Prevalence of chronic kidney disease among the high risk population in South-Western Ghana; a cross sectional study

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Prevalence of chronic kidney disease among the high risk population in South-Western Ghana; a cross sectional study

Richard KD Ephraim1*, Sylvester Biekpe1, Samuel A. Sakyi23, Prince Adoba1, Hope Agbodjakey1 and Enoch O. Antoh2

Author Affiliations

1 Department of Medical Laboratory Technology, School of Allied Health Sciences, University of Cape Coast, Cape Coast, Ghana

2 Department of Molecular Medicine, School of Medical Sciences, College of Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

3 Noguchi Memorial Institute for Medical Research, Legon, Ghana

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Canadian Journal of Kidney Health and Disease 2015, 2:40  doi:10.1186/s40697-015-0076-3

The electronic version of this article is the complete one and can be found online at: http://www.cjkhd.org/content/2/1/40

Received: 29 April 2014
Accepted: 28 August 2015
Published: 3 November 2015

© 2015 Ephraim et al.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Abstract

Background

Chronic Kidney Disease (CKD) is a major global health problem. CKD is one of the most common complications of diabetes mellitus and hypertension and carries a risk of cardiovascular morbidity and mortality and progression to end-stage kidney disease.

Objectives

This study sought to use the 2012 Kidney Disease Improving Global Outcomes (KDIGO) definitions to establish the prevalence and risk factors for CKD among a high risk population in the Sekondi-Takoradi metropolis.

Design

Cross sectional study.

Setting

Effia-Nkwanta regional and the Takoradi Government hospitals in South Western Ghana.

Patients

Two hundred eight consecutive adults with diabetes, hypertension or both.

Measurements

Serum creatinine and urine albumin-creatinine ratio respectively. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) was used to estimate glomerular filtration rate (GFR).

Methods

CKD was classified according to KDIGO.

Results

The prevalence of CKD was 30 %: 27 % in patients with diabetes, 22 % in patients with hypertension only and 74 % in patients with both diabetes and hypertension. GFR category G3a CKD was most prevalent stage (9 %). Albuminuria was highest among people with diabetes (39 %).

Limitations

A convenience sample of patients attending clinics.

Conclusion

CKD was prevalent in these high-risk patients.

Abrégé

Contexte

L’insuffisance rénale chronique (IRC) est un problème majeur de santé globale. Elle se révèle l’une des plus fréquentes complications du diabète sucré et de l’hypertension. De plus, l’IRC pose un risque accru pour les patients de souffrir, voire de mourir de cardiopathie, ou alors de voir leur état progresser vers l’insuffisance rénale terminale.

Objectifs de l’étude

L’étude a cherché à établir la prévalence et les facteurs de risque de l’IRC dans la population prédisposée de la métropole de Sekondi-Takoradi (Ghana) en utilisant les définitions proposées par « Kidney Disease Improving Global Outcomes » (KDIGO) en 2012.

Type d’étude

Il s’agit d’une étude transversale.

Contexte de l’étude

L’étude a été effectuée sur des patients de l’hôpital régional Effia-Nkwanta et de l’hôpital gouvernemental de Takoradi, dans le sud-ouest du Ghana.

Patients

L’étude était constituée d’une cohorte de 208 adultes atteints de diabète, d’hypertension ou d’une comorbidité.

Mesures

Le rapport albumine-créatinine dans l’urine ainsi que le taux de créatinine sérique ont été mesurés, puis le débit de filtration glomérulaire (GFR) a été déterminé à l’aide de l’équation du « Chronic Kidney Disease Epidemiology Collaboration » (CKD-EPI).

Méthodologie

L’IRC a été déterminée selon les critères de KDIGO.

Résultats

À la suite de cette étude, la prévalence d’IRC a été établie à 30 % parmi les patients de la cohorte. Elle s’établissait à 27 % chez les patients atteints de diabète seulement, 22 % chez les patients atteints d’hypertension seulement et de 74 % chez les patients présentant à la fois du diabète et de l’hypertension. Le stade d’IRC (9 %) le plus prévalent était de catégorie G3a. La prévalence d’albuminurie était plus élevée chez les patients diabétiques (39 %).

Limites de l’étude

Il s’agit d’un échantillon de commodité formé de patients fréquentant les deux cliniques mentionnées plus haut.

Conclusions

La prévalence d’insuffisance rénale chronique était plus élevée chez ce groupe de patients considérés à haut risque.

Background

Chronic Kidney Disease (CKD) is defined as abnormalities of kidney structure or function, present for more than 3 months, with implications for health [1], [2]. It is characterized by either decreased glomerular filtration rate (GFR) or albuminuria, or both, and carries a risk of cardiovascular morbidity and mortality and progression to end-stage renal disease (ESRD) [3]. Chronic kidney disease is thought to be prevalent in sub-Saharan Africa and to be a major public health problem [4]. Resources for recognition and management aiming at reduction in progression are limited, and resources for the treatment of ESRD severely limited [4].

Chronic kidney disease (CKD) is one of the most common complications of diabetes mellitus [5] and hypertension [5]. Screening for CKD is not routinely performed in many diabetic clinics in sub-Saharan Africa because of limited human resource, diagnostic facilities and the cost of the tests [5]–[7].

Several studies within sub-Saharan Africa have examined the prevalence of CKD in people at high risk, including those with diabetes and hypertension. Janmohamed et al., [7] recorded 84 % prevalence in adult outpatients with diabetes in Tanzania, and Osafo et al. [8] showed a CKD prevalence of 47 % among Ghanaian patients, mainly from the Greater Accra region, with hypertension. In addition, Sumaili et al., [9] recorded 44 % prevalence in patients with hypertension, 39 % in those with diabetes; 16 % in people with obesity and 12 % in those who had human immunodeficiency virus (HIV) or acquired immunodeficiency syndrome (AIDS). We used the 2012 guidelines of the kidney disease improving global outcomes (KDIGO) to classify CKD among patients with diabetes, hypertension and both and also identified the associated risk factors for CKD in the Sekondi-Takoradi metropolis in south western Ghana.

Methods

Study design and study site

A cross-sectional study was conducted at the outpatient diabetes and hypertension clinics of the Effia-Nkwanta Regional hospital (ERH) and the Takoradi Government Hospital (TGH) in the Sekondi-Takoradi metropolis between December 2012 and May 2013. These serve as the major healthcare facilities in the metropolis providing primary, secondary and tertiary healthcare services for a population of 445,000. The healthcare system is accessible to those who contribute or pay the minimum of GH 20.0 yearly premium, equivalent to about three times the daily minimum wage of GH 6.0; about 66 % of the population is covered. In Ghana, patients with diabetes or hypertension receive specialized care in teaching, regional or municipal hospitals since they are the only facilities with the capacity to diagnose and manage this condition. Sekondi-Takoradi is the administrative capital of the Western Region. It has a land area of 385 km 2 and is located in the South-Western part of Ghana and about 242 km west of Accra, the capital city of Ghana.

Inclusion and exclusion criteria

We enrolled eligible adult (>18 years) outpatients receiving medical care at the diabetes and hypertension clinics of the hospitals during the study period. Patients diagnosed with high blood pressure or on anti-hypertensive drugs, diabetes or both hypertension and diabetes were included in this study. We excluded patients with other kidney diseases (such as glomerulonephritis, vasculitis, kidney infection, connective tissue disease or adult polycystic kidney disease), those undergoing peritoneal or hemodialysis, and those with inflammatory bowel disease or rheumatoid arthritis. We also excluded people with known hepatitis B or C and HIV/AIDS.

Patient screening, recruitment and data collection

We screened 382 consecutive patients with diabetes, hypertension or both who visited the outpatient department of the two hospitals for routine evaluation. Diabetes was defined as a diagnosis of diabetes or taking a hypoglycaemic drug, and hypertension as a diagnosis of hypertension or taking an anti-hypertensive drug. Information on age, gender, fasting blood glucose, body mass index (BMI), systolic blood pressure and diastolic blood pressure, medication used, duration on medication, and duration of diabetes was obtained using a pre-tested questionnaire and the patient medical records.

Measurement of blood pressure

Trained personnel used a mercury sphygmomanometer (ACCOSON, England) with a standard or a large cuff, appropriate to the patient’s size, to measure blood pressure after patients rested for 5 min, in accordance with recommendations of the American Heart Association Council on High Blood Pressure Research [10]. We report mean values of duplicate measurements.

Body mass index (BMI)

Height (nearest centimetre) and weight (nearest 0.1 kg), without shoes and in light clothing were measured. Participants were weighed on a bathroom scale (Zhongshan Camry Electronic Co. Ltd, Guangdong, China) and their height measured with a wall-mounted ruler. BMI was calculated by dividing weight (kg) by height squared (m 2 ), and categorized according to WHO criteria into normal weight (BMI 18.5–24.9), underweight (<18.5), overweight (25.0–29.9), obese (30.0–39.9) [11].

Blood sample collection and processing

A 4 ml venous blood sample was collected from each participant and 1 and 3 ml were dispensed into a fluoride oxalate tube and a serum gel separator tube respectively. After centrifugation at 1500 g for 3 min, the plasma and serum were stored in cryovials at −80 °C until assays were performed.

Biochemical analysis

Plasma fasting blood sugar (FBS), serum urea and creatinine were estimated using automated chemistry analyzer (Selectra JR). Estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI equation using the coefficients for black ethnicity in all [12].

Urine sample collection and processing

Urine protein was quantitatively estimated using the method of [13]. Estimation of urine creatinine was done using automated analyzer (ENVOY500/BT 3000 chemistry analyzer. Urine protein-creatinine ratio (uPCR) was calculated by the following formula: uPCR (mg/mmol = urine protein (mg/dl)/urine creatinine (mmol/dl). The urine protein/creatinine ratio (uPCR) was reported as mg/mmol. Resources were not available to measure albuminuria. The relationship between uACR and uPCR is not a simple one, so we did not attempt to convert between the two [14]. Instead we report uPCR in uACR categories, recognizing that this leads to overestimation of the proportions of patients with problems.

Statistical analysis

Analysis was performed using Graphpad prism version 5.0 (GraphPad software, San Diego California USA, www.graphpad.com). Two-sample Student’s t test and chi-squared or Fisher’s exact test, as appropriate, and one-way analysis of variance (ANOVA) were used to compare groups. A P-value ≤0.05 was considered statistically significant.

Ethical considerations

The study was approved by the University of Cape Coast institutional review board (UCC/IRB) and the committee of ethics of TGH and ERH. Written informed consent was obtained from all participants.

Funding

Study procedures including collection of clinical data and the laboratory tests were funded by the authors.

Results

We screened 382 consecutive patients, of whom 76 were less than 18 years, and 67 declined to participate: of the 239 consenting participants, 208 provided both blood and urine samples (i.e., 87 % with complete data) and are the subject of this report. Mean age was 60 and 71 % were female (Table 1); blood pressure was higher in participants with known hypertension. The distribution of body weight did not vary with diagnosis (diabetes, hypertension or both) (Table 1). Figures 1 and 2 report the types of medication used by participants and the types of medications used by hypertensives and diabetics respectively.

Table 1. Demographic, clinical and biochemical characteristics of study participants stratified by clinical conditions

thumbnailFig. 1. Types of medications used by participants

thumbnailFig. 2. Medications used by participants with diabetes and hypertension

Overall, 13 of 208 participants (6.2 %) had GFR less than 30, and 50 (24 %) had eGFR less than 60 mL/min/1.73 m 2 (Table 2); 4 of 40 participants (10 %) had uPCR > 30 mg/mmol, and 43 (96 %) had uPCR 3–30 mg/mmol. GFR less than 30 mL/min/1.73 m 2 and uPCR > 3 were more prevalent in those with both diabetes and hypertension than in patients with just one diagnosis (Table 3). Age and gender were similar across eGFR categories, but patients with the lowest eGFR had the highest systolic and diastolic blood pressures, and systolic blood pressure was above 140 in 17 and 26 % respectively of those in eGFR categories 3a and 3b, and in 36 % of those in eGFR category 4 (eGFR less than 30 mL/min/1.73 m 2 ).

Table 2. Prevalence of albuminuria, estimated glomerular filtration rate (eGFR) and stages of CKD stratified by clinical conditions

Table 3. Prevalence of albuminuria and estimated glomerular filtration rate (eGFR) stratified by clinical conditions

Table 3 show the distribution of patients by GFR and albuminuria categories, for those with hypertension, diabetes and both diabetes and hypertension. Overall, 30 % of participants fell into the category defined by KDIGO as ‘very high risk’: 23 % of patients with hypertension, 27 % of patients with diabetes and 74 % of those with both diabetes and hypertension.

Multivariable predictors of the presence of CKD were diagnosis category and duration on medication, both with odds ratios around 10, but not age, gender or BMI (Table 4).

Table 4. Multivariable associations of clinical variables with CKD in high-risk population

Discussion

We identified a prevalence of CKD in patients with hypertension of 22 % and in patients with diabetes of 27 %. In patients with both hypertension and diabetes, the prevalence was 74 %, and 26 % had category G4 CKD. Clinical factors associated with a greater risk of CKD were the presence of both hypertension and diabetes, and duration on medication (antidiabetic and antihypertensive).

Osafo and colleagues [8] reported a 47 % prevalence of CKD among patients with hypertension in Ghana, in a multicenter study conducted predominantly among people with hypertension in the Greater Accra area (known for a high prevalence of hypertension). This is higher than the overall prevalence of 30 % among our study participants, and 22 % in people with hypertension. The difference may, in part, be owing to our having used the CKD-Epi [12] equation rather the MDRD equation, which was used in the study by Osafo and colleagues. MDRD is known to overestimate the prevalence of CKD compared with CKD-Epi, and this has also been shown by Kitiyakara and colleagues in their study of the high risk population in South East Asia [15]. In all patients with diabetes (with or without hypertension) we observed a prevalence of 48 %, which is lower than the 80 % prevalence observed among African adults with diabetes in a cross-sectional study conducted in Tanzania by Janmohamed and colleagues [7]. Again this is not directly comparable, with differences arising from their use of Cockroft-Gault equation to calculate the eGFR (which overestimates true GFR and underestimates prevalence); and urine albumin concentration as a measure of proteinuria (which is the recommended method of assessing proteinuria; our use of uPCR overestimates prevalence) but both these differences would result in a tendency for our prevalence by our methods to be higher than by their methods; so it may be that true differences exist. However, neither study used IDMS calibrated creatinine measurement and the direction of biases resulting from this limitation cannot be assessed.

Osafo and colleagues [8] observed a 51 % prevalence of CKD in patients with coexistent diabetes and hypertension, based on data from 712 participants in a multicenter study in Accra, Ghana, and we observed 74 % prevalence of CKD in our participants. Since their use of the MDRD formula would have biased their findings towards a greater incidence of CKD, it is possible that the prevalence is truly higher in this group in Ghana.

Good blood pressure control and ACE inhibitors are known to have a reno-protective effect, particularly in people with albuminuria [16]. In our study, 30 % overall, 11.1 % of people with diabetes and hypertension, 9.2 % of people with albuminuria and hypertension received ACE inhibitor therapy. We are unable to determine from our study to what extent this relatively low prevalence represents true contraindications or previous adverse effects, or whether this is a possible missed treatment opportunity that results from the economic costs of the drug (most of which are borne by the patients) or a reluctance on the part of physicians to prescribe ACE inhibitors without access to repeated monitoring of renal function (laboratory tests are paid for by the patient).

Our study has several limitations. First our findings cannot be generalized to other low income and low resource countries because it was not community based and was conducted within a population at risk of developing CKD with genetic and cultural differences. Further, there may be differences in the practices that lead to a patient being identified as having hypertension or diabetes, and differences in access to treatments for, and monitoring of those conditions. The study was conducted in the Sekondi-Takoradi metropolis. It is likely that there would be significant variation in prevalence rates in other urban and rural towns in the Western region and across Ghana as a whole. This study is also limited by the small sample size, use of the single measurement of serum creatinine (whereas to truly fulfill definitions of CKD, two measurements at least 3 months apart are needed), and by our lack of standardization of serum creatinine to isotope mass dilution spectrophotometry (IMDS). Third, although the CKD-EPI eGFR equation has been used in previous studies in this population [8], [18], [19] it has not been validated for use in the black Ghanaian population. Strengths of our study are the consecutive sampling and completeness of data collection.

Conclusion

CKD was detected among 30 % of this high-risk population. Further research is needed into optimal approaches to screening and treatment, including research on the effects of lowering economic barriers to known effective treatments. This is particularly important in resource-constrained practice settings such as ours, because the impact of the development of end-stage renal disease when dialysis cannot be provided is so much greater.

