Where Are the Newly Diagnosed HIV Positives in Kenya? Time to Consider Geo-Spatially Guided Targeting at a Finer Scale to Reach the “First 90”

Background: The UNAIDS 90-90-90 Fast-Track targets provide a framework for assessing coverage of HIV testing services (HTS) and awareness of HIV status – the “first 90.” In Kenya, the bulk of HIV testing targets are aligned to the five highest HIV-burden counties. However, we do not know if most of the new HIV diagnoses are in these five highest-burden counties or elsewhere. Methods: We analyzed facility-level HTS data in Kenya from 1 October 2015 to 30 September 2016 to assess the spatial distribution of newly diagnosed HIV-positives. We used the Moran's Index (Moran's I) to assess global and local spatial auto-correlation of newly diagnosed HIV-positive tests and Kulldorff spatial scan statistics to detect hotspots of newly diagnosed HIV-positive tests. For aggregated data, we used Kruskal-Wallis equality-of-populations non-parametric rank test to compare absolute numbers across classes. Results: Out of 4,021 HTS sites, 3,969 (98.7%) had geocodes available. Most facilities (3,034, 76.4%), were not spatially autocorrelated for the number of newly diagnosed HIV-positives. For the rest, clustering occurred as follows; 438 (11.0%) were HH, 66 (1.7%) HL, 275 (6.9%) LH, and 156 (3.9%) LL. Of the HH sites, 301 (68.7%) were in high HIV-burden counties. Over half of 123 clusters with a significantly high number of newly diagnosed HIV-infected persons, 73(59.3%) were not in the five highest HIV-burden counties. Clusters with a high number of newly diagnosed persons had twice the number of positives per 1,000,000 tests than clusters with lower numbers (29,856 vs. 14,172). Conclusions: Although high HIV-burden counties contain clusters of sites with a high number of newly diagnosed HIV-infected persons, we detected many such clusters in low-burden counties as well. To expand HTS where most needed and reach the “first 90” targets, geospatial analyses and mapping make it easier to identify and describe localized epidemic patterns in a spatially dispersed epidemic like Kenya's, and consequently, reorient and prioritize HTS strategies.publishedVersio

Evaluation of Kenya’s readiness to transition from sentinel surveillance to routine HIV testing for antenatal clinic-based HIV surveillance

BACKGROUND: Sentinel surveillance for HIV among women attending antenatal clinics using unlinked anonymous testing is a cornerstone of HIV surveillance in sub-Saharan Africa. Increased use of routine antenatal HIV testing allows consideration of using these programmatic data rather than sentinel surveillance data for HIV surveillance. METHODS: To gauge Kenya’s readiness to discontinue sentinel surveillance, we evaluated whether recommended World Health Organization standards were fulfilled by conducting data and administrative reviews of antenatal clinics that offered both routine testing and sentinel surveillance in 2010. RESULTS: The proportion of tests that were HIV-positive among women aged 15–49 years was 6.2 % (95 % confidence interval [CI] 4.6–7.7 %] in sentinel surveillance and 6.5 % (95 % CI 5.1–8.0 %) in routine testing. The agreement of HIV test results between sentinel surveillance and routine testing was 98.0 %, but 24.1 % of specimens that tested positive in sentinel surveillance were recorded as negative in routine testing. Data completeness was moderate, with HIV test results recorded for 87.8 % of women who received routine testing. CONCLUSIONS: Additional preparation is required before routine antenatal HIV testing data can supplant sentinel surveillance in Kenya. As the quality of program data has markedly improved since 2010 a repeat evaluation of the use of routine antenatal HIV testing data in lieu of ANC sentinel surveillance is recommended

Automating indicator data reporting from health facility EMR to a national aggregate data system in Kenya: An Interoperability field-test using OpenMRS and DHIS2

Introduction:Developing countries are increasingly strengthening national health information systems (HIS) for evidence-based decision-making. However, the inability to report indicator data automatically from electronic medical record systems (EMR) hinders this process. Data are often printed and manually re-entered into aggregate reporting systems. This affects data completeness, accuracy, reporting timeliness, and burdens staff who support routine indicator reporting from patient-level data. Method: After conducting a feasibility test to exchange indicator data from Open Medical Records System (OpenMRS) to District Health Information System version 2 (DHIS2), we conducted a field test at a health facility in Kenya. We configured a field-test DHIS2 instance, similar to the Kenya Ministry of Health (MOH) DHIS2, to receive HIV care and treatment indicator data and the KenyaEMR, a customized version of OpenMRS, to generate and transmit the data from a health facility. After training facility staff how to send data using DHIS2 reporting module, we compared completeness, accuracy and timeliness of automated indicator reporting with facility monthly reports manually entered into MOH DHIS2. Results: All 45 data values in the automated reporting process were 100% complete and accurate while in manual entry process, data completeness ranged from 66.7% to 100% and accuracy ranged from 33.3% to 95.6% for seven months (July 2013-January 2014). Manual tally and entry process required at least one person to perform each of the five reporting activities, generating data from EMR and manual entry required at least one person to perform each of the three reporting activities, while automated reporting process had one activity performed by one person. Manual tally and entry observed in October 2013 took 375 minutes. Average time to generate data and manually enter into DHIS2 was over half an hour (M=32.35 mins, SD=0.29) compared to less than a minute for automated submission (M=0.19 mins, SD=0.15). Discussion and Conclusion: The results indicate that indicator data sent electronically from OpenMRS-based EMR at a health facility to DHIS2 improves data completeness, eliminates transcription errors and delays in reporting, and reduces the reporting burden on human resources. This increases availability of quality indicator data using available resources to facilitate monitoring service delivery and measuring progress towards set goals

