Evaluating turnaround times for early infant diagnosis samples in Kenya from 2011-2014: A retrospective analysis of HITSystem program data
Evaluating turnaround times for early infant diagnosis samples in Kenya from 2011-2014: A retrospective analysis of HITSystem program data
Catherine Wexler 0 1 2
An-Lin Cheng 0 2
Brad Gautney 0 2
Sarah Finocchario-Kessler 0 1 2
Kathy Goggin 0 2
Samoel Khamadi 0 2
HITSystem Team 0 2
0 Data Availability Statement: Our data set consists largely of specific dates, which are considered potentially identifying. Since it is impossible to completely de-identify our data set, and per the requirements of the IRB at the Kenya medical Research Institute, we have not made the data publicly available. Data can be requested from Natabhona Mabachi at
1 University of Kansas Medical Center, Department of Family Medicine, Kansas City, Kansas, United States of America, 2 University of Missouri-Kansas City, School of Medicine, Kansas City, Missouri, United States of America, 3 Global Health Innovations, Kansas City, Missouri, United States of America, 4 Children's Mercy Hospitals and Clinics, Health Services and Outcomes Research, Kansas City, Missouri, United States of America, 5 University of Missouri-Kansas City, School of Medicine, Kansas City, Missouri, United States of America, 6 University of Missouri-Kansas City, School of Pharmacy, Kansas City, Missouri, United States of America, 7 Kenya Medical Research Institute , Nairobi , Kenya
2 Editor: Sten H Vermund, Yale University Yale School of Public Health , UNITED STATES
Long turnaround times (TAT) for the processing and posting of results of infant HIV DNA PCR samples can hinder the success of early infant diagnosis (EID) programs. The HITSystem is an eHealth intervention that alerts staff when services are overdue or results are delayed. We conducted a retrospective analysis of 3669 HIV-exposed infants enrolled in 15 Kenya hospital EID programs and three laboratories using the HITSystem from 2011±2014. We assessed mean and median TAT from when a sample was: 1) obtained to when it was shipped to the laboratory, 2) shipped to when it was received at the laboratory, 3) received to when a result was posted, and 4) the total time from obtaining the sample (step 1) to posting the result (step 3). TAT were compared by laboratory, clinic, year, and month of sample collection. 3625 infant samples had results posted by end of 2014. Mean TAT from sample collection to shipping was 5.2 days, from shipping to laboratory receipt was 2.0 days, and from laboratory receipt to result posting was 17.4 days. Altogether, it took an average of 24.7 days from sample collection until result posting. There was significant variation between laboratories, particularly in laboratory processing times (step 3). TAT showed a
Funding: Funding for this study was provided by
private donations to Global Health Innovations and
with other Kenyan studies, TAT in these HITSystem enrolled settings were shorter.
Significant variation between laboratories, however, indicates the need to strengthen protocols
and infrastructure to ensure that all laboratories can provide rapid, high-quality services.
the National Institutes of Child Health and
Development, R01HD076673 (Finocchario-Kessler
PI). The funders had no role in study design, data
collection and analysis, decision to publish, or
preparation of the manuscript.
Early identification and initiation of ART for HIV-infected infants can reduce mortality by up
to 76% and slow disease progression [
] and are critical goals of early infant diagnosis (EID)
programs. Prior to 2008, guidelines for infant HIV-testing in Kenya focused on the
identification of HIV in infants who presented at pediatric or tuberculosis wards with symptoms [
However, in 2008, Kenya revised its guidelines to advocate for EID services to be provided to
all HIV-exposed infants [
]. According to these revised guidelines, polymerase chain reaction
(PCR) testing for HIV-exposed infants should occur when the infant is 6 weeks of age or at the
first contact thereafter. Antibody testing should then occur at 9- and 18- months of age or 6
weeks after cessation of breastfeeding, with confirmatory PCR tests for infants with positive
antibody tests [
In Kenya, the shift from using whole blood to using dried blood spots (DBS) in 2006 for
infant PCR testing allowed for substantial scale-up of EID and made HIV testing possible even
in remote areas [
]. For DBS, capillary blood samples are collected (usually by heel stick,
minimizing the need for extensive phlebotomy training) on filter paper. DBS samples are stable at
room temperature, require a minimal amount of blood from the infant, are easily transported,
and pose minimal risk of infection [
]. The shift to using DBS, coupled with the new EID
guidelines, contributed to a dramatic increase in the number of infants tested annually and
resulted in a three-fold increase from 18,848 in 2008 to 59,413 in 2014 .