Competing interests

The authors declare that there is no conflict of interest associated with this manuscript.

Authors’ contributions

RKDE, SB, SAS and HA were involved in conception of the idea and study design and data analysis. RKDE, SB, PA and EOA were involved in recruitment of participants, data collection and compilation. RKDE, SB, SAS and EOA were involved in laboratory work, literature search and drafting of the manuscript. RKDE, PA and HA were involved in revision and final approval of the manuscript. All authors read and approved the final manuscript.

Acknowledgement

The authors appreciate the contributions Mr. Samuel K. Danquah and the staff and management of the Effia Nkwanta Regional Hospital (ENRH) and the Takoradi Government Hospital (TGH) especially the Laboratory Department in making this work a success. We also appreciate the contributions of the humble people of the Sekondi-Takoradi metropolis for availing themselves for this research.

References

  1. Stevens PE, Levin A. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013;158(11):825–30.
  2. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl. 2013;3:1–150.
  3. Hallan SI, Dahl K, Oien CM, Grootendorst DC, Aasberg A, Holmen J et al.. Screening strategies for chronic kidney disease in the general population: follow-up of cross sectional health survey. BMJ. 2006; 333(7577):1047. PubMed Abstract | Publisher Full Text OpenURL
  4. Naicker S. End-stage renal disease in sub-Saharan Africa. Ethn Dis. 2009; 19(1 Suppl 1):S1-13-15. OpenURL
  5. Gill GV, Mbanya JC, Ramaiya KL, Tesfaye S. A sub-Saharan African perspective of diabetes. Diabetologia. 2009; 52(1):8-16. PubMed Abstract | Publisher Full Text OpenURL
  6. Perico N, Remuzzi G. Chronic kidney disease in sub-Saharan Africa: a public health priority. Lancet Glob Health. 2014; 2(3):e124-5. PubMed Abstract | Publisher Full Text OpenURL
  7. Janmohamed MN, Kalluvya SE, Mueller A, Kabangila R, Smart LR, Downs JA et al.. Prevalence of chronic kidney disease in diabetic adult out-patients in Tanzania. BMC Nephrol. 2013; 14:183. PubMed Abstract | BioMed Central Full Text OpenURL
  8. Osafo C, Mate-Kole M, Affram K, Adu D. Prevalence of chronic kidney disease in hypertensive patients in Ghana. Ren Fail. 2011; 33(4):388-92. PubMed Abstract | Publisher Full Text OpenURL
  9. Sumaili EK, Cohen EP, Zinga CV, Krzesinski JM, Pakasa NM, Nseka NM. High prevalence of undiagnosed chronic kidney disease among at-risk population in Kinshasa, the Democratic Republic of Congo. BMC Nephrol. 2009; 10:18. PubMed Abstract | BioMed Central Full Text OpenURL
  10. Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves J, Hill MN, Jones DW, Kurtz T, Sheps SG, Roccella EJ: Recommendations for blood pressure measurement in humans and experimental animals: Part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Hypertension 2005;45(1):142–161.
  11. Physical status: the use and interpretation of anthropometry, Report of a WHO expert committee. Technical report series. WHO, Geneva; 1995. OpenURL
  12. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI et al.. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009; 150(9):604-12. PubMed Abstract | Publisher Full Text OpenURL
  13. Pesce MA, Strande CS. A new micromethod for determination of protein in cerebrospinal fluid and urine. Clin. Chem. 1973; 19:1265-1267. PubMed Abstract | Publisher Full Text OpenURL
  14. Johnson DW, Jones GR, Mathew TH, Ludlow MJ, Chadban SJ, Usherwood T et al.. Chronic kidney disease and measurement of albuminuria or proteinuria: a position statement. Med J Aust. 2012; 197(4):224-5. OpenURL
  15. Kitiyakara C, Yamwong S, Vathesatogkit P, Chittamma A, Cheepudomwit S, Vanavanan S et al.. The impact of different GFR estimating equations on the prevalence of CKD and risk groups in a Southeast Asian cohort using the new KDIGO guidelines. BMC Nephrol. 2012; 13:1. PubMed Abstract | BioMed Central Full Text OpenURL
  16. Baltatzi M, Savopoulos C, Hatzitolios A: Role of angiotensin converting enzyme inhibitors and angiotensin receptor blockers in hypertension of chronic kidney disease and renoprotection. Study results. Hippokratia 2011, 15(Suppl 1):27–32
  17. van der Meer V, Wielders HPM, Grootendorst DC, de Kanter JS, Sijpkens YWJ, Assendelft WJJ et al.. Chronic kidney disease in patients with diabetes mellitus type 2 or hypertension in general practice. Br J Gen Pract. 2010; 60:884-90. PubMed Abstract | Publisher Full Text OpenURL
  18. Owiredu W, Ephraim R, Amidu N, Eghan Jnr BA, Quaye L. Predictive performance of renal function equations among Ghanaians presenting with chronic kidney disease. J Med Sci. 2008; 8:491-7. Publisher Full Text OpenURL
  19. Eastwood JB, Kerry SM, Plange-Rhule J, Micah FB, Antwi S, Boa FG et al.. Assessment of GFR by four methods in adults in Ashanti, Ghana: the need for an eGFR equation for lean African populations. Nephrol Dial Transplant. 2010; 25(7):2178-87. PubMed Abstract | Publisher Full Text OpenURL

The effectiveness of interventions to improve uptake and retention of HIV-infected pregnant and breastfeeding women and their infants in prevention of mother-to-child transmission care programs in low- and middle-income countries: protocol for a systematic review and meta-analysis

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The effectiveness of interventions to improve uptake and retention of HIV-infected pregnant and breastfeeding women and their infants in prevention of mother-to-child transmission care programs in low- and middle-income countries: protocol for a systematic review and meta-analysis

Lisa M. Puchalski Ritchie123, Monique van Lettow45*, Mina C. Hosseinipour67, Nora E. Rosenberg67, Sam Phiri8, Megan Landes39, Fabian Cataldo4, Sharon E. Straus12 and For the PURE consortium

Author Affiliations

1 Department of Medicine, University of Toronto, Toronto, ON, Canada

2 Li Ka Shing Knowledge Institute, St. Michaels Hospital, University of Toronto, Toronto, ON, Canada

3 University Health Network, Toronto, ON, Canada

4 Dignitas International, Zomba, Malawi

5 Dalla Lana School of Public Health, University of Toronto, Toronto, Canada

6 Division of Infectious Diseases, University of North Carolina, Chapel Hill, NC, USA

7 University of North Carolina Project, Lilongwe, Malawi

8 Lighthouse Trust, Lilongwe, Malawi

9 Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada

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Systematic Reviews 2015, 4:144  doi:10.1186/s13643-015-0136-x

The electronic version of this article is the complete one and can be found online at: http://www.systematicreviewsjournal.com/content/4/1/144

Received: 5 June 2015
Accepted: 15 October 2015
Published: 3 November 2015

© 2015 Puchalski Ritchie et al.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Abstract

Background

Despite recent improvements, uptake and retention of mothers and infants in prevention of mother-to-child transmission (PMTCT) services remain well below target levels in many low- and middle-income countries (LMICs). Identification of effective interventions to support uptake and retention is the first step towards improvement. We aim to complete a systematic review and meta-analysis to evaluate the effectiveness of interventions at the patient, provider or health system level in improving uptake and retention of HIV-infected mothers and their infants in PMTCT services in LMICs.

Methods/Design

We will include studies comparing usual care or no intervention to any type of intervention to improve uptake and retention of HIV-infected pregnant or breastfeeding women and their children from birth to 2 years of age attending PMTCT services in LMICs. We will include randomized controlled trials (RCTs), cluster RCTs, non-randomized controlled trials, and interrupted time series. The primary outcomes of interest are percentage of HIV-infected women receiving/initiated on anti-retroviral prophylaxis or treatment, percentage of infants receiving/initiated on anti-retroviral prophylaxis, and percentage of women and infants completing the anti-retroviral regimen/retained in PMTCT care. The following databases will be searched from inception: Ovid MEDLINE and EMBASE, The WHO Global Health Library, CAB abstracts, EBM Reviews, CINAHL, HealthSTAR and Web of Science databases, Scopus, PsychINFO, POPLINE, Sociological Abstracts, ERIC, AIDS Education Global Information System, NLM Gateway, LILACS, Google Scholar, British Library Catalogue, DARE, ProQuest Dissertation & Theses, the New York Academy of Grey Literature, Open Grey, The Cochrane Library, WHO International Clinical Trials Registry, Controlled Clinical Trials, and clinicaltrials.gov. Reference lists of included articles will be hand searched and study authors and content experts contacted to inquire about eligible unpublished or in progress studies. Screening, data abstraction, and risk of bias appraisal using the Cochrane Effective Practice and Organization of Care criteria will be conducted independently by two team members. Results will be synthesized narratively and a meta-analysis conducted using the DerSimonian Laird random effects method if appropriate based on assessment of clinical and statistical heterogeneity.

Discussion

Our findings will be useful to PMTCT implementers, policy makers, and implementation researchers working in LMICs.

Systematic review registration

PROSPERO CRD42015020829

Keywords:

HIV; Prevention of mother-to-child transmission; Interventions; Retention; Uptake

Background

Although the incidence of pediatric HIV acquisition is falling, over 240,000 children were newly infected with HIV in 2013, primarily through mother-to-child transmission [1]. Prevention of mother-to-child transmission (PMTCT) therapeutic regimens have been proven to reduce the risk of mother-to-child transmission from 20–45 % to 2 % in non-breastfeeding populations and 5 % or less in breastfeeding populations [2]. However, despite recent improvements in PMTCT clinical service coverage in low- and middle-income countries (LMICs) from 10 % in 2004 to 67 % in 2013, uptake and retention of mothers and newborns in PMTCT clinical services remain well below target levels in many LMICs [1], [2]. PMTCT services begin with maternal HIV testing and counseling and for HIV-infected women include the following: initiation and maintenance of pregnant and nursing women and their infants on PMTCT medication regimens for the duration of treatment as defined by the specific regimen employed; and completion of appropriate infant HIV testing. As a result of the 2010–2015 PMTCT strategic vision, the World Health Organization (WHO) has called for renewed commitment and effort towards achieving universal PMTCT coverage. The identification of interventions to support PMTCT uptake and retention is the first step towards improvement.

To date, two systematic reviews have been published that specifically evaluated the effectiveness of interventions to improve PMTCT coverage. Both were limited to specific interventions—male involvement [3] and integration of services [4] —and found too few studies meeting inclusion criteria to assess or make recommendations regarding effectiveness. A third systematic review indentified nine completed studies and five ongoing trials which examined initiation of antiretroviral (ARV) treatment in pregnant women [5]. While the authors report several promising interventions for improving ARV initiation, the quality of evidence was insufficient to support recommendations. In addition, results for ARV initiation in pregnant women were not independently examined, and maternal retention in PMTCT care and exposed infant care were not assessed. However, in our preliminary search, we identified a number of additional interventions including integration of HIV and antenatal care, peer-based programs, and community health worker programs [6]–[8] that have been evaluated to improve PMTCT uptake and retention in LMICs.

Given the paucity of synthesized evidence to date, we propose to complete a systematic review to identify what interventions are effective in improving uptake and retention of HIV-infected mothers and their infants in PMTCT services in LMICs. While we anticipate a relatively small number of evaluations of any given intervention type, which may preclude meta-analysis, a narrative synthesis of the evidence to date is urgently needed to inform LMIC PMTCT program development and policy. With the exception of Option B+ (lifelong triple ARV therapy for all HIV+ pregnant and breastfeeding women, regardless of clinical stage or CD4 count) recommended by WHO in April 2012 for which evidence is not yet available, the effectiveness of PMTCT regimens is well established and will therefore not be included in the present search [9].

Methods/Design

Protocol

A preliminary systematic review protocol was developed based on the Cochrane Handbook [10]. The protocol was revised with input from the PURE Malawi Consortium, a research partnership of governmental, non-governmental, and academic organizations working to improve PMTCT programming in Malawi. The final protocol was registered with the PROSPERO database (CRD42015020829, available at: http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42015020829#.VXHCNUZBn5I), with reporting of the protocol guided by the PRISMA-P [11].

Eligibility criteria

We will include studies of HIV-infected pregnant and breastfeeding women and their children from birth to 2 years of age or termination of breastfeeding in LMICs. For the purpose of this review, we will utilize the EPOC filter to identify low- and middle-income countries [12] updated using the most recent World Bank World Country and Lending group classification [13] to define LMICs. Based on the unique challenges facing PMTCT health services in LMICs and intended use of the findings of this review to inform PMTCT service development in Malawi and other LMICs, we chose to limit the review to studies conducted in LMICs. Studies conducted only in high-income countries or where LMIC results cannot be separated will not be eligible for inclusion.

We will include studies comparing usual care or no intervention to any type of intervention (including patient, provider, or health system level interventions) to improve uptake and retention of HIV-infected pregnant or breastfeeding women and their children from birth to 2 years of age in PMTCT services. Patient level interventions are those focused on the patient and may include patient education programs, peer support programs, or efforts to improve patient support through engagement of partners or family members. Provider level interventions may include provider training, incentive programs, or tools to improve care provided. Health system level interventions may include restructuring of services and task shifting or other mechanisms to address human resource shortages.

The primary outcomes of interest are percentage of HIV-infected women receiving or initiated on ARV prophylaxis or treatment, percentage of infants born to HIV-infected mothers receiving or initiated on ARV prophylaxis, and percentage of women and infants retained in PMTCT care/completing the ARV regimen as defined by the PMTCT regimen utilized. Secondary outcomes of interest include the following: percentage of infants completing post-exposure HIV testing at 4–6 weeks after birth and percentage of infants completing post-exposure HIV testing at 6 weeks following termination of breastfeeding for all infants with known HIV exposure as recommend by the WHO [14]; percentage of HIV-exposed infants testing positive for HIV; and adverse events including negative impact(s) on resources/delivery and/or effectiveness of other health care programs (including economic impact), major (e.g., heart defects, neural tube defects, major limb malformations, hypospadias) or minor (e.g., syndactyly, cutis aplasia, accessory digit) congenital malformations, small for gestational age, premature delivery, still birth, and infant death within the first 2 years of life).

We will include controlled experimental studies (randomized controlled trials, cluster randomized controlled trials, non-randomized controlled trials) and controlled quasi-experimental studies (interrupted time series). We chose to include non-randomized controlled trials and quasi-experimental designs based on the results of our scoping searches, in which we found few randomized controlled trials that evaluated interventions to improve uptake and retention of HIV-infected women and their children in PMTCT services conducted in LMICs. Language of publication will be restricted to the language spoken by the study team and includes English only. No restrictions will be placed on publication status, study time frame, or duration of follow-up.

Information sources and literature search

Our search strategy was developed in consultation with an experienced information specialist and peer reviewed by two additional information specialists with expertise in systematic reviews using the Peer Review of Electronic Search Strategies checklist [15].

We will search the following electronic databases from inception to June 2015 using medical subject headings (MeSH) and text words related to HIV, pregnancy, breastfeeding, mother-to-child transmission, interventions, treatment uptake and retention, and low- and middle-income countries, using MEDLINE (OVID interface, 1946 to July Week 4 onwards), EMBASE (OVID interface, 1974 onward), The WHO Global Health Library (http://www.globalhealthlibrary.net/php/index.php), CAB abstracts (OVID interface, 1973 onward), EBM Reviews (OVID interface, 1991 onward), CINAHL (EBSCOhost Research Databases interface, 19,814 onward), HealthSTAR (OVID interface, 1966 onward) and Web of Science databases (Thompson Reuters interface, 1975 onward), Scopus (Elsevier Interface, 1823 onward), PsychINFO (OVID interface, 1806 onward), POPLINE (www.POPLINE.org, 1970 onward), Sociological Abstracts (Proquest interface, 1953 onward), ERIC (EBSCOhost Research Databases interface, 1966 onward), AIDS Education Global Information System (http://www.aegis.org), NLM Gateway (http://gateway.nlm.nih.gov/), LILACS (http://bases.bireme.br/cgi-bin/wxislind.exe/iah/online/?IsisScript=iah/iah.xis&base=LILACS&lang=i), Google Scholar (https://scholar.google.ca), British Library Catalogue (http://explore.bl.uk/primo_library/libweb/action/search.do?dscnt=1&dstmp=1445538063587&vid=BLVU1&fromLogin=true), DARE (LexisNexis Academic interface, 2010 onward), ProQuest Dissertation & Theses (Proquest Interface, 1637 onward), the New York Academy of Grey Literature (http://library.tmc.edu/website/new-york-academy-of-medicine-library-grey-literature-collection/), OpenGrey (http://www.opengrey.eu/), The Cochrane Library (http://www.cochranelibrary.com/), WHO International Clinical Trials Registry (http://www.who.int/ictrp/en/), Controlled Clinical Trials (http://www.controlled-trials.com/), and clinicaltrials.gov (https://clinicaltrials.gov/). In addition, we will search reference lists of included articles and will contact experts in the field to inquire about eligible unpublished or in progress studies. Low- and middle-income countries will be searched utilizing the EPOC LMIC filter [12], updated based on the most recent World Bank LMIC list [13], see Additional file 1 for full MEDLINE search strategy. We will employ the Cochrane highly sensitive search strategy for identifying randomized trials in OVID MEDLINE: sensitivity and precision maximizing version [16], with the following two changes: Random* was used in place of randomized or randomly and trials ti was not used as an isolated term.

