Research Article
Austin Med Sci. 2023; 8(2): 1077.
The Role of COVID-19 Pandemic on Malaria Incidence; Meta-Analysis and Systematic Review Study
Mahsa Jalili1,2; Fatemeh Sadat Abolhasani3; Hamed Afkhami4,5; Somayeh Sharifi1,2; Ali Noori Zadeh6; Morvarid Shafiei7*
1Department of Medical Microbiology, Faculty of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
2Reference Laboratory of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
3Department of Microbiology, Faculty of Biological Sciences, Alzahra University, Tehran, Iran
4Nervous System Stem Cells Research Center, Semnan University of Medical Sciences, Semnan, Iran
5Department of Medical Microbiology, School of Medicine, Shahed University, Tehran, Iran
6Department of Clinical Biochemistry, School of Medicine, Ilam University of Medical Sciences, Ilam, Iran
7Department of Bacteriology, Pasteur Institute of Iran, Tehran, Iran
*Corresponding author: Morvarid Shafiei 7Department of Bacteriology, Pasteur Institute of Iran, Tehran, IR Iran. Tel: +98-9128047530, +98-216411223 Email: Dr.m.shafiei@pasteur.ac.ir
Received: August 22, 2023 Accepted: September 29, 2023 Published: October 06, 2023
Abstract
Background: Today, COVID-19 and malaria are the leading causes of death in the worldwide. Malaria and COVID-19 have common aspects and may be was a high potential for mutual influence. Hence, consequences and outcomes of COVID-19 become very dangerous and problematic. The aim of this study, investigate of the influence of COVID-19 mortality and malaria prevalence by systematic review and meta-analysis study.
Method: We searched authentic research databases by using the following keywords in English language. In additional, for statistical analysis, meta-analysis and random effect model and I2 index were used. Statistical analysis was performed with STAT (version 11.2).
Result: In the present study, nine articles were selected from 1,014 articles that examined co-infection in COVID-19 and malaria. This study revealed that co-infection between malaria and COVID-19 has very interesting and surprising results. OR (odds ratio) =-1.2 (95% CI: -1.8 to -0.6). We encounter high values of I2 in the study (l2=98.549).
Conclusion: People with malaria who show symptoms such as fever should be evaluated by COVID-19 to prevent serious complications. The present study provided information on malaria and COVID-19 co-infection. Nevertheless, further prospective studies are needed to investigate the burden and consequences of COVID-19 in malaria endemic areas.
Keywords: COVID-19; SARS-CoV-2; Malaria; Meta analysis; Systematic Review
Introduction
Acute respiratory syndrome-coronavirus (SARS-CoV-2) is a viral infection that is considered one of the most persistent, destructive and deadly infectious diseases [1]. On January 30, 2020, the World Health Organization declared the outbreak of the coronavirus as a public health emergency with international concern, and on March 11, 2020, it was recognized as a global pandemic [2]. Nowadays, the SARS-CoV-2 virus, the causative agent of coronavirus disease (COVID-19), has caused more than 690 million cases and 6.8 million deaths globally. The clinical manifestations of the corona virus infectious disease include fever, cough, fatigue, myalgia, headache, shortness of breath and in some cases lead to severe pneumonia and acute respiratory distress syndrome in patients [3]. Furthermore, the effects of the COVID-19 infection can significantly affect other diseases such as malaria. However, malaria and COVID-19 may resemble each other and present similar symptoms such as fever, fatigue and headache, cough, sweating, and breathing problems that are often misdiagnosed [4]. Despite this, malaria is one of the most dangerous and deadly parasitic diseases in the world and is widely distributed in tropical and subtropical regions, especially in Africa and Southeast Asia [5]. Despite being preventable and treatable, the parasitic disease of malaria still has a devastating effect on people's health and livelihood [6]. According to the some reports published by the WHO; this parasitic disease has accounted for a high percentage of deaths in African countries, while only about 3% of malaria cases have been reported in Southeast Asian regions such as India, Indonesia, Bangladesh, Nepal, Thailand, Sri Lanka, Myanmar and the Maldives [7]. Some statistical evidence shows that since the end of August 2020, the incidence rate of the COVID-19 disease in Africa is not high compared to many regions around the world [8]. The relatively low spread of COVID-19 in Africa may depend on host genetic epidemiology and other relevant factors that protect the African population from SARS-CoV-2 infection. Thus, the infectious disease COVID-19 can affects the efforts of health care providers in malaria control and ultimately leads to significant changes in the statistics of malaria patients [9]. Epidemiological analysis of the global malaria situation in the corona virus pandemic helps to understand the dynamics of the changing malaria situation in different continents and helps us to implement control strategies and allocate financial and medical resources [10]. However, there is a significant knowledge gap regarding the co-infection of these two diseases. A better understanding of co-infection may lead to the development of control strategies for different regions. Hence, in this systematic review, we reviewed reports of co-infection with malaria and COVID-19 and assessed aspects such as symptoms, diagnosis and mortality.
