Translating efficacious interventions to prevent mother-to-child HIV-1 transmission (PMTCT) into effective programs has been slow and uneven. In sub-Saharan Africa, despite high antenatal care (ANC) utilization,1 bottlenecks occur at each step of the PMTCT cascade, from HIV testing of pregnant women, to uptake of antiretrovirals (ARVs) or combination antiretroviral therapy (cART), to screening HIV-exposed infants (HEI) with HIV polymerase chain reaction testing, to adoption and maintenance of appropriate infant feeding.2–4 Facility-level barriers include human resources shortages,5 lack of service integration,3,5,6 lack of ongoing mentoring,7 and poor patient–provider interactions.5 Although vertical transmission rates of <1% have been achieved in high-income countries,8 actual rates in sub-Saharan Africa are estimated to be several times higher.9–11
The PMTCT cascade is uniquely complex. First, it encompasses more than 2 years and crosses multiple biological phases for women and infants. Second, women and infants must navigate multiple sectors, from ANC, to maternity, postpartum care, and finally integration into long-term HIV care. Each transition between services is an opportunity for patients to be lost to follow-up. Finally, completion of the sequential steps in the PMTCT cascade is conditional on completion of previous steps; therefore, even modest inefficiencies across individual services compound one another.
The advent of lifelong cART for all pregnant or breastfeeding HIV-positive women, termed Option B+, has theoretical logistical advantages, as clinicians prescribe a uniform, fixed dose combination regimen without previous CD4 testing.12 The 3 countries in which this randomized trial was conducted have each implemented Option B (in which cART is given for the duration of pregnancy and breastfeeding) or B+. Côte d'Ivoire adopted Option B in November 2012 and gradually rolled it out during 2013.13 Mozambique implemented Option B+ in June 2013.7 Kenya introduced Option B+ at research sites in 2011 and adopted it as national policy in 2014.14
However, Option B/B+ also presents new obstacles.15 A sizable proportion of women—who may feel healthy—refuse or default from cART4,16,17 or receive no ARVs at all,18 putting them at risk of increased MTCT. Data on long-term retention and adherence are as yet lacking. In addition, Option B/B+ has not addressed—and may even worsen, if women are more likely to default from care—the urgent need for PMTCT programs to improve HEI screening as part of a “child-centric” approach.19 Increasing HEI screening promotes long-term engagement in care and prompt recognition and treatment of infant HIV infection,19 but also could promote family planning and other services for mothers, which are used by only a fraction of women at 2 months postpartum.20
Systems engineering is a multidisciplinary approach to optimize complex processes.21 Value stream mapping and quality improvement (QI) are systems engineering tools, which seek to maximize value while minimizing waste, and have been successfully applied to health care.22 QI has been applied to increase maternal HIV status documentation in the United States,23 cotrimoxazole prophylaxis among adult patients with HIV in Uganda, Mozambique, Namibia, and Haiti,24 uptake of early infant diagnosis of HIV in rural Mozambique,25 and decrease turn-around time for HEI screening test results in Tanzania.26 QI has proven feasible to apply to PMTCT programs in South Africa27–29 and Zambia,30 although previous PMTCT-focused QI interventions in sub-Saharan Africa have lacked a comparison group27,28 or were nonrandomized.30 None have evaluated QI to improve PMTCT services in the era of Option B+, although one other randomized QI intervention is ongoing in Nigeria.31
We sought to quantify the effectiveness of a package of systems engineering tools, including QI, to improve PMTCT services in sub-Saharan Africa, as measured by its impact on 3 key steps in the PMTCT cascade: HIV testing coverage during first ANC visit, ARV coverage among HIV-positive pregnant women, and screening infants exposed to HIV.
Details of the study design of this pragmatic, 2-arm, longitudinal cluster-randomized trial have been published elsewhere, including eligibility criteria, locations, HIV prevalence, and other characteristics of study facilities.32 Briefly, we randomized 36 health facilities 1:1 to either the study intervention or usual care, stratified by country and volume of first antenatal care visits (ANC1), to test whether our package of systems engineering tools could improve PMTCT service delivery at the facility (ie, cluster) level. Option B/B+ rollout at study facilities began in March 2013 and continued throughout the study. For a list of roll-out dates at each facility, see Table, Supplemental Digital Content 1, http://links.lww.com/QAI/A815.