Study selection process

All titles and abstracts identified by the database search will be entered into a reference manager and duplicates removed manually into the duplicate folder, with companion papers for the same study retained for further evaluation at the full article phase of the review. Citations will be screened in two phases, level 1 (titles and abstracts) and level 2 (full-text review). A screening checklist will be developed and pilot tested by the reviewers on a random sample of 50 citations for each screening phase. Inter-rater agreement will be calculated for the pilot test and the form revised and re-piloted if percent agreement is <90 %. Once adequate agreement has been achieved, two team members will independently screen citations using the screening checklist. Differences at each stage will be resolved by consensus and if necessary through discussion with a third team member who is a content expert. Reference lists of included studies will be reviewed independently by the same two team members and again differences resolved through consensus and if necessary consultation with a third team member. A review log will be maintained in order to provide a record of resolution of discrepancies, decisions regarding studies described in >1 report, and reasons for exclusion.

Data abstraction and management

Data abstraction forms will be developed and pilot tested. Two team members will independently abstract directly into excel spreadsheets, corresponding to outcome tables, with additional space for comments and reasons for exclusion. Inter-rater reliability will be measured for data abstraction on a sample of excluded and included articles (approximately 10 %), and if percent agreement is found to be below 90 %, abstraction is conducted by a third team member. All discrepancies will be reviewed and consensus reached through discussion.

Data abstraction will be based on the PICOST [17] format including population, intervention, comparator, context, outcomes, study DESIGN, and time frame. Population characteristics to be abstracted include maternal age, number of children, marital status, place of residence (rural/urban), level of education, primary language, first infant HIV testing (4–6 weeks), and at end of study. Study characteristics of interest include study design, country and geographical location within country (rural/urban), setting (home, hospital or health center clinic, maternity ward), detailed description of intervention and comparator (usual care/no intervention), number of participants per group at study baseline and follow-up, duration of intervention and follow-up period, source of data (self-report, clinical records, pill counting), and publication status. Outcome data to be abstracted include percentage of HIV-infected women and their infants receiving or initiating PMTCT treatment, retained in or completing PMTCT as defined by the PMTCT regimen(s) used. Where data necessary for analysis are missing, corresponding authors will be contacted.

Although improved in recent years, examples of cluster trails inappropriately analyzed (without adjustment for cluster randomization) may be found among older trials. Data on appropriateness of analysis will be abstracted and reported as part of the review findings.

Methodological quality/risk of bias appraisal

Risk of bias assessment will be conducted using the Cochrane Effective Practice and Organization of Care (EPOC) criteria for assessing risk of bias [18]. Categories of bias assessed by this tool for randomized controlled trials, and non-randomized controlled trails include: allocation concealment, measurement of baseline characteristics and outcomes, management of incomplete data, blinding of outcome assessment, protection against contamination, selective reporting, and other categories of bias [18]. Categories of bias assessed by this tool for interrupted time series and repeated measures studies include independence of intervention from other changes, pre-specification of the intervention effect shape, effect of data collection on the intervention, allocation concealment, management of incomplete data, selective reporting, and other sources of bias [18]. Two team members will independently assess the studies for risk of bias at both study and outcome levels with disagreement resolved by consensus and discussion with a third team member if necessary. Studies will not be excluded based on risk of bias assessment, but the information will be used in the analysis and reporting of findings. Risk of bias will be categorized as low, high, or unclear risk of bias, using the EPOC-suggested risk of bias criteria [18]. We have elected not to use GRADE for this review given that the review findings are urgently needed to inform PMCTC program development and policy and that the need to build capacity in the use of grade across the team which would significantly prolong the review timeline.

Risk of publication bias will be examined using funnel plots. For studies in which selected reporting bias is suspected, planned outcomes will be reviewed for registered trials and authors contacted for missing outcomes and for unregistered trials, and risk of selected reporting bias rated as unclear if response not received within 8 weeks of our initial email request.

Evidence synthesis

A flow diagram will be utilized to visually present the results of the search strategy and reasons for exclusion of articles. Included articles will be synthesized and reported narratively and in tabular form to provide an overview of findings, assessment of bias and its potential impact on reported findings, and strengths and weaknesses of included studies. Summary statistics for continuous outcomes will be expressed as mean difference and standardized mean difference with 95 % CIs, for outcomes reported using the same and different scales, respectively. Summary statistics for dichotomous data will be expressed as risk ratio with 95 % CI.

If meta-analysis is possible, it will be conducted using the DerSimonian Laird random effects method. Summary statistics will be expressed as risk ratios with 95 % confidence interval. Clinical heterogeneity will be determined based on patient, intervention, and outcome characteristics of included studies. Statistical heterogeneity will be determined visually and the impact of heterogeneity assessed using the I 2 test, with I 2 of 75 % considered significant. Given the time constraints for this review, re-analysis for unit of analysis errors will not be conducted and cluster trials with unit of analysis errors will be excluded from the primary meta-analysis, and their impact assessed with sensitivity analysis comparing meta-analysis with and without studies with unit of analysis errors included. Interventions at the patient, provider, and health system level will be reported separately and analyzed separately if possible to do so.

Discussion

The findings of this review will have significant implications for PMTCT program development and policy in LMICs. If high-quality evidence of intervention effectiveness is identified, this will provide important guidance to ongoing efforts to address low rates of uptake and retention of HIV-infected mothers and their infants in PMTCT services in LMICs. If high-quality evidence is not identified, findings of the systematic review may identify gaps in evidence and promising interventions providing direction for future intervention research.

To ensure our findings reach audiences who may benefit from the review findings, we plan to disseminate the results through publication in open access peer-reviewed journals, presentations at relevant international conferences, and direct communication within the professional networks of PURE consortium members.

Abbreviations

ARV: anti-retroviral

EPOC: Effective Practice and Organization of Care

HIV: human immunodeficiency virus

LMIC: low- and middle-income country

MeSH: medical subject headings

PMTCT: prevention of mother-to-child transmission

WHO: World Health Organization

Competing interests

LPR was funded by a KT Canada Strategic Training Initiative in Health Research Fellowship award in 2014. SS is funded by a Tier 1 Canada Research Chair in Knowledge Translation and Quality of Care. The authors have declared that no competing interests exist. The authors alone are responsible for the writing and content of the paper.

Authors’ contributions

LPR and MvL conceived the study. LPR and SS were responsible for developing the search strategy. LPR was responsible for preparing and registering the protocol and for manuscript preparation. LPR, MvL, and SS were responsible for finalizing the protocol. MCH, NER, SP, ML, and FC provided content expertise and assisted with preparation of the protocol and manuscript. All authors provided critical revision of the protocol and manuscript. All authors read and approved the final manuscript.

Additional file

Additional file 1:. Ovid MEDLINE search strategy. (DOC 39 kb)

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This file can be viewed with: Microsoft Word ViewerOpen Data

Acknowledgements

We thank Melanie Anderson for her assistance with developing the search strategy and Elise Cogo and Becky Skidmore for peer reviewing our MEDLINE search strategy.

References

  1. World Health Organization, 2014. Global update on the health sector reponse to HIV, 2014. http://apps.who.int/iris/bitstream/10665/128196/1/WHO_HIV_2014.15_eng.pdf. Accessed March 2015.
  2. World Health Organization, 2010. PMTCT strategic vision 2010–2015: preventing mother-to-child transmission of HIV to reach the UNGASS and Millennium Development Goals. www.who.int/hiv/pub/mtct/strategic_vision/en/index.html. Accessed March 2015.
  3. Brusamento S, Ghanotakis E, Tudor Car L, van-Velthoven MHMMT, Majeed A, Car J. Male involvement for increasing the effectiveness of prevention of mother-to-child HIV transmission (PMTCT) programmes (Review). The Cochrane library, 2012, issue 10. accessible at. http://www. cochranelibrary.com.myaccess.library.utoronto.ca/cochrane-database-of-systematic-reviews/index.html OpenURL
  4. Tudor Car L, van-Velthoven MHMMT, Brusamento S, Elmoniry H, Car J, Majeed A, Atun R. Integrating prevention of mother-to-child HIV transmission (PMTCT) programmes with other health services for preventing HIV infection and improving HIV outcomes in developing countries (Review). The Cochrane library, 2011, issue 6. accessible at. http://www. cochranelibrary.com.myaccess.library.utoronto.ca/cochrane-database-of-systematic-reviews/index.html OpenURL
  5. Govindasamy D, Meghij J, Negussi EK, Baggaley RC, Ford N, Kranzer K. Interventions to improve or facilitate linkage to or retention in pre-ART (HIV) care and initiation of ART in low- and middle income settings—a systematic review. J Int AIDS Soc. 2014; 17:19032. PubMed Abstract | Publisher Full Text OpenURL
  6. Turan JM, Onono M, Steinfeld RL, Shade SB, Owuor K, Washington S 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 Defic Syndr. 2015; 69(5):e172-e181. PubMed Abstract | Publisher Full Text OpenURL
  7. Decroo T, Telfer B, Biot M et al.. Distribution of antiretroviral treatment through self-forming groups of patients in Tete Province. Mozambique J Acquir Immune Defic Syndr. 2011; 56:e39-e44. PubMed Abstract | Publisher Full Text OpenURL
  8. Kim MH, Ahmed S, Buck WC et al.. The Tingathe programme: a pilot intervention using community health workers to create a continuum of care in the prevention of mother to child transmission of HIV (PMTCT) cascade of services in Malawi. J Int AIDS Soc. 2012; 15(suppl 2):17389. PubMed Abstract | Publisher Full Text OpenURL
  9. Teasdale C, Marais B, Abrams E. HIV: prevention of mother-to-child transmission. Clin Evid. 2011; 01:909. OpenURL
  10. Higgins JPT, Green S (editors). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration, 2011. Available from www.cochrane-handbook.org. Accessed January 2015.
  11. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew P, Stewart L A, and PRISMA-P Group. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews 2015, 4:1 doi:10.1186/2046-4053-4-1
  12. EPOC filter to identify Low and middle income countries http://epoc.cochrane.org/lmic-filters. Accessed March 2015.
  13. The world bank data: Country and lending groups http://data.worldbank.org/about/country-classifications/country-and-lending-groups. Accessed January 2015.
  14. World Health Organizaiton (2010). WHO recommendations on the diagnosis of HIV infection in infants and children. http://whqlibdoc.who.int/publications/2010/9789241599085_eng.pdf?ua=1, accessed march 2015.
  15. Sampson M, McGowan J, Cogo E, Grimshaw J, Moher D, Lefebvre C. An evidence-based practice guideline for the peer review of electronic search strategies. J Clin Epidemiol. 2009; 62(9):944-952. PubMed Abstract | Publisher Full Text OpenURL
  16. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration, 2011. The Cochrane Highly Sensitive Search Strategies for identifying randomized trials in MEDLINE (section6.4.11.1, 2008 revision). Available from www.cochrane-handbook.org. Accessed January 2015.
  17. Stone PW. Popping the (PICO) question in research and evidence-based practice. Appl Nurs Res. 2002; 15(3):197-198. PubMed Abstract | Publisher Full Text OpenURL
  18. Effective Practice and Organisation of Care (EPOC). EPOC Resources for review authors. Oslo: Norwegian Knowledge Centre for the Health Services; 2013. Available at: http://epoc.cochrane.org/epoc-specific-resources-review-authors. Accessed January 2015.

Increased circulating follicular helper T cells with decreased programmed death-1 in chronic renal allograft rejection

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Increased circulating follicular helper T cells with decreased programmed death-1 in chronic renal allograft rejection

Jian Shi1, Fengbao Luo1, Qianqian Shi1, Xianlin Xu1, Xiaozhou He1* and Ying Xia2*

Author Affiliations

1 Third Clinical College of Soochow University, Changzhou, Jiangsu, China

2 The University of Texas Medical School at Houston, Houston, TX, USA

For all author emails, please log on.

BMC Nephrology 2015, 16:182  doi:10.1186/s12882-015-0172-8

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2369/16/182

Received: 26 May 2015
Accepted: 19 October 2015
Published: 3 November 2015

© 2015 Shi et al.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Abstract

Background

Chronic antibody-mediated rejection is a major issue that affects long-term renal allograft survival. Since follicular helper T (Tfh) cells promote the development of antigen-specific B cells in alloimmune responses, we investigated the potential roles of Tfh cells, B cells and their alloimmune-regulating molecules in the pathogenesis of chronic renal allograft rejection in this study.

Methods

The frequency of Tfh, B cells and the levels of their alloimmune-regulating molecules including chemokine receptor type 5 (CXCR5), inducible T cell co-stimulator (ICOS), programmed death-1 (PD-1), ICOSL, PDL-1 and interleukin-21 (IL-21), of peripheral blood were comparatively measured in 42 primary renal allograft recipients within 1–3 years after transplantation. Among them, 24 patients had definite chronic rejection, while other 18 patients had normal renal function.

Results

Tfh-cell ratio was significantly increased with PD-1 down-regulation in the patients with chronic renal allograft rejection, while B cells and the alloimmune-regulating molecules studied did not show any appreciable change in parallel.

Conclusions

The patients with chronic renal allograft rejection have a characteristic increase in circulating Tfh cells with a decrease in PD-1 expression. These pathological changes may be a therapeutic target for the treatment of chronic renal allograft rejection and can be useful as a clinical index for monitoring conditions of renal transplant.

Keywords:

Chronic renal allograft rejection; Tfh cells; PD-1

Background

Renal transplantation remains an effective treatment for end-stage renal dysfunction [1], facilitating a return to normal health and prolonging life. However, antibody-mediated rejection is a major issue that affects long-term renal allograft survival. Despite the rapid development of new immunosuppressive drugs for attenuating acute rejection, improving the long-term survival of grafts is still a challenge mainly because of chronic allograft rejection. The features of chronic renal allograft rejection are hypertension, proteinuria, progressive deterioration of graft function, peritubular capillary C4d deposition, presence of donor-specific antibodies (DSA) and morphological changes with transplant vasculopathy, glomerulopathy, fibrosis and lymphocyte infiltration. However, the causes leading to chronic rejection are complex and not well understood yet [2].

Allograft rejection is characterized by an increase in activated CD4+ T-lymphocytes, especially regulatory and cytotoxic T cells, leading to an imbalance of immune responses in the transplant recipients [3], [4]. Functionally, CD4+ T helper cells that interact with antigen-specific B cells are required for the production of alloantibodies [5]. Among them, Tfh cells, a recently defined subset of CD4+ T cells, play a particular role in mediating B cell-driven allogeneic responses. Tfh cells can migrate into germinal centers and promote B-cell activation and differentiation into immunoglobulin-producing plasmablasts or plasma cells [5]. They can express PD-1, CXCR5, ICOS, IL-21 and the transcription factor B-cell lymphoma 6 (Bcl-6) [6], [7], thereby displaying their regulatory functions.

Circulating Tfh cells, peripheral counterparts of conventional Tfh cells, express PD-1, CXCR5, ICOS and IL-21, but not Bcl-6 [5]–[7]. They play an important role in human humoral immunity through these functional molecules. Their abnormal activities are critically involved in the onset of several human diseases such as autoimmune disorders, cancer and infective diseases [7]–[10]. Therefore, an alteration in circulating Tfh cells may be correlated with disease conditions and might be used as a biomarker of certain diseases [11]–[13]. Moreover, recent clinical studies have shown that peripheral Tfh cells in the kidney transplant recipients with acute rejection can regulate B-cell alloreactivity and the number of these Tfh cells alters the immunization status and DSA levels [14]. However, their function and relevance to chronic renal allograft rejection are not known yet.

