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. 2022 Sep 14;15:324. doi: 10.1186/s13071-022-05432-2

Malaria prevalence in HIV-positive children, pregnant women, and adults: a systematic review and meta-analysis

Seyedeh-Tarlan Mirzohreh 1,2, Hanieh Safarpour 1,2, Abdol Sattar Pagheh 3, Berit Bangoura 4, Aleksandra Barac 5, Ehsan Ahmadpour 6,7,✉
PMCID: PMC9472338  PMID: 36104731

Abstract

Background

Malaria in human immunodeficiency virus (HIV)-positive patients is an ever-increasing global burden for human health. The present meta-analysis summarizes published literature on the prevalence of malaria infection in HIV-positive children, pregnant women and adults.

Methods

This study followed the PRISMA guideline. The PubMed, Science Direct, Google Scholar, Scopus and Cochrane databases were searched for relevant entries published between 1 January 1983 and 1 March 2020. All peer-reviewed original papers evaluating the prevalence of malaria among HIV-positive patients were included. Incoherence and heterogeneity between studies were quantified by the I2 index and Cochran’s Q test. Publication and population biases were assessed with funnel plots, and Egger’s regression asymmetry test.

Results

A total of 106 studies were included in this systematic review. The average prevalence of malaria among HIV-positive children, HIV-positive pregnant women and HIV-positive adults was 39.4% (95% confidence interval [CI]: 26.6–52.9), 32.3% (95% CI = 26.3–38.6) and 27.3% (95% CI = 20.1–35.1), respectively. In adult patients with HIV, CD4+ (cluster of differentiation 4) < 200 cells/µl and age < 40 years were associated with a significant increase in the odds of malaria infection (odds ratio [OR] = 1.5, 95% CI = 1.2–1.7 and OR = 1.1, 95% CI = 1–1.3, respectively). Antiretroviral therapy (ART) and being male were associated with a significant decrease in the chance of malaria infection in HIV-positive adults (OR = 0.8, 95% CI = 0.7–0.9 and OR = 0.2, 95% CI = 0.2–0.3, respectively). In pregnant women with HIV, CD4+ count < 200 cells/µl was related to a higher risk for malaria infection (OR = 1.5, 95% CI = 1.1–1.9).

Conclusions

This systematic review demonstrates that malaria infection is concerningly common among HIV-positive children, pregnant women and adults. Among HIV-positive adults, ART medication and being male were associated with a substantial decrease in infection with malaria. For pregnant women, CD4+ count of < 200 cells/µl was a considerable risk factor for malaria infection.

Graphical Abstract

graphic file with name 13071_2022_5432_Figa_HTML.jpg

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05432-2.

Keywords: AIDS, Anopheles, People living with HIV, Plasmodium, Protozoan parasite

Background

Infectious diseases pose a concerning threat to the health systems of both developed countries and countries with limited resources such as, for example, sub-Saharan countries [1, 2]. With 228 million malaria cases globally in 2018, future declines in the malaria burden caused by Plasmodium spp. infections are uncertain [3, 4]. Approximately 3.3 billion people are residing in malaria-endemic regions (parts of the Africa, Southeast Asia and Middle East) [5, 6].

The human immunodeficiency virus (HIV) is an emerging infectious disease agent defined by cellular immune system impairment [7]. HIV is a well-established global health burden, with > 36 million HIV-infected patients and > 1 million HIV-related deaths in 2017 [8]. While Plasmodium parasites causing human malaria are transmitted mainly by mosquitoes (Anopheles spp.) serving as biological vectors, malaria can also be transmitted directly via blood transfusion, needle sticks with contaminated needles and vertical transmission [9, 10]. The infection routes bypassing the biological vector are transmission routes shared by HIV and malaria [11]. Since HIV infection affects the immune system, the infected individuals are more susceptible to other infections [12–15]. Therefore, people living with HIV (including children, pregnant women and adults) are at risk for significant disease and may have fatal complications following infection [11, 16]. The vertical transmission option for both malaria and HIV facilitates co-transmission from infected pregnant women to their infants [17]. Since the co-infections of malaria and HIV can induce anemia, blood transfusion is often required, but blood transfusion can also contribute to the transmission of HIV and malaria [18, 19].

