Malaria parasite prevalence and Haematological parameters in HIV seropositive patients attending the regional hospital Limbe, Cameroon: a hospital-based cross-sectional study

The study was carried out in the Regional Hospital Limbe (RHL), Fako Division, South West Region of Cameroon. Limbe is a coastal town situated at the foot of Mount Cameroon and opens the South West Region to the rest of the world through the Atlantic Ocean. It covers an area of 185 km² and as of 2014 had a population of 84,500 inhabitants [18]. Its climate is typically equatorial with annual rainfall exceeding 4000 mm, temperatures ranging from 23 °C to 32 °C and 80% relative humidity as reported by the Cameroon Development Corporation. Limbe is characterised by two distinct seasons; a rainy season running from mid-March to October and a dry season running from November to mid-March.

The study participants included HIV positive patients registered at the Voluntary Counselling Testing and Treatment Centre of the RHL and presumed HIV negative individuals presenting themselves at the Out Patient Department of the hospital for consultation during the study period. Participants were recruited irrespective of their age, sex, marital status, level of education, and with or without malaria-related signs and symptoms. Only those who gave their consent for blood collection and answered the questionnaire were enrolled in the study.

This cross-sectional study was conducted from March–June, 2014. An information sheet and a brief talk were given to the participants, explaining the objectives and benefits of the study. Participants were then invited to participate in the study by signing an informed consent form. In the case of children (≤ 16 years) their parents signed a proxy consent form on their behalf. Before sample collection, a clinical examination was carried out followed by the administration of structured questionnaires to the participants. HIV-sero-negative individuals were persons at the hospital Out Patient Department who consented to be screened for HIV during the process and who tested negative for HIV.

The sample size was determined using the formula n = Z²pq/d² [19] where n represented the sample size evaluated, Z was 1.96, which is the standard normal deviate (for the 95% confidence interval, CI), p was 58.9 and 51.1% [10], the malaria prevalence in HIV patients on ART and those not on ARV, respectively, q was 1-p and d was 0.05. The optimum sample size calculated was n = 380.

A pre-tested structured questionnaire was administered to all the consented participants enrolled in the study to obtain socio-demographic data such as age, sex, level of education, marital status; while clinical data such as HIV status, ART usage, duration on treatment were collected from each patient’s file at the HIV centre. The questionnaire also assessed the utilization of insecticide-treated nets and insecticide residual spray as preventive methods against malaria. An additional file shows the questionnaire used in detail (see Additional file 1).

Before blood sample collection, a participant’s axillary temperature was recorded using a digital thermometer. Fever was defined as temperature ≥ 37.5 °C. Under sterile conditions, a vacuum holder and vacutainer needle were used to collect 4 mL of venous blood into 2 well-labelled ethylenediaminetetraacetate (EDTA) tubes for each participant. A portion of the blood was used to prepare thick and thin blood films. The blood in one of the tubes was used for HIV rapid test (in the case of presumed HIV negative participants) and to conduct full blood count. Plasma was separated from the other portion of the blood, stored in an ice-filled cooler and transported to the Malaria Research Laboratory of the University of Buea for use in the HIV BISPOT assay. The prepared thin and thick blood films were Giemsa-stained and observed under the microscope following standard procedures [20] by two qualified microscopists. A complete blood count including values for white blood cell (WBC), red blood cell (RBC) and platelet counts, haemoglobin concentration (Hb), haematocrit (Hct), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), mean platelet volume (MPV), red cell distribution width (RDW), platelet distribution width (PDW), red blood cell distribution width coefficient of variation (RDW-CV), and red blood cell distribution width standard deviation (RDW-SD) was obtained using the URIT-3300 automated haematology analyser (URIT Medical Electronic CO., LTD. Jiuhua Road, Gungxi, China), following the manufacturer’s instructions. Anaemia was defined as Hb < 11.0 g/dL and further classified as described by Cheesbrough (2009) as severe (Hb <7 g/dl), moderate (Hb between 7.0 g/dl and 10.0 g/dl) and mild (>10.0 g/dl and <11 g/dl) [20, 21].

The Determine Rapid Test Kit (Abott Laboratories, CO., Ltd. Minato-Ku, Tokyo Japan) was used to test the supposed HIV negative subjects for HIV-1. Test results were read after 15 min from corresponding colour changes on the strip according to the manufacturer’s instructions.