This study was conducted to explore the potential association between circulating Tfh cells and chronic rejection in kidney transplant recipients. The outcome results may provide a useful hint for clinical prediction of renal status after transplantation and for a potential new therapy for chronic allograft rejection.

Methods

This study was approved by the Institutional Ethics Committee of Third Affiliated Hospital of Soochow University, Jiangsu Province, China. Written-informed consent was obtained from all participants of the study.

Subjects

The patients with primary renal transplantation for 1–3 years were enrolled from October 2013 to December 2014. Totally 42 recipients were studied in this work, including 24 patients with chronic rejection (CR group) and 18 patients with normal renal function as the normal control (NC group). All of them received the treatment with cyclosporine A, methylprednisolone and mycophenolate mofetil or azathioprine after the renal transplantation. The diagnosis for chronic allograft rejection was confirmed by renal biopsy, biochemical measurements and immunological assays, including DSA as described in other studies [15]–[18]. In specific,the diagnostic criteria included 1) clinical evidence of slowly deteriorating graft function; 2) biopsy evidence and diffuse deposition of C4d; and 3) the presence of circulating DSA at the time of biopsy. Their peripheral blood samples were collected in a standard way by the clinical laboratory of the hospital. All the subjects had no infective disease when sampling blood.

Surface staining and flow cytometry analysis

The whole blood was subjected to flow cytometry detection by a BD bioscience FACSCantoII cytometer with FACSDiva software for measuring the frequency of circulating Tfh cells and B cells as well as the expression of their surface markers. The following conjugated monoclonal antibodies were used to stain the cells: CD4-FITC, CXCR5-APC, ICOS-PE, PD-1-PE, CD19-PE-Cy5.5, ICOSL-PE and PDL-1-APC. The cells were incubated with the antibodies for 30 min at room temperature in the dark. Totally 50,000 lymphocytes were acquired in each sample. Data were analyzed using Flow Jo software 7.6.1.

Enzyme-linked immunosorbent assay (ELISA)

The levels of serum IL-21 were quantified by using the human IL-21 ELISA kit (eBioscience) according to the manufacturer’s instructions. The concentration in each individual sample was calculated according to the standard curve.

Statistical analyses

All experimental data were analyzed by Graph Prism version 5.0. The results were expressed as mean ± SD and subjected to t test for statistical comparisons between the NC and CR groups. If a p-value was found to be less than 0.05, the result would be considered statistically significant.

Results

Demographics

The general information of the renal transplant recipients was summarized in Table 1. Gender and age were similar between CR and NC groups. The mean serum creatinine (sCr) and blood urea nitrogen (BUN) were almost three-fold higher in the CR patients than those of NC group (p < 0.001).

Table 1. General information of the renal transplant patients

Increased circulating Tfh cells in the CR patients

To determine if chronic rejection was associated with an alteration in circulating Tfh cells in the renal transplantation recipients, we first evaluated the frequency of CD4 + CXCR5+ Tfh cells through flow cytometry. As shown in Fig. 1, the percentage of CD4 + CXCR5+ Tfh cells among total CD4+ T cells was significantly increased in the CR group as compared to that of the NC group (35.3 ± 8.5 % vs.19.0 ± 5.0, P < 0.001).

thumbnailFig. 1. Frequency of Tfh cells in the patients with renal allograft. a, representative contour plots of the ratio of Tfh cells in the NC and CR groups, b, mean values of the frequency of Tfh cells in the two groups. ***, P < 0.001. Note a significant increase in Tfh cells in the CR group compared to that of the control group

Differential changes in PD-1, CXCR5, ICOS, and IL-21 of Tfh cells in the CR patients

We further detected the changes of PD-1, CXCR5 and ICOS in the high frequency of Tfh cells and found that PD-1 was significantly down-regulated in these Tfh cells (Fig. 2a). In sharp contrast, there were no significant changes in CXCR5 and ICOS in the CR group as compared to the NC group (Fig. 2b and c).

thumbnailFig. 2. Comparative changes in PD-1, CXCR5 and ICOS expression on Tfh-cell surfaces. a,PD-1. b, CXCR5. c, ICOS. * P < 0.05. Note a significant decrease in PD-1 expression with no appreciable changes in CXCR5 and ICOS expression in the CR patients as compared to the NC group

IL-21 is a biological hallmark of Tfh cells because this immune cytokine is produced by Tfh cells and is involved in mediating Tfh-B-cell interaction [6]. Therefore, we further measured the concentration of IL-21 in the serum of the CR patients (Fig. 3). Similarly as the changes in CXCR5 and ICOS, serum IL-21 did not show any significant change in the CR patients as compared to that of the control group (406.9 ± 123.9 pg/ml vs. 449.1 ± 101.7 pg/ml, P > 0.05).

thumbnailFig. 3. The level of serum IL-21 in renal allograft patients. Note that there was no statistic difference in the IL-21 level between the CR and NC groups

No changes in circulating B cells, PDL-1 and ICOSL in the CR patients

Since B cells are key players in the graft rejection [19], we further determined the frequency of total B cells and compared their change with that of Tfh cells. The ratio of B lymphocytes was not significantly different between CR and NC groups (Fig. 4), unlike Tfh cells. Because PDL-1-expressing B cells interact with PD-1+ Tfh cells to regulate the maturation and survival of B cells [20], we next detected the expression of PDL-1 in B cells. Unlike the change in the PD-1 of Tfh cells, PDL-1 did not decrease in B cells at all in the CR group (Fig. 5a). Also, ICOSL had no significant change in the CR patients (Fig. 5b).

thumbnailFig. 4. The frequency of B cells. Note that there was no significant difference between two groups

thumbnailFig. 5. The expression of PDL-1 and ICOSL in B cells. Note that there were no appreciable differences in both PDL-1 (a) and ICOSL (b) between two groups

All these results suggest that the increase in circulating Tfh cells with PD-1 down-regulation is a specific and characteristic change in the CR patients.

Discussion

We have made a novel finding in this work, i.e., a major increase in circulating Tfh cells with a significant decrease in PD-1 in the patients with chronic renal allograft rejection. In sharp contrast, B cells and the alloimmune-regulating molecules such as CXCR5, ICOS, ICOSL, PDL-1 and IL-21 did not show any appreciable change in parallel.

Tfh cells display multiple features for their helper functions in secondary lymphoid organs or tissues with inflammation [21]. They migrate into B cell follicles of germinal centers [22] and thereby help B cells generate antibodies for humoral immunity [3], [6], [23]. In fact, Tfh cells, as an immune regulator, are critically involved in the pathological processes of many immune diseases [8]. In renal allograft rejection, the germinal center reactions are dependent on Tfh, while B cells are indispensable for the immune attack to the newly transplanted kidney [24].

Tfh cells express PD-1 [6], while B cells produce PDL-1, an endogenous ligand of PD-1 [19]. The PD-1/PDL-1 signaling has been shown to play an important role in regulating immune functions and affecting the activation of regulatory T cells, cytotoxic T cells and Dendritic cells [25]. Such signaling also influences the generation and differentiation of Tfh and B cells themselves [26]. Recent evidence suggests that blocking the PD-1 signaling induces an up-regulation of Tfh generation and differentiation, which may directly lead to autoimmune encephalomyelitis [27]. In contrast, stimulating this pathway can prolong the survival of the patients after cardiac allograft transplantation [28]. More recently, PD-1 ligands are found to protect the kidneys from ischemia reperfusion injury [29]. In fact, PDL-1, the endogenous ligand of PD-1, has been demonstrated as a required factor for peripheral transplantation tolerance and protection aganist chronic allograft rejection [30].

Taken together, PD-1 signaling is a key regulator for attenuating the Tfh cells and down-regulating the overreaction of humoral immunity against the transplanted kidneys. Therefore, the novel finding of the present study strongly suggests that the deficiency of PD-1 expression causes the increase in Tfh cells, thereby leading to an overreaction of humoral immunity against the allergenic organ, which may be a major reason for chronic allograft rejection.

Tfh and B cells also express many other immune-regulating molecules such as CXCR5, ICOS, ICOSL, and IL-21 [6], [19], [26]. All these molecules are actively involved in the regulation of immune function [26]. For example, an increase in the expression of CXCR5 can enable Tfh cells to migrate into germinal centers [21]. On the other hand, ICOS, another surface receptor like PD-1, also mediate the generation, development and function of Tfh cells by activating ICOS/ICOSL signaling [31], [32]. Moreover, IL-21,a pro-inflammatory cytokine secreted by Tfh cells,has an important role in Tfh cell differentiation, B cell proliferation [33], and the expression of PD-1 [34] and CXCR5 [26]. However, all of these immune regulators did not shown any change in the patients with chronic allograft rejection. We are therefore confident that the deficient PD-1 expression with increased circulating Tfh cells is a specific and characteristic change in chronic allograft rejection.

In the lymph nodes, CD4+ CXCR5+ Tfh cells are more effective in helping B cells than their peripheral counterparts [7]. Humoral response in the lymph nodes can be suppressed by anti-CD40 mAb via regulating Tfh cells [23]. In the transplanted kidneys with acute rejection, infiltrated Tfh cells have been found to participate in the antibody-mediated rejection [14]. However, little is known about the role of these special Tfh cells in the transplanted kidneys with chronic rejection. We speculate that the increase in circulating Tfh cells with a decrease in PD-1 expression might, at least partially, contributes to the genesis of the renal chronic rejection by migrating and infiltrating into germinal centers of renal allografts and lymphoid organs. We will further clarify this issue in our future work.

In addition, pre-existent DSA storing before renal transplantation and de-novo DSA developing after renal transplantation are associated with antibody-mediated rejection and allograft failure [35]. However, recent studies have shown that despite the numbers of circulating Tfh cells were higher in the patients with pre-existent DSA than those without pre-existent DSA, the levels of circulating Tfh cells were not different among the patients with or without de-novo DSA [35], [36]. Therefore, the relationship between the frequency of circulating Tfh cells and the level of DSA in renal allograft rejection is not clear yet and needs more investigations.

Conclusions

Our first data show that decreased PD-1 expression may contribute to the increase in circulating Tfh cells in the patients with chronic renal allograft rejection. This finding provides a potential hint for a new target for the treatment of chronic rejection. Moreover, a dynamic change in the expression of PD-1 and the number of circulating Tfh cells may be used as an index for monitoring chronic allograft rejection after kidney transplantation as.

Abbreviations

Tfh: Follicular helper T cells

CXCR5: Chemokine receptor type 5

ICOS: Inducible T cell co-stimulator

PD-1: Programmed death-1

ICOSL: Inducible T cell co-stimulator ligand

PDL-1: Programmed death-1 ligand

IL-21: Interleukin-21

Bcl-6: Transcription factor B-cell lymphoma 6

CR: Chronic rejection

NC: Normal control

ELISA: Enzyme-linked immunosorbent assay

sCr: Serum creatinine

BUN: Blood urea nitrogen

DSA: Donor-specific antibodies.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

JS, XX, XH and YX conceived and designed the experiments. JS, FL and QS performed the experiments. JS analyzed the data. XH provided research reagents. JS and YX wrote the paper. All authors read and approved the final manuscript.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No.81273267) . YX was partially supported by NIH (AT-004422).

References

  1. Nankivell BJ, Alexander SI. Rejection of the kidney allograft. N Engl J Med. 2010; 363:1451-62. PubMed Abstract | Publisher Full Text OpenURL
  2. Halloran PF. Call for revolution: a new approach to describing allograft deterioration. Am J Transplant. 2002; 2:195-200. PubMed Abstract | Publisher Full Text OpenURL
  3. Giaretta F, Bussolino S, Beltramo S, Fop F, Rossetti M, Messina M et al.. Different regulatory and cytotoxic CD4+ T lymphocyte profiles in renal transplants with antibody-mediated chronic rejection or long-term good graft function. Transpl Immunol. 2013; 28:48-56. PubMed Abstract | Publisher Full Text OpenURL
  4. Nankivell BJ, Chapman JR. Chronic allograft nephropathy: current concepts and future directions. Transplantation. 2006; 81:643-54. PubMed Abstract | Publisher Full Text OpenURL
  5. Morita R, Schmitt N, Bentebibel SE, Ranganathan R, Bourdery L, Zurawski G et al.. Human blood CXCR5(+)CD4(+) T cells are counterparts of T follicular cells and contain specific subsets that differentially support antibody secretion. Immunity. 2011; 34:108-21. PubMed Abstract | Publisher Full Text OpenURL
  6. Crotty S. Follicular helper CD4 T cells (TFH). Annu Rev Immunol. 2011; 29:621-63. PubMed Abstract | Publisher Full Text OpenURL
  7. Tangye SG, Ma CS, Brink R, Deenick EK. The good, the bad and the ugly – TFH cells in human health and disease. Nat Rev Immunol. 2013; 13:412-26. PubMed Abstract | Publisher Full Text OpenURL
  8. King C, Tangye SG, Mackay CR. T follicular helper (TFH) cells in normal and dysregulated immune responses. Annu Rev Immunol. 2008; 26:741-66. PubMed Abstract | Publisher Full Text OpenURL
  9. Shi W, Li X, Cha Z, Sun S, Wang L, Jiao S et al.. Dysregulation of circulating follicular helper T cells in nonsmall cell lung cancer. DNA Cell Biol. 2014; 33:355-60. PubMed Abstract | Publisher Full Text OpenURL
  10. Slight SR, Rangel-Moreno J, Gopal R, Lin Y, Fallert-Junecko BA, Mehra S et al.. CXCR5(+) T helper cells mediate protective immunity against tuberculosis. J Clin Invest. 2013; 123:712-26. PubMed Abstract | Publisher Full Text OpenURL
  11. Bentebibel SE, Lopez S, Obermoser G, Schmitt N, Mueller C, Harrod C et al.. Induction of ICOS + CXCR3 + CXCR5+ TH cells correlates with antibody responses to influenza vaccination. Sci Transl Med. 2013; 5:176ra132. Publisher Full Text OpenURL
  12. Locci M, Havenar-Daughton C, Landais E, Wu J, Kroenke MA, Arlehamn CL et al.. Human circulating PD-1 + CXCR3-CXCR5+ memory Tfh cells are highly functional and correlate with broadly neutralizing HIV antibody responses. Immunity. 2013; 39:758-69. PubMed Abstract | Publisher Full Text OpenURL
  13. Pallikkuth S, Parmigiani A, Silva SY, George VK, Fischl M, Pahwa R et al.. Impaired peripheral blood T-follicular helper cell function in HIV-infected nonresponders to the 2009 H1N1/09 vaccine. Blood. 2012; 120:985-93. PubMed Abstract | Publisher Full Text OpenURL
  14. Carla CB, Gretchen NG, Karin B. T Follicular Helper Cells in Transplantation: The Target to Attenuate Antibody-Mediated Allogeneic Responses? Curr Transpl Rep. 2014; 1:166-72. Publisher Full Text OpenURL
  15. Rascio F, Pontrelli P, Accetturo M, Oranger A, Gigante M, Castellano G et al.. A type I interferon signature characterizes chronic antibody-mediated rejection in kidney transplantation. J Pathol. 2015; 237(1):72-84. PubMed Abstract | Publisher Full Text OpenURL
  16. Hong YA, Kin HG, Choi SR, Sun IO, Park HS, Chung BH et al.. Effectiveness of Rituximab and Intravenous Immunoglobulin Therapy in Renal Transplant Recipients with Chronic Active Antibody-Mediated Rejection. Transplant Proc. 2012; 44:182-4. PubMed Abstract | Publisher Full Text OpenURL
  17. Chow KM, Szeto CC, Lai FM, Luk CC, Kwan BC, Leung CB et al.. Functional and histological improvement after everolimus rescue of chronic allograft dysfunction in renal transplant recipients. Ther Clin Risk Manag. 2015; 11:829-35. PubMed Abstract | Publisher Full Text OpenURL
  18. Kim MG, Kim YJ, Kwon HY, Park HC, Koo TY, Jeong JC et al.. Outcomes of combination therapy for chronic antibody-mediated rejection in renal transplantation. Nephrology(Carlton). 2013; 18:820-6. Publisher Full Text OpenURL
  19. Nouel A, Simon Q, Jamin C, Pers JO, Hillion S. Regulatory B cells: an exciting target for future therapeutics in transplantation. Front Immunol. 2014; 5:11. PubMed Abstract | Publisher Full Text OpenURL
  20. Good-Jacobson KL, Szumilas CG, Chen L, Sharpe AH, Tomayko MM, Shlomchik MJ. PD-1 regulates germinal center B cell survival and the formation and affinity of long-lived plasma cells. Nat Immunol. 2010; 11:535-42. PubMed Abstract | Publisher Full Text OpenURL
  21. Schmitt N, Bentebibel SE, Ueno H. Phenotype and functions of memory Tfh cells in human blood. Trends Immunol. 2014; 35:436-42. PubMed Abstract | Publisher Full Text OpenURL
  22. Schaerli P, Willimann K, Lang AB, Lipp M, Loetscher P, Moser B. CXC chemokine receptor 5 expression defines follicular homing T cells with B cell helper function. J Exp Med. 2000; 192:1553-62. PubMed Abstract | Publisher Full Text OpenURL
  23. McHeyzer-Williams LJ, Pelletier N, Mark L, Fazilleau N, McHeyzer-Williams MG. Follicular helper T cells as cognate regulators of B cell immunity. Curr Opin Immunol. 2009; 21:266-73. PubMed Abstract | Publisher Full Text OpenURL
  24. Kim EJ, Kwun, Gibby AC, Hong JJ, Farris AB, Iwakoshi NN et al.. Costimulation blockade alters germinal center responses and prevents antibody-mediated rejection. Am J Transplant. 2014; 14:59-69. PubMed Abstract | Publisher Full Text OpenURL
  25. Tsang JY, Li D, Ho D, Peng J, Xu A, Lamb J et al.. Novel immunomodulatory effects of adiponectin on dendritic cell functions. Int Immunopharmacol. 2011; 11:604-9. PubMed Abstract | Publisher Full Text OpenURL
  26. Park HJ, Kim DH, Lim SH, Kim WJ, Youn J, Choi YS et al.. Insights into the role of follicular helper T cells in autoimmunity. Immune Netw. 2014; 14:21-9. PubMed Abstract | Publisher Full Text OpenURL
  27. Ansari MJ, Salama AD, Chitnis T, Smith RN, Yagita H, Akiba H et al.. The programmed death-1 (PD-1) pathway regulates autoimmune diabetes in nonobese diabetic (NOD) mice. J Exp Med. 2003; 198:63-9. PubMed Abstract | Publisher Full Text OpenURL
  28. Ozkaynak E, Wang L, Goodearl A, McDonald K, Qin S, O’Keefe T et al.. Programmed death-1 targeting can promote allograft survival. J Immunol. 2002; 169:6546-53. PubMed Abstract | Publisher Full Text OpenURL
  29. Jaworska K, Ratajczak J, Huang L, Whalen K, Yang M, Stevens BK, et al. Both PD-1 Ligands Protect the Kidney from Ischemia Reperfusion Injury. J Immunol. 2015;194(1):325–33.
  30. Tanaka K, Albin MJ, Yuan X, Yamaura K, Habicht A, Murayama T et al.. PDL1 is required for peripheral transplantation tolerance and protection from chronic allograft rejection. J Immunol. 2007; 179:5204-10. PubMed Abstract | Publisher Full Text OpenURL
  31. Gigoux M, Shang J, Pak Y, Xu M, Choe J, Mak TW et al.. Inducible costimulator promotes helper T-cell differentiation through phosphoinositide 3-kinase. Proc Natl Acad Sci. 2009; 106:20371-6. PubMed Abstract | Publisher Full Text OpenURL
  32. Hams E, McCarron MJ, Amu S, Yagita H, Azuma M, Chen L et al.. Blockade of B7-H1 (programmed death ligand 1) enhances humoral immunity by positively regulating the generation of T follicular helper cells. J Immunol. 2011; 186:5648-55. PubMed Abstract | Publisher Full Text OpenURL
  33. Nurieva RI, Chung Y, Hwang D, Yang XO, Kang HS, Ma L et al.. Generation of T follicular helper cells is mediated by interleukin-21 but independent of T helper 1, 2, or 17 cell lineages. Immunity. 2008; 29:138-49. PubMed Abstract | Publisher Full Text OpenURL
  34. Jacob J, Przylepa J, Miller C, Kelsoe G. In situ studies of the primary immune response to (4-hydroxy-3-nitrophenyl)acetyl. III. The kinetics of V region mutation and selection in germinal center B cells. J Exp Med. 1993; 178:1293-307. PubMed Abstract | Publisher Full Text OpenURL
  35. de Graav GN, Dieterich M, Hesselink DA, Boer K, Clahsen-van Groningen MC, Kraaijeveld R et al.. Follicular T helper cells and humoral reactivity in kidney transplant patients. Clin Exp Immunol. 2015; 180:329-40. PubMed Abstract | Publisher Full Text OpenURL
  36. Lee PC, Zhu L, Terasaki PI, Everly MJ. HLA-specific antibodies developed in the first year posttransplant are predictive of chronic rejection and renal graft loss. Transplantation. 2009; 8:568-74. Publisher Full Text OpenURL