The Systems Analysis and Improvement Approach (SAIA) study intervention was a 5-step, iterative package of systems analysis and improvement tools developed using multiple systems engineering techniques, including continuous quality improvement. Full details of the study intervention, protocols, and tools are available at healthallianceinternational.org. The first 2 steps helped facility staff understand barriers to PMTCT service delivery in their health facility using decision support tools. Staff used the Excel-based PMTCT cascade analysis tool (PCAT)33 to quantify drop-offs along the cascade and identify the number of additional mother–infant pairs who would complete the cascade if drop-off at each individual step were eliminated, holding all other steps constant (step 1). Then, staff mapped patient flow through PMTCT services across sectors at their facility and identified which specific step to adapt, within one service element of the cascade (step 2). The final 3 steps used continuous quality improvement methodology. Staff developed and implemented a “microintervention” to mitigate bottlenecks in the cascade (step 3). Next, they updated the PCAT and assessed the impact of the microintervention on drop-offs (step 4). Finally, staff would either modify the initial microintervention or implement a new one if it had been successful. Steps 1−4 were then repeated in an iterative cycle (step 5).
After a 4-day workshop at each intervention facility to orient staff to the intervention's purpose and methods, including how to select metrics to assess the impact of microinterventions, follow-up visits were conducted weekly for 4 weeks, biweekly for the next 8 weeks, and thereafter monthly or as determined by facility and study staff. Implementation barriers were addressed by interviewing both leadership and frontline health workers at follow-up visits. Impacts of microinterventions were evaluated monthly through the PCAT, although staff often collected additional data (eg, total time for ANC1 visit) to assess impacts in real time. The implementation period was initially 6 months but was extended to 9 months (February 2014–November 2014) because of recognition that more time was essential for facility staff to become proficient at systems improvement and see the impact of multiple, incremental improvements in service delivery. Staff did not implement the intervention during December 2014, as services are reduced or suspended during December because of staff absences for the holidays.
The primary outcomes were: the proportions of (1) pregnant women tested for HIV during ANC1, (2) HIV-positive pregnant women receiving ARVs, and (3) HEI screened for HIV with polymerase chain reaction test by 6–8 weeks of age. These outcomes reflect key steps across the PMTCT cascade (see Figure, Supplemental Digital Content 2, http://links.lww.com/QAI/A815) and have been previously validated as measures of performance.34 All primary outcomes applied to the cluster level. We estimated the denominator for the last outcome, HEI screening, with a weighted average using the distribution of the gestational ages at ANC1 visits at each facility and the number of ANC1 visits in previous months. For details of primary outcome definitions, see Document, Supplemental Digital Content 3, http://links.lww.com/QAI/A815. Outcomes were calculated using monthly data from facility registries; outcomes greater than 100% were capped at 100%. Data collection began in January 2014. Data from 2013 were collected retrospectively; all other data were collected prospectively. Data were double-collected on site by 2 trained abstractors. Any differences between abstractors were re-reviewed on site until they reached consensus.
For each outcome, we tested whether the change between the baseline period's mean (January 2013–January 2014) and postintervention period's mean (January 2015–March 2015) differed between study arms using a 2-sided t-test. Specifically, for each facility, we subtracted the baseline mean from the postintervention mean. Then, the resulting 18 values in control facilities were compared with the 18 values in intervention facilities using a t test. We also conducted one prespecified sub-group analysis: stratification by country. No other subgroup analyses were conducted. We did not adjust for covariates, as the intervention was randomized. We did not need to account for clustering, as the unit of randomization and the unit of analysis were each at the cluster (ie, facility) level.35 All analyses were intent-to-treat.
