A national household survey on HIV prevalence and clinical cascade among children aged ≤15 years in Kenya (2018).

We analyzed data from the 2018 Kenya Population-Based HIV Impact Assessment (KENPHIA), a cross-sectional, nationally representative survey, to estimate the burden and prevalence of pediatric HIV infection, identify associated factors, and describe the clinical cascade among children aged < 15 years in Kenya. Interviewers collected information from caregivers or guardians on child's demographics, HIV testing, and treatment history. Blood specimens were collected for HIV serology and if HIV-positive, the samples were tested for viral load and antiretrovirals (ARV). For participants <18 months TNA PCR is performed. We computed weighted proportions with 95% confidence intervals (CI), accounting for the complex survey design. We used bivariable and multivariable logistic regression to assess factors associated with HIV prevalence. Separate survey weights were developed for interview responses and for biomarker testing to account for the survey design and non-response. HIV burden was estimated by multiplying HIV prevalence by the national population projection by age for 2018. Of 9072 survey participants (< 15 years), 87% (7865) had blood drawn with valid HIV test results. KENPHIA identified 57 HIV-positive children, translating to an HIV prevalence of 0.7%, (95% CI: 0.4%-1.0%) and an estimated 138,900 (95% CI: 84,000-193,800) of HIV among children in Kenya. Specifically, children who were orphaned had about 2 times higher odds of HIV-infection compared to those not orphaned, adjusted Odds Ratio (aOR) 2.2 (95% CI:1.0-4.8). Additionally, children whose caregivers had no knowledge of their HIV status also had 2 times higher odds of HIV-infection compared to whose caregivers had knowledge of their HIV status, aOR 2.4 (95% CI: 1.1-5.4)". From the unconditional analysis; population level estimates, 78.9% of HIV-positive children had known HIV status (95% CI: 67.1%-90.2%), 73.6% (95% CI: 60.9%-86.2%) were receiving ART, and 49% (95% CI: 32.1%-66.7%) were virally suppressed. However, in the clinical cascade for HIV infected children, 92% (95% CI: 84.4%-100%) were receiving ART, and of these, 67.1% (95% CI: 45.1%-89.2%) were virally suppressed. The KENPHIA survey confirms a substantial HIV burden among children in Kenya, especially among orphans.

Methods for conducting trends analysis: roadmap for comparing outcomes from three national HIV Population-based household surveys in Kenya (2007, 2012, and 2018).

BACKGROUND: For assessing the HIV epidemic in Kenya, a series of independent HIV indicator household-based surveys of similar design can be used to investigate the trends in key indicators relevant to HIV prevention and control and to describe geographic and sociodemographic disparities, assess the impact of interventions, and develop strategies. We developed methods and tools to facilitate a robust analysis of trends across three national household-based surveys conducted in Kenya in 2007, 2012, and 2018. METHODS: We used data from the 2007 and 2012 Kenya AIDS Indicator surveys (KAIS 2007 and KAIS 2012) and the 2018 Kenya Population-based HIV Impact Assessment (KENPHIA 2018). To assess the design and other variables of interest from each study, variables were recoded to ensure that they had equivalent meanings across the three surveys. After assessing weighting procedures for comparability, we used the KAIS 2012 nonresponse weighting procedure to revise normalized KENPHIA weights. Analyses were restricted to geographic areas covered by all three surveys. The revised analysis files were then merged into a single file for pooled analysis. We assessed distributions of age, sex, household wealth, and urban/rural status to identify unexpected changes between surveys. To demonstrate how a trend analysis can be carried out, we used continuous, binary, and time-to-event variables as examples. Specifically, temporal trends in age at first sex and having received an HIV test in the last 12 months were used to demonstrate the proposed analytical approach. These were assessed with respondent-specific variables (age, sex, level of education, and marital status) and household variables (place of residence and wealth index). All analyses were conducted in SAS 9.4, but analysis files were created in Stata and R format to support additional analyses. RESULTS: This study demonstrates trends in selected indicators to illustrate the approach that can be used in similar settings. The incidence of early sexual debut decreased from 11.63 (95% CI: 10.95-12.34) per 1,000 person-years at risk in 2007 to 10.45 (95% CI: 9.75-11.2) per 1,000 person-years at risk in 2012 and to 9.58 (95% CI: 9.08-10.1) per 1,000 person-years at risk in 2018. HIV-testing rates increased from 12.6% (95% CI: 11.6%-13.6%) in 2007 to 56.1% (95% CI: 54.6%-57.6%) in 2012 but decreased slightly to 55.6% [95% CI: 54.6%-56.6%) in 2018. The decrease in incidence of early sexual debut could be convincingly demonstrated between 2007 and 2012 but not between 2012 and 2018. Similarly, there was virtually no difference between HIV Testing rates in 2012 and 2018. CONCLUSIONS: Our approach can be used to support trend comparisons for variables in HIV surveys in low-income settings. Independent national household surveys can be assessed for comparability, adjusted as appropriate, and used to estimate trends in key indicators. Analyzing trends over time can not only provide insights into Kenya's progress toward HIV epidemic control but also identify gaps.

