ABSTRACT
SETTING: In 2008, the Kenya tuberculosis (TB) program reported low (31%) antiretroviral therapy (ART) uptake among human immunodeficiency virus (HIV) infected TB patients. OBJECTIVE: To confirm ART coverage and identify factors associated with HIV clinic enrollment and ART initiation among TB patients. DESIGN: Retrospective chart abstraction of adult TB patients newly diagnosed with HIV and eligible for ART at 58 Nyanza Province TB clinics between October 2006 and April 2008. TB data were linked to HIV clinic data at 50 facilities that provided ART. Associations with HIV clinic enrollment and ART were evaluated. RESULTS: Among 1137 ART-eligible TB patient records sampled, 32% documented HIV clinic enrollment and 29% ART. Date fields were largely incomplete; 11% of the patient records included HIV testing dates and ≤1% had dates for cotrimoxazole prophylaxis, HIV clinic enrollment and ART initiation. Adding HIV clinic data increased HIV clinic enrollment and ART documentation to respectively 62% and 44%. Among TB patients in HIV care, female sex, older age group and baseline CD4 documentation were associated with ART initiation. CONCLUSION: Linking data increased documentation of HIV clinic enrollment and ART uptake. Continued efforts are required to improve the documentation of HIV service delivery, especially in TB clinics. Interventions to increase ART uptake are needed for younger patients and men.
ABSTRACT
For some diseases, the transmission of infection can cause spatial clustering of disease cases. This clustering has an impact on how one estimates the rate of the spread of the disease and on the design of control strategies. It is, however, difficult to assess such clustering, (local effects on transmission), using traditional statistical methods. A stochastic Markov-chain model that takes into account possible local or more dispersed global effects on the risk of contracting disease is introduced in the context of the transmission dynamics of tuberculosis. The model is used to analyse TB notifications collected in the Asembo and Gem Divisions of Nyanza Province in western Kenya by the Kenya Ministry of Health/National Leprosy and Tuberculosis Program and the Centers for Disease Control and Prevention. The model shows evidence of a pronounced local effect that is significantly greater than the global effect. We discuss a number of variations of the model which identify how this local effect depends on factors such as age and gender. Zoning/clustering of villages is used to identify the influence that zone size has on the model's ability to distinguish local and global effects. An important possible use of the model is in the design of a community randomised trial where geographical clusters of people are divided into two groups and the effectiveness of an intervention policy is assessed by applying it to one group but not the other. Here the model can be used to take the effect of case clustering into consideration in calculating the minimum difference in an outcome variable (e.g. disease prevalence) that can be detected with statistical significance. It thereby gauges the potential effectiveness of such a trial. Such a possible application is illustrated with the given time/spatial TB data set.