The ethics review boards of the Ministries of Health in Mozambique and Côte d'Ivoire, and of Kenyatta National Hospital in Nairobi, Kenya each approved the study. The study was reviewed by the Institutional Review Board at the University of Washington and qualified for federal exempt status category 2. All procedures were conducted in accordance with the Helsinki Declaration of 1975, as revised in 2000. All analyses were completed using Stata version 13.1 (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP.).
Thirty-six facilities were randomized. One high-volume facility in Kenya (facility AA) randomized to the intervention declined to participate despite repeated attempts to engage staff. Primary outcome data from this site were included in the analysis. Two facilities in Kenya (one low-volume intervention, one low-volume control) were excluded because of an ongoing World Health Organization–sponsored QI intervention, which overlapped sufficiently with our own to warrant the facilities' replacement. In Côte d'Ivoire, 2 facilities (one high-volume intervention, one low-volume control) were excluded because of the absence of on-site delivery or postpartum care, which precluded gathering primary outcome data. These 4 facilities were replaced with similar volume sites that were randomly selected from the remaining eligible facilities in each country. There were no significant differences between study arms in experience offering PMTCT, volume, staffing levels, or infrastructure (Table 1).
Over the 9-month intervention period, the 17 facilities that implemented the SAIA intervention tested 158 microinterventions (range: 4–31). On average, each facility tested 9.3 microinterventions or approximately 1 per month. Microinterventions focused on 1 of 5 categories: reorganizing services, strengthening existing norms, educating patients, improving health worker communication (within the facility or with outside laboratories), and improving routine data quality. For the distribution of microintervention types, see Table, Supplemental Digital Content 4, http://links.lww.com/QAI/A815. Most microinterventions were modest alterations to reorganize existing services (36%), such as sending new ANC patients to the laboratory for blood draw before they queued to see the nurse, or to strengthen existing norms (30%), such as conducting trainings for health workers on Option B+. Among the 3 primary outcomes, a similar proportion of microinterventions targeted ARV coverage (27%), HEI screening (26%), and other aspects of PMTCT services not captured by the primary outcomes [(28%), eg, HIV testing during subsequent ANC visits or at the time of delivery, procuring stethoscopes for exclusive use in maternal and child health sectors, etc.]. A smaller proportion targeted testing coverage (18%), as baseline coverage of testing was already high, and the PCAT suggested that improving other aspects of the cascade would be more effective.
Patient volume and HIV prevalence varied substantially across study sites and countries. For raw numbers used to calculate primary outcomes, see Table, Supplemental Digital Content 5, http://links.lww.com/QAI/A815. For example, the mean volume of ANC1 visits during the 13-month baseline period per facility was 1078 in Côte d'Ivoire compared with 2034 in Kenya and 2089 in Mozambique.
In Kenya, facilities were subject to the nationwide shortage of HIV test kits in mid-2014. However, this period was included in neither baseline nor endline periods. Two control facilities in Kenya (facilities K and L) were located in coastal areas near the Kenya–Somalia border, which were unfortunately subject to violence beginning in the spring of 2014.36 All 3 primary outcomes declined in these facilities, most dramatically for ARV coverage (–65% and –44%). For safety reasons, study staff could not return to collect data directly from these facilities' registries; instead, data were extracted from Kenya's national health information system. In Mozambique, national elections during the last quarter of 2014 may have impacted health service delivery across sectors, although again, this period was not included in both baseline and endline periods. In Côte d'Ivoire, the ANC registry form was updated in mid-2013, which may have impacted data quality for both control and intervention sites during the baseline period.
In the overall analysis, increases in ARV coverage and HEI screening, but not HIV testing in ANC1, were substantially greater in intervention facilities compared with controls (Fig. 1). For country-specific trend lines, see Figures, Supplemental Digital Content 6–8, http://links.lww.com/QAI/A815. HIV testing in ANC1 increased, on average, in intervention facilities from 90.5% to 95.9%, a gain of +5.3% points (95% CI: −1.7 to 9.0) and in control facilities from 87.8% to 93.4% [+5.5% points (−2.8 to 13.8); P = 0.97] (Table 2). In intervention sites, ARV coverage increased from 66.4% to 77.7% [+13.3% points (0.5 to 26.0)], and in control sites, ARV coverage increased from 64.0% to 65.9% on average [+4.1% points (−12.6 to 20.7); P = 0.36] (Table 3). The proportion of HEI screened for HIV increased from 34.5% to 46.1% [+11.6% points (−2.6 to 25.7)] in intervention sites, on average, and increased from 31.3% to 32.0% in controls, on average [+0.7% points (−12.9 to 14.4); P = 0.25] (Table 4). Although ARV provision and HEI screening declined >10% in 4 facilities each, mean coverage levels were not significantly lower in intervention facilities compared with controls for any outcome.