Automating indicator data reporting from health facility EMR to a national aggregate data system in Kenya: An Interoperability field-test using OpenMRS and DHIS2

Introduction: Developing countries are increasingly strengthening national health information systems (HIS) for evidence-based decision-making. However, the inability to report indicator data automatically from electronic medical record systems (EMR) hinders this process. Data are often printed and manually re-entered into aggregate reporting systems. This affects data completeness, accuracy, reporting timeliness, and burdens staff who support routine indicator reporting from patient-level data.  Method: After conducting a feasibility test to exchange indicator data from Open Medical Records System (OpenMRS) to District Health Information System version 2 (DHIS2), we conducted a field test at a health facility in Kenya. We configured a field-test DHIS2 instance, similar to the Kenya Ministry of Health (MOH) DHIS2, to receive HIV care and treatment indicator data and the KenyaEMR, a customized version of OpenMRS, to generate and transmit the data from a health facility. After training facility staff how to send data using the module, we compared completeness, accuracy and timeliness of automated indicator reporting with facility monthly reports manually entered into MOH DHIS2.Results: All 45 data values in the automated reporting process were 100% complete and accurate while in manual entry process, data completeness ranged from 66.7% to 100% and accuracy ranged from 33.3% to 95.5% for seven months (July 2013-January 2014). Manual tally and entry process required at least one person to perform each of the five reporting activities, generating data from EMR and manual entry required at least one person to perform each of the three reporting activities, while automated reporting process had one activity performed by one person. Manual tally and entry observed in October 2013 took 375 minutes. Average time to generate data and manually enter into DHIS2 was over half an hour (M=32.35 mins, SD=0.29) compared to less than a minute for automated submission (M=0.19 mins, SD=0.15).Discussion and Conclusion: The results indicate that indicator data sent electronically from OpenMRS-based EMR at a health facility to DHIS2 improves data completeness, eliminates transcription errors and delays in reporting, and reduces the reporting burden on human resources. This increases availability of quality indicator data using available resources to facilitate monitoring service delivery and measuring progress towards set goals

A comparison of the general eligible and randomly sampled population of HIV infected individuals aged >15 years at the start of combination antiretroviral therapy using data from the national data warehouse (April 01^st 2018 to March 31^st 2021, N = 352,322).

A comparison of the general eligible and randomly sampled population of HIV infected individuals aged >15 years at the start of combination antiretroviral therapy using data from the national data warehouse (April 01st 2018 to March 31st 2021, N = 352,322).</p

Effect of the SARS-CoV-2 pandemic, defined as the period after the first documented case when compared to the period prior to the pandemic, on virologic non-suppression amongst HIV infected individuals >15 years starting combination antiretroviral therapy using data from the national data warehouse sampling framework in Kenya (April 01^st 2018 to March 31^st 2021, N = 7,406)^b'*'.

Effect of the SARS-CoV-2 pandemic, defined as the period after the first documented case when compared to the period prior to the pandemic, on virologic non-suppression amongst HIV infected individuals >15 years starting combination antiretroviral therapy using data from the national data warehouse sampling framework in Kenya (April 01st 2018 to March 31st 2021, N = 7,406)b'*'.</p

Overall effect of the COVID-19 pandemic, defined as period after the first documented case when compared to the period prior to the pandemic, on initial virologic non-suppression (VnS) amongst HIV infected individuals aged >15 years using data from the national data warehouse sampling framework in Kenya (April 2018 to March 2021, N = 7,046).

Overall effect of the COVID-19 pandemic, defined as period after the first documented case when compared to the period prior to the pandemic, on initial virologic non-suppression (VnS) amongst HIV infected individuals aged >15 years using data from the national data warehouse sampling framework in Kenya (April 2018 to March 2021, N = 7,046).</p

Overall effect of the COVID-19 pandemic, defined as period after the first documented case when compared to the period prior to the pandemic, on time from a HIV diagnosis to combination antiretroviral therapy start (Same day ART initiation) amongst HIV infected individuals aged >15 years using data from the national data warehouse sampling framework in Kenya (April 2018 to March 2021, N = 7,046).

Overall effect of the COVID-19 pandemic, defined as period after the first documented case when compared to the period prior to the pandemic, on time from a HIV diagnosis to combination antiretroviral therapy start (Same day ART initiation) amongst HIV infected individuals aged >15 years using data from the national data warehouse sampling framework in Kenya (April 2018 to March 2021, N = 7,046).</p

Overall effect of the COVID-19 pandemic, defined as period after the first documented case when compared to the period prior to the pandemic, on attrition from combination antiretroviral therapy start amongst HIV infected individuals aged >15 years using data from the national data warehouse sampling framework in Kenya (April 2018 to March 2021, N = 7,046).

Overall effect of the COVID-19 pandemic, defined as period after the first documented case when compared to the period prior to the pandemic, on attrition from combination antiretroviral therapy start amongst HIV infected individuals aged >15 years using data from the national data warehouse sampling framework in Kenya (April 2018 to March 2021, N = 7,046).</p