Quantitative analysis of a Māori and Pacific admission process on first-year health study

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Quantitative analysis of a Māori and Pacific admission process on first-year health study

Elana Curtis1*, Erena Wikaire1, Yannan Jiang2, Louise McMillan2, Robert Loto1, Airini3 and Papaarangi Reid1

Author Affiliations

1 Te Kupenga Hauora Māori, Faculty of Medical and Health Sciences, University of Auckland, Private Bag 92015, Auckland, New Zealand

2 Department of Statistics, Faculty of Science, University of Auckland, Private Bag 92015, Auckland, New Zealand

3 Faculty of Human, Social and Educational Development, Thompson Rivers University, Thompson, BC, Canada

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BMC Medical Education 2015, 15:196  doi:10.1186/s12909-015-0470-7

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1472-6920/15/196

Received: 15 December 2014
Accepted: 20 October 2015
Published: 3 November 2015

© 2015 Curtis et al.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Abstract

Background

Universities should provide flexible and inclusive selection and admission policies to increase equity in access and outcomes for indigenous and ethnic minority students. This study investigates an equity-targeted admissions process, involving a Multiple Mini Interview and objective testing, advising Māori and Pacific students on their best starting point for academic success towards a career in medicine, nursing, health sciences and pharmacy.

Methods

All Māori and Pacific Admission Scheme (MAPAS) interviewees enrolled in bridging/foundation or degree-level programmes at the University of Auckland were identified (2009 to 2012). Generalised linear regression models estimated the predicted effects of admission variables (e.g. MAPAS Maths Test; National Certificate in Educational Achievement (NCEA) Rank Score; Any 2 Sciences; Followed MAPAS Advice) on first year academic outcomes (i.e. Grade Point Average (GPA) and Passes All Courses) adjusting for MAPAS interview year, gender, ancestry and school decile.

Results

368 First Year Tertiary (bridging/foundation or degree-level) and 242 First Year Bachelor (degree-level only) students were investigated. NCEA Rank Score (estimate 0.26, CI: 0.18-0.34, p< 0.0001); MAPAS Advice Followed (1.26, CI: 0.18-1.34, p = 0.0002); Exposure to Any 2 Sciences (0.651, CI: 0.15-1.15, p = 0.012); and MAPAS Mathematics Test (0.14, CI: 0.02-0.26, p = 0.0186) variables were strongly associated with an increase in First Year Tertiary GPA. The odds of passing all courses in First Year Tertiary study was 5.4 times higher for students who Followed MAPAS Advice (CI: 2.35-12.39; p< 0.0001) and 2.3 times higher with Exposure to Any Two Sciences (CI: 1.15-4.60; p = 0.0186). First Year Bachelor students who Followed MAPAS Advice had an average GPA that was 1.1 points higher for all eight (CI: 0.45-1.73; p = 0.0009) and Core 4 courses (CI: 0.60-2.04; p = 0.0004).

Conclusions

The MAPAS admissions process was strongly associated with positive academic outcomes in the first year of tertiary study. Universities should invest in a comprehensive admissions process that includes alternative entry pathways for indigenous and ethnic minority applicants.

Keywords:

Admission; Selection; Indigenous; Ethnic minority; Health professional; Higher education; Widening participation; Workforce development; Māori; Pacific

Background

Worldwide, tertiary institutions are attempting to widen participation to historically underserved populations including indigenous and ethnic minority students [1] Often driven by social inclusion and social accountability policies, universities have devised a number of strategies to increase diversity. Within an indigenous and ethnic minority health workforce context, a pipeline approach is recommended to address well-known barriers to accessing and succeeding in university-level studies. A pipeline approach often includes early exposure interventions aimed at raising aspirations and academic preparation for a career in health [2]–[4]; addressing educational disadvantage via the provision of bridging/foundation programmes [5], [6] and improving student performance by providing comprehensive support programmes [7]–[9]. Given the highly competitive context of health professional programme selection, it is also recommended that universities provide more flexible and inclusive selection and admission policies for students from underserved populations [1], [10].

Universities have a choice of selection tools that can be used to inform student admission including prior academic performance, interview scores and results from aptitude tests. Both cognitive and non-cognitive tools are used by universities when selecting students; however it is arguable that prior academic performance remains a dominant tool for medical selection in many universities [11]. Given this reality, indigenous and ethnic minority students are required to aim to achieve a high level of academic performance within the pathways used for future selection into medical or health professional programmes of study [12]. Unfortunately, students from underserved populations are less likely to receive access to science-rich subjects and are more likely to leave high school with lower qualifications than their peers [5], [10], [13]. Providing an admissions process that can determine whether indigenous and ethnic minority applicants are academically (and socially) ready to achieve success in pre-medical degree pathways and the provision of alternative entry pathways is recommended for tertiary institutions committed to widening participation [14], [15].

An extensive body of research identifies the tertiary conditions and factors that impact on academic success within the first year of study at university [16]–[20]. Indicators of prior academic performance such as: secondary school grade point averages [21]; secondary school factors including markers of socio-geographic status (e.g. school decile) [22]; and student characteristics (e.g. autonomy, confidence, motivation, control) [17], [23] have been identified as important factors impacting on academic performance in the first year of study. In addition, factors associated with the environment of the tertiary institution also impact on student engagement; such factors include: opportunities for teachers and students to engage with each other [18]; levels of institutional support to provide environments conducive to learning [20]; and the provision of academic, social and personal support [16].

To date, few studies have explored the effect of equity-targeted admission processes on the academic performance of indigenous and ethnic minority students in their first year of tertiary study. As a result, tertiary institutions have little empirical evidence to understand the effect of equity-targeted selection processes and whether such initiatives are likely to support a widening participation agenda.

This article explores the predictive effect of admission variables associated with an equity-targeted admission process on academic outcomes for Māori (the indigenous peoples of Aotearoa New Zealand) and Pacific (a heterogeneous composite of peoples with Pacific nation ancestry born and/or living in New Zealand) applicants applying under the Māori and Pacific Admission Scheme (MAPAS) to the Faculty of Medical and Health Sciences (FMHS) at the University of Auckland (UoA).

Methods

FMHS entry pathways

Admission into FMHS health professional programmes is generally via direct entry into First Year Bachelor level undergraduate study for those applicants who meet the necessary entry requirements [24]. The FMHS also offers a one-year, MAPAS-specific bridging/foundation programme, the Certificate in Health Sciences (CertHSc) through which Māori and Pacific students who achieve a CertHSc GPA above B+ can gain alternative entry into First Year Bachelor undergraduate study. Hence, Māori and Pacific First Year Tertiary students within FMHS could either be enrolled in the CertHSc bridging foundation programme, or, the first year of bachelor level study (Table 1). The first year of bachelor level study also acts as a ‘pre-medical’ year prior to admission into the FMHS Medical programme in year 2. Table 1 provides definitions of the Certificate in Health Sciences, First Year Tertiary, and First Year Bachelor terms used within this study (Table 1).

Table 1. Definition of terms used within the FMHS context

Māori and Pacific Admission Scheme (MAPAS)

MAPAS operates an equity-targeted admissions process for applicants with indigenous Māori and Pacific ancestry. The process aims to gather a broad range of information about Māori and Pacific applicant preparation for tertiary health study. The December interview process involves a Multiple Mini Interview (MMI), an English test and a mathematics test.

The MMI is an alternative form of admission interview that aims to reduce interviewer bias by consisting of a number of short interview stations with multiple interviewers. The MMI has been shown to be reliable, acceptable and feasible in a variety of tertiary health study contexts [25]. In building on the original pilot of the MMI [26], other studies have taken advantage of the intended benefit of the flexibility of station development in their own contexts [27], [28]. Whilst the original authors aimed to assess suitability of applicants as health professionals, the MAPAS MMI aims to assess Māori and Pacific applicant preparation for and potential to succeed in FMHS programmes. In the MAPAS context, the MMI has been redeveloped to include four 8-min stations assessing career aspirations; academic preparation; family support and student information. The MAPAS mathematics and English testing are used in addition to the MAPAS MMI to objectively assess academic numeracy and literacy skills. Using MMI and testing information, two assessments are made about: 1) potential to succeed within the CertHSc, and 2) potential to succeed within the Bachelor of: Health Sciences; Science (Biomedicine)1 ; Nursing; or Pharmacy. Potential to succeed is assessed as: pass, borderline or fail (objective testing) for the English and mathematics testing and few, some, or major concerns (subjective testing) for each MMI station. A MAPAS Recommendations Team reviews the combination of results and provides a provisional MAPAS recommendation (advice regarding the applicant’s recommended best starting point given their intended health career) for applicants (and families) on the day of their interview. Recommended starting points are reflected within three categories: (1) Bachelor i.e. start at degree-level; (2) CertHSc i.e. start at bridging/foundation; or (3) Not FMHS i.e. start in a pathway not provided by FMHS (likely to need further academic preparation not offered by the FMHS). Following the release of secondary school results in January, all information is re-reviewed and a final MAPAS recommendation is provided. MAPAS recommendations are not binding if an applicant has met guaranteed entry criteria for any FMHS programme. In this context, the applicant can choose to follow MAPAS advice (or not)2 .

Methodology

This study used a Kaupapa Māori Research (KMR) approach, broadly defined and responsive to Pacific research methodologies [29], [30]. This approach recognises that issues associated with power, privilege and agency within society are hypothesised to act similarly on both Māori and Pacific students [31], [32]. In this instance KMR aims to: ensure research outputs are positive for Māori and Pacific students; explicitly challenge ‘victim blame’ or ‘cultural deficit’ analyses that may blame Māori or Pacific students for educational failure; and provide a structural analysis to promote institutional change targeting Māori and Pacific student success [14], [33]. This research was led by senior Māori and Pacific researchers with input from a FMHS advisory group.

Study design

The predictive effect of MAPAS admission process variables on academic outcomes in the first year of tertiary study was explored. Applicant data were obtained from the MAPAS admissions database and the university’s centralised student data management system for all MAPAS interviewees (2008 – 2011) who subsequently enrolled in relevant tertiary health programmes (2009 – 2012) within the FMHS at the UoA. Approval to complete this research was granted by the University of Auckland Human Participant Ethics Committee (Ref 8110). As per ethics protocols, written informed consent was not required for this research project due to the use of secondary administrative data sources. All secondary data obtained from these datasets were de-identified by an independent research member with no student contact or teaching responsibilities and data analysis occurred via a coding system. Two student cohorts are identified: First Year Tertiary Students i.e. students enrolled in either the CertHSc or the first year of a bachelor programme in the year following their MAPAS interview; and First Year Bachelor Students i.e. students enrolled in a bachelor programme in either the first or second year following their MAPAS interview (may include CertHSc graduates).

Variables

Demographic variables include: Year of Admission (2009–2012); Gender (Female, Male); Ancestry (Māori, Pacific, Both) and School Decile (High, Medium and Low). Secondary schools with a mid-low decile rating have been linked to higher levels of deprivation associated with reduced access to, and outcomes from, tertiary education [34] (Table 2).

Table 2. Descriptive summary of first year tertiary and first year bachelor student demographic and outcome variables

Admission predictor variables include: MAPAS Testing results (%); MMI Station results (Some or Major Concerns (SMC) versus Few Concerns (FC)); Provisional December Recommendation (CertHSc, Bachelor, Not FMHS); secondary school results including New Zealand’s NCEA Rank Score3 (out of 320); Level 3 NCEA Subject Credits (number of credits achieved in English, biology, chemistry, physics, mathematics); Exposure to Any 2 Sciences of senior biology, chemistry or physics (yes, no)4 ; Followed MAPAS Advice (yes, no); and Final January Recommendation made in January (CertHSc, Bachelor, Not FMHS).

Academic outcome variables include: Grade Point Average (GPA) Eight Courses, 09 (i.e. GPA achieved across a total of eight courses over the year); GPA Core 4 Courses, 09 (i.e. GPA achieved across four core courses5 taken in the first year of bachelor study that are specifically assessed for selection into second year medicine at the UoA); Passes All Courses, yes/no (i.e. across total of eight courses); Passes All Core 4 Courses, yes/no (i.e. across the four core courses).