In prespecified subgroup analyses stratified by country, notable differences emerged. For HIV testing coverage in ANC1 (Table 2), in Kenya, intervention sites improved substantially more than control sites (+9.2% vs. −0.4%, P = 0.30). For ARV coverage (Table 3), differences between study arms in Kenya were statistically significant; intervention sites improved from 52.5% to 73.4%, whereas control sites declined from 59.7% to 38.5% (P = 0.02). For HEI screening (Table 4), intervention sites in Mozambique improved significantly more than control sites (+23.1% vs. +3.7%; P = 0.04).
For ANC1 testing coverage, ARV coverage, and HEI screening, 4.1%, 4.8%, and 8.9% of values were >100% and were capped at 100%. In sensitivity analyses, there were no meaningful or significant changes in the results when values were left uncorrected.
In this first randomized, controlled trial of a package of systems engineering tools to improve PMTCT services in sub-Saharan Africa, ARV coverage among HIV-positive pregnant women and screening of HEI increased substantially in the overall analysis and increased significantly in selected countries in a prespecified subgroup analyses. HIV testing coverage during first ANC visit, near 90% at baseline, improved modestly in both arms. There was no evidence of harm. Health facilities were not required to implement only those microinterventions that could plausibly impact the study's primary outcomes, and this may have diluted our ability to measure the intervention's true impact. However, this approach was consistent with our aim to quantify the intervention's real-world effectiveness to improve the flow of mother–infant pairs through the PMTCT cascade.
HIV testing coverage in ANC1 improved similarly across study arms, which may reflect several factors. First, a smaller proportion of microinterventions targeted this step of the PMTCT cascade (18% of all microinterventions) compared with the other 2 primary outcomes. Furthermore, baseline levels were high (88% in control and 91% in intervention facilities), leaving relatively little room for improvement. By the end of the study, HIV testing in ANC1 achieved near-universal coverage levels in both arms. This is encouraging, as testing is the essential first step to access PMTCT. Moreover, PMTCT programs have historically provided the largest proportion of HIV testing and counseling services for adults7 and therefore represent the most common entry point into HIV care. Missing an HIV test in ANC represents a dual failure to protect the infant from HIV acquisition and provide the mother with timely cART. This also reinforces that the PMTCT cascade magnifies inefficiencies, as each step is conditional on completing previous steps.
ARV coverage among HIV-positive women improved 3-fold more in intervention (+13.3%) than control facilities (+4.1%), although this difference was not significant. ARV coverage in Kenya improved significantly more among intervention facilities (+20.9%) compared with control facilities (−21.2%). However, the decline in the 2 coastal facilities near the Kenya–Somalia border may have contributed to the magnitude and significance of this difference. Facilities in Côte d'Ivoire were able to achieve 100% ARV coverage, although facilities in Côte d'Ivoire had much smaller volumes because of lower HIV prevalences.11
The nearly 17-fold larger improvement in HEI screening in intervention vs. control facilities is particularly encouraging. Although not statistically significant, this promising result merits further investigation as it represents a meaningful increase in retention in longer-term care.19 Ongoing engagement in the later steps of the PMTCT cascade is critical to the success of PMTCT in general, and to Option B+ in particular, although it has been difficult to improve.37 When the analysis was restricted to Mozambican facilities, where microinterventions targeted HEI screening more frequently than the other 2 primary outcomes, intervention facilities improved 6-fold more than control facilities (23.1% vs. 3.7%), a difference that was statistically significant. The facility in Mozambique (II) that integrated HEI screening into immunization clinics saw the largest increase, from 6.2% to 35.9%. Service integration of HEI screening into immunization clinics has previously been shown to be associated with increased uptake, younger age at screening, increased receipt of test results, and increased enrollment in HIV care.38 Furthermore, the high HIV incidence among postpartum women in Mozambique39 and the higher risk of viral rebound in the first 340 to 641 months postpartum highlight the importance of engaging postpartum women in ongoing care to preserve the mother's health and to prevent vertical transmission, including counseling on appropriate feeding, HEI screening, and prevention of future unplanned pregnancies.