Material and Methods
Study Protocol and Registration
This study was conducted in Hamadan University of Medical Sciences, Hamadan, Iran. This review is based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyzes) guidelines was done.
Search Strategy
In this study the reports of co-infection of COVID-19 and malaria in databases including PubMed, Web of Science, and Scopus from the beginning to February 2022 were collected and analyzed and keywords including “COVID-19”, “malaria”, “2019-nCoV”, “Plasmodium falciparum”, “2019 nCoV Infection”, “2019-nCoV”, “Coronavirus Disease-19”, “Coronavirus Disease 19”, “2019 Novel Coronavirus”, “SARS Coronavirus 2”, “SARS-CoV-2” were used.
Also, words “combinations and / or operators” were used as keywords. In this study, only the terms in the Medical Subject (MeSH) headings in the search strategy were used.
Inclusion and Exclusion Criteria
The inclusion criteria in the current study include observational and epidemiological articles such as cross-sectional studies, case-control studies and cohort studies in English that were conducted on the human population and published in relation to the co-infection of COVID-19 and malaria.The study excluded review papers with insufficient data, irrelevant to the subject, authors' comments, authors' corrections, news, letters to the editor, studies with short reports, laboratory studies. Also, in the present study, articles on COVID-19 and unrelated parameters, authors' predictions on COVID-19 models, molecular studies, case reports, systematic or limited studies, community assessment studies, clinical trials, animal studies, and analytical studies were excluded.
Articles Selection
In this study, the initial search of articles was done by two authors (S. SH and M.J). Any initial disagreement about the eligibility of the studies was resolved by discussion among all authors and then agreement between all authors. The authors reviewed the topics, titles, and abstracts of all identified articles. The full text of the articles was evaluated according to the inclusion and exclusion criteria of the present study. Any initial disagreement about the eligibility of the articles was resolved by discussion among all authors. Finally, articles using the STROBE checklist (strengthening the reporting of observational studies in epidemiology) were evaluated. Eventually, articles that received a minimum score of 16 were selected as finalists.
Data Extraction and Data Item
The fallowing data extracted from each study for further analysis included first author, year of publication, place of study including country/ continent, year of study, mean age and standard deviation, number of patients with co-infection, population size of studies including number of case, control, total, male and female in case and control groups, number of patients with SARS-CoV-2, and number of patients infected with Plasmodium spp, P-value and odds ratios and other data (Table 1). Also, all data excluded from the studies were placed in a standard experimental datasheet before further analysis.
Ref.
Author
Years
Country
Continent
Study type
Age
OR
CLOW
CLUPER
P Value
Quality
1
Yahaya
2020
Nigeria
Africa
CS
-
0.02
0.001
0.06
0.05
Moderate
2
Michael
2021
Uganda
Africa
CS
31
1.04
1.03
1.05
0.001
Moderate
3
Marissa
2020
Hamburg
Europe
CC
55
0.6
0.226
1.61
0.3
High
4
Junior
2020
Congo
Africa
CO
54
0.01
0.0
0.03
0.05
High
5
Niraj
2020
India
Asia
CS
32
0.53
0.33
0.855
0.009
Low
6
Jane
2021
London
Europe
CS
36
8.7
1.0
75.5
0.015
Moderate
7
Amoo
2020
Nigwria
Africa
CS
34
0.02
0.001
0.06
-
High
8
Jane
2020
Sanfrancisco
North America
CS
23
0.87
0.78
0.97
-
High
9
Vanroye
2021
Belgium
Europe
CC
42.5
5.07
3.506
7.34
0.001
Moderate
OR: Odds Ratio, CC: Case-Control Studies, CS: Cross-Sectional Study, CO: Cohort study, N= Number
Table 1: Characteristics of studies.