A clinical decision support system is associated with reduced loss to follow-up among patients receiving HIV treatment in Kenya: a cluster randomized trial.

BACKGROUND: Loss to follow-up (LFTU) among HIV patients remains a major obstacle to achieving treatment goals with the risk of failure to achieve viral suppression and thereby increased HIV transmission. Although use of clinical decision support systems (CDSS) has been shown to improve adherence to HIV clinical guidance, to our knowledge, this is among the first studies conducted to show its effect on LTFU in low-resource settings. METHODS: We analyzed data from a cluster randomized controlled trial in adults and children (aged ≥ 18 months) who were receiving antiretroviral therapy at 20 HIV clinics in western Kenya between Sept 1, 2012 and Jan 31, 2014. Participating clinics were randomly assigned, via block randomization. Clinics in the control arm had electronic health records (EHR) only while the intervention arm had an EHR with CDSS. The study objectives were to assess the effects of a CDSS, implemented as alerts on an EHR system, on: (1) the proportion of patients that were LTFU, (2) LTFU patients traced and successfully linked back to treatment, and (3) time from enrollment on the study to documentation of LTFU. RESULTS: Among 5901 eligible patients receiving ART, 40.6% (n = 2396) were LTFU during the study period. CDSS was associated with lower LTFU among the patients (Adjusted Odds Ratio-aOR 0.70 (95% CI 0.65-0.77)). The proportions of patients linked back to treatment were 25.8% (95% CI 21.5-25.0) and 30.6% (95% CI 27.9-33.4)) in EHR only and EHR with CDSS sites respectively. CDSS was marginally associated with reduced time from enrollment on the study to first documentation of LTFU (adjusted Hazard Ratio-aHR 0.85 (95% CI 0.78-0.92)). CONCLUSION: A CDSS can potentially improve quality of care through reduction and early detection of defaulting and LTFU among HIV patients and their re-engagement in care in a resource-limited country. Future research is needed on how CDSS can best be combined with other interventions to reduce LTFU. Trial registration NCT01634802. Registered at www.clinicaltrials.gov on 12-Jul-2012. Registered prospectively.

%svy_freqs: A Generic SAS Macro for Creating Publication-Quality Three-Way Cross-Tabulations.

Cross-tabulations are a simple but important tool for understanding the distribution of socio-demographic characteristics among participants in epidemiological studies. We developed a generic SAS macro, %svy_freqs, to create publication-quality tables from cross-tabulations between a factor and a by-group variable given a third variable using survey or non-survey data. The macro also performs two-way cross-tabulations and provides extra features not available in existing procedures such as ability to incorporate parameters for survey design and replication-based variance estimation methods, performing validation checks for input parameters, transparently formatting variable values from character into numeric and allowing for generalizability. We demonstrate the macro using the 2013-2014 National Health and Nutrition Examination Survey (NHANES), a complex survey designed to assess the health and nutritional status of adults and children in the United States.