Although numerous studies have highlighted malaria prevalence in patients with HIV, there has been no comprehensive meta-analysis to demonstrate this prevalence in children, adults and pregnant women. Therefore, the aims of this systematic review and meta-analysis are to summarize malaria prevalence among HIV-positive children, pregnant women and adults, and to identify risk factors that increase the probability of HIV-positive patients being infected with malaria.

Methods

Search strategy

For inclusion in the present systematic review, the PubMed, Science Direct, Google Scholar, Scopus and Cochrane databases were searched for relevant English-language, full-text articles and abstracts published between 1 January 1983 (date of HIV discovery) and 1 March 2020. As the aim was to evaluate the prevalence of positive test results for malaria among HIV-positive and HIV-negative individuals, the following Medical Subject Headings (MeSH) terms were used: “Malaria” OR “Plasmodium” AND “prevalence” OR “epidemiology” OR “co-infection” AND “HIV” OR “AIDS” OR “acquired immune deficiency syndrome” OR “immunocompromised” OR “immunosuppressed” OR “immunodeficiency” AND “pregnancy women” OR “children” OR “adult” alone OR combined using “OR” and/or “AND”.

Study selection and data extraction

After an initial search of the databases, subject-related topics and their abstracts were double-checked, and then full texts of potentially eligible articles were selected for downloading. All potentially relevant full texts were reviewed by three independent reviewers (TM, HS, ASP). Discrepancies were resolved by discussion and consensus. The studies were assessed for quality using the Joanna Briggs Institute (JBI) checklist (Additional files 2, 3, 4, 5: Tables S1–S4). The required data were extracted by the reviewers and then re-checked. The criteria for inclusion in the review were: (i) peer-reviewed original research papers; (ii) cross-sectional and cohort studies that estimated the prevalence of malaria infection in HIV-positive and HIV-negative individuals; (iii) published papers in English; (iv) published online before 1 March 2020; and (v) sufficient sample size (n > 10). Any article that did not satisfy the above criteria were excluded. The reference lists of the eligible articles were also browsed manually to identify relevant papers that were not initially identified in the database search. Finally, details of each study were extracted using a data extraction form, including country, year of publication, first author, number of HIV+ and malaria-positive cases, education status of patients, alcohol consumption status, number of partners, marital status, level of CD4+ (cluster of differentiation 4) in HIV-positive patients, ART (antiretroviral therapy) status, sex protection status and diagnostic method (microscopy, serology or molecular). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to report the findings [20].

Meta-analysis

The point estimate and corresponding confidence interval (CI) for the prevalence of malaria in HIV-positive individuals for each study were calculated. Incoherence and heterogeneity among studies were assessed using the I2 index and Cochran’s Q test, respectively, and the random-effects model (DerSimonian-Laird) was used for analysis. The heterogeneity among subgroups was tested by meta-regression analysis. The relationship between prevalence, year of publication and sample size was estimated by meta-regression. Additionally, a funnel plot relying on the Egger’s regression asymmetry test was used to assess the small effects of the study and the population bias. For the meta-analysis, the included studies were evaluated as a random sample of each study population, and the analyses were performed using StatsDirect (version 2.7.2) statistical software (StatsDirect Ltd., Altrincham, UK).

Results

The systematic search of the electronic databases identified 24,311 potentially relevant papers. The full-text of 212 articles was assessed, resulting in exclusion from the study of 106 papers due to their small sample size, the review or case report nature of the report, duplication and insufficient data. The remaining 106 papers fulfilled the inclusion criteria and were included in the present systematic review and meta-analysis. All of these 106 articles were published between 1983 and 2020 and present data from malaria-endemic regions in Africa (n = 103) and Asia (n = 3). The inclusion/exclusion criteria at each step of screening and eligibility and the number of selected papers are shown in Fig. 1.

Fig. 1.

