https://orcid.org/0000-0002-2918-1373
Figures
Abstract
Region-specific laboratory reference intervals (RIs) are important for clinical trials and these data are often sparse in priority areas for research, including Africa. We reviewed data on RIs from Africa to identify gaps in the literature with a systematic review of PubMed for RI studies from Africa published ≥2010. Search focus included clinical analytic chemistry, hematology, immunological parameters and RIs. Data from adults, adolescents, children, pregnant women, and the elderly were included. We excluded manuscripts reporting data from persons with conditions that might preclude clinical trial participation in studies enrolling healthy volunteers. Of 179 identified manuscripts, 80 were included in this review, covering 20 countries with the largest number of studies in Ethiopia (n = 23, 29%). Most studies considered healthy, nonpregnant adults (n = 55, 69%). Nine (11%) studies included pregnant women, 13 (16%) included adolescents and 22 (28%) included children. Recruitment, screening, enrollment procedures and definition of age strata varied across studies. The most common type of RIs reported were hematology (66, 83%); 14 studies (18%) included flow cytometry and/or T cell counts. Other common tests or panels included liver function assays (32, 40%), renal function assays (30, 38%), lipid chemistries (17, 21%) and serum electrolytes (17, 21%). The number of parameters characterized ranged from only one (three studies characterized either CD4+ counts, D-dimer, or hemoglobin), to as many as 40. Statistical methods for calculating RIs varied. 56 (70%) studies compared their results to international RI databases. Though most presented their data side-by-side with international data with little accompanying analysis, nearly all reported deviation from comparator RI data, sometimes with half or more of otherwise healthy participants having an “out of range” result. We found there is limited local RI data available in sub-Saharan Africa. Studies to fill this gap are warranted, including efforts to standardize statistical methods to derive RIs, methods to compare with other RIs, and improve representative participant selection.
Citation: Price MA, Fast PE, Mshai M, Lambrick M, Machira YW, Gieber L, et al. (2022) Region-specific laboratory reference intervals are important: A systematic review of the data from Africa. PLOS Glob Public Health 2(11): e0000783. https://doi.org/10.1371/journal.pgph.0000783
Editor: Nikita Mehra, Cancer Institute Women’s India Association, INDIA
Received: April 3, 2022; Accepted: October 6, 2022; Published: November 14, 2022
Copyright: © 2022 Price et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: This is a systematic review. The source data (i.e., data abstracted from reviewed manuscripts) are in an Excel file in the Supporting Information files.
Funding: This IAVI study was entirely funded by The Bill & Melinda Gates Foundation (reference number INV-024949). No author received any funding from any other source. The funder played no role in the data collection, analysis or writing of the manuscript, or the decision to submit it for publication.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Laboratory reference intervals (RIs) for a laboratory test are defined as that interval within which falls 95% of the healthy population. Accurate and appropriate RIs are important for the interpretation of clinical laboratory data, and are vital to guide the design, conduct and interpretation of clinical trials to ensure volunteer and product safety. Since 1992, systematic guidelines on the creation of reference intervals have been available from the Clinical Laboratory Standards Institute (CLSI) and the International Federation of Clinical Chemistry (IFCC) [1] and more recently new methodology has been proposed as part of a global effort under the IFCC Committee on Reference Intervals and Decision Limits (C-RIDL) to standardize generation of RIs [2–4]. Because establishing RIs is expensive, logistically demanding, and time-consuming, many laboratories rely on RIs from assay package inserts, textbooks, or published data, which may be derived from very different populations. However, international studies employing systematic methodologies have demonstrated important differences between populations for some analytes [5, 6].
Other issues hamper the interpretation of regional RIs. Research Investigators often employ convenience sampling methods, enrolling blood donors, institute staff or students for their studies of reference intervals. While often easier and less expensive, this could introduce a selection bias if the population studied is systematically different from the population against which the RIs are meant to be used (e.g., younger, different patterns in alcohol and/or tobacco consumption, not pregnant, or living with HIV). Selection of study participants should be documented and should include factors such as age, sex, evaluation of health (e.g., at a minimum with a questionnaire, ideally with a physical examination and laboratory testing for pathological conditions) so that partitions of meaningful subclasses may be well described [1]. Additional methodology problems include insufficient sample size, poorly defined statistical methods, incomplete or non-standardized data collection (e.g., demographics, medical history), and inconsistent screening and enrollment procedures.