Testing coverage levels at baseline were similar to comparable data from the most recent national reports9–11 and post-B+ facilities in Malawi,42 which verified the study methodology and representativeness of the study facilities. Comparable ARV coverage estimates were not available, as Mozambique's and Côte d'Ivoire's most recent national reports were published before Option B+ implementation. Facilities in Kenya reported lower ARV coverage (53% in intervention facilities and 60% in controls) than estimates in Kenya's 2014 national report (71% in 2013), although internal inconsistencies in this section of the report and lack of clear definitions limit their utility as a comparison point.9 Estimated HEI screening coverage in study clinics exceeded that in national reports, although intervention facilities in Kenya had an estimated coverage level identical to that in reported in 2013 (45%).9 These differences could be because we extrapolated the denominator for HEI screening.
The study intervention has several unique advantages. Our intervention directly addresses 3 of the 4 prongs of PMTCT identified by the World Health Organization43: primary prevention of HIV infection through strengthened counseling and testing services, preventing mother-to-child transmission, and linking HIV-infected women and infants to long-term care. Many intervention facilities chose to address the fourth prong—preventing unintended pregnancies among women living with HIV—by integrating family planning services into newborn immunization and HEI screening visits. The intervention was feasible and well accepted; only one facility declined to implement it. In future publications, our group will explore facility-level factors that explain variations in the fidelity of SAIA implementation, such as leadership engagement, using qualitative data from focus group discussions with health facility staff at the study's conclusion.44 Our intervention intrinsically incorporates context, a key consideration in implementation science15: microinterventions were responsive to facility-specific barriers, as identified by staff themselves. Although being inherently context specific, the intervention is also applicable to diverse settings and health care contexts, even those beyond PMTCT. Frontline health workers are at the heart of this intervention, and the skills they gained to develop, test, and monitor the performance of microinterventions could be useful for other staff in any health care sector with minimal expense and oversight. Our intervention would translate easily to adult HIV services, which will be increasingly important as early and rapid cART initiation becomes the norm.45 Systems engineering tools are particularly appropriate for management of chronic diseases that require continuity within and/or across health sectors, such as PMTCT, but also cardiovascular disease, depression, diabetes, and others. Systems engineering could reorient African health systems to respond more nimbly to a shifting disease landscape.
Our study has several strengths. First, this was the first randomized study to investigate the impact of systems engineering on PMTCT. Second, the study was conducted in geographically diverse countries, in western, eastern, and southern Africa, which increases generalizability. Third, the study provides pragmatic data; we tested the intervention in real-world health facilities using routinely available data from facility registries, as would likely occur should the study intervention be scaled up. Consequently, our data could easily and inexpensively be compared with data generated from future implementation. Finally, microinterventions were not artificially restricted to those that would impact the primary outcomes; rather, we encouraged staff to implement microinterventions that they believed would improve PMTCT services holistically.
This study was not without limitations. We could not follow mother–infant dyads through the PMTCT cascade or beyond it and therefore cannot measure improvements in direct health outcomes, nor long-term retention and adherence. We had to extrapolate denominators to calculate HEI screening. However, this formula was applied uniformly across study sites and over time, and therefore should not impact our ability to analyze trends. Our power to rule out chance as an explanation for observed differences was limited by the small sample size; in essence, our study had similar power to an individually randomized trial with 36 people. We used routine health facility records to collect data, which were not collected primarily for research purposes. However, routine data quality is high in Mozambique,46 and data were double-collected directly from facility registries by 2 trained abstractors to maximize accuracy (except when unrest prevented travel to facilities K and L). As with any study investigating trends over time, we could not rule out the impact of secular events on our outcomes, although the randomized nature of the study strengthens the inference that the intervention itself was responsible for observed differences between study arms. Measures of fidelity to the SAIA intervention were not included in this intent-to-treat analysis, although this will be explored in the future.