Fig. 1

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Flowchart of study selection process

All analyses were conducted in three subgroups: children (n = 17; Table 1; Fig. 2), adults (n = 57; Table 2; Fig. 3) and pregnant women (n = 32; Table 3; Fig. 4). The pooled malaria prevalence among HIV-positive children was 39.4% (95% CI = 26.6–52.9). The combined prevalence of malaria in HIV-positive adults was 27.3% (95% CI = 20.1–35.1), and the collective malaria prevalence among HIV-positive pregnant women was 32.3% (95% CI = 26.3–38.6) (Figs. 2, 3, 4). The funnel plot showing a statistically significant Egger’s regression suggests the possibility of publication bias (Additional file 1: Figure S1). The published risk factors associated with HIV and malaria, namely CD4+ level, ART consumption, sex, education, gravidity and age, were analyzed (Table 4). In adult patients with HIV, CD4+ count < 200 cells/µl predisposes the patient to malaria infection (odds ratio [OR] = 1.5, 95% CI = 1.2–1.7). In adult HIV-positive patients, age < 40 years old was found to be associated with a significant increase in the odds of being infected with malaria (OR = 1.1, 95% CI = 1–1.3). Also, for adult HIV-positive patients, being male and being treated with ART medication have been associated with a significant decrease in the odds of being infected with malaria (OR = 0.8, 95% CI = 0.7–0.9 and OR = 0.2, 95% CI = 0.2–0.3, respectively). CD4+ count < 200 cells/µl was found to predispose pregnant women with HIV to malaria infection (OR = 1.5, 95% CI = 1.1–1.9) (Table 4).

Table 1.

Baseline characteristics of the included studies on malaria and human immunodeficiency virus co-infection in children

No. Year of publication Country/region Study design No. of HIV-positive patients  No. of malaria-positive patients  Laboratory diagnostic method Quality assessment Reference
1 1987 Zaire (Democratic Republic of Congo) Case–control 40 15 Blood smear 6/10 [21]
2 2003 Tanzania Cross-sectional 44 5 Blood smear 6/8 [22]
3 2006 Kenya Cross-sectional 23 15 Blood smear 7/8 [23]
4 2007 Kenya Cohort 73 16 Blood smear 8/11 [24]
5 2008 Uganda Cohort 35 31 Blood smear 8/11 [25]
6 2009 Kenya Case–control 262 133 Blood smear 8/10 [26]
7 2010 Uganda Prospective cohort 135 120 Blood smear 8/11 [27]
8 2011 Uganda Case–control 15 12 Blood smear 9/10 [28]
9 2012 Tanzania Cohort 255 4 Blood smear 7/11 [29]
10 2013 Ghana Cross-sectional 443 108 Rapid Test Kit 6/8 [30]
11 2014 Malawi Cohort 45 26 Blood smear 9/11 [31]
12 2015 Malawi Cohort 19 15 Autopsy 8/11 [32]
13 2016 Tanzania Prospective cohort 52 20 Blood smear; rapid diagnostic test; PCR 8/11 [33]
14 2016 Cameroon Cross-sectional 234 58 Blood smear 8/8 [34]
15 2017 Cameroon Cross-sectional 15 4 Blood smear 6/8 [35]
16 2017 Nigeria Cross-sectional 162 56 Blood smear 7/8 [36]
17 2017 Nigeria Cross-sectional 67 67 Blood smear 5/8 [37]
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Fig. 2.

Fig. 2

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Forest plot diagram of malaria prevalence in human immunodeficiency virus-positive children (first author, year and country)

Table 2.

Baseline characteristics of the included studies on malaria and human immunodeficiency virus co-infection in adults