All HIV positive results by the test kit were confirmed using the HIV-1 and 2 BISPOT assay (ORGENICS, Yavne, Israel). After bringing all components of the test kit and plasma samples to room temperature, 50 μL of each specimen and controls was dispensed into separate wells of row A of the developing plate and gently mixed. The comb was then introduced into the wells of row A and incubated for 10 min. The comb was later introduced into other wells serially with incubation and results were read by recognition of black dots on the comb teeth.

Ten (10) μL of Guava Auto CD4/CD4% antibody cocktail was placed into a 1.5 mL sample tube labelled with the patient’s serial number. A reverse pipette of 10 μL of whole blood (from the purple-top EDTA) was added onto the bottom of each tube containing the antibody cocktail. The tubes were vortexed immediately. The mixture was then incubated for 30 min at room temperature in the dark. Taking one tube at a time, 380 μL Guava 1X lysing solution was added into it and then the mixture vortexed immediately. The mixture was incubated again in the dark at room temperature for 15 min. Then each sample was acquired on a Guava PCA instrument with the Guava Auto CD4/CD4% software module and the CD4⁺ T-cell reading was recorded. CD4⁺ T-cell counts were categorized as low or advanced stage (< 200/μL), moderate or chronic stage (200–499/μL) and high or asymptomatic stage (≥500/μL) [12].

A total of 411 participants (309 HIV positive patients and 102 HIV negative patients) with a median age of 37 (range 1–72) years participated in the study. The study population consisted of 299 (72.7%) females and 112 (27.3%) males distributed in 3 different age groups. The mean (± SD) ages of HIV positive and HIV negative subjects were 40.8 (± 10.2) years and 27.9 (± 15.7), respectively. The highest number of participants in the overall population was recorded in the 26–40 years age group (191, 46.5%). Only 8.3% (34) of the participants presented with fever. Amongst HIV positive patients, a total of 292 (94.5%) were under ART while 17 (5.5%) were not yet on ART, and 35.3% had CD4 T cell count ≥500 cells/μL of blood. The socio-economic, demographic and clinical data by HIV status of the study population is presented in Table 1.

The overall prevalence of malaria in the study population was 14.1% (58). Malaria parasite prevalence was significantly higher (χ² = 41.36, P <  0.001) in HIV negative (33.3%, 34) than HIV positive participants (7.8%, 24). The highest malaria parasite prevalence was recorded in the youngest age group in both HIV positive (36.4%, 4) and HIV negative (41.7%, 20) participants. As shown in Table 2, female participants among both HIV positive and HIV negative participants had a higher malaria parasite prevalence (8%, 19 in HIV positive and 34.4%, 31 in HIV negative) than their male counterparts (7% in HIV positive and 31.7% in HIV negative); although only statistically significant in HIV positive patients (χ² = 11.98, P = 0.003). Malaria parasite prevalence was significantly higher (χ² = 20.82, P = 0.001) in HIV positive patients who presented with fever (37.5%) than those who did not (6.1%). The same trend of malaria parasite prevalence was observed amongst HIV negative participants, although the difference was not statistically significant (χ² = 1.29, P = 0.20). Anaemic patients had a higher prevalence of malaria parasite (11% in HIV positive and 39% in HIV negative) than non-anaemic patients (3.7% in HIV positive and 29.5% in HIV negative). This difference was statistically significant only among HIV positive patients (χ² = 5.67, P = 0.01) as shown in Table 2. Out of the 214 anaemic patients, those with moderate anaemia had the statistically significant (χ² = 7.44, P = 0.024) highest prevalence of malaria parasite (24.2%, 22). With respect to HIV status, analysis revealed that participants who had moderate anaemia (18.8% in HIV positive and 40.9% in HIV negative) had the highest prevalence of malaria parasite as compare to their respective counterparts; though the difference was only significant for HIV positive patients (χ² = 7.53, P = 0.02) (Table 2).

Among the 17 HIV positive participants who had not started the ART, prevalence of malaria parasite was higher (23.5%, 4) compared with the 292 patients under ARV therapy (6.8%, 20). This difference was statistically significant (χ² = 6.23, P = 0.03). With respect to duration on ART, HIV positive participants who have been on therapy for ≤12 months had the highest prevalence of malaria parasite (18.2%, 6) compared with those who had been taking their medications for a longer period. However, the difference was not statistically significant (χ² = 6.05, P = 0.06).

