BACKGROUND
In 2012, an estimated 260,000 children were newly infected with HIV worldwide1 2 3 4 5–7
Current WHO guidelines for resource-limited settings recommend virologic testing for HIV-exposed infants at 4–6 weeks of age, but also allow for consideration of the addition of testing at birth.8 9 10–12 13 early infant diagnosis (EID) services are often not available, are poorly functioning, and have low follow-up rates; in 2012, only 39% of HIV-exposed infants in resource-limited settings had an HIV DNA PCR test performed within 2 months of birth.1 14–18 19
One proposal for increasing early HIV testing and decreasing the time to infant ART initiation in resource-limited settings involves conducting the first HIV DNA PCR test shortly after birth.8,20 21
METHODS
We used Microsoft Excel to create a decision-tree model of the continuum of care for HIV-infected women and their infants in sub-Saharan Africa (Table 1 ). Our model included decision points for HIV-infected pregnant women during receipt of antenatal services through delivery and for their infants from birth through breastfeeding until 18 months of age. We searched PubMed and abstracts from relevant meetings [Conference on Retroviruses and Opportunistic Infections (CROI) and the International AIDS Society (IAS)] to identify rates from sub-Saharan Africa for each of the decision branch points along the cascade; we used a conservative median estimate for the model and conducted sensitivity analyses on assumptions used to gauge the effect of changing assumptions.
TABLE 1.: Assumption Values, Including High and Low Estimates of Each Model Parameter Used in Sensitivity Analysis, and Source
TABLE 1-A.: Assumption Values, Including High and Low Estimates of Each Model Parameter Used in Sensitivity Analysis, and Source
Assumptions for Pregnancy, Delivery, and Early MTCT
Our model was constructed using a theoretical cohort of 1,400,000 HIV-infected pregnant women, which is the number of HIV-infected pregnant women estimated to be living in sub-Saharan Africa in 2012.1 22 1 33 33–35 23,36
Assumptions for HIV Testing of Exposed Infants at Birth
For our model of newborn HIV testing, we estimated that 80% of HIV-exposed infants born in facilities would have a dried blood spot sample for HIV DNA PCR test collected before discharge. This estimate was based on uptake of BCG vaccination, another service provided at birth in resource-limited settings.37 38,39 40
We assumed that HIV DNA PCR testing at birth would detect 60% of all infections acquired perinatally, but no postnatal infections. This sensitivity was based on data demonstrating that 68% of perinatally infected infants identified by HIV DNA PCR testing at 6 weeks of age had also tested positive at birth, and other studies that tested infants at birth that showed lower sensitivities.12,25–27,41 37 19,28,42–46
Assumptions for HIV Testing at 6 Weeks
We assumed that 39% of all HIV-exposed infants received HIV DNA PCR testing at 6 weeks and that this test had a sensitivity of 100% for perinatal HIV infections.1,47 32
Assumptions for HIV Testing After 6 Weeks
We assumed that HIV-exposed breastfeeding infants who initially tested negative, regardless of the timing of the first test, had an additional HIV DNA PCR test schedule at 9 months, consistent with WHO recommendations.8 32
Assumptions for Enrollment in HIV Care and Treatment
Based on current published data on linkage to care for HIV-infected infants in sub-Saharan Africa, we estimated that 52% of infants identified as HIV infected would enroll into care,16,19,28–31 16,19,31 14,17,31
Estimates of Infant Survival
To estimate the survival of HIV-infected infants, we assumed that HIV-infected women experienced rates of stillbirths of 2% for those not receiving any PMTCT and 4% for those receiving PMTCT.24 3 3,5
Model Endpoints
Our model endpoints were the number of infants initiated on ART by ages 3 and 18 months and the number of HIV-related deaths prevented by 12 months. We compared these endpoints for infants who had a 6-week HIV DNA PCR test and for infants who had additional newborn DNA PCR testing.