Statistical analysis

All downloaded data were recorded in Microsoft Office Excel spread sheets. Statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC, USA). Continuous variables were presented as mean and standard deviation (SD); categorical variables as frequencies (n) and percentages (%) (Tables 2 and 3). Generalised linear and logistic regression models were used to estimate the predicted effects of individual admission variables on academic outcomes (i.e. GPA and Passes All); adjusting for pre-defined demographic variables (i.e. MAPAS interview year, gender, ancestry and school decile) (Tables 4, 5, 6 and 7). Admission variables that showed significant single predictive effect (i.e. MAPAS Maths Test, NCEA Rank Score, Any 2 Sciences and Followed MAPAS Advice) were included in the multiple regression analyses to determine their joint effects on the academic outcomes of interest (Tables 8 and 9). All statistical tests were two-sided at 5 % significance level.

Table 3. Descriptive summary of first year tertiary and first year bachelor student predictor variables

Table 4. Univariate regression analysis results – GPA eight courses

Table 5. Univariate regression analysis results – GPA core 4 courses

Table 6. Univariate regression analysis results – passes all eight courses

Table 7. Univariate regression analysis results: passes all core 4 courses

Table 8. Multiple regression analysis results – linear regression a

Table 9. Multiple regression analysis results – logistic regression a

Results

Descriptive variables

A total of 368 students were identified in the First Year Tertiary cohort. Of these, 37 % were Māori, 57 % Pacific and 6 % had Both Māori and Pacific ancestry. Two thirds were female (67 %), the mean age was 19.2 years (SD 4.2 %) and 70 % or more came from a secondary school with a medium or low school decile (representing more deprived communities). The First Year Bachelor cohort had a total of 242 students with a similar demographic profile to First Year Tertiary students (Table 2).

Predictor variables

Mathematics and english testing

The First Year Tertiary cohort had a mean percentage mark for the mathematics test of 79.0 % (SD 18.3 %) and 68.4 % (SD 13.6 %) for the English test. This represents a borderline-fail result for bachelor-level study and a pass result for CertHSc-level study as the best starting point of entry across both assessments. The First Year Bachelor cohort had a slightly higher mean mark for both the mathematics (80.4 %, SD 18.3 %) and English tests (70.6 %, SD 12.8 %) (Table 3).

MMI

Over 80 % of all students from both cohorts were assessed as having few concerns for CertHSc-level entry across the four MMI stations. Forty-four percent of all First Year Tertiary students were assessed as having some or major concerns for bachelor-level entry at the Academic Preparation and Student Information MMI stations. For First Year Bachelor students, the stations with the highest proportion of some or major concerns for bachelor-level entry were Career Aspirations (48 %) and Student Information (39 %) (Table 3).

School results

The average NCEA rank score (out of a total of 320) was 190.5 (SD 51.3) for First Year Tertiary and 201.8 (SD 52.7) for First Year Bachelor students. Both averages fall below requirements for guaranteed entry within FMHS (set at a rank score between 210 – 250 depending on the programme). The average number of subject credits for both cohorts were 0.3–3.4 credits below requirements for guaranteed entry (i.e. 16 – 18 subject credits depending on programme) (Table 3). At least two thirds of all students admitted into either the CertHSc or bachelor programmes had taken two or more science subjects in their final year of secondary school (Table 3).

MAPAS recommendations

For First Year Tertiary students, MAPAS recommended CertHSc to 72 % of all students, followed by Bachelor (26 %) and Not FMHS (2 %). For First Year Bachelor students, 58 % were recommended to start at the CertHSc level, followed by 39 % Bachelor and 3 % Not FMHS (Table 3).

Followed MAPAS advice

Over 83 % of all students followed MAPAS advice regarding the best starting point for success with only 12 – 17 % of students from each cohort not following their final MAPAS recommendation (Table 3).

Outcome variables

GPA All eight courses and core 4 courses

The average GPA for all eight courses (out of a total of 9) was 4.3 (SD 2.0) for First Year Tertiary and 4.1 (SD 2.1) for First Year Bachelor students. The average GPA achieved for the Core 4 Courses was 3.8 (SD 2.4) for First Year Bachelor students.

Passes All eight courses and passes All core 4 courses

Seventy-five percent of First Year Tertiary students and 60 % of First Year Bachelor students passed all eight courses. Sixty-four percent of First Year Bachelor students passed all Core 4 Courses (Table 2).

Multiple regression analysis

First year tertiary – GPA

As shown in Table 8, all predictors had a statistically significant effect on First Year Tertiary GPA, with the most significant predictor being NCEA Rank Score, then MAPAS Advice Followed, Any 2 Sciences and MAPAS Mathematics Test results. First year Tertiary GPA increased by an average of 0.3 (out of a total 9) for every 20 point increase in NCEA Rank Score (CI: 0.18-0.34; p < 0.0001). Students who followed MAPAS advice had on average a GPA that was 1.2 points higher (out of a total 9) than students who did not (CI: 0.57-1.78; p = 0.0002).

First year tertiary – passes All courses

The odds of passing all eight courses was 5.4 times higher for those students who followed MAPAS advice versus those students who did not (CI: 2.36-12.39; p < 0.0001) (Table 8). The odds of passing all eight courses was 2.3 times higher for those students who had exposure to Any 2 Sciences versus those students who did not (CI: 1.15-4.61; p = 0.019) (Table 8).

First year bachelor – GPA

For every 20 point increase in NCEA Rank Score, the GPA achieved by First Year Bachelor students increased by an average of 0.4 for all 8 courses (CI: 0.30-0.50; p < 0.0001) and for Core 4 courses (CI: 0.26-0.50; p < 0.0001) (Table 7). Students who followed MAPAS advice had on average a GPA that was 1.1 points higher than students who did not follow MAPAS advice for all eight courses (CI: 0.45-1.73; p = 0.0009) and Core 4 courses (CI: 0.60-2.04; p = 0.0004) (Table 8).

First year bachelor – passes All courses

A 20 point increase in NCEA Rank Score increased the odds of passing all first year bachelor courses by a factor of 1.5 (CI: 1.24-1.74; p < 0.0001), with similar results for passing all Core 4 courses (Table 8). The odds of passing all first year bachelor courses (CI: 1.45-7.69; p = 0.005) and all Core 4 courses (CI: 1.39-7.69; p = 0.007) was 3.3 times higher for those students who followed MAPAS advice versus those students who did not (Table 9).

Discussion

Our findings confirm that the MAPAS admissions process is strongly associated with positive academic outcomes in the first year of tertiary study. Our results reinforce the evidence-base showing a strong association between secondary school performance via NCEA rank score (a marker of the quality of grades achieved) and positive tertiary academic outcomes [35]. The existing literature base has also been extended, given our identification of a strong association between exposure to two or more senior science subjects (a marker of breadth of knowledge) and first year academic outcomes. Similar to other studies, our findings show that the number of credits achieved within NCEA subjects appear to be less strongly correlated with tertiary outcomes [35].

Overall, our findings suggest that there is value in providing a comprehensive admissions process for indigenous and ethnic minority students applying under equity targeted admission programmes. Students admitted into tertiary institutions under targeted admission programmes have been shown to experience peer/educator stigma and ‘everyday racism’. Demonstrating the effectiveness of targeted admission programmes may assist some indigenous and ethnic minority students to override this societal (and potentially internalised) stigma to receive the benefits that targeted admission programmes have to offer.

Increasing the odds of passing all first year courses has relevance for all students. This is important for applicants pursuing medicine as even small increments in first year bachelor GPA, particularly within the Core 4 courses used for medical selection, may have a profound impact on potential selection [12], [19]. A student’s progress towards completion of total point requirements within their degree has been shown to improve student retention and increase the likelihood of degree completion [36]. Aligning MAPAS admission to a comprehensive process focussed on achieving equity in access and performance is likely to have contributed to the recent increase in numbers and improved performance observed for Māori and Pacific students within the FMHS [5], [37].

Our data suggests secondary schooling is yet to demonstrate the ability to prepare Māori and Pacific students adequately for tertiary health professional study. Both teaching and subject selection are critical factors. Māori and Pacific students and their families are not to blame for the observed inequities in secondary education. Rather, Māori and Pacific students and their families should receive greater support to navigate NCEA subject selection and ensure that students achieve the right number and quality of credits [38]. This is consistent with international evidence showing that indigenous and ethnic minority students are less likely to receive high-quality careers or university advice [38], [39] and in some instances may be actively discouraged from pursuing a health professional career [2].

Based on our findings, it appears that the secondary education sector is failing to ensure that indigenous and ethnic minority students are ‘university-ready’ for health-professional study. Unfortunately, this is not a new issue [5], [14], [40], [41] and nor is it unique to New Zealand [3], [42]. Action by secondary schools and educators to address their own role in the creation and maintenance of ethnic inequities in academic outcomes is recommended [43]. Likewise, tertiary institutions are expected to be part of the solution [44]. Pechenkina & Anderson (2011) call for “more effective institutional response to the lack of adequate preparation of indigenous students…via greater investment in the pipeline and provision of transitioning programmes” (p. 5-6). Our findings further support the delivery of bridging/foundation programmes targeting indigenous and ethnic minority students.

Strengths

This study explores a unique application of the MMI within an equity-targeted context [14], [26]. Although we identified varied associations between individual MMI stations and academic outcomes, we believe that our overall findings support maintaining the MMI within the MAPAS admissions process. This reflects the strong association observed between following MAPAS advice (a predictor variable that is determined by the combined assessment of all results) and higher academic outcomes.

Using both cognitive (e.g. NCEA school results, MAPAS Maths and English test) and non-cognitive (e.g. MMI results) tools for student selection within the total MAPAS admission process supports a widening participation agenda and is consistent with recommendations to use more inclusive selection tools [10], [45]-[47]. This is particularly important when assessing the potential of alternative admission or older applicants who may possess maturity shown to be positively associated with tertiary programme completion [36], [48].

Limitations

This study has a number of limitations. The analysis relied on secondary data and is therefore limited by the quality of data sources. However, combining central university and MAPAS datasets has reduced the potential for data misclassification by using verified ancestry and increased the admission variables available for analysis [49], [50]. Our research was limited to first-year outcomes due to resource and time constraints. Ideally, the effect of predictor variables on long-term outcomes across all FMHS programmes should be examined. Comparing academic outcomes across all ethnic groups may also highlight issues of disadvantage and privilege [51]. This research is in progress and is drawing on the methods developed within this study. We acknowledge that combining Māori and Pacific data is not ideal from an indigenous rights or Pacific-centric perspective. However, this is consistent with our methodological approach as it maximises statistical power (to aid student success) and supports a structural critique of the effect of ‘society’ on ‘ancestry’ [14]. As the quantum of Māori and Pacific data increases, further research should investigate Māori-specific and Pacific-specific predictors of academic success.

Conclusion

Tertiary institutions committed to widening participation should prioritise the funding and delivery of a comprehensive, flexible and inclusive admissions process that includes alternative entry pathways for indigenous and ethnic minority applicants [10], [52], [53].

Ethical approval

This project was approved by the University of Auckland Human Participants Ethics Committee, Ref 8110.

Abbreviations

CertHSc: Certificate in Health Sciences (Hikitia Te Ora)

CIE: Cambridge International Exam

FMHS: Faculty of Medical and Health Sciences

GPA: Grade Point Average

IB: International Baccalaureate

KMR: Kaupapa Māori Research

MAPAS: Māori and Pacific Admission Scheme

NCEA: National Certificate of Educational Achievement

UoA: University of Auckland

Competing interest

The authors declare that they have no competing interest.

Authors’ contributions

EC led the study design, methodological approach, interpretation of the data analysis, and drafted the manuscript. EW contributed to study design and provided research assistance to obtain and clean data variables. She contributed to drafting and revising the manuscript and was responsible for producing the data tables. YJ provided senior statistical expertise for data analysis. She contributed to drafting and revising the manuscript. LM provided junior statistical expertise and contributed to drafting and revising the manuscript. RL contributed to the study design and provided Pacific research methodological expertise in the drafting and revising of the manuscript. A provided senior Pacific educational and research expertise and contributed to drafting and revising the manuscript. PR provided senior Māori educational, institutional and KMR expertise and contributed to drafting and revising the manuscript. All authors read and approved the final manuscript for submission. All authors agreed to be accountable for all aspects of the work

Authors’ information

EC (Te Arawa, FNZCPHM, MPH (Distinc), MBChB) is a specialist in public health medicine who has experience in research and policy concerned with eliminating ethnic and indigenous inequalities in health. Elana is a Senior Lecturer and the Director Vision 20:20 at Te Kupenga Hauora Māori, The University of Auckland. She is a postgraduate Doctor of Medicine (MD) candidate (exploring indigenous and ethnic minority health workforce development) and has ongoing research interests in ethnic inequities in service utilisation and health outcomes.

EW (Ngāti Hine, PGDipPH (Distinc), BHSc) is a Māori Physiotherapist who has experience in research concerned with Māori and Indigenous health workforce development, cultural competence, and psycho-oncology in Māori and Indigenous populations. Erena is currently completing a Masters in Public Health whilst working as Researcher at Te Kupenga Hauora Māori, University of Auckland. Ongoing research interests include Māori health workforce development and addressing ethnic inequalities in health.

YJ (Chinese, PhD) is a Senior Research Fellow at the Department of Statistics and Senior Statistical Consultant at the Statistical Consulting Centre (SCC), Faculty of Science, University of Auckland, New Zealand. Ongoing research interests include: randomised controlled trial design and analysis, national surveys, longitudinal and case-control studies with response-selective sampling and missing data problems.

LM (Pākehā, MSc, MMath) is an Assistant Analyst at the Statistical Consulting Centre (SCC), Faculty of Science, The University of Auckland, New Zealand. She is a PhD candidate in the Department of Mathematics and Statistics.

RL (Samoa, PGDipPsych-Community, MSocS-Hons) proudly hails from the villages of Fagamalo and Avao (Savai’i) where he was raised as a young child. Rob is a Professional Teaching Fellow within Hikitia Te Ora – Certificate in Health Sciences programme at Te Kupenga Hauora Māori, FMHS, UoA. Rob is a Registered Community Psychologist and his aspirations are firmly rooted in the wellbeing and development of Māori and Pacific communities in regards to identity and health.

A (PhD, MEd (Distinc), MBA, BA, DipTchg, CertARM) specialises in higher education research, with a particular focus on Pasifika, indigenous and under-served students. Airini has Samoan ancestry, has a national and international record in Pasifika education research and recognised expertise in Pasifika methodologies. Airini is Dean, Faculty of Human, Social and Educational Development, Thompson Rivers University, BC, Canada. With a view to informing further education system reform in New Zealand and internationally, as a Fulbright Scholar based in Washington DC Airini investigated how to convert education policy into better results for under-served students.

PR (Te Rarawa, DipComH, BSc, MBChB, DipObst, FNZCPHM) is Tumuaki and Head of Department of Māori Health at the Faculty of Medical and Health Sciences, University of Auckland, New Zealand. She is a specialist in public health medicine and her research interests include analysing disparities between indigenous and non-indigenous citizens as a means of monitoring government commitment to indigenous rights.

Acknowledgements

The authors would like to thank members of the Te Hā Advisory Group: Dr Teuila Percival; Dr Vili Nosa; Dr Malakai Ofanoa; Associate Professor Mark Barrow; Lynley Pritchard; James Clark and Carolyn (Shaoxun) Huang. Andrew Sporle and Joanna Stewart are acknowledged for providing input into the early stages of project design from a statistical perspective. Dr Elana Curtis was supported by Te Kete Hauora, Ministry of Health (New Zealand) to conduct this research via the provision of a Research Fellowship (Contract 414953/337535/00). We also thank Ngā Pae o Te Māramatanga for their support for Erena Wikaire to attend and present these research findings at the Leaders in Indigenous Medical Education (LIME) Connection V conference in Darwin, Australia 2013.