Sub-Saharan Africa is increasingly being recognized as an important region to conduct clinical trials [7, 8], and thus appropriate RIs are needed to manage volunteer wellbeing and guide the interpretation of clinical trial results. Between 2004 and 2006 IAVI conducted a large, multisite study (including teams from Kenya, Uganda, Rwanda and Zambia) to establish RIs among persons potentially suitable for participation in HIV prevention trials [5, 9]. Since then, large-scale, systematic efforts to characterize regional RIs in India, China and elsewhere around the world have been published [2, 6, 10, 11] but data from sub-Saharan Africa has been more limited. Here we review publications presenting the generation of newer RIs specific to sub-Saharan Africa.
Methods
A systematic review of Pubmed and Pubmed Central was conducted in November 2020 to search for laboratory reference intervals in sub-Saharan African populations published in 2010 and later. The search was repeated in October 2021 to update any new publications in the interim. IAVI’s laboratory RI study included work done in 2004–6 [5, 9]; in the interest of focusing on more recent work, we excluded work conducted prior to 2006 (regardless of publication date). The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (http://www.prisma-statement.org/, accessed 7 January 2022). We did not pool data for any type of meta-analyses, as study designs, source populations, the nature of the data, and the rationale for this review made this irrelevant. The search focus included concepts related to standards in clinical, analytic chemistry, hematology, metabolism, immunological and other assays or markers of important substances found in blood, urine, tissues, and other human biological fluids and reference values/intervals, i.e., the range or frequency distribution of a measurement in a population that has not been selected for the presence of disease or abnormality. Manuscripts that considered only clinical decision limits (i.e., thresholds or ranges of analytes associated with disease or negative clinical outcomes) were not considered. We limited our initial search to studies conducted in Africa. Highly relevant papers were also reviewed for citations and cited papers and with the Pubmed feature find similar articles. Relevant manuscripts that were identified were checked for any additional manuscripts not initially identified. This review was not registered, and a protocol was not prepared. The following terms were searched as MESH terms and as free text words:
blood proteins/standards, c reactive protein/standards, blood sedimentation/standards, ferritins/standards, cytokines/standards, chemokines/standards, vitamin d/standards, blood cell count/standards, clinical laboratory techniques/standards, hematology/standards, reference standards/standards, reference values/standards, bilirubin/standards, metabolism/standards, eosinophils/metabolism, hemoglobins/standards, immunoglobulin g/standards, l lactate dehydrogenase/standards, neutrophils/methods, allergy and immunology/standards, medical laboratory science/standards, blood proteins/standards, blood chemical analysis/standards, clinical chemistry tests/standards, reference intervals.
A secondary search for “African reference standards” and synonyms was performed in African Journal Online (https://www.ajol.info/index.php/ajol), Sabinet (https://www.sabinet.co.za/), Google Scholar, Dimensions (https://www.digital-science.com/products/dimensions/), and Project Smile (https://resources.psmile.org/resources/equipment/reference-intervals/reference-ranges). Additional manuscripts were identified by review of selected publication references or by notification from an author.
One author (MP) reviewed each manuscript to extract details including year(s) of study, country or region of study, sample size, study population (i.e., healthy adults, pregnant women, adolescents [typically ages 13–17], children [typically 12 and under and age-stratified], or other), RI calculation methods, and analytes included. Then two authors (VK, PF) reviewed the abstracts independently and, where necessary, the extracted details to further screen manuscripts; both VK and PF had access to all papers. Studies were excluded if they were too old (study conduct was prior to 2006), RIs were not calculated or were calculated for a group with a specific pathological condition or environmental exposure that may not be broadly relevant for clinical trial participants in sub-Saharan Africa (we did seek studies of healthy persons living with HIV, though we did not find any), or the study was not conducted in sub-Saharan Africa. Some manuscripts did not specify when the study was conducted; we reviewed the manuscript for context about timing and opted to include these studies, as it did not appear that they were done prior to 2006. Reviews and editorials were also excluded, though their references were checked for additional manuscripts missed in our initial searches.