Sensitivity Analysis and Country Examples
A 1-way sensitivity analysis was conducted for key variables in the model to determine their influence on the model endpoints. For this sensitivity analysis, we used the higher and lower published estimates of the following variables: facility births, PMTCT coverage, sd-NVP usage, newborn HIV testing rates, newborn HIV DNA PCR sensitivity, receipt of newborn test results, 6-week HIV testing rates, receipt of 6-week HIV test results, enrollment into care, and initiation of treatment (Table 2 ). We calculated outcomes using country-specific data for these key variables in 2 countries, Kenya and Swaziland, to illustrate the potential impact of newborn HIV testing in 2 sub-Saharan African settings with different HIV epidemics and health care systems (Table 3 ).
TABLE 2.: Sensitivity Analysis of the Impact of the Highest and Lowest Published Value for Model Parameters on the Number of HIV-Infected Infants Receiving ART by Age 3 Months, HIV-Infected Infants Receiving ART by Age 18 Months, and HIV-Related Deaths Prevented
TABLE 3.: Key Variables for Models of the Impact of Newborn HIV Testing in Kenya and Swaziland
RESULTS
In our model, 600,600 (43%) of 1,400,000 HIV-infected women delivered in a health facility. Of these women, 378,378 (63%) received a WHO highly effective PMTCT regimen and 222,222 (37%) did not receive any PMTCT intervention. Among live births, 71,514 (67%) infants were infected with HIV perinatally and an additional 34,377 (33%) were infected postnatally.
Compared with the standard 6-week HIV testing algorithm, the addition of DNA PCR testing at birth increased the number of infants initiated on ART at age 3 months by 543% (1907 with newborn and 6-week testing versus 351 with 6-week testing alone) and increased the number of infected infants initiated on ART by age 18 months by 209% (9141 versus 4372, respectively) (Fig. 1 ). The increase in the number of infants receiving early ART led to a corresponding increase in survival at 12 months of age, with 5108 fewer infant deaths (49,658 versus 44,550) as a result of earlier HIV diagnosis and ART initiation.
FIGURE 1.: Model estimates of the number of children initiated on ART by 3 and 18 months with current testing practices and with the addition of newborn testing.
The sensitivity analysis showed that facility birth rates and enrollment in care are the 2 decision-tree points that have the most effect on the impact of newborn HIV testing (Figs. 2A–C ). When facility birth rates reached 90%, as they are in the Republic of Congo,22 Figs. 2A–C ). The next largest influencer on the impact of newborn HIV testing was utilization of PMTCT. Here, the relationship was inverse, as the lower the usage of PMTCT, and correspondingly a larger number of peripartum transmissions, the larger the impact of newborn testing; if rates were as low as 6%, as they are in Nigeria (1), then the number of infants on ART by 3 months increased by 109% (2071 additional infants), leading to a 107% increase in deaths prevented (1828 additional lives saved) (Figs. 2A–C ).
FIGURE 2.: A, Sensitivity analysis of the impact of newborn testing in settings with high and low estimates of key variables in the model compared with average estimates in percent change of infants on ART by age 3 months. B, Sensitivity analysis of the impact of newborn testing in settings with high and low estimates of key variables in the model compared with average estimates in percent change of infants on ART by 18 months. C, Sensitivity analysis of the impact of newborn testing in settings with high and low estimates of key variables in the model compared with average estimates in percent change in number of additional infant deaths prevented at age 12 months.
Country Examples
Because of the wide variations in program performance across sub-Saharan Africa and the impact of various program factors on the outcome of the model, we sought to apply the model to specific country contexts. We used published data from Swaziland and Kenya to highlight the difference that key variables could have on the impact of newborn HIV testing. Compared with Kenya, Swaziland has a higher facility birth rate (74% versus 41%), higher HIV prevalence (31% versus 6.2%), and a higher current rate of 6-week HIV testing (81% versus 39%). Swaziland also has a much higher PMTCT utilization rate (83% versus 53%) (Table 3 ). Using these country-specific assumptions, our model showed that newborn HIV testing would have a larger impact in Kenya, increasing the number of infants on ART at age 3 months by 359% compared with 249% in Swaziland (Table 4 ). This translates into 66 more infant deaths prevented each year in Kenya using newborn testing compared with 9 infant deaths per year prevented using newborn testing in Swaziland (Table 4 ).