No. Year of publication Country/region Study design No. of HIV-positive patients  No. of malaria-positive patients  Laboratory diagnostic method Quality assessment Reference
1 2001 Uganda Case–control 65 14 Blood smear and ELISA 7/10 [38]
2 2002 Nigeria Cross-sectional 91 23 Blood smear 6/8 [39]
3 2005 Nigeria Cross-sectional 490 103 Serology 6/8 [40]
4 2005 Malawi Cross-sectional 83 12 Blood smear 7/8 [41]
5 2006 Malawi Cross-sectional 660 325 Blood smear and serology 7/8 [42]
6 2007 Nigeria Cross-Sectional 81 72 Blood smear 6/8 [43]
7 2007 Nigeria Prospective study 149 28 RDT 7/11 [44]
8 2008 Cameron Prospective cohort 258 201 Blood smear 6/11 [45]
9 2009 Nigeria Cross-sectional 560 476 Blood smear 7/8 [46]
10 2011 Nigeria Cross-sectional 300 79 RDT 6/8 [47]
11 2012 India Cohort 460 45 PCR 7/11 [48]
12 2012 Cameroon Cross-sectional 312 7 Blood smear 8/8 [49]
13 2012 Nigeria Cross-sectional 285 6 Blood smear 7/8 [50]
14 2012 Nigeria Cross-sectional 2000 87 Blood smear 7/8 [51]
15 2012 Nigeria Cross-sectional 1080 343 Blood smear 6/8 [52]
16 2012 Nigeria Cross-sectional 97 24 Blood smear 8/8 [53]
17 2013 Nigeria Cross-sectional 65 31 Blood Smear and ELISA 6/8 [54]
18 2013 Nigeria Cohort 317 31 Blood smear and PCR 7/11 [55]
19 2013 Ethiopia Retrospective 377 73 Blood smear 9/11 [56]
20 2013 Nigeria Cross-sectional 342 254 Blood smear 7/8 [57]
21 2013 Nigeria Cross-sectional 387 74 RDT and blood smear 8/8 [58]
22 2013 Ghana Cross-sectional 933 15 Blood smear 7/8 [59]
23 2013 Nigeria Case–control 68 17 Blood smear 8/10 [60]
24 2013 Nigeria Cross-sectional 363 117 Blood smear 7/8 [61]
25 2014 Mozambique Cross-Sectional 128 70 Serology and PCR 6/8 [62]
26 2014 Nigeria Cross-sectional 200 37 PCR 7/8 [63]
27 2015 Kenya Cross-sectional 46 27 ELISA and blood Smear 7/8 [64]
28 2015 Ethiopia Cross-Sectional 1819 13 Blood smear and serology 6/8 [65]
29 2015 Uganda Cross-sectional 160 30 Blood smear 6/8 [66]
30 2015 Nigeria Cross-sectional 350 159 Blood smear 8/8 [67]
31 2015 Ghana Cross-sectional 400 47 Blood Smear and serology 7/8 [68]
32 2016 Niagara Cross-sectional 83 53 Blood smear 7/8 [69]
33 2016 Uganda Cross-sectional 131 26 LAMP and serology 7/8 [70]
34 2016 Cameroon Cross-sectional 35 6 Blood smear 7/8 [71]
35 2016 Niagara Cross-sectional 226 56 Blood smear 6/8 [72]
36 2017 Niagara Case–control 179 61 PCR and serology 8/10 [73]
37 2017 Equatorial Guinea Cross-sectional 101 14 Blood smear and ELISA 8/8 [74]
38 2017 Ethiopia Cross-sectional 528 92 RDT 8/8 [75]
39 2017 India Prospective cohort 202 14 Blood smear and PCR 8/11 [76]
40 2017 India Prospective cohort 131 8 Blood smear and PCR 8/11 [76]
41 2017 Ethiopia Cross-sectional 172 86 Blood smear 7/8 [77]
42 2017 Nigeria Cross-sectional 761 211 RDT 7/8 [78]
43 2017 Gabon Cross-sectional 856 61 Blood smear 6/8 [79]
44 2018 Nigeria Case–control 35 5 PCR and serology 6/8 [80]
45 2018 Ethiopia Cross-sectional 53 12 Blood smear 7/8 [81]
46 2018 Niagara Cross-sectional 324 254 Blood smear 7/8 [82]
47 2018 Nigeria Cross-sectional 200 130 Blood smear 8/8 [83]
48 2018 Mozambique Retrospective 701 232 RDT 8/11 [84]
49 2018 Ghana Cross-sectional 466 64 Blood smear 8/8 [85]
50 2018 Cameroon Cross-sectional 15 5 Blood smear 7/8 [86]
51 2019 Nigeria Cross-sectional 262 60 Blood smear 8/8 [87]
52 2019 Sudan Cross-sectional 70 1 PCR 6/8 [88]
53 2019 Cameroon Cross-sectional 309 24 Blood Smear 8/8 [89]
54 2019 Nigeria Cross-sectional 268 116 Blood smear 7/8 [90]
55 2020 Niagara Retrospective 1472 1101 n.a 7/11 [91]
56 2020 Nigeria Cross sectional 94 40 Serology 8/8 [92]
57 2020 Malawi Cohort 30 11 Blood smear 8/11 [93]
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ELISA enzyme-linked immunosorbent assay, LAMP loop-mediated isothermal amplification, n.a. information not available, RDT rapid diagnostic test

Fig. 3.

Fig. 3

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Forest plot diagram of malaria prevalence in human immunodeficiency virus-positive adults (first author, year, and country)

Table 3.