End notes

    1. Completion of the first year of study within either the Bachelor of Health Sciences or the Bachelor of Science (Biomedicine) programme is required for an undergraduate application to the medical programme at the UoA

    1. For additional information, see previous publications 5. Curtis E, Reid P. Indigenous health workforce development: Challenges and successes of the Vision 20: 20 programme. Australian & New Zealand Journal of Surgery. 2013;83(2013):49-54, 13. Curtis E, Reid P, Jones R. Decolonising the Academy: The process of re-presenting indigenous health in tertiary teaching and learning. In: Cram F, Phillips H, Sauni P, Tuagalu C, editors. Māori and Pasifika Higher Education Horizons. Bingley, U.K.: Emerald Group Publishing Limited; 2014. p. 147-66, 14. Curtis, E., Wikaire, E., Jiang, Y., McMillan, L., Loto, R., Airini, & Reid, P. (2015). A tertiary approach to improving equity in health: Quantitative analysis of the Māori and Pacific admission scheme (MAPAS) process, 2008-2012. International Journal for Equity in Health, 14(7). 10.1186/s12939-015-0133-7. or https://www.fmhs.auckland.ac.nz/en/faculty/for/future-undergraduates/maori-and-pacific-admission-scheme.html

    1. The National Certificate of Educational Achievement (NCEA) is the major assessment method used in New Zealand secondary schools. The NCEA Rank Score reflects the best 80 credits at Level 3 or higher, over a maximum of five approved subjects. It reflects a system of Grade Point Average and is used by the UoA to assist with admission to limited entry programmes 23. Shulruf B, Hattie J, Tumen S. New Zealand’s standard-based assessment for secondary schools (NCEA): implications for policy makers. Asia Pacific Journal of Education. 2010;30(2).

    1. Exposure to a minimum of two final year secondary school science subjects is recommended for success within the CertHSc (alongside English and mathematics rich subjects). This variable includes secondary school results from NCEA, International Baccalaureate (IB) and Cambridge International Examinations (CIE).

  1. The Core 4 courses include: CHEM110 (Chemistry of the living world), POPLHLTH 111 (Population Health), MEDSCI 142 (Biology for Biomedicine Science: Organ Systems) and BIOSCI 107 (Biology for Biomedicine Science: Cellular Processes and Development).

References

  1. Whitehead G, Shah M, Nair C. Equity and excellence are not mutually exclusive: A discussion of academic standards in an era of widening participation. Quality Assurance in Education. 2013;21(3):299-310.
  2. Hollow W, Buckley A, Patterson DG, Olsen P, Medora R, Morin L et al.. Clearing the Path to Medical School for American Indians and Alaska Natives: New Strategies. School of Medicine, University of Washington and WWAMI Centre for Health Workforce Studies, Washington; 2006. OpenURL
  3. Sullivan L. Missing person: Minorities in the health professions. A report of the Sullivan Commission on diversity in the healthcare workforce. 2004.
  4. Cooney R, Kosoko-Lasaki O, Slattery B, Wilson MR. Proximal versus distal influences on underrepresented minority students pursuing health professional careers. J Natl Med Assoc. 2006; 98(9):1471-5. OpenURL
  5. Curtis E, Reid P. Indigenous health workforce development: Challenges and successes of the Vision 20: 20 programme. Aust N Z J Surg. 2013; 83(2013):49-54. Publisher Full Text OpenURL
  6. Bediako MR, McDermott BA, Bleich ME, Colliver JA. Ventures in education: A pipeline to medical education for minority and economically disadvantaged students. Acad Med. 1996; 71(2):190-2. Publisher Full Text OpenURL
  7. Farrington S, Page S, DiGregorio KD. The things that matter: Understanding the factors that affect the participation and retention of Indigenous students in the Cadigal Program at the Faculty of Health Sciences, University of Sydney. In: Joint Annual Conference of the Australian Association for Research in Education (AARE) and New Zealand Association for Research in Education (NZARE). Melbourne; 1999.
  8. Ratima M, Brown R, Garrett N, Wikaire E, Ngawati R, Aspin C et al.. Rauringa Raupa: Recruitment and rentention of Maori in the health and disability workforce. Division of Public Health and Psychosocial Studies, Faculty of Health and Environmental Sciences. AUT University, Auckland: Taupua Waiora; 2008. OpenURL
  9. Anderson M, Lavallee B. The development of the First Nations, Inuit and Metis medical workforce. Med J Aust. 2007; 186(10):539-40. OpenURL
  10. Powis D, Hamilton J, McManus IC. Widening access by changing the criteria for selecting medical students. Teach Teach Educ. 2007; 23:1235-45. Publisher Full Text OpenURL
  11. Zhou Y-X, Zhao Z-T, Wan C-S, Peng C-H, Yang J, Ou C-Q. Predictors of first-year GPA on medical students: A longitudinal study of 1285 matriculates in China. BMC Med Educ. 2014; 14:87. BioMed Central Full Text OpenURL
  12. Poole P, Moriarty H, Wearn T, Wilkinson T, Weller J. Medical Student Selection in New Zealand: Looking to the future. N Z Med J. 2009; 122(1306):88-100. OpenURL
  13. Curtis E, Reid P, Jones R. Decolonising the Academy: The process of re-presenting indigenous health in tertiary teaching and learning. In: Māori and Pasifika Higher Education Horizons. 5th ed. Cram F, Phillips H, Sauni P, Tuagalu C, editors. Emerald Group Publishing Limited, Bingley, U.K; 2014: p.147-66. Publisher Full Text OpenURL
  14. Curtis E, Wikaire E, Jiang Y, McMillan L, Loto R, Airini et al.. A tertiary approach to improving equity in health: Quantitative analysis of the Māori and Pacific admission scheme (MAPAS) process, 2008-2012. Int J Equity Health. 2015; 14:7. BioMed Central Full Text OpenURL
  15. Salmi J, Bassett R. The equity imperative in tertiary education: Promoting fairness and efficiency. Int Rev Educ. 2014;60(3):1-18.
  16. Tinto, V. Taking Retention Seriously: rethinking the first year of college. NACADA Journal. 1997;19(2):5-9.
  17. Fazey D, Fazey J. The potential for autonomy in learning: Perceptions of competence, motivation and locus of control in first-year undergraduate students. Stud Higher Educ. 2001; 26(3):345-61. Publisher Full Text OpenURL
  18. Krause K-L, Coates H. Students’ engagement in first-year university. Assess Eval High Educ. 2008; 33(5):493-505. Publisher Full Text OpenURL
  19. Shulruf B, Poole P, Wang G, Rudland J, Wilkinson T. How well do selection tools predict performance later in a medical programme? Adv Health Sci Educ. 2012; 17:615-26. Publisher Full Text OpenURL
  20. Zepke N, Leach L. Improving student engagement: Ten proposals for action. Active Learn High Educ. 2010; 11(3):167-77. Publisher Full Text OpenURL
  21. Shulruf B, Hattie J, Tumen S. The predictability of enrolment and first-year university results from secondary school performance: The New Zealand National Certificate of Educational Achievement. Studies High Educ. 2008; 33(6):685-98. Publisher Full Text OpenURL
  22. Shulruf B, Hattie J, Tumen S. Individual and school factors affecting students’ participation and success in higher education. High Educ. 2008; 56:613-32. Publisher Full Text OpenURL
  23. Flett R, Gavala J. Influencial factors moderating academic enjoyment/motivation and psychological well-being for Māori university students at Massey University. N Z J Psychology. 2005; 34(1):52-7. OpenURL
  24. The University of Auckland Faculty of Medical and Health Sciences undergraduate prospectus. The University of Auckland, Auckland; 2015. OpenURL
  25. Pau A, Jeevaratnam K, Chen Y, Fall A, Khoo C, Nadarajah V. The Multiple Mini-Interview (MMI) for student selection into health professoins training – A systematic review. Med Teach. 2013; 35:1027-41. Publisher Full Text OpenURL
  26. Eva K, Rosenfeld J, Reiter H, Norman G. An admmission OSCE: The multiple mini-interview. Med Educ. 2004; 38:314-26. Publisher Full Text OpenURL
  27. Harris S, Owen C. Discerning quality: Using the multiple mini-interview in student selection for the Australian National University Medical School. Med Educ. 2007; 41:234-41. Publisher Full Text OpenURL
  28. Brownell K, Lockyer J, Collin T, Lemay J. Introduction of the multiple mini interview into the admission process at the University of Calgary: Acceptability and feasibility. Med Teach. 2007; 29:394-6. Publisher Full Text OpenURL
  29. Smith L. Decolonizing Methodologies: Research and Indigenous Peoples. 2nd ed. Zed Books, London & New York; 2012. OpenURL
  30. Health Research Council of New Zealand. Guidelines for Pacific health research. In. Auckland: Health Research Council of New Zealand; 2005.
  31. Eketone A. Theoretical underpinnings of Kaupapa Māori directed practice. MAI Review. 2008; 1:1-11. OpenURL
  32. Reid P, Robson B. Understanding Health Inequities. In: Hauora: Māori Standards of Health IV A study of the years 2000-2005. Robson B, Harris R, editors. Te Rōpū Rangahau Hauora a Eru Pōmare, Wellington; 2007: p.3-10. OpenURL
  33. Valencia RR. The Evolution of Deficit Thinking: Educational Thought and Practice. The Palmer Press, Washington DC; 1997. OpenURL
  34. McKinley E, Madjar I. From Schools in Low-income Communities to University: Challenges of Transition For Māori and Pacific Students. In: Diversity in Higher Education Māori and Pasifika Higher Education Horizons. Volume 15, First edn. Edited by Cram F, Phillipa H, Sauni P, Tuagalu C. Bingley, UK: Emerald Group Publishing Limited; 2014: 241-252.
  35. Shulruf B, Meisong L, McKimm J, Smith M. Breadth of knowledge vs. grade: What best predicts achievement in the first year of health sciences programmes? J Educ Eval Health Prof. 2012; 9:7. Publisher Full Text OpenURL
  36. Tumen S, Shulruf B, Hattie J. Student pathways at the university: Patterns and predictors of completion. Studies High Educ. 2008; 33(3):233-52. Publisher Full Text OpenURL
  37. Skene J, S E. Does access equal success? The critical role of the FYE in achieving equity in higher education. In: 12th Pacific Rim First Year in Higher Education Conference, ‘Preparing for tomorrow today: The First Year as foundation’. Townsville: The University of Western Australia; 2009.
  38. McKinley E, Madjar I. From schools in low-income communities to university. Māori and Pacifika higher education horizons. In: Māori and Pasifika higer education horizons (Diversity in higher education, Volume 15). edn. Edited by Cram F, Phillips H, Sauni P, Tuagalu C: Bingley: Emerald Group Publishing Limited; 2014: 1-19.
  39. Drysdale M, Faulkner S, Chesters J. Footprints forwards: Better strategies for the recruitment, retention and support of Indigenous medical students. In.: Monash University School of Rural Health, Moe; 2006.
  40. Curtis E, Wikaire E, Stokes K, Reid P. Addressing indigenous health workforce inequities: A literature review exploring ‘best’ practice for recruitment into tertiary health programmes. Int J Equity Health. 2012; 11:13. BioMed Central Full Text OpenURL
  41. Ratima M, Brown R, Garrett N, Wikaire E, Ngawati R, Aspin C, Potaka U. Rauringa Raupa. Recruitment and retention of Māori in the health and disability workforce. In. Auckland: Taupua Waiora: Division of Public Health and Psychosocial Studies. Faculty of Health and Environmental Sciences: AUT University; 2008.
  42. Pechenkina E, Anderson I. Background paper on Indigenous Australian Higher Education: Trends, Initiatives and Policy Implications. Prepared for The Review of Higher Educaiton Access and Outcomes for Aboriginal and Torres Strait Islander People. Commonwealth of Australia, Canberra; 2011. OpenURL
  43. Bishop R, Berryman M, Tiakiwai S, Richardson C. Te Kotahitanga: The Experiences of Year 9 and 10 Māori Students in Mainstream Classrooms. University of Waikato, Hamilton; 2003. OpenURL
  44. Initial Plan Guidance for 2013 Plans: Guidance for all TEOs. Tertiary Education Commission, Wellington; 2012. OpenURL
  45. Siu E, Reiter H. Overview: what’s worked and what hasn’t as a guide towards predictive admissions tool development. Adv in Health Sci Educ. 2009; 14:759-75. Publisher Full Text OpenURL
  46. Wilkinson D, Zhang J, Byrne GHL, Ozolins 1Z, Parker M, Peterson R. Medical school selection criteria and the prediction of academic performance. Evidence leading to change in policy and practice at the University of Queensland. Med Educ. 2008; 188:349-54. OpenURL
  47. Poole P, Shulruf B. Shaping the future medical workforce: take care with selection tools. J Prim Health Care. 2013; 5(4):269-75. OpenURL
  48. O’Shea, S. Avoiding the manufacture of ‘sameness’: first-in family students, cultural capital and the higher education environment. Higher Education. 2015; ePub 16 September:1-20.
  49. Ethnicity Data Protocols for the Health and Disability Sector. Ministry of Health, Wellington; 2004. OpenURL
  50. Educational Performance Indicators: Definitions and Methodology. Measuring Student Achievement for Tertiary Education Organisations. All funds reported through SDR. Version 7. Tertiary Education Commission, Wellington; 2014. OpenURL
  51. McIntosh P. White privilege and Male privilege: A personal account of coming to see corresponsdences through work in women’s studies. Working paper 189. In., vol. MA 02181. Wellesley: Wellesley College Center for Research on Women; 1988.
  52. Salmi J, Bassett R. The equity imperative in tertiary education: Promoting fairness and efficiency. International Review of Education. 2014;60(3):1-18.
  53. Clancy P, Goastellec G. Exploring Access and Equity in Higher Education: Policy and Performance in a Comparative Perspective. Higher Education Quarterly. 2007; 61(2):136-154. Publisher Full Text OpenURL

An epidemiological investigation to reconstruct a probable human immunodeficiency virus -1 transmission network: a case report

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An epidemiological investigation to reconstruct a probable human immunodeficiency virus -1 transmission network: a case report

Sara Serafino1, Eleonora Cella12, Claudia Montagna3, Eugenio Nelson Cavallari1, Pietro Vittozzi1, Alessandra Lo Presti2, Marta Giovanetti24, Laura Mazzuti3, Ombretta Turriziani3, Giancarlo Ceccarelli1, Gabriella d’Ettorre1, Vincenzo Vullo1 and Massimo Ciccozzi256*

Author Affiliations

1 Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy

2 Department of Infectious Parasitic and Immunomediated Diseases, Reference Centre on Phylogeny, Molecular Epidemiology and Microbial Evolution (FEMEM)/Epidemiology Unit, National Institute of Health, Rome, Italy

3 Department of Molecular Medicine, Laboratory of Virology, Sapienza University of Rome, Rome, Italy

4 Department of Biology, University of Rome Tor Vergata, Rome, Italy

5 University of Biomedical Campus, Rome, Italy

6 Epidemiology Unit, Department of Infectious, Parasitic and Immune-Mediated Diseases, Istituto Superiore di Sanità- V.le Regina Elena, Roma, 299 – 00161, Italy

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Journal of Medical Case Reports 2015, 9:253  doi:10.1186/s13256-015-0717-2

The electronic version of this article is the complete one and can be found online at: http://www.jmedicalcasereports.com/content/9/1/253

Received: 31 October 2014
Accepted: 28 September 2015
Published: 3 November 2015

© 2015 Serafino et al.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Abstract

Background

Recently published studies have highlighted the importance of phylogenetic and phylodynamic analyses in supporting epidemiological investigations to reconstruct the transmission network of human immunodeficiency virus. Here, we report a case of sexual transmission of human immunodeficiency virus type 1 between a man and a woman that marks once more the importance of a tightened collaboration between phylogeny and epidemiology.

Case presentation

We describe a case of human immunodeficiency virus type 1 subtype B transmission in a stable Caucasian heterosexual couple. The man was 30 years old and the woman was 21 years old at the time of their presentation to the Department of Public Health and Infectious Diseases of the University of Rome “Sapienza”. The couple reported a history of drug abuse.

Conclusion

Phylogenetic analysis is a powerful technique that if properly used can prove valuable in research investigations. In the case presented here, a phylogenetic analysis alongside epidemiological evidence allowed us to determine the most probable source of the human immunodeficiency virus infection. The dated tree allowed us to date the transmission event, the time point, and the direction of transmission based on the phylogeny, which agreed with the presumptive time of infection determined from clinical history-taking.

Keywords:

Case report; HIV-1; Phylogeny

Background

The human immunodeficiency virus (HIV) is characterized by great genetic heterogeneity driven by several factors, such as the lack of proofreading ability of the reverse transcriptase [1], [2], the rapid turnover of HIV-1 in vivo[3], host selective immune pressures [4], and recombination events during replication [5].

The majority of HIV-1 strains cluster within a large group called M (for Main), which includes nine subtypes (A–D, F–H, J, and K) with distinct phylogeny. Subtypes A and F can be further divided into sub-subtypes A1–A4 and F1 and F2, respectively. A number of inter-subtype recombinant viruses have also been observed [6]–[8].

