR-Egger, weighted median, and weighted mode methods. Sensitivity analyses, including Cochran Q test, MR-Egger intercept test, leave-one-out analysis, and MR-Steiger directionality test, were performed to ensure robustness.

Causality↗

Statistical issues in life course epidemiology.

There is growing recognition that the risk of many diseases in later life, such as type 2 diabetes or breast cancer, is affected by adult as well as early-life variables, including those operating prior to conception and during the prenatal period. Most of these risk factors are correlated because of common biologic and/or social pathways, while some are intrinsically ordered over time. The study of how they jointly influence later ("distal") disease outcomes is referred to as life course epidemiology. This area of research raises several issues relevant to the current debate on causal inference in epidemiology. The authors give a brief overview of the main analytical and practical problems and consider a range of modeling approaches, their differences determined by the degree with which associations present (or presumed) among the correlated explanatory variables are explicitly acknowledged. Standard multiple regression (i.e., conditional) models are compared with joint models where more than one outcome is specified. Issues arising from measurement error and missing data are addressed. Examples from two cohorts in the United Kingdom are used to illustrate alternative modeling strategies. The authors conclude that more than one analytical approach should be adopted to gain more insight into the underlying mechanisms.

Causality↗