The baseline characteristics of the included studies on malaria and human immunodeficiency virus co-infection in pregnant women

No. Year of publication Country/region Study design Number of HIV-positive patients  No. of malaria-positive patients  Laboratory diagnostic method Quality assessment Reference
1 1999 Malawi Cross-sectional 159 90 Blood smear 8/8 [94]
2 2002 Rwanda Cohort 228 19 Blood smear 7/11 [95]
3 2003 Kenya Cross-sectional 599 179 Blood smear 7/8 [96]
4 2004 Malawi Cross-sectional 480 61 Blood smear 7/8 [97]
5 2004 Kenya Cross-sectional 512 128 Blood smear 7/8 [17]
6 2004 Malawi Cross-sectional 205 44 Blood smear 8/8 [98]
7 2005 Kenya Cohort 83 34 Smear and/or PCR 7/11 [99]
8 2008 Uganda Cohort 170 63 IHC 8/11 [100]
9 2008 Uganda Cohort 170 52 ICT 7/11 [100]
10 2009 Uganda Cross-sectional 161 30 Blood smear 6/8 [101]
11 2009 Ethiopia Cross-sectional 92 41 RDT and smear 6/8 [102]
12 2010 Tanzania Cross-sectional 1006 185 Blood smear 8/8 [103]
13 2011 Malawi Clinical trial 251 108 Blood smear 11/13 [104]
14 2012 Malawi Cross-sectional 185 70 Blood smear 8/8 [105]
15 2012 Nigeria Cross-sectional 82 43 Blood smear 6/8 [106]
16 2013 Ethiopia Cross-sectional 23 2 Blood smear 7/8 [107]
17 2013 Nigeria Cohort 203 145 Blood smear 8/10 [108]
18 2013 Rwanda Cross-sectional 980 130 Blood smear 7/8 [109]
19 2013 Nigeria Cross-sectional 44 34 Blood smear 7/8 [110]
20 2013 Kenya Cohort 489 119 Blood smear 8/11 [111]
21 2013 Ghana Prospective 443 60 RDT 7/11 [30]
22 2014 Nigeria Cohort 432 45 Smear or RDT 8/11 [112]
23 2014 Tanzania Cross-sectional 420 19 RDT 8/8 [113]
24 2014 Nigeria Cross-sectional 159 53 Blood smear 7/8 [114]
25 2014 Nigeria Cross-sectional 28 28 Blood smear 7/8 [115]
26 2014 Nigeria Cross-sectional 301 150 Blood smear 6/8 [116]
27 2014 Africa Randomized controlled trial 973 54 Blood smear 13/13 [117]
28 2015 Congo Cross-sectional 25 19 Smear and PCR 8/8 [118]
29 2015 Zambia Cross-sectional 140 49 Blood smear 8/8 [119]
30 2015 Zambia Cross-sectional 138 90 PCR 7/8 [119]
31 2015 Tanzania Prospective 2378 376 Clinical 8/11 [120]
32 2015 Benin Cross-sectional 432 87 Blood smear 7/8 [121]
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ICT Immunochromatography, IHC immunohistochemistry

Fig. 4.

Fig. 4

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Forest plot diagram of malaria prevalence in human immunodeficiency virus-positive pregnant women (first author, year, and country)

Table 4.

Risk factors associated with malaria infection in human immunodeficiency virus-positive patients

Risk factors Categories No. study Odds ratio (95% CI) P-value I2 (inconsistency), % Cochran Q Egger regression test (bias) P-value
Children
 ART

Yes

No

 2 1.3 (0.2–6.6)  0.7342 - 7.3 -  0.0069
 CD4+ 

 < 200 cells/µl

 ≥ 200 cells/µl

 2 1.8 (0.8–3.8)  0.1195 - 1.8 -  0.1681
Adults
 Sex

Male

Female

 24 0.8 (0.7–0.9)  0.1393 81.4 (72.9–86.3) 123.4 0.6  0.007
 Age (years)