However, there are many points in the cascade of care where long turnaround times (TAT)
delay the initiation of lifesaving ART for HIV-infected infants. Without point-of-care PCR
testing technologies, samples must be shipped to one of seven centralized testing laboratories
in Kenya [
]. This presents barriers for excessive TAT for infant samples to occur if the
hospital delays routine shipment of samples to their designated central laboratory, if the courier
service is delayed, or if samples are lost or damaged. Once the sample is received at the central
laboratory, receipt is documented and the sample is processed [
]. Delays in sample processing
at the laboratory can occur due to stock-outs of reagents, maintenance issues with the
automated PCR testing equipment, or inadequate staffing to handle the volume of samples [
Once available, laboratories either ship paper-based results back to the hospital via commercial
courier or use email and SMS printers and rely on clinical staff to record and communicate
findings to mothers. This very manual process accounts for additional delays and each step
presents a unique barrier for the sample or the result to be misplaced or mishandled,
compromising the care, retention, and clinical outcomes for HIV-exposed infants [
Studies from Kenya suggest that delays at each step of this process vary substantially
between settings and may create long turnaround times from when a sample is obtained to
when the result is available to the clinician. It can take up to three weeks from the time a
sample is obtained until it is received at the central laboratory and an additional one to three weeks
from when it is received at the laboratory until the result is available to the hospital [
said, it can take six to eight weeks from when a sample is collected to when the result is
available at the hospital [
]. More recently, in standard of care settings, it took between 6.3 and
8.1 weeks from the time a sample was collected until the caregiver was informed of the infant's
result; however, the study did not directly report on the turnaround time between sample
collection and hospital receipt of result . Studies in other parts of East Africa report variable
turnaround times, ranging from 9 days to 21 weeks [
The HITSystem is a web-based eHealth intervention that has three mechanisms that may
improve EID sample turnaround time. First, the HITSystem tracks samples from the time
that they are collected until the result is posted and generates electronic alerts when samples
are delayed. These alerts allow providers to easily identify which samples do not have a result
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posted and follow up with the laboratory if s/he expects that the sample was lost, rejected, or
forgotten. Second, if the sample was lost or rejected, a built in text messaging service allows
providers to easily reach out to mothers and ask them to return to the hospital for a repeat
blood draw. Third, once the sample is processed, results are entered directly into the
HITSystem and become available to providers online. This eliminates any potential loss or delay
that may occur when paper-based results are delivered via courier to the hospital, which is
the standard of care. In a preliminary analysis conducted from one hospital and one
laboratory from 2011±2012, TAT from DBS collection to the availability of results decreased from
4.08 weeks before HITSystem introduction to 2.48 weeks after HITSystem introduction
The purpose of this study was to more fully describe TAT for EID samples collected as part
of the HITSystem intervention in Kenya from 2011±2014. We report changes in TAT over
time and identify points in the EID cascade most vulnerable to delays.
We conducted a retrospective analysis of 3669 HIV-exposed infants enrolled in the HITSystem
at 15 hospitals over a 3-year period, served by three central laboratories in Kenya. All hospitals
and laboratories included in the study started using the HITSystem for EID between April
2011 and June 2014 (staggered start dates). The laboratories served at least one hospital
(ranging from one to nine hospitals) that implemented the HITSystem. In 2015, labs 1, 2, and 3
processed an average of 1496, 595, and 1803 EID samples per month from all 361, 166, and 422
facilities served, respectively [
]. While all the labs were government run, they received varying
support from outside funding agencies. All of the hospitals were government run and had
Ministry of Health clinical staff implementing the HITSystem as an added service to their EID
standard of care. All clinics and labs received training prior to HITSystem implementation.
