Human immunodeficiency virus (HIV) case-based surveillance (CBS) systematically and continuously collects available demographic and health event data (sentinel events*) about persons with HIV infection from diagnosis and, if available, throughout routine clinical care until death, to characterize HIV epidemics and guide program improvement (1,2). Surveillance signals such as high viral load, mortality, or recent HIV infection can be used for rapid public health action. To date, few standardized assessments have been conducted to describe HIV CBS systems globally (3,4). For this assessment, a survey was disseminated during May-July 2019 to all U.S. President's Emergency Plan for AIDS Relief (PEPFAR)-supported countries with CDC presence(dagger) (46) to describe CBS implementation and identify facilitators and barriers. Among the 39 (85%) countries that responded,( section sign) 20 (51%) have implemented CBS, 15 (38%) were planning implementation, and four (10%)( paragraph sign) had no plans for implementation. All countries with CBS reported capturing information at the point of diagnosis, and 85% captured sentinel event data. The most common characteristic (75% of implementation countries) that facilitated implementation was using a health information system for CBS. Barriers to CBS implementation included lack of country policies/guidance on mandated reporting of HIV and on CBS, lack of unique identifiers to match and deduplicate patient-level data, and lack of data security standards. Although most surveyed countries reported implementing or planning for implementation of CBS, these barriers need to be addressed to implement effective HIV CBS that can inform the national response to the HIV epidemic.

OBJECTIVES: Achieving the Sustainable Development Goals will require data-driven public health action. There are limited publications on national health information systems that continuously generate health data. Given the need to develop these systems, we summarised their current status in low-income and middle-income countries. SETTING: The survey team jointly developed a questionnaire covering policy, planning, legislation and organisation of case reporting, patient monitoring and civil registration and vital statistics (CRVS) systems. From January until May 2017, we administered the questionnaire to key informants in 51 Centers for Disease Control country offices. Countries were aggregated for descriptive analyses in Microsoft Excel. RESULTS: Key informants in 15 countries responded to the questionnaire. Several key informants did not answer all questions, leading to different denominators across questions. The Ministry of Health coordinated case reporting, patient monitoring and CRVS systems in 93% (14/15), 93% (13/14) and 53% (8/15) of responding countries, respectively. Domestic financing supported case reporting, patient monitoring and CRVS systems in 86% (12/14), 75% (9/12) and 92% (11/12) of responding countries, respectively. The most common uses for system-generated data were to guide programme response in 100% (15/15) of countries for case reporting, to calculate service coverage in 92% (12/13) of countries for patient monitoring and to estimate the national burden of disease in 83% (10/12) of countries for CRVS. Systems with an electronic component were being used for case reporting, patient monitoring, birth registration and death registration in 87% (13/15), 92% (11/12), 77% (10/13) and 64% (7/11) of responding countries, respectively. CONCLUSIONS: Most responding countries have a solid foundation for policy, planning, legislation and organisation of health information systems. Further evaluation is needed to assess the quality of data generated from systems. Periodic evaluations may be useful in monitoring progress in strengthening and harmonising these systems over time.

BACKGROUND: A universal health care identifier (UHID) facilitates the development of longitudinal medical records in health care settings where follow up and tracking of persons across health care sectors are needed. HIV case-based surveillance (CBS) entails longitudinal follow up of HIV cases from diagnosis, linkage to care and treatment, and is recommended for second generation HIV surveillance. In the absence of a UHID, records matching, linking, and deduplication may be done using score-based persons matching algorithms. We present a stepwise process of score-based persons matching algorithms based on demographic data to improve HIV CBS and other longitudinal data systems. OBJECTIVE: The aim of this study is to compare deterministic and score-based persons matching algorithms in records linkage and matching using demographic data in settings without a UHID. METHODS: We used HIV CBS pilot data from 124 facilities in 2 high HIV-burden counties (Siaya and Kisumu) in western Kenya. For efficient processing, data were grouped into 3 scenarios within (1) HIV testing services (HTS), (2) HTS-care, and (3) within care. In deterministic matching, we directly compared identifiers and pseudo-identifiers from medical records to determine matches. We used R stringdist package for Jaro, Jaro-Winkler score-based matching and Levenshtein, and Damerau-Levenshtein string edit distance calculation methods. For the Jaro-Winkler method, we used a penalty (р)=0.1 and applied 4 weights (ω) to Levenshtein and Damerau-Levenshtein: deletion ω=0.8, insertion ω=0.8, substitutions ω=1, and transposition ω=0.5. RESULTS: We abstracted 12,157 cases of which 4073/12,157 (33.5%) were from HTS, 1091/12,157 (9.0%) from HTS-care, and 6993/12,157 (57.5%) within care. Using the deterministic process 435/12,157 (3.6%) duplicate records were identified, yielding 96.4% (11,722/12,157) unique cases. Overall, of the score-based methods, Jaro-Winkler yielded the most duplicate records (686/12,157, 5.6%) while Jaro yielded the least duplicates (546/12,157, 4.5%), and Levenshtein and Damerau-Levenshtein yielded 4.6% (563/12,157) duplicates. Specifically, duplicate records yielded by method were: (1) Jaro 5.7% (234/4073) within HTS, 0.4% (4/1091) in HTS-care, and 4.4% (308/6993) within care, (2) Jaro-Winkler 7.4% (302/4073) within HTS, 0.5% (6/1091) in HTS-care, and 5.4% (378/6993) within care, (3) Levenshtein 6.4% (262/4073) within HTS, 0.4% (4/1091) in HTS-care, and 4.2% (297/6993) within care, and (4) Damerau-Levenshtein 6.4% (262/4073) within HTS, 0.4% (4/1091) in HTS-care, and 4.2% (297/6993) within care. CONCLUSIONS: Without deduplication, over reporting occurs across the care and treatment cascade. Jaro-Winkler score-based matching performed the best in identifying matches. A pragmatic estimate of duplicates in health care settings can provide a corrective factor for modeled estimates, for targeting and program planning. We propose that even without a UHID, standard national deduplication and persons-matching algorithm that utilizes demographic data would improve accuracy in monitoring HIV care clinical cascades.

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.