Data presented here include year(s) of study; country or regions of study; study population size, recruitment methods, and composition (including ages and methods to describe “health”); methods used to generate RIs; number, type, and category of analytes included; and whether comparisons were made with international findings and how these comparisons were presented (e.g., were they quantified by presenting the number of ‘out of range’ values when compared against another RI range? Were statistical tools employed to test reported RIs against existing ranges?). Bias was assessed by a narrative evaluation of selected study populations and statistical methods. Analytes are listed individually and broadly grouped into 8 categories in Table 1, including hematology, immunology/ lymphocyte subsets, liver/pancreas function, kidney function, blood gas parameters, serum electrolytes, lipid parameters, and others (including thyroid tests, diabetes, tumor markers, markers of inflammation, etc.). A list of abbreviations is shown in Table 2. Text on participant health and RI comparisons shown in Table 3 are often copied or paraphrased directly from the source manuscript.
Results
Populations studied
2,383 manuscripts were screened to identify 179 manuscripts (Including 5 from reviewing reference sections or by word-of-mouth; Fig 1) of potential interest; 80 were included in this review, covering 20 countries across sub–Saharan Africa (Fig 2) with the largest number of studies in Ethiopia (n = 23, 29%); only one study included more than one country (Malawi and Uganda).
* Included studies of how values varied under differing laboratory/storage conditions, studies relevant for comparative patient care but not RCTs, studies of lung function, focus on decision limits and not RIs etc. LMIC: Lower Middle-Income Country; RIs: Reference Intervals; RCT: Randomized Controlled Trial; SSA: sub-Saharan Africa.
Only one study included data from more than one country, Malawi, and Uganda. Rows and columns do not sum to total studies (80) as some studies include multiple study populations.
Most studies considered healthy, nonpregnant adults (n = 55, 69%). Nine (11%) studies included pregnant women, 13 (16%) included adolescents (ages 11–19; one study included 20 year olds) and 22 (28%) included children (<14 years old, typically stratified by age); three studies included elderly participants (4%) though specific age ranges varied and in one case the age range was not reported (Table 3). “Adult age” was not consistently defined, with 8 papers focused on “adult” RIs that included adolescents as adults, ranging in age starting from 13–17, and five papers started adult enrollment with volunteers over 18, with the lower age limit ranging from 19–25 years old; 6 studies also did not report either the upper and/or lower limit for adult ages (typically reporting median ages, Table 3). Several papers (10, 13%) considered multiple age groups (e.g., infants, children and adolescents were common for papers addressing RIs in the context of malaria prevention and control). Data for infants, children and adolescents were all age-stratified, though the age strata were not consistent across studies. Data for adolescents were typically also gender-stratified, but not always.
Thirteen studies (16%) omitted the year(s) when the study was conducted. In two cases, hematology and chemistry were published separately for the same study population (i.e., four publications, two study populations). We found no manuscripts including RIs for people living with HIV.
Recruitment, screening, and enrollment procedures varied significantly across studies. Blood donors represented the largest source of volunteers, with 15 (19%) studies recruiting from persons donating blood. Fourteen studies (18%) reported recruiting volunteers for RIs from screening and/or enrollment into clinical trials or observational studies. Only 23 (29%) studies reported efforts to enroll a representative sample, describing methods to randomly select volunteers from a well-defined source population. All studies attempted to enroll “healthy” volunteers, however the description of inclusion and exclusion criteria varied widely. Those studies that drew from volunteers of other studies or from blood donors may have involved prescreening on “health”, however this was often not reported (Table 3). Laboratory tests employed in screening volunteers for participation varied and was sometimes not made explicit. As reported in the reviewed papers, 53 (66%) studies screened for HIV (including maternal screening, where infants were enrolled), 37 (46%) for hepatitis B, 29 (36%) for hepatitis C, 20 (25%) for malaria, 18 (23%) for sexually transmitted infections (STI- typically syphilis), 4 (5%) for intestinal parasites (often not specified), and 1 (1%) for hepatitis A. One study reported “serological tests for viral infection” without reporting those tests, three others reported laboratory screening for “hepatitis” without providing further details (Table 3). Eighteen studies (23%) did not report any laboratory screening tests for enrollment (i.e., volunteers were enrolled if they or their caregivers reported good health); five of these 18 studies were in children and infants.