 < 40

 ≥ 40

 20 1.1 (1 -1.3)  0.4716 53 (10.8–70.6) 40.3 0.04  0.0148
 ART

Yes

No

 7 0.2 (0.2–0.3)  0.0029* 82.5 (49.5–90.8) 92.9 1.09 < 0.0001
 CD4+ 

 < 200 cells/µl

 ≥ 200 cells/µl

 12 1.5 (1.2–1.7)  0.0428* 90.4 (85.7–93.1) 114.9 1.1  < 0.0001
 Education

Primary level

Higher-level

 3 0.9 (0.7–1.2)  0.8935 0 (0–72.9) 0.5 –  0.9389
Pregnant women
 Gravidity

Primigravida

Multigravida

 9 0.96 (0.7–1.2)  0.9758 38.2 (0–70.2) 12.9 0.2  0.7916
 ART

Yes

No

 4 1.06 (0.7–1.5)  0.96 51.8 (0–82.3) 6.2 0.01  0.1012
 CD4+ 

 < 200 cells/µl

 ≥ 200 cells/µl

 4 1.5 (1.1–1.9)  0.7949 92.3 (83.2–95.4) 38.7 − 5.2  0.0012
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ART Antiretroviral therapy, CD4 Cluster of differentiation 4, CI confidence interval

*Significant association (P = 0.05) with malaria infection

Discussion

Although extensive studies have been conducted on both HIV and Plasmodium spp. infections, a comprehensive meta-analysis aimed at precisely evaluating the prevalence of malaria infections among HIV-positive patients and related risk factors is lacking. Therefore, the aim of the present meta-analysis was to provide the pooled prevalence of malaria infection in HIV-positive children, pregnant women and adults and evaluate the related risk factors. The included studies represent African and Asian regions where both HIV and Plasmodium spp. are endemic. The pooled malaria prevalence in HIV-positive children, adults and pregnant women included in these studies was 39.4% (95% CI = 26.6–52.9), 27.3% (95% CI = 20.1–35.1) and 32.3% (95% CI = 26.3–38.6), respectively. In adult patients with HIV, receiving ART and having CD4+ count > 200 cells/µl were two factors significantly associated with malaria infection (P < 0.05).

Due to widespread ART coverage, mortality due to HIV as the main cause of death has decreased drastically over the years [8]. Notwithstanding the extensive efforts to end the acquired immunodeficiency syndrome (AIDS) epidemic by 2030 (set down in the Joint United Nations Program on HIV/AIDS), a lot of the work remains to be done [122]. The troublesome high prevalence of HIV, the increased life expectancy of affected patients, the common co-transmission of HIV and malaria and a remarkable geographical overlap between malaria and HIV high prevalence areas have paved the way for higher rates of co-infections in HIV-positive individuals [123].

Although the incidence of malaria and mortality due to malaria declined significantly by 62% and 41%, respectively, between 2000 and 2015, WHO reported that malaria remained an endemic disease in 76 countries at the beginning of 2016 [124], with approximately 216 million malaria cases in that year. Fifteen countries of the sub-Saharan African region alone were reported to be responsible for 80% of the total malaria burden [125]. Therefore, it is believed that many challenges remain to be overcome in order to eliminate malaria [126]. Regarding the burden of HIV and malaria and the immunosuppressive nature of HIV, there is an urgent need to clarify malaria prevalence in HIV-infected patients and the related risk factors.

According to the results of this systematic review and meta-analysis, the majority of published HIV/malaria studies to date have been in African countries. Socioeconomic conditions and a desirable climate for the biological vector, both of which can facilitate malaria transmission, may be the main reasons underlying this result [127]. Based on our findings, more than one-third of pregnant and HIV-positive women have been infected by malaria, which is worrisome because of the vertical transmission nature of malaria and HIV, which predisposes neonates to other infectious diseases [128, 129]. Indeed, pregnant women are among the most susceptible and vulnerable groups infected with malaria due to the altered immune system during pregnancy [3, 130]. The weakened immune response and HIV infection can lead to even deeper attenuation of the immune system. It is well-recognized that a decline in CD4+ cell numbers is associated with attenuation of the cell immune system and an increased vulnerability to being infected with other infections [131]. Our finding that CD4+ cell count < 200 cells/µl is linked to increased susceptibility to malaria infection (OR = 1.5, 95% CI = 1.1–1.9) confirms this association. In essence, AIDS and malaria are each controlled by adaptive and innate immune mechanisms, and declining immunity caused by HIV infection will cause an increase in malaria severity. CD4+ cells are depleted by the HIV virus, which leads to an impaired immune response to many pathogens, including Plasmodium spp. [43]. This pattern was corroborated by Grimwade et al. [132] who observed that malaria incidence in persons with CD4+ T cell count ≥ 500/µl, between 200 and 499/μl and < 200/μl was 57, 93 and 140 per 1000 person-year, respectively, in Uganda. It has been postulated that HIV increases malaria incidence in adults based on CD4+ cell count categories [133].