Training consisted of one 6-hour day for key hospital providers and one 3-hour day for key
All initial DBS samples collected during this period were included in the analysis. Data are
reported for the infant's first PCR test result only. Since HIV DNA PCR tests at 9- and
18months are only run for the small proportion of infants who have a positive antibody test,
any confirmatory PCR run at these time points was not included in the analysis. Samples
from HIV-exposed infants receiving routine care were collected, packaged, and shipped per
the National EID guidelines and protocols established at each hospital. All clinics used a
single courier system for sample shipment to lab. At sample collection, infants were enrolled in
the HITSystem. A detailed description of the HITSystem has been published previously
] but briefly, at enrollment infant and mother demographic data and the date of the
initial DBS collection is entered into the HITSystem. The following dates are also recorded
in the HITSystem: 1) date the sample was shipped from the hospital to the central
laboratory, 2) date the sample was received at the laboratory, and 3) date the result was entered
into the HITSystem. Automated alerts are sent to the laboratory technicians and the
clinicians if any of these steps are not completed within a specified amount of time. This
facilitates frequent communication and follow up between clinical staff and laboratory
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All mothers were informed about the HITSystem. Mothers provided oral informed consent to
participate or were given the option to use only the paper-based registry without text messages
or electronic alerts. Less than 1% declined to be entered in the HITSystem. Oral consent was
deemed appropriate since participation involved minimal risk and a signed consent form
would have been the only document outside of medical records that linked participants to
their HIV status. The study was approved by the Institutional Review Board at the Kenya
Medical Research Institute (SSC 1890).
The primary outcome was overall TAT. This was defined as the number of days from when a
sample was obtained to when the result was available to the hospital via the HITSystem.
Overall TAT time was also broken down into three steps: 1) the number of days from when a
sample was obtained to when a sample was shipped to the laboratory, 2) the number of days from
when a sample was shipped until it was recorded as received at the laboratory, and 3) the
number of days from when it was received at the laboratory until the result was posted in the
HITSystem and became available for the clinician to see (Fig 1).
We calculated mean and median days for each TAT by laboratory, hospital, year and month.
Due to the natural distribution of TAT data (counted days), Poisson regression models were
conducted to examine differences in overall TAT by laboratory and clinic.
Fig 1. Flow of DBS sample processing from collection to results.
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Of the 3669 infant samples collected, 3625 (98.8%) had results posted by end of 2014. A total
of 3407 (94%) samples were negative, 192 (5.3%) were positive, and 26 (0.7%) were
For all laboratories, the mean TAT from when a sample was obtained to when it was
shipped was 5.2 days (median = 3; range = 0±698 days), the mean TAT from when a sample
was shipped to when it was received at the laboratory was 2.0 days (median = 0; range = 0±253),
and the mean TAT from when it was received at the laboratory to when a result was posted
was 17.4 days (median = 13; range = 0±744). All said, it took a mean of 24.7 (median = 19,
range = 0±776) days from when a sample was obtained until the result was available to the
clinician. 76% of samples had results posted within 30 days of sample collection, 19% had results
posted between 31 and 60 days of sample collection, and 5% had results posted over 60 days
after sample collection.
The results from the Poisson regression models showed significant variation in the
turnaround times between labs (Table 1). The mean TAT from obtained to shipped ranged from
1.7 days to 6.5 days, the mean TAT from shipped to received ranged from 0.3 days to 3.1 days,
the mean TAT from received to results ranged from 12.5 days to 40.2 days, and the mean
overall TAT ranged from 20.1 days to 41.9 days. It took Lab 3 nearly 2.5 times longer than the
mean to process samples.
TAT by clinic also showed variation based on Poisson regression models (Table 2). The
mean number of days from when a sample was obtained to when it was shipped to the
laboratory ranged from 1.2 days to 11.7 days, with five of the fifteen hospitals averaging longer
than one week to ship samples. Mean TAT from when a sample was shipped to the lab until it
was received at the lab ranged from 0 days (received the same day it was shipped) to 4.2 days.
Mean TAT from when a clinic obtained a sample to when the result was posted ranged from a
low of 16.8 days to a high of 49.9 days.
Mean TAT by year are reported in Fig 2. Labs 1 and 2 implemented the HITSystem in
2011, while Lab 3 implemented it in 2013. Across all labs, there was an overall decreasing trend
from 2011 to 2014 (28.3 days from sample collection to result in 2011 to 25.3 days in 2014)
(Fig 2A). This improvement was primarily driven by reductions in the time at the laboratory
(i.e., from when sample received at laboratory until the test result was posted). While Labs 1
and 2 showed more modest improvements in mean TAT from when a sample was received to
when a result was posted from 2011 to 2014 (from 29.8 days to 25.8 days and from 22.6 days to
20.1 days, respectively) (Fig 2B and 2C), Lab 3 nearly halved its mean TAT from when a
sample was received to when results were posted (from 62.7 days to 33.8 days) between from 2013
to 2014 (Fig 2D).
While overall TAT from samples collected in January through November were similar
(ranging from a low of 18.8 in June to a high of 26.0 in November), samples collected in
December had a substantially longer overall TAT (42.1 days). This difference was driven
primarily by longer TAT for processing and posting results at the laboratory.
