Does Level of Access to Tuberculosis (TB) Treatment Predict Default Patterns? A multilevel analysis. 公开

Driver, Stevi (2014)

Permanent URL: https://etd.library.emory.edu/concern/etds/sn009z330?locale=zh
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Abstract

Setting: KwaZulu-Natal (KZN), a province in South Africa, has one of the highest prevalence of tuberculosis (TB) and multi-drug resistance TB in the world, which presents challenges for effective TB control and treatment outcomes, lending to the highest TB death rate in South Africa. Poor TB treatment outcomes, such as defaulting from treatment, are associated with increased risk for morbidity, mortality, acquisition of drug-resistance, and continued transmission of TB in the community.

Objective: To evaluate the association between access to care metrics (i.e. direct observed therapy, health facility density per population, composite health quality score) and risk for default among persons diagnosed with TB.

Design: A multilevel analysis of existing surveillance and administrative data on all patients registered with active TB and with an available treatment outcome in KZN between 2010 and 2011.

Results: A total of 3,261 (11.3%) TB patients defaulted from treatment during the study period. Patients who received direct observed therapy (DOT) throughout the prescribed treatment and patients who lived in an area with a high density of health facilities were significantly less likely to default than persons without DOT or who lived in areas with a lower concentration of facilities (RR=0.3, 95% CI=0.2, 0.3 and RR=0.4, 95% CI=0.3, 0.5, respectively). However, patients who received DOT during either the intensive or the continuation phase of treatment had an increased risk of defaulting compared to patients who were not provided any DOT during treatment (RR=1.8, 95% CI=1.1, 2.8 and RR=2.1, 95% CI=1.6, 2.8, respectively). There was no association between health quality score and defaulting from treatment.

Conclusions: This study presents key insights on the importance of access to care with regard to defaulting. By linking existing surveillance and administrative data, we demonstrated that consistent provision of DOT and spatial density of health facilities are independent predictors of tuberculosis default. Although this work needs to be replicated, these findings can guide program managers into better understanding the effect of access to care metrics on default patterns in low and middle-income countries.

Table of Contents

Introduction ___________________________________________________________ 1-2

Background____________________________________________________________ 3-18

General Information about TB_____________________________________________ 3-6

Tuberculosis Control____________________________________________________ 6-7

TB Treatment Compliance as a Threat to TB Control__________________________ 7-8

Risk Factors Associated with Treatment Default______________________________ 8-11

Access to Care (definition and measures) ___________________________________ 11-12

Access to Care and treatment adherence - all diseases________________________ 12-15

Access to Care and Treatment Adherence - Tuberculosis______________________ 15-16

Study Objectives_______________________________________________________ 16-18

Methods_______________________________________________________________ 19-25

Setting _______________________________________________________________ 19

Study Population_______________________________________________________ 19

Data Sources__________________________________________________________ 19-20

Definitions and Coding__________________________________________________ 21-24

Statistical Analyses____________________________________________________ 24-25

Ethical Considerations__________________________________________________ 26

Results________________________________________________________________ 27-35

Discussion_____________________________________________________________ 36-38

Conclusions ___________________________________________________________ 39

References_____________________________________________________________40-45

Figures________________________________________________________________ 46-53

Tables_________________________________________________________________ 54-99

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