To achieve the elimination of pediatric HIV, health facilities must function at near-optimal levels. Systems engineering has the potential to optimize any health service along the PMTCT cascade, while at the same time building capacity among staff to leverage these tools for other health services. This study's findings indicate that systems engineering could substantially increase the coverage of critical aspects of the PMTCT cascade, namely ARV coverage and HEI screening. However, because of limited sample size and the lack of restriction of microinterventions to those that would impact our chosen primary outcomes, the results—although substantial in overall analyses—achieved statistical significance only in subgroups. Although seemingly contradictory, there were specific explanations for each significant subgroup result—Mozambican facilities focused on HEI screening, and violence undermined ARV provision in Kenyan control facilities—which were tempered in the overall analysis. Future studies evaluating systems engineering applications to HIV services are urgently needed to expand our body of knowledge about these powerful tools.
The authors gratefully acknowledge the dedication, insight, and hard work of all members of the SAIA Study Team. Members include Catherine Henley, Ahoua Koné, Julia Robinson, S. Adam Granato, Seydou Kouyaté, Grace Mbatia, Grace Wariua, Martin Maina, Peter Mwaura Njuguna, Joana Coutinho, Emelita Cruz, Quincy Moore, Justina Zucule, Bradley Wagenaar, and James Pfeiffer.
1. Wang W, Alva S, Wang S, et al. Levels and Trends in the Use of Maternal Health Services in Developing Countries. DHS Comparative Reports 26. Demographic and Health Survey; 2011. Available at: https://dhsprogram.com/pubs/pdf/CR26/CR26.pdf
2. Sibanda EL, Weller IV, Hakim JG, et al. The magnitude of loss to follow-up of HIV-exposed infants along the prevention of mother-to-child HIV transmission continuum of care: a systematic review and meta-analysis. AIDS. 2013;27:2787–2797.
3. van Lettow M, Bedell R, Mayuni I, et al. Towards elimination of mother-to-child transmission of HIV: performance of different models of care for initiating lifelong antiretroviral therapy for pregnant women in Malawi (Option B+). J Int AIDS Soc. 2014;17:18994.
4. Tenthani L, Haas AD, Tweya H, et al. Retention in care under universal antiretroviral therapy for HIV-infected pregnant and breastfeeding women (“Option B+”) in Malawi. AIDS. 2014;28:589–598.
5. Gourlay A, Birdthistle I, Mburu G, et al. Barriers and facilitating factors to the uptake of antiretroviral drugs for prevention of mother-to-child transmission of HIV in sub-Saharan Africa: a systematic review. J Int AIDS Soc. 2013;16:18588.
6. Herlihy JM, Hamomba L, Bonawitz R, et al. Integration of PMTCT and antenatal services improves combination antiretroviral therapy cART uptake for HIV-positive pregnant women in Southern Zambia: a prototype for Option B+?. J Acquir Immune Defic Syndr. 2015;70:e123–129.
7. Conselho Nacional de Combate au HIV e SIDA (CNCS). Global AIDS Response Progress Report: Mozambique. UNAIDS; 2014. Available at: http://www.unaids.org/sites/default/files/country/documents/MOZ_narrative_report_2014.pdf
8. Townsend CL, Cortina-Borja M, Peckham CS, et al. Low rates of mother-to-child transmission of HIV following effective pregnancy interventions in the United Kingdom and Ireland, 2000-2006. AIDS 2008;22:973–981.
9. Kenyan National AIDS Control Council. Kenya AIDS Response Progress Report 2014: progress towards zero. UNAIDS; 2014. Available at: http://www.unaids.org/sites/default/files/country/documents/KEN_narrative_report_2014.pdf
12. Schouten EJ, Jahn A, Midiani D, et al. Prevention of mother-to-child transmission of HIV and the health-related millennium development goals: time for a public health approach. Lancet. 2011;378:282–284.