This study provides the most comprehensive description of TAT for EID in Kenya, to date.
The mean turnaround time from when a DBS sample was collected from an infant to when a
result was posted was 24.7 days. This is faster than the 1.5±2 months reported in other studies
in Kenya [
]. The TAT in this study was slightly longer than the 2.5 weeks observed in a
preliminary analysis of the HITSystem at one hospital and one laboratory (Lab 2) by Gautney
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Fig 2. Mean turnaround times from 2011±2014. (A) Mean turnaround times by year for all labs. (B) Mean
turnaround times by year for lab 1. (C) Mean turnaround times by year for lab 2. (D) Mean turnaround times by
year for lab 3.
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et al . This analysis, however; includes 3 laboratories and 15 hospitals, increasing variability
of TAT across hospitals and laboratories. While this increases the representativeness of these
data it also contributed to the higher TAT reported here, partially due to the lack of consistent
practices and capacity among sites. Clinic and laboratory characteristics, including varying
levels of HITSystem ownership and varying rates of HITSystem adoption among facilities [
and staffing levels and commitment at the different facilities may have contributed to
differences in TAT. Furthermore, distance and road conditions between the hospital and the
laboratory and the role of outside funding agency and varying EID volume among labs may have
contributed to variations in sample shipment and sample processing times, respectively.
Time between receipt of sample at the laboratory to having a result posted accounted for
the majority of total processing, followed by the time between a sample being obtained and
being shipped. Shipping time was the shortest segment of the total TAT. In our study, the 17.4
day TAT from when a sample was received at the laboratory until a result was posted,
reflecting efficiency of processing at the central laboratory, was slightly longer than the 13.5 days
reported in Khamadi, et al [
], the only study we found from Kenya that reported on TAT
between receipt of sample at the lab and availability of results to the hospital. However, data
from Khamadi was collected from 2006±2008, prior to the substantial scale up of EID services
in Kenya [
]. The increased TAT observed in this study could indicate an inadequate
investment in laboratory infrastructure and personnel over the last several years to meet the growing
demand for EID laboratory services and introduction of viral load testing for all HIV+ patients
in 2014 [
]. Such improvements in the monitoring of HIV care will necessitate substantial
scale up of laboratory capacity including purchases of additional laboratory diagnostic
equipment, hiring and in-depth training of more laboratory technicians, and/or introduction of
more health facilities with the capacity to run on-site testing. Without these investments,
laboratories will be hard pressed to meet the growing demands for both EID and viral load sample
processing. Point-of-care tests for EID is an emerging technology that has the potential to
improve timely diagnosis by enabling same day results, significantly reducing the burden on
laboratories, and improving pediatric HIV programming [
Delays at both the clinic and the laboratory contributed to the wide range of turnaround
times observed in this study. Five of the 15 hospitals exceeded the 1 week target for shipping
samples and excessive delays (> 60 days) to post test results were documented for 5% of the
samples; indicating that improved oversight and management of such outliers could
significantly improve overall TAT. We also observed some variability between sample shipment and
receipt at lab between hospitals. While a single courier service is used across the country,
distance from the hospitals to the designated central laboratory or road conditions could account
for differences in TAT from shipping from hospital to receipt at laboratory. It may be
important that courier companies with different capacities and efficiencies be identified to help
improve efficiency of the sample transportation to testing laboratory.
Samples collected in December had the longest TAT, likely reflecting holiday travel and
reduced availability of laboratory technicians. Staggering staff and support during the holiday
season could help avoid lengthy delays. Since it is the excessive outliers that drive patient and
provider dissatisfaction and can lead to repeat testing [
], improving TAT could minimize
the need for repeat testing, and increase the percent of caregivers who collect their infant's
results in a timely manner [
]. Long TAT also delays infant ART initiation and can seriously
compromise the health of HIV-infected infants. Tightening the range of TATs for EID samples
is essential in order to optimize infant health outcomes, cost-effectiveness, and patient
In our sample, only 1.2% of DBS samples did not have a result posted by the end of the data
collection period. This represents a significant improvement on results returned compared to
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the estimated 10%-63% of all infant samples in East Africa that never make it back to the
]. The HITSystem's mechanisms for sample tracking and hospital/laboratory
communication when test results are delayed may have contributed to the small number of unreturned
A number of limitations to the current study should be noted. First, as a retrospective
review of HITSystem data, we did not have a comparison group. Thus, we were unable to
comment on TATs in standard of care settings during the same period of time and whether these
data represent an improvement to standard of care. A more rigorous randomized control trial
to evaluate the HITSystem is underway [
], which will enable us to make such comparisons.
