Improving Early Infant Diagnosis Observations

Estimates of Timely HIV Testing and Mortality Among HIV-Exposed Infants

Karen Webb, MSc; Vivian Chitiyo, MPhil; Nyikadzino Mahachi, MD, MPH; Solomon Huruva Mukungunugwa, MD, MBChB, MPH; Angela Mushavi, MBChB, MMed; Simukai Zizhou, MBChB, MPH; Barbara Engelsmann, MD, MPH; Rashida Abbas Ferrand, MD, MSc, PhD; Melissa Neuman, ScD, MS; Wendy Hartogensis, PhD, MPH; Elvin Geng, MD, MPH


J Acquir Immune Defic Syndr. 2020;83(3):235-239. 

In This Article


In a representative sample of Mashonaland East Province of Zimbabwe, after tracing a sample of MB pairs identified as LTFU for EID, our corrected estimate of EID almost doubled (from 31.2% to 60.0%). These findings underscore the risk of equating LTFU of MB pairs in health information systems with disengagement from care where such systems do not facilitate electronic, longitudinal tracing of service uptake within and between health facilities.[19] MOHCC is currently expanding efforts to strengthen facility-based documentation, retention monitoring, and longitudinal outcome reporting through use of the Mother-Baby Pair Register to track longitudinal outcomes of MB Pairs, together with appointment diary systems.[20] As Zimbabwe transitions toward electronic health record systems, we indicate the value of sampling-based methods for improving accuracy of EID coverage estimates and informing current progress toward EMTCT validation.[21]

We found high mortality among HIV-exposed infants, with "my child died" being the most frequently cited reason for no EID testing. Passive facility-based monitoring may underestimate true mortality by up to 80%,[22] and under-reporting of infant mortality in high HIV burden settings is acknowledged to bias child mortality estimates downward.[23,24] Our findings reinforce the need to act early among HIV-positive mother–exposed infant pairs at high risk of defaulting or adverse clinical outcomes with enhanced PMTCT interventions.[25,26] Subsequent MOHCC guidelines recommending prioritization of viral load monitoring for pregnant and lactating mothers, birth testing for high-risk infants,[20,27] and case-based surveillance of infants testing HIV positive[20,28] are intended strengthen program evidence and action among high-risk MB pairs. Our findings emphasize that monitoring of facility-level implementation fidelity, data quality, and robust analysis of resulting data will be central to realizing the benefit of such efforts for improved PMTCT program strategies and impact.

Finally, we not only improved our understanding of "true EID" rates but also developed evidence on factors influencing timely infant HIV testing for informing quality improvement. At patient-level, the predominance of psychosocial reasons ("I didn't know") for failure to uptake EID among mothers of living infants emphasizes need to provide information about the importance of timely infant HIV testing during the initial engagement in ANC and continued emphasis at every subsequent visit. Reasons for silent transfer are consistent with studies of adult ART retention and highlight the role of structural[29] and clinic-based factors[30] for optimizing retention in care along the PMTCT cascade. Findings have guided PMTCT program planning and actions including strengthening of patient education and problem solving, appointment monitoring, active follow-up, and outcome documentation in Zimbabwe and other settings.[20,31–33]


Although oversampling enabled achievement of our targeted sample of at least 10% of MB pairs LTFU for EID (we traced 22%), our findings highlight need to strengthen routine documentation and other forms of observational data to ascertain uptake of services and mortality.[34] We acknowledge nonresponse due to incomplete tracing details may introduce some uncertainty in the resulting estimates. However, our findings of lower LTFU and higher mortality after tracing are concordant with larger, more robust LTFU studies.[35] In the absence of effective interfacility electronic patient-monitoring systems, we demonstrate the value of leveraging routine observational data in "real-life" program settings for improving accuracy of estimates, identifying bottlenecks and guiding programmatic actions at the local level.[34,36,37]