In the overall population, participants not using insecticide treated nets (ITNs) had a higher prevalence of malaria parasites (16%, 26) than those who used ITNs (12.9%, 32), but the difference was not significant (χ² = 0.57, P = 0.27). Participants using IRS as their preventive measure had a non-statistically higher prevalence of malaria parasites (16.3%, 17) than those not using it (13.4%, 41).

Among HIV positive participants, those who did not use ITN nor IRS had a higher malaria parasite prevalence (10.6% [12/113]), 8.2% [19/231]) than those who used ITN or IRS (6.1% [12/196], 6.4% [5/78]), respectively. However, the differences were not significant (P = 0.41, P = 0.12). Among HIV negative participants, those who did not use ITN nor IRS had a lower malaria parasite prevalence (28.6%, 14; 28.9%, 22 respectively) as compared to their respective counterparts (37.7%, 20; 46.2%, 12). These differences were not statistically significant (P = 0.8, P = 0.15).

As shown in Fig. 1, when the two preventive methods were compared, the lowest malaria parasite prevalence was recorded among participants who used both preventive measures. When comparing the usage of both preventive measures among HIV positive and HIV negative participants, it was observed that those who did not implement any preventive measure had the highest malaria parasite prevalence in HIV positive patients (10.1%, 8/79) which was the opposite among HIV negative participants; those not implementing preventive measures had the lowest malaria parasite prevalence (22.6%, 7/31) (Fig. 1).

The overall prevalence of anaemia in the study population was 52.1% (214). Anaemia prevalence was significantly higher (χ² = 7.00, P = 0.004) in HIV positive patients (56%, 173) than their HIV negative counterparts (40.2%, 41). HIV positive patients and HIV negative participants in the age groups 26–40 years (59.0%) and ≤ 25 years (52.1%), respectively had the highest prevalence of anaemia as compared to their age-group counterparts, though the differences were not statistically significant. Anaemia prevalence was higher in female participants (64.7%, 154 in HIV positive and 49.2%, 30 in HIV negative) compared to male participants (26.8%, 19 in HIV positive and 26.8%, 11 in HIV negative). These differences were statistically significant for both HIV positive patients (P = 0.001) and their HIV negative counterparts (P = 0.02). With respect to malaria status, both in HIV positive and HIV negative participants, anaemia prevalence was higher in those who were positive for malaria parasite than those who were not. Within both categories of patients, febrile patients had a higher prevalence of anaemia (56.2%, 9 in HIV positive and 55.6%, 10 in HIV negative) than their afebrile counterparts (56%, 164 in HIV positive and 36.9%, 31 in HIV negative). HIV positive patients with CD4 T-cell count < 200 cells had the highest prevalence of anaemia (73.2%) that approached significance (P = 0.05) when compared to their counterparts. As shown in Table 3, although not significant (P = 0.31), HIV patients not on ART had a higher prevalence of anaemia (64.7%, 11) than those on ART (55.5%, 162).

With respect to anaemia severity, 51.9% (111) of the anaemic patients had mild anaemia, 42.5% (91) had moderate anaemia and 5.9% (12) had severe anaemia in the overall study population.

As shown in Fig. 2, HIV positive patients had lower mean haemoglobin level when compared to HIV negative participants; though the difference in mean was not statistically significant (P = 0.40).

Malaria/HIV co-infection was observed in 24 patients (5.8%) while those who had only malaria were 34 (8.3%). The mean values of 14 haematological parameters were compared between malaria/HIV co-infected patients and only HIV-infected patients. Seven haematological parameters (Hb, RBC, MCV, MCH, Lymphocyte count, Hct, PDW) showed lower mean value among malaria/HIV co-infected patients when compared with HIV-infected patients. The difference was statistically significant for Hb (P = 0.002), RBC (P = 0.002) and Hct (P = 0.001). Conversely, co-infected patients were observed to have higher mean values for WBC, PLT, MCHC, Granulocyte count, MPV, RDW-SD and RDW-CV than HIV-infected patients without malaria. The difference was not statistically significant as shown in Table 4.