13. Ministre de la Sante et de la Lutte contre le SIDA. Plan d'élimination de la Transmission mère-enfant du VIH (2012–2015). UNAIDS: Abidjan, Côte d'Ivoire; 2012.
14. Kieffer MP, Mattingly M, Giphart A, et al. Lessons learned from early implementation of option B+: the Elizabeth Glaser Pediatric AIDS Foundation experience in 11 African countries. J Acquir Immune Defic Syndr. 2014;67(suppl 4):S188–S194.
15. Bhardwaj S, Carter B, Aarons GA, et al. Implementation research for the prevention of mother-to-child HIV transmission in sub-Saharan Africa: existing evidence, current gaps, and new opportunities. Curr HIV/AIDS Rep. 2015;12:246–255.
16. Tweya H, Gugsa S, Hosseinipour M, et al. Understanding factors, outcomes and reasons for loss to follow-up among women in Option B+ PMTCT programme in Lilongwe, Malawi. Trop Med Int Health. 2014;19:1360–1366.
17. Kim MH, Ahmed S, Hosseinipour MC, et al. Implementation and operational research: the impact of option B+ on uptake, retention, and transmission: a pre/post study in Lilongwe, Malawi. J Acquir Immune Defic Syndr. 2015;68:e77–e83.
18. Dryden-Peterson S, Lockman S, Zash R, et al. Initial programmatic implementation of WHO option B in Botswana associated with increased projected MTCT. J Acquir Immune Defic Syndr. 2015;68:245–249.
19. Ghadrshenas A, Ben Amor Y, Chang J, et al. Improved access to early infant diagnosis is a critical part of a child-centric prevention of mother-to-child transmission agenda. AIDS. 2013;27(suppl 2):S197–S205.
20. Winfrey W, Rakesh K. Use of Family Planning in the Postpartum Period. DHS Comparative Reports 36. Demographic and Health Survey; 2014. Available at: https://dhsprogram.com/pubs/pdf/CR36/CR36.pdf
21. National Aeronautic and Space Administration. NASA Systems Engineering
Handbook (NASA/SP-2007-6105 Rev1). National Aeronautic and Space Administration Scientific and Technical Information; Hanover, MD; 2007.
22. Womack JP, Byrne AP, Fiume OJ, et al. Going Lean in Health Care. Institution for Healthcare Improvement; Cambridge, MA: 2005. Available at: https://www.entnet.org/sites/default/files/GoingLeaninHealthCareWhitePaper-3.pdf
23. Paydar-Darian N, Pursley DM, Haviland MJ, et al. Improvement in perinatal HIV status documentation in a Massachusetts Birth Hospital, 2009–2013. Pediatrics. 2015;136:e234–e241.
24. Bardfield J, Agins B, Palumbo M, et al. Improving rates of cotrimoxazole prophylaxis in resource-limited settings: implementation of a quality improvement approach. Int J Qual Health Care. 2014;26:613–622.
25. Ciampa PJ, Burlison JR, Blevins M, et al. Improving retention in the early infant diagnosis of HIV program in rural Mozambique by better service integration. J Acquir Immune Defic Syndr. 2011;58:115–119.
26. Manumbu S, Smart LR, Mwale A, et al. Shortening turnaround times for newborn HIV testing in rural Tanzania: a report from the field. PLoS Med. 2015;12:e1001897.
27. Youngleson MS, Nkurunziza P, Jennings K, et al. Improving a mother to child HIV transmission programme through health system redesign: quality improvement, protocol adjustment and resource addition. PLoS One. 2010;5:e13891.
28. Mate KS, Ngubane G, Barker PM. A quality improvement model for the rapid scale-up of a program to prevent mother-to-child HIV transmission in South Africa. Int J Qual Health Care. 2013;25:373–380.
29. Bhardwaj S, Barron P, Pillay Y, et al. Elimination of mother-to-child transmission of HIV in South Africa: rapid scale-up using quality improvement. S Afr Med J. 2014;104:239243.