TABLE 4.: Infant ART Initiation at 3 and 18 Months, and Lives Saved by Adding Newborn Testing to Routine Infant HIV Testing Algorithms in Kenya and Swaziland
DISCUSSION
Our model shows that newborn HIV DNA PCR testing in sub-Saharan Africa has the potential to increase the number of infected infants initiated on treatment before the age of 3 months and significantly reduce infant mortality due to perinatally acquired HIV; the addition of newborn HIV testing would increase the number of infants on ART by 3 months by 543% and help prevent 5108 HIV-related infant deaths at 12 months of age. Although this model provides insight into the potential impact of newborn testing in sub-Saharan Africa, the real impact of newborn testing will be highly variable based on a country's specific burden of HIV and program performance. Our sensitivity analysis showed that newborn testing would have the largest impact and prevent the most deaths in countries with a high proportion of facility births and the countries with the lowest PMTCT rates. In our country examples, although Swaziland has stronger systems once infected children are identified, the fact that Kenya has lower PMTCT rates, translating into more peripartum infections, increases the potential of newborn testing to find more HIV-infected infants in that country.
Our model has several limitations. First, our model assumptions were based on published data on the cascade of care for HIV-infected women and their infants across sub-Saharan Africa. The published rates may not reflect the most recent performance and may present a biased picture of program performance by ignoring improvements in care delivery that have been made in recent years. Although we used the most recent data available at the time of our analysis, new data have likely become available in the interim that could change the modeled impact of birth testing. Second, our model assumptions about uptake of newborn HIV testing and return of test results were based on data from usage of other services, including vaccination services. One of the largest programmatic hurdles for a successful newborn HIV testing program would be the return of results at the 6-week immunization visit, especially as immunization services are often more decentralized than maternity facilities and that women may choose not to return to the same clinic where they delivered for their infant's immunizations. A study to better estimate these rates and understand the programmatic feasibility, including costs, of newborn testing is needed. Third, many of our assumptions are derived from studies that did not focus on HIV-infected women who deliver in health care facilities. The women who choose to deliver in a health care facility may not be representative of the larger population of HIV-infected women, as they may have greater access to health care and may be more likely to use PMTCT and HIV-exposed infant follow-up services including EID. In our model, we assumed that HIV-infected and HIV-uninfected women would deliver in health facilities at the same rates; however, as HIV is often more common in urban areas, where the density of health facilities is higher and where women have higher uptake of facility births, HIV-infected women may be more likely to deliver in health care facilities, thus underestimating the effect of newborn testing. Our model could not account for these issues, mainly because this type of stratified data is not available. Finally, newborn testing would not replace the need for an additional HIV DNA PCR test at a later age, as false negatives could occur, a number of intrapartum infections would be missed, and early breastfeeding transmissions would not be detected. Therefore, if a country implemented a newborn HIV testing model, infants testing negative at birth would require at least 2 HIV DNA PCR tests, meaning that additional commodities and laboratory capacity would be required. A formal costing analysis of newborn HIV testing is needed to inform international and country-specific decisions regarding the feasibility of implementing a newborn HIV testing program.
Newborn testing should continue to be evaluated as a potential addition to other strategies for earlier HIV diagnosis and treatment initiation in infants. Pilot studies and operational research are urgently needed to better determine the true impact that this strategy could have, and how postnatal transmission changes might influence the timing of subsequent testing, especially in settings where PMTCT services, including universal ART for life for HIV-infected pregnant and breastfeeding women, are rapidly expanding.49
In addition to pilot testing, additional modeling should be performed to account for the introduction of new technologies, such as ultrasensitive and point-of-care HIV DNA PCR tests or PMTCT program improvements. In addition, new algorithms for infant HIV testing should be explored, particularly combinations of newborn testing with a second HIV test that could be performed at a time later than the 6-week visit to capture more postnatal transmissions; 10 weeks has been suggested.50
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