Most studies were cross-sectional; two studies (3%) were prospective, with follow up visits that compared resultant RIs over time, one in infants in Zimbabwe another in pregnant and postpartum women in Kenya (Table 3).
Laboratory analytes tested
We found 77 different laboratory parameters across the 80 studies in this review (Table 1). 66 (83%) papers include hematology parameters, 23 (29%) included immunology parameters ranging from immunoglobulin and CD4+ T cell counts, to more in-depth delineation of T cell subsets (Tables 1 and 3), 32 (40%) papers included liver function parameters, and 30 (38%) included renal function parameters. The number of parameters characterized in each manuscript ranged from only one (three studies each characterized either CD4+ counts, D-dimer, or hemoglobin), to as many as 40 (Table 3). A total of 54 different analysis platforms were reported, among the most common machines were Sysmex KX-21N (11), Beckton Dickson FACSCount (7) and FACSCalibur (8) cytometers, and Beckman Coulter ACT 5 Diff Hematology analyzer (9); two studies did not report their analysis platforms (Table 3). Statistical methods for calculating RIs varied; 46 manuscripts (58%) cited CLSI guidelines for defining RIs, five manuscripts (6%, all published since 2018) cited the newer C-RIDL guidelines for defining RIs, and 36 (45%) manuscripts described other methods, typically using nonparametric methods by reporting the observed 2.5th and 97.5th percentiles to describe a 95% RI (i.e., similar to the CLSI guidelines).
Comparisons with RIs for other populations
While all studies noted the importance of regionally appropriate RIs, only 56 (70%) studies presented their results compared to other RIs in an effort to describe regional differences. Most (35/56, 63%) presented them side by side with limited analysis or discussion. The remainder (21, 37%) presented some information on the number and percentage of their study population that would be tallied as “out of range” against these comparator RIs, with the most common comparator range for this exercise being the US Division of AIDS (DAIDS) adverse events grading tables (n = 9). Four (7%) manuscripts presented statistical analysis to show that the number of out of range participants differed significantly from comparator ranges, though these statistical methods were often unclear; one additional manuscript tested whether or not male or female RIs fell ‘out of range’ more frequently, but did not test their generated RIs against comparator RIs. None reported perfect correlation with values in Europe or North America; only one study from South Africa with a high prevalence of ‘mixed race’ volunteers and individuals found that the hematological parameters measured did not vary meaningfully from international RIs and recommended ongoing use of the internationally derived RIs (Table 3).
Discussion
Regionally appropriate reference intervals are a vital component to guiding the development, design, conduct and interpretation of clinical trials, as well as serving to support patient care. There is a paucity of published, rigorously conducted RI data available from sub-Saharan Africa. In this report, we summarize the diverse range of studies that describe laboratory reference intervals published after 2009 and before October 2021 (and conducted since 2006). We found significant regional heterogeneity; nearly one-third of all studies originated in Ethiopia, while central African countries were largely unrepresented. Many of the studies were at risk of bias that limited their suitability for use as reference intervals for future clinical trials; Most studies included significant issues including unrepresentative study populations, varying definitions of “healthy,” and different age cut offs for population strata. Misunderstanding what is ‘normal’ in a population can lead to difficulties in clinical research. Selection of study populations and inclusion/exclusion criteria for clinical trials may be set in such a way as to inhibit enrolment or bias outcomes, if based on inappropriate RIs. Setting hemoglobin requirements that will be met by African men but not by most African women of reproductive age, for example, could skew a study population away from gender equality. Additionally, we observed that study populations were highly varied and selected populations underrepresented (e.g., the elderly, people living with HIV). Though methods to define RIs have been available during the period of this review, nearly half the studies do not cite the CLSI or C-RIDL guidelines. The parameters measured were diverse, as were the analysis platforms used to measure them. There was near consensus across all studies that regionally appropriate RIs are important, but many investigators failed to present strong, consistent evidence for this. We recognize the limitations of searching published work for this type of data; Although we searched a diverse set of publication databases, some health care facilities may not have published their RIs, and we may have missed these data.