Secondly, for this study, we are unable to know the exact reason for delays for individual
samples or labs. However, we hope that through the ongoing randomized controlled trial to
evaluate the HITSystem, [
] we will have more documentation on sample processing that will
allow us to identify reasons for delays among specific samples, labs, and clinics. Thirdly, the
interpretation of TAT changes over time is limited given the staggered start date of hospitals
and their associated central laboratory over the study period. Two of the three laboratories
began using the HITSystem in 2011, while Lab 3 began in 2013, and each showed
improvement in the year after HITSystem implementation. Lastly, of the seven central laboratories in
Kenya, the current study includes data for only three laboratories and therefore we are unable
to comment on the TAT of the other four laboratories. Despite these limitations, these data
provide an evaluation of TAT with a large sample size, inclusion of multiple central
laboratories and hospitals, and the ability to observe changes over time.
This study provides the most comprehensive and up to date description of turnaround time
for EID in Kenya. While overall TAT showed modest improvement from 2011 to 2014,
investments in infrastructure and laboratory personnel are needed to accelerate TAT, reduce the
number of samples with excessive delays, and ensure that all laboratories providing EID
diagnostic services are capable of keeping up with the increasing workload. While lack of a control
group limits our ability to compare these results from HITSystem facilities to standard of care,
this study provide promising evidence that the HITSystem can effectively monitor and manage
TAT for EID samples by identifying TAT segments and time periods that need targeted
support (i.e. sample processing at laboratories, staggered support during holiday months).
We are grateful to the mothers and infants who participated in the HITSystem intervention
and the clinical staff who were integral to these efforts. We also acknowledge the contributions
of Vincent Okoth. We acknowledge the support we have received from Kenya Medical
Research Institute and the Walter Reed Project, Kericho Field Station in executing this work.
We also acknowledge the critical role of our government partners at NASCOP; Nancy Bowen,
Dr. Martin Sirengo, Dr. Irene Mukui, Dr. Ibrahim Mohamed and Dr. William Maina. We are
also appreciative of the support of KEMRI Director, Dr. Yeri Kombe and Deputy Director of
Research, Dr. Evans Amukoye, and research mentors Drs. Andrea Ruff and Michael Sweat.
Without the generous pro-bono contributions of HITSystem software developers at OnTarget
LLC, Terry Oehrke and Brian Hickey, these efforts would not have been possible.
HITSystem Team Author List:
Study coordination: Charles Bawcom (Global Health Innovations, Kansas City, KS), Martin
Ochieng (Kenya Medical Research Institute, Nairobi, Kenya)
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Site Investigators: Sharon Koech (Kenya Medical Research Institute, Nairobi, Kenya), Irene
Odera (Kenya Medical Research Institute, Nairobi, Kenya), Joey Odeke (Kenya Medical
Research Institute, Nairobi, Kenya), Nancy Ndung'e (Kenya Medical Research Institute,
Nairobi, Kenya), and Patrick Mwinamo (Kenya Medical Research Institute, Nairobi, Kenya).
Lead author for HITSystem Team: Charles Bawcom
Conceptualization: Catherine Wexler, Brad Gautney, Sarah Finocchario-Kessler, Kathy
Goggin, Samoel Khamadi.
Formal analysis: An-Lin Cheng.
Funding acquisition: Brad Gautney, Sarah Finocchario-Kessler.
Investigation: Catherine Wexler, Brad Gautney, Sarah Finocchario-Kessler, Kathy Goggin,
Samoel Khamadi, .
Methodology: Catherine Wexler, An-Lin Cheng, Brad Gautney, Sarah Finocchario-Kessler,
Kathy Goggin, Samoel Khamadi.
Project administration: Catherine Wexler, Brad Gautney, Sarah Finocchario-Kessler, .
Supervision: Brad Gautney, Sarah Finocchario-Kessler, Kathy Goggin, Samoel Khamadi, .
Visualization: An-Lin Cheng.
Writing ± original draft: Catherine Wexler.
Writing ± review & editing: Catherine Wexler, An-Lin Cheng, Brad Gautney, Sarah
Finocchario-Kessler, Kathy Goggin, Samoel Khamadi, .
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