HIV-1 group M subtypes are responsible for most of the HIV infections worldwide. In Italy the estimated percentage of non-B subtype infections has been reported to range from 2.4 to 19.4 %, thus confirming a significant increase in non-B subtypes prevalence [9]–[15], but, in this country, the first phase of the HIV epidemic was mainly confined to the intravenous drug users risk group, with an absolute predominance of HIV-1 B clade, as other Western Countries [16].

Phylogeny is a branch of molecular biology that infers knowledge about taxonomy and the evolution of species [17]. It is a powerful tool widely used in the study of rapidly evolving RNA viruses that cause chronic infections. The present case report underlines the importance of phylogenetic analysis to support epidemiological investigations into the reconstruction of transmission networks.

We present a case of HIV-1 subtype B transmission in a stable heterosexual couple living together was described to mark once more the importance of the “tightened collaboration” between phylogeny and epidemiology.

Case presentation

We describe a case of HIV-1 subtype B transmission in a stable Caucasian heterosexual couple. The man was 30 years old and the woman was 21 years old at the time of their presentation to the Department of Public Health and Infectious Diseases of the University of Rome “Sapienza”. He became addicted to injected drugs in 1996 at the age of 14 and entered a rehabilitation community after 14 years of drug abuse, during which he practiced needle exchange. She was addicted to injected drugs from the age of 12 until the age of 19 and also practiced an unsafe needle exchange. They met after she joined his rehabilitation community.

Our epidemiological investigation was conducted in two different phases. At the beginning of April 2013, he came to our attention because of a suspected infection with hepatitis C virus (HCV). He reported a virulent and recent episode of shingles on his right hemi-thorax. During a physical examination we noticed the presence of several genital lesions, suggestive of condylomas. On the basis of his epidemiological history and these clinical findings, we proposed our patient be tested for HIV infection. He was found to be positive for HCV IgG (Anti HCV Advia Centaur Immunoassay System, Siemens Healthcare Diagnostic, Tarrytown, NY, USA) and negative for HCV RNA (Versant HCV RNA 1.0 assay (kPCR), Siemens Healthcare Diagnostics). He was HIV-Ag/Ab-positive (Advia Centaur Systems HIV Ag/Ab Combo assay, Siemens Healthcare Diagnostics) with an HIV RNA count of 102,900 copies/mL (Versant HIV 1.0 RNA assay (kPCR), Siemens Healthcare Diagnostics) and a CD4+ T cell count of 20 cells/μL (1.55 %). A genotypic resistance test (Trugene HIV-1 genotyping assay, Siemens Healthcare Diagnostics) showed a wild-type virus. Our patient started combination antiretroviral therapy (cART) with tenofovir, emtricitabine, atazanavir, and ritonavir. Owing to these findings, we tested our patient’s partner for HIV and HCV infections. She was HIV-Ag/Ab-negative, HCV IgG-positive, and HCV RNA-negative.

After two weeks, the woman presented to the emergency department of an urban hospital with an elevated temperature and a skin rash on every part of her body. Results of a blood test showed a white blood cell count of 1200 cell/mL. She was discharged with a diagnosis of a viral infection and instructed to present for ambulatory care to an infectious diseases clinic.

The day after this discharge, our patient arrived at our center and reported that her menses was two days late. She tested positive for beta-human chorionic gonadotropin. The test for HIV-Ag/Ab was repeated, again with negative results, but we tested her anyway for HIV RNA with a result of 4161 copies/mL. As stated by the US Department of Health and Human Services guidelines, in consideration of <10,000 copies/mL of HIV RNA with a negative HIV-Ag/Ab test, we repeated the HIV RNA test using a different specimen from the same patient, and found an HIV RNA count of 1,302,000 copies/mL. At this time, an HIV-Ag/Ab test had “undetermined” results, with a single gp41 positive band on a confirmatory western blot test. cART was initiated with tenofovir, emtricitabine, and raltegravir. As was the case with her partner, a genotypic resistance test revealed a wild-type virus. A genotyping test revealed coincident viral tropism within the couple, with CXCR4 tropism as predicted by a geno2pheno algorithm set at a false-positive rate of 10 % [18]. At the beginning of May, our patient had a spontaneous abortion due to acute retroviral syndrome.

To support the epidemiological investigation, we reconstructed the transmission network within a calendar timescale on the basis of a recently described phylogenetic–statistical framework, using the env viral sequences from our two patients [19], [20]. For virologic and phylogenetic analysis, we performed peripheral blood mononuclear cell isolation and DNA extraction as previously described [21]. The env region was amplified by a nested polymerase chain reaction and the following primers were used for the sequencing reaction: 5′-CTGTTAAATGGCAGTCTAGC-3′, 5′-GCAATGTATGCCCCTCCCATC-3′, and 5′GCTCCATGTTTTTCCAGGTC-3′. Sequence analyses were performed by Sequencher and Bioedit software packages. The subtypes of the two sequences was determined by uploading the sequences individually into the REGA HIV-1 automated Subtyping Tool v2.0 [22].

We built two different datasets: one with both the male and female sequences, and one without the female sequence to date the male infection. Nucleotide sequences were aligned with 35 reference HIV-1 subtype B sequences of known provenance and date (26 from Italy and six from other countries) using CLUSTAL W software and edited manually according to their codon-reading frame by BioEdit [23], [24]. These reference sequences were obtained with the Blast similarity search.

We performed a hierarchical likelihood ratio test using ModelTest 3.7 implemented in the PAUP* 4.0 software [23], [24], and identified the evolutionary model as the best-fitting nucleotide substitution model.

Dated phylogenies were obtained by simultaneously inferring the evolutionary rate and population and model parameters using a Bayesian Markov Chain Monte Carlo (MCMC) method implemented in the BEAST package version 1.8 [18], [25]. Statistical support for specific clades was obtained by calculating the posterior probability of each monophyletic clade. The trees were generated using the HKY +I+G model of substitution, chosen by ModelTest.

The MCMC was run for 50×10 6 generations, under both strict and relaxed clock conditions, until convergence was achieved on the basis of the effective sampling size (ESS). Only ESS values of >250 were accepted. As coalescent priors, we compared three parametric models (constant, exponential, expansion growth) and a Bayesian Skyline plot non-parametric model.

For the first dataset, the expansion growth model under a relaxed (uncorrelated log normal) clock was selected, whereas for the second dataset, the exponential growth model under a relaxed (uncorrelated log normal) clock was selected.

REGA subtyping analysis classified the two sequences as subtype B. Our patients’ isolates formed a significant monophyletic cluster (posterior probability = 1) (Fig. 1a), showing a strong relationship and affirming infection by the same virus.

thumbnailFig. 1. Bayesian time-scaled tree of the HIV-1 B sequences. The asterisks (*) along a branch represent significant statistical support for the clade subtending that branch (posterior probability = 100 %). The numbers at the internal node represent the estimated date of the origin and the uncertainty indicated by 95% highest posterior density intervals. a Couple (male and female) tree. b Dated male viral phylogeny

In the male dated phylogeny (Fig. 1b), the male sequence was related to a sequence from the USA, and the time to most recent common ancestor was estimated to be 1998 (95 % highest posterior density, 1991 to 2004).

The Bayesian analysis confirmed the transmission network and allowed us to date the transmission event with a probability of 99 %. Moreover, the existence of an epidemiological relationship between the two patients confirmed the phylogenetic analysis and agreed with the presumptive date of infection on the basis of clinical history-taking. The Bayesian analysis also confirmed that our male patient probably acquired the infection about two years after starting illicit drug use and before having a relationship with our female patient.

Conclusions

HIV-1 and HIV-2 transmission networks are already described in nosocomial and non-hospital-acquired infections [19], [20], [26]. A report of healthcare workers infection with HIV-1 by a needle stick injury was recently reported and published in 2010 [23]. A connection between epidemiological investigations and phylogenetic analyses was also recently demonstrated in case report analyses and population studies [24], [26]. Prospective surveillance studies conducted throughout the world report that the risk of HIV transmission ranges from 0.09 to 0.3 % [27].

A phylodynamic reconstruction, created using Bayesian methods, of the transmission network within a calendar timescale, that agreed with the epidemiological data, provided a well-documented transmission framework to significantly improve the investigation in our case.

Phylogenetic analysis is a powerful technique that if properly used can prove valuable in research investigations. In our case, we found it remarkable how the phylogenetic analysis and epidemiological evidence aligned to allow us to determine the most probable source of HIV infection. Our findings in these cases has strengthened the evidence that Bayesian phylogenetic analysis can be an important way of tracing epidemiological relationships.

Consent

Written informed consent was obtained from the patients for publication of this case report and any accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.

Abbreviations

cART: combined antiretroviral therapy

ESS: effective sampling size

HCV: hepatitis C virus

HIV: human immunodeficiency virus

MCMC: Markov Chain Monte Carlo

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

SS, EC, ALP and MG performed the phylogenetic analysis and contributed to manuscript writing. ENC, GC, GdE and PV collected the clinical data and revised the manuscript; CM, LM, OT and SS performed sequencing and data analysis. VV supervised and coordinated the study. MC supervised the phylogenetic analysis and wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgements

The authors wish to thank Dr Valerio Ciccozzi for the English revision of the manuscript.

References

  1. Op De Coul ELM, Prins M, Cornelissen M, Van Der Schoot A, Boufassa F, Brettle RP et al.. European and Italian Seroconverter Studies. Using phylogenetic analysis to trace HIV-1 migration among western European injecting drug users seroconverting from 1984 to 1997. AIDS. 2001; 15:257-66. PubMed Abstract | Publisher Full Text OpenURL
  2. Roberts JD, Bebenek K, Kunkel TA. The accuracy of reverse transcriptase from HIV-1. Science. 1988; 242:1171-3. PubMed Abstract | Publisher Full Text OpenURL
  3. Ho DD, Neumann AU, Perelson AS, Chen W, Leonard JM, Markowitz M. Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infections. Nature. 1995; 373:123-6. PubMed Abstract | Publisher Full Text OpenURL
  4. Michael NL. Host genetic influences on HIV-1 pathogenesis. Curr Opin Immunol. 1999; 11:466-74. PubMed Abstract | Publisher Full Text OpenURL
  5. Temin HM. Retrovirus variation and reverse transcription: abnormal strand transfers result in retrovirus genetic variation. Proc Natl Acad Sci USA. 1993; 90:6900-3. PubMed Abstract | Publisher Full Text OpenURL
  6. Robertson DL, Anderson JP, Bradac JA, Carr JK, Foley B, Funkhouser RK et al.. HIV-1 nomenclature proposal. Science. 2000; 288:55-6. PubMed Abstract | Publisher Full Text OpenURL
  7. McCutchan FE. Understanding the genetic diversity of HIV-1. AIDS. 2009; 14:S31-44. OpenURL
  8. Salminen MO, Carr JK, Burke DS, McCutchan FE. Identification of breakpoints in intergenotypic recombinants of HIV type 1 by bootscanning. AIDS Res Hum Retroviruses. 1995; 11:1423-5. PubMed Abstract | Publisher Full Text OpenURL
  9. Balotta C, Facchi G, Violin M, Van Dooren S, Cozzi-Lepri A, Forbici F et al.. Increasing prevalence of non-clade B HIV-1 strains in heterosexual men and women, as monitored by analysis of reverse transcriptase and protease sequences. J Acquir Immune Defic Syndr. 2001; 27:499-505. PubMed Abstract | Publisher Full Text OpenURL
  10. Tramuto F, Vitale F, Bonura F, Romano N. Group for HIV-1 Antiretroviral Studies in Sicily. Detection of HIV type 1 non-B subtypes in Sicily, Italy. AIDS Res Hum Retroviruses. 2004; 20:251-4. PubMed Abstract | Publisher Full Text OpenURL
  11. Monno L, Brindicci G, Lo Caputo S, Punzi G, Scarabaggio T, Riva C et al.. HIV-1 subtypes and circulating recombinant forms (CRFs) from HIV-infected patients residing in two regions of central and southern Italy. J Med Virol. 2005; 75:483-90. PubMed Abstract | Publisher Full Text OpenURL
  12. Ciccozzi M, Montieri S, Salemi M, De Oliveira T, Dorrucci M, Sinicco A et al.. An outbreak of HIV-1 subtype G among Italian injecting drug users. AIDS. 2007; 21:1213-5. PubMed Abstract | Publisher Full Text OpenURL
  13. Ciccozzi M, Santoro MM, Giovanetti M, Andrissi L, Bertoli A, Ciotti M. HIV-1 non-B subtypes in Italy: a growing trend. New Microbiol. 2012; 35:377-86. PubMed Abstract | Publisher Full Text OpenURL
  14. Buonaguro L, Petrizzo A, Tagliamonte M, Vitone F, Re MC, Pilotti E et al.. Molecular and phylogenetic analysis of HIV-1 variants circulating in Italy. Infect Agent Cancer. 2008; 10:3-13. OpenURL
  15. Lai A, Riva C, Marconi A, Balestrieri M, Razzolini F, Meini G et al.. Changing patterns in HIV-1 non-B clade prevalence and diversity in Italy over three decades. HIV Med. 2010; 11:593-602. PubMed Abstract | Publisher Full Text OpenURL
  16. Ciccozzi M, Bon I, Ciotti M et al.. Do the HIV-1 subtypes circulating in Italy resemble the Red Queen running in Carroll’s novel. NEW MICROBIOL. 2012; 35:377-386. PubMed Abstract | Publisher Full Text OpenURL
  17. Lemey P, Salemi M, Vandamme AM. The phylogenetic handbook: a practical approach to phylogenetic analysis and hypothesis testing. Cambridge University Press, New York; 2009. OpenURL
  18. Svicher V, D’Arrigo R, Alteri C, Andreoni M, Angarano G, Antinori A et al.. Performance of genotypic tropism testing in clinical practice using the enhanced sensitivity version of Trofile as reference assay: results from the OSCAR Study Group. New Microbiol. 2010; 33:195-206. PubMed Abstract | Publisher Full Text OpenURL
  19. Ciccozzi M, Madeddu G, Lo Presti A, Cella E, Giovanetti M, Budroni C et al.. HIV type 1 origin and transmission dynamics among different risk groups in Sardinia: molecular epidemiology within the close boundaries of an Italian island. AIDS Res Hum Retroviruses. 2013; 29:404-10. PubMed Abstract | Publisher Full Text OpenURL
  20. Ciccozzi M, Callegaro A, Lo Presti A, Cella E, Giovanetti M, Salpini R et al.. When phylogenetic analysis complements the epidemiological investigation: a case of HIV-2 infection. Italy. New Microbiol. 2013; 36:93-6. PubMed Abstract | Publisher Full Text OpenURL
  21. Falasca F, Montagna C, Maida P, Bucci M, Fantauzzi A, Mezzaroma I et al.. Analysis of intracellula human immunodeficiency virus (HIV)-1 drug resistance mutations in multi-failed HIV-1-infected patients treated with a salvage regimen: 72-week follow-up. Clin Microbiol Infect. 2013; 19:E318-21. PubMed Abstract | Publisher Full Text OpenURL
  22. de Oliveira T, Deforche K, Cassol S, Salminen M, Paraskevis D, Seebregts C et al.. An automated genotyping system for analysis of HIV-1 and other microbial sequences. Bioinformatics. 2005; 21:3797-800. PubMed Abstract | Publisher Full Text OpenURL
  23. Bon I, Ciccozzi M, Zehender G, Biagetti C, Verrucchi G, Lai A et al.. HIV-1 subtype C transmission network: the phylogenetic reconstruction strongly supports the epidemiological data. J Clin Virol. 2010; 48:212-4. PubMed Abstract | Publisher Full Text OpenURL
  24. Ciccozzi M, Lo Presti A, Cenci A, Staltari O, Buttò S, Equestre M et al.. May phylogenetic analysis support epidemiological investigation in identifying the source of HIV infection? AIDS Res Hum Retroviruses. 2011; 27:455-7. PubMed Abstract | Publisher Full Text OpenURL
  25. BEAST package version 1.8. Available at. http://beast. bio.ed.ac.uk OpenURL
  26. Callegaro A, Svicher V, Alteri C, Lo Presti A, Valenti D, Goglio A et al.. Epidemiological network analysis in HIV-1 B infected patients diagnosed in Italy between 2000 and 2008. Infect Genet Evol. 2011; 11:624-32. PubMed Abstract | Publisher Full Text OpenURL
  27. Progress report 2011: Global HIV/AIDS response. Epidemic update and health sector progress towards universal access. WHO, Geneva; 2011. OpenURL