This meta-analysis also revealed the worrying situation of malaria infection among HIV-positive children. Approximately 39.4% of HIV-positive children in the analyzed studies were infected with malaria. This is a much higher prevalence than that observed in several studies investigating general children populations in African countries, with the prevalence in these studies ranging from 1% in Kenya to 22% in Uganda, with 14.5% prevalence in Tanzania and 20% in the Democratic Republic of Congo [134, 135]. The observation of increased malaria prevalence in HIV-positive children supports our assumption that susceptibility to co-infection is high in HIV-positive individuals. It is interesting to note that much of the pathogenesis of malaria during pregnancy is mediated by the accumulation of Plasmodium-infected red blood cells in the placental intervillous space, termed ‘placental malaria.’ The placenta is also the key interface in mother-to-child transmission of HIV, especially that involving in utero transfer [136]. No remarkable association between receiving ART and HIV infection status has been noted in HIV-positive children (OR = 1.3, 95% CI = 0.2–6.6). Moreover, there has been no significant association between the CD4+ cell count and the probability of malaria infection (P > 0.05), possibly due to the small number of studies that have considered this factor.

The present meta-analysis reveals that, on average, 27.3% of HIV-positive adults are infected with malaria in endemic countries. One of the consequences of this alarmingly high figure can be manifested in blood transfusion. With the ever-increasing need for a blood transfusion due to environmental and heredity diseases such as sickle cell anemia [137], the prevalence of transfusion-transmitted HIV/malaria can be expected to be high. A study conducted in the sub-African region has demonstrated that about 10–15% of HIV transmission is related to blood transfusion [138]. Ahmadpour et al. [19] reported that transfusion-transmitted malaria is a significant challenge in sub-Saharan African regions. In terms of risk factors, CD4+ cell count of < 200 cells/µl predisposes HIV-positive adults to Plasmodium spp. infection (OR = 1.5, 95% CI = 1.2–1.7). However, the association between malaria and HIV is more complex than expected. Some studies have corroborated that CD4+ T cells, as the prime targets for reproduction by HIV-1, play a vital role in immune responses to malaria [131, 139]. Malaria infection leads to upregulation of proinflammatory cytokines and stimulates CD4+ cell activation, thus providing the ideal microenvironment for the spread of the HIV virus among the CD4+ cells. On the other hand, the selective infection of CD4+ cells by HIV leads to the loss of these cells [140]. It is assumed that the increased susceptibility of HIV-seropositive individuals to malaria is related to some immune system-modulating mechanisms, such as depletion of CD4+ cells [131, 141].

Age < 40 years has also been associated with a significant increase in the chance of HIV-positive adults becoming infected with malaria (OR = 1.1, 95% CI = 1–1.3). In HIV-positive adults, being male and receiving ART have been associated with a significant decrease in the risk of being infected with Plasmodium spp. (OR = 0.8, 95% CI = 0.7–0.9 and OR = 0.2, 95% CI = 0.2–0.3, respectively). This is an interesting finding when compared to individual studies that described a higher risk of malaria infections in males compared to females in the general population in north-east Tanzania, irrespective of their HIV status [134]. Thus, it appears that HIV status may potentially alter malaria susceptibility differently in male patients than in female patients. It is worth emphasizing that the reported figures may not reflect the current status of this co-infection because these endemic areas are limited in terms of healthcare resources, and testing may not be conducted on all people unless they show clinical symptoms. Furthermore, there is insufficient evidence to determine whether or not malaria-induced changes in CD4+ T cell counts or viral loads translate to accelerated HIV disease progression or death in areas of stable malaria transmission.