30. Kim YM, Banda J, Hiner C, et al. Assessing the quality of HIV/AIDS services at military health facilities in Zambia. Int J STD AIDS. 2013;24:365–370.
31. Oyeledun B, Oronsaye F, Oyelade T, et al. Increasing retention in care of HIV-positive women in PMTCT services through continuous quality improvement-breakthrough (CQI-BTS) series in primary and secondary health care facilities in Nigeria: a cluster randomized controlled trial. The Lafiyan Jikin Mata Study. J Acquir Immune Defic Syndr. 2014;67(suppl 2):S125–S131.
32. Sherr K, Gimbel S, Rustagi A, et al. Systems analysis and improvement to optimize pMTCT (SAIA): a cluster randomized trial. Implement Sci. 2014;9:55.
33. Gimbel S, Voss J, Mercer MA, et al. The prevention of mother-to-child transmission of HIV cascade analysis tool: supporting health managers to improve facility-level service delivery. BMC Res Notes. 2014;7:743.
34. Gimbel S, Voss J, Rustagi A, et al. What does high and low have to do with it? performance classification to identify health system factors associated with effective prevention of mother-to-child transmission of HIV delivery in Mozambique. J Int AIDS Soc. 2014;17:18828.
35. Donner A, Klar N. Design and Analysis of Cluster Randomized Trials in Health Research. New York, NY: Oxford University Press; 2000.
36. Kenya Attack: Mpeketoni Near Lamu Hit by Al-Shabab Raid: BBC; London, England: 2014. Available at: http://www.bbc.com/news/world-africa-27862510
37. Turan JM, Onono M, Steinfeld RL, et al. Implementation and operational research: Effects of antenatal care and HIV treatment integration on elements of the PMTCT cascade: results from the SHAIP cluster-randomized controlled trial in Kenya. J Acquir Immune Syndr. 2015;69:e172–e181.
38. McCollum ED, Johnson DC, Chasela CS, et al. Superior uptake and outcomes of early infant diagnosis of HIV services at an immunization clinic versus an “under-five” general pediatric clinic in Malawi. J Acquir Immune Syndr. 2012;60:e107–e110.
39. De Schacht C, Nédio M, Ferreira OC, et al. High HIV incidence in the postpartum period sustains vertical transmission in settings with generalized epidemics: a cohort study in Southern Mozambique. J Int AIDS Soc. 2014;17.
40. Huntington S, Thorne C, Newell ML, et al. The risk of viral rebound in the year after delivery in women remaining on antiretroviral therapy. AIDS. 2015;29:2269–2278.
41. Matthews LT, Ribaudo HB, Kaida A, et al. HIV-infected Ugandan women on antiretroviral therapy maintain HIV-1 RNA suppression across periconception, pregnancy, and postpartum periods. J Acquir Immune Defic Syndr. 2016;71:399–406.
42. Herce ME, Mtande T, Chimbwandira F, et al. Supporting option B+ scale up and strengthening the prevention of mother-to-child transmission cascade in central Malawi: results from a serial cross-sectional study. BMC Infect Dis. 2015;15:328.
43. World Health Organization. PMTCT Strategic Vision 2010–2015: Preventing Mother-to-Child Transmission of HIV to Reach the UNGASS and Millenium Development Goals. World Health Organization HIV/AIDS Department; Geneva, Switzerland: 2010. Available at: http://www.who.int/hiv/pub/mtct/strategic_vision/en/
44. Damschroder LJ, Aron DC, Keith RE, et al. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4:50.
45. World Health Organization. Guideline on When to Start Antiretroviral Therapy and on Pre-Exposure Prophylaxis for HIV. World Health Organization HIV/AIDS Department; Geneva, Switzerland: 2015. Available at: http://www.who.int/hiv/pub/guidelines/earlyrelease-arv/en/
46. Gimbel S, Micek M, Lambdin B, et al. An assessment of routine primary care health information system data quality in Sofala Province, Mozambique. Popul Health Metr. 2011;9:12.