%svy_logistic_regression: A generic SAS macro for simple and multiple logistic regression and creating quality publication-ready tables using survey or non-survey data.

INTRODUCTION: Reproducible research is increasingly gaining interest in the research community. Automating the production of research manuscript tables from statistical software can help increase the reproducibility of findings. Logistic regression is used in studying disease prevalence and associated factors in epidemiological studies and can be easily performed using widely available software including SAS, SUDAAN, Stata or R. However, output from these software must be processed further to make it readily presentable. There exists a number of procedures developed to organize regression output, though many of them suffer limitations of flexibility, complexity, lack of validation checks for input parameters, as well as inability to incorporate survey design. METHODS: We developed a SAS macro, %svy_logistic_regression, for fitting simple and multiple logistic regression models. The macro also creates quality publication-ready tables using survey or non-survey data which aims to increase transparency of data analyses. It further significantly reduces turn-around time for conducting analysis and preparing output tables while also addressing the limitations of existing procedures. In addition, the macro allows for user-specific actions to handle missing data as well as use of replication-based variance estimation methods. RESULTS: We demonstrate the use of the macro in the analysis of the 2013-2014 National Health and Nutrition Examination Survey (NHANES), a complex survey designed to assess the health and nutritional status of adults and children in the United States. The output presented here is directly from the macro and is consistent with how regression results are often presented in the epidemiological and biomedical literature, with unadjusted and adjusted model results presented side by side. CONCLUSIONS: The SAS code presented in this macro is comprehensive, easy to follow, manipulate and to extend to other areas of interest. It can also be incorporated quickly by the statistician for immediate use. It is an especially valuable tool for generating quality, easy to review tables which can be incorporated directly in a publication.

HIV incidence in western Kenya during scale-up of antiretroviral therapy and voluntary medical male circumcision: a population-based cohort analysis.

BACKGROUND: In Kenya, coverage of antiretroviral therapy (ART) among people with HIV infection has increased from 7% in 2006, to 57% in 2016; and, in western Kenya, coverage of voluntary medical male circumcision (VMMC) increased from 45% in 2008, to 72% in 2014. We investigated trends in HIV prevalence and incidence in a high burden area in western Kenya in 2011-16. METHODS: In 2011, 2012, and 2016, population-based surveys were done via a health and demographic surveillance system and home-based counselling and testing in Gem, Siaya County, Kenya, including 28 688, 17 021, and 16 772 individuals aged 15-64 years. Data on demographic variables, self-reported HIV status, and risk factors were collected. Rapid HIV testing was offered to survey participants. Participants were tracked between surveys by use of health and demographic surveillance system identification numbers. HIV prevalence was calculated as a proportion, and HIV incidence was expressed as number of new infections per 1000 person-years of follow-up. FINDINGS: HIV prevalence was stable in participants aged 15-64 years: 15% (4300/28 532) in 2011, 12% (2051/16 875) in 2012, and 15% (2312/15 626) in 2016. Crude prevalences in participants aged 15-34 years were 11% (1893/17 197) in 2011, 10% (1015/10 118) in 2012, and 9% (848/9125) in 2016; adjusted for age and sex these prevalences were 11%, 9%, and 8%. 12 606 (41%) of the 30 520 non-HIV-infected individuals enrolled were seen again in at least one more survey round, and were included in the analysis of HIV incidence. HIV incidence was 11·1 (95% CI 9·1-13·1) per 1000 person-years from 2011 to 2012, and 5·7 (4·6-6·9) per 1000 person-years from 2012 to 2016. INTERPRETATION: With increasing coverage of ART and VMMC, HIV incidence declined substantially in Siaya County between 2011 and 2016. VMMC, but not ART, was suggested to have a direct protective effect, presumably because ART tended to be given to individuals with advanced HIV infection. HIV incidence is still high and not close to the elimination target of one per 1000 person-years. The effect of further scale-up of ART and VMMC needs to be monitored. FUNDING: Data were collected under Cooperative Agreements with the US Centers for Disease Control and Prevention, with funding from the President's Emergency Fund for AIDS Relief.