This is the first meta-analysis on malaria prevalence among HIV-positive patients. We broke down the data into three categories, namely infancy, pregnancy and adulthood, and identified the available risk factors for each group. Since there has been little research on the prevalence of malaria in HIV patients in malaria endemic areas, further studies are needed in this regard. Also, due to the incomplete data in the studies included in our meta-analysis, we were unable to evaluate some risk factors, including duration of illness, time of diagnosis and response to treatment. Unfortunately, no data on the health status of individuals having both malaria and HIV infection were provided in these studies. On the other hand, publication bias is one of the main concerns in systematic review studies. As expected, publication bias was observed in the analyzed studies. The main limitation of this systematic review and meta-analysis is related to the different study designs and varying laboratory methods used to determine infection status. Diagnostic methods have varying sensitivity and specificity and, therefore, the heterogeneous prevalence data reported may partially be caused by flaws in methodology. The use of an accurate, reliable and uniform diagnostic techniques would support the correct interpretation of results.

Conclusions

The current systematic review has revealed concerning prevalence data for malaria among HIV-positive persons, including children, adults and pregnant women. In view of the fact that malaria can quickly become a life-threatening condition in risk groups (e.g. people living with HIV), prevention, chemoprophylaxis, early diagnosis and treatment of clinical malaria are recommended. Recent information also indicates that malaria is associated with the availability of ART and CD4+ cell count numbers in adults. Therefore, the related risk factors should be given appropriate attention in HIV/malaria co-infected patients. As HIV infection affects the host immune response, future studies are needed to elucidate the pathogenesis aspects of this co-infection, as well as the severity of its complications, and to investigate possible drugs and drug effectiveness.

Supplementary Information

13071_2022_5432_MOESM1_ESM.doc (53KB, doc)

Additional file 1: Figure S1. Funnel plot of standard error by logit event rate to assess publication or other types of bias across prevalence studies. Studies based on the prevalence of malaria in HIV patients: children (A), adults (B), and pregnant women (C).

13071_2022_5432_MOESM2_ESM.doc (138.5KB, doc)

Additional file 2: Table S1. Summary score for methodological quality of analytic cross-sectional studies.

13071_2022_5432_MOESM3_ESM.doc (46.5KB, doc)

Additional file 3: Table S2. Summary score for methodological quality of analytic case–control studies.

13071_2022_5432_MOESM4_ESM.doc (81KB, doc)

Additional file 4: Table S3. Summary score for methodological quality of analytic cohort studies.

13071_2022_5432_MOESM5_ESM.doc (33.5KB, doc)

Additional file 5: Table S4. Summary score for methodological quality of analytic RCT studies.

Acknowledgements

Not applicable.

Abbreviations

AIDS

Acquired immunodeficiency syndrome

ART

Antiretroviral therapy

CD4

Cluster of differentiation 4

HIV

Human immunodeficiency virus

Author contributions

TM, EA and AB designed the study. TM, HS, MAS and ASP were involved in searching the databases. TM, HS, MAS, ASP and BB screened the papers and extracted the data. AB and EA performed the statistical analysis. MAS, ASP and BB wrote the manuscript, with revision by EA and AB. All authors read and approved the final manuscript.

Funding

The authors received no financial support for the research.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Seyedeh-Tarlan Mirzohreh, Email: tarlan_mirzohre@yahoo.com.

Hanieh Safarpour, Email: hanie.safarpour@yahoo.com.

Abdol Sattar Pagheh, Email: satar2011@gmail.com.

Berit Bangoura, Email: bbangour@uwyo.edu.

Aleksandra Barac, Email: aleksandrabarac85@gmail.com.

Ehsan Ahmadpour, Email: ehsanahmadpour@gmail.com, Email: ahmadpoure@tbzmed.ac.ir.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

13071_2022_5432_MOESM1_ESM.doc (53KB, doc)

Additional file 1: Figure S1. Funnel plot of standard error by logit event rate to assess publication or other types of bias across prevalence studies. Studies based on the prevalence of malaria in HIV patients: children (A), adults (B), and pregnant women (C).

13071_2022_5432_MOESM2_ESM.doc (138.5KB, doc)

Additional file 2: Table S1. Summary score for methodological quality of analytic cross-sectional studies.

13071_2022_5432_MOESM3_ESM.doc (46.5KB, doc)

Additional file 3: Table S2. Summary score for methodological quality of analytic case–control studies.

13071_2022_5432_MOESM4_ESM.doc (81KB, doc)

Additional file 4: Table S3. Summary score for methodological quality of analytic cohort studies.

13071_2022_5432_MOESM5_ESM.doc (33.5KB, doc)

Additional file 5: Table S4. Summary score for methodological quality of analytic RCT studies.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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