Since 1992, the Clinical and Laboratory Standards Institute (CLSI) in collaboration with the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) has published guidelines on the generation of new RIs. Now in its third edition [1], this document provides instruction for readers to create clinical laboratory RIs that “meet the minimum requirements for reliability and usefulness.” The document lays out recommendations to select an appropriate study population, possible exclusion criteria to stimulate discussion of how to approach enrolling “healthy” participants, suggestions on partitioning criteria (e.g., age, sex, race, pregnancy / stage of pregnancy), and an example study questionnaire to modify as needed and administer to participants. Topics also include pre-analytical considerations (e.g., participant preparation including fasting, timing of sample collection, abstinence from certain drugs, etc.) and sample collection, handling, and testing. The report recommends 120 persons per partition to ensure adequate numbers to establish a RI using non-parametric methods. Discussion of methods to identify and exclude outliers in the data are included. Once outliers are removed, the lower limit of the RI is thus the 2.5th percentile of the data set, and the upper limit is the 97.5th percentile. All this should be done in the context of a robust quality control program, including equipment maintenance and EQA, to ensure precise and accurate data.
Increasingly common are large, multi-center studies for region specific RIs [12]. In concert with these studies, the IFCC Committee on Reference Intervals and Decision Limits (C-RIDL) set out to develop more robust guidelines for developing RIs than available via the above described CLSI document, and to explore the feasibility of conducting large scale studies to compare data across countries and regions. Initial efforts focused on three chemistry assays, ALT, AST and GGT, done in Turkey, China, Spain and across the Nordic countries; data were similar for ALT and AST, suggesting the use of common RIs were appropriate in some cases, but that was not the case for GGT [12]. Despite the obvious gap of data from Latin American and Africa, it was encouraging to see some harmonization was possible.
Building on this work, C-RIDL led a worldwide project to harmonize the generation of RIs, publishing a protocol and SOPs [3] to help others generate comparable data. A major change was to increase the recommended sample size from the CLSI-recommended 240 (i.e., 120 men and 120 women) to 500. This larger sample size allows the comparison of data across multiple sites and enables investigators to control for independent variables (e.g., smoking, diet, race, alcohol consumption) and to establish whether some RIs might be suitable for global standardization. Additional age-stratification among adults (e.g., 10-year age brackets among adults) was also recommended. Newer methods have been described to help identify outlier values (e.g., latent abnormal value exclusion, LAVE [13]) and to better describe when post-enrollment exclusion can be done. The newer C-RIDL protocol includes more guidance on subject selection, data and sample collection and management, standardization of assay results, additional statistical methods to generate the RIs, and greater detail on when to partition data.
As whole genome data becomes more available and inexpensive, a future avenue for RI research may include incorporating genetics as partitioning factor or an independent variable. Studies have shown the influence of genetic variation on laboratory RIs and the integration of these data into efforts to generate new RIs could become routine. Integrating genetic data into the generation of RIs should allow physicians and clinical trialists greater precision when interpreting individual laboratory parameter results [14, 15]. Lowering costs and more widespread technology means these data may become more available for African researchers [16]. Unfortunately, these data are more prevalent for European populations, further highlighting the importance of doing this work in underrepresented regions [17].
There remains a strong need to generate region-specific RIs to guide clinical trials and patient care in sub-Saharan Africa. Newer methods adopted globally are streamlining generation, comparison, and interpretation of these RIs [2, 3, 13]. Additional resources and studies to fill this gap are warranted, including a focus on improving awareness of newer methods.
Acknowledgments
We thank Jackson Dykman at Explicom and Nicole Sender from IAVI for their help with Fig 2, the mapping of studies and populations. We thank Carl Verlinde for his early support on helping start this project.
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