The KEMRI/Centers for Disease Control and Prevention (CDC) Health and Demographic Surveillance System (HDSS) is located in Rarieda, Siaya and Gem Districts (Siaya County), lying northeast of Lake Victoria in Nyanza Province, western Kenya. The KEMRI/CDC HDSS, with approximately 220 000 inhabitants, has been the foundation for a variety of studies, including evaluations of insecticide-treated bed nets, burden of diarrhoeal disease and tuberculosis, malaria parasitaemia and anaemia, treatment strategies and immunological correlates of malaria infection, and numerous HIV, tuberculosis, malaria and diarrhoeal disease treatment and vaccine efficacy and effectiveness trials for more than a decade. Current studies include operations research to measure the uptake and effectiveness of the programmatic implementation of integrated malaria control strategies, HIV services, newly introduced vaccines and clinical trials. The HDSS provides general demographic and health information (such as population age structure and density, fertility rates, birth and death rates, in- and out-migrations, patterns of health care access and utilization and the local economics of health care) as well as disease- or intervention-specific information. The HDSS also collects verbal autopsy information on all deaths. Studies take advantage of the sampling frame inherent in the HDSS, whether at individual, household/compound or neighbourhood level.
To achieve global targets for universal treatment set forth by the Joint United Nations Programme on human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) (UNAIDS), viral load monitoring for HIV-infected persons receiving antiretroviral therapy (ART) must become the standard of care in low- and middle-income countries (LMIC) (1). CDC and other U.S. government agencies, as part of the President's Emergency Plan for AIDS Relief, are supporting multiple countries in sub-Saharan Africa to change from the use of CD4 cell counts for monitoring of clinical response to ART to the use of viral load monitoring, which is the standard of care in developed countries. Viral load monitoring is the preferred method for immunologic monitoring because it enables earlier and more accurate detection of treatment failure before immunologic decline. This report highlights the initial successes and challenges of viral load monitoring in seven countries that have chosen to scale up viral load testing as a national monitoring strategy for patients on ART in response to World Health Organization (WHO) recommendations. Countries initiating viral load scale-up in 2014 observed increases in coverage after scale-up, and countries initiating in 2015 are anticipating similar trends. However, in six of the seven countries, viral load testing coverage in 2015 remained below target levels. Inefficient specimen transport, need for training, delays in procurement and distribution, and limited financial resources to support scale-up hindered progress. Country commitment and effective partnerships are essential to address the financial, operational, technical, and policy challenges of the rising demand for viral load monitoring.
BackgroundNyanza Province, Kenya, had the highest HIV prevalence in the country at 14.9% in 2007, more than twice the national HIV prevalence of 7.1%. Only 16% of HIV-infected adults in the country accurately knew their HIV status. Targeted strategies to reach and test individuals are urgently needed to curb the HIV epidemic. The family unit is one important portal.MethodsA family model of care was designed to build on the strengths of Kenyan families. Providers use a family information table (FIT) to guide index patients through the steps of identifying family members at HIV risk, address disclosure, facilitate family testing, and work to enrol HIV-positive members and to prevent new infections. Comprehensive family-centred clinical services are built around these steps. To assess the approach, a retrospective study of patients receiving HIV care between September 2007 and September 2009 at Lumumba Health Centre in Kisumu was conducted. A random sample of FITs was examined to assess family reach.ResultsThrough the family model of care, for each index patient, approximately 2.5 family members at risk were identified and 1.6 family members were tested. The approach was instrumental in reaching children; 61% of family members identified and tested were children. The approach also led to identifying and enrolling a high proportion of HIV- positive partners among those tested: 71% and 89%, respectively.ConclusionsThe family model of care is a feasible approach to broaden HIV case detection and service reach. The approach can be adapted for the local context and should continue to utilize index patient linkages, FIT adaption, and innovative methods to package services for families in a manner that builds on family support and enhances patient care and prevention efforts. Further efforts are needed to increase family member engagement.
ObjectiveTo assess the performance of symptom-based screening for tuberculosis (TB), alone and with chest radiography among people living with HIV (PLHIV), including pregnant women, in Western Kenya.DesignProspective cohort studyMethodsPLHIV from 15 randomly-selected HIV clinics were screened with three clinical algorithms [World Health Organization (WHO), Ministry of Health (MOH), and “Improving Diagnosis of TB in HIV-infected persons” (ID-TB/HIV) study], underwent chest radiography (unless pregnant), and provided two or more sputum specimens for smear microscopy, liquid culture, and Xpert MTB/RIF. Performance of clinical screening was compared to laboratory results, controlling for the complex design of the survey.ResultsOverall, 738 (85.6%) of 862 PLHIV enrolled were included in the analysis. Estimated TB prevalence was 11.2% (95% CI, 9.9–12.7). Sensitivity of the three screening algorithms was similar [WHO, 74.1% (95% CI, 64.1–82.2); MOH, 77.5% (95% CI, 68.6–84.5); and ID-TB/HIV, 72.5% (95% CI, 60.9–81.7)]. Sensitivity of the WHO algorithm was significantly lower among HIV-infected pregnant women [28.2% (95% CI, 14.9–46.7)] compared to non-pregnant women [78.3% (95% CI, 67.3–86.4)] and men [77.2% (95% CI, 68.3–84.2)]. Chest radiography increased WHO algorithm sensitivity and negative predictive value to 90.9% (95% CI, 86.4–93.9) and 96.1% (95% CI, 94.4–97.3), respectively, among asymptomatic men and non-pregnant women.ConclusionsClinical screening missed approximately 25% of laboratory-confirmed TB cases among all PLHIV and more than 70% among HIV-infected pregnant women. National HIV programs should evaluate the feasibility of laboratory-based screening for TB, such as a single Xpert MTB/RIF test for all PLHIV, especially pregnant women, at enrollment in HIV services.
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