Quality Assessment
The quality of this study was independently evaluated by two authors (A.M and A.NZ) according to the Newcastle-Ottawa Scale (NOS) to assess the quality of non-randomized studies (case-control and cohort study). The authors also used the Newcastle-Ottawa scale to evaluate cross-sectional studies. In addition, disputes between the two authors were resolved with the evaluation and guidance of the third arbitrator (F.AJ). To evaluate the quality of the studies, the quality of "high", "average" or "low" for each study was defined as: low quality (score less than 5), average quality (score 6-7) and high score (score 8-9).
Data Analysis
In this study, the prevalence of Plasmodium spp among infected individuals (among the studies included in this article) was estimated by using random effect models (DerSimonian and Laird). Also, data analysis was performed by comprehensive meta-analysis software version 2. In addition OR with 95% CI confidence interval was used as a selective correlation index. Forest plot was also used to present combined results and individual studies. Q-Cochran test and I2 statistics were used to evaluate the heterogeneity of the studies (I2 index less than 25%, 25% -75% and more than 75% were considered as a low, medium and high heterogeneity, respectively). Also, a random effect model was used to detect of substantial heterogeneity in this study.
Also, to evaluated no significant heterogeneity the fixed effect models were performed to effect measure. To evaluated publication bias, funnel plot and Egger’s and Begger’s tests for asymmetry was performed (if number of included article was more than 10). Furthermore, statistical analysis was performed by using STATA Statistical Software version 15.0.The Significance level was evaluated <0.05. The result of analysis was reported as a frequencies or graphs.
Result
Included of Study
762 articles were selected based on their relevance to COVID-19 and Malaria. Some articles were removed from the project due to duplication. Then, from the remaining studies, abstract and full text articles were reviewed. Some of these articles were deleted due to irrelevance to the main topic as well as lack of criteria and calibration (Figure 1). Eventually, nine articles were conducted to investigate the influence of COVID-19 mortality and malaria prevalence in the form of systematic review and meta-analysis study.
Figure 1: PRIMA Flowchart.
Characteristics of Research Articles
Inclusion criteria were articles on COVID-19 and malaria. The selected articles were in English. Also, exclusion criteria included studies with inappropriate sample size, lack of relevance to the main topic, letter to the editor, and case report reports. Studies with these characteristics were excluded from the project.
Quality of the Included Studies and Publication Bias
Two authors (M.J and S, Sh) evaluated the studies in terms of methodological aspects including sampling methods, description of sampling conditions, measurement parameters, statistical analysis. In the event of a dispute between the two authors, the third person (F.A) acted as the third reviewer. To evaluate the quality, "high", "medium" or "low" quality was considered for the articles. Hence, for each study that had a score of more than 7 "high quality", a score of 4-6 "average quality" and a score less than 4 "low quality" was awarded (Table 1).
Synthesis of Results
The forest Plot was designed for the included article in this study (Figure 2). This figure provides information about Odds ratio with 95 percent of the confidence interval, Lower limit, Upper limit, Z-value, and P-value for each of article. The initial analysis to evaluate the impact of the COVID-19 epidemic on malaria mortality has shown in this figure (Figure 2). In the present study, data were summarized based on two models. The two models include the random effect model and the fixed effect model. In fix effect model, all results were constant and unaltered. In random effect, the final answer model includes a return of numbers and we are faced with a random result. Also, there are no fixed results.
Figure 2: Forest Plot of 9 included studies fulfilled the inclusion criteria. In this presentation, information about the impact of the COVID-19 epidemic on malaria mortality was demonstrated.
Figure 3: The funnel plot for 9 included studies; For interpretation of any publication bias among studies, visual inspection of the generated funnel plot under random-effects model employed to evaluate the asymmetry.
Indeed, there are some studies that analyze their data according to the random effects models. On the other hand, in this study, we calculated I2 and showed its information in Table 2. We encounter high values of I2 in the study. (l2=98.549). Practically, in this report, we consider the random effect model because it is a more accurate and appropriated model. Since, this random effect model includes the return of numbers, the results are more realistic. As, in nature we encounter different sizes of plantain. Also, we consider a plantain tree in nature with different sizes of leaves. Following this example, in random effects model, there is a distributed of results. Also, random effects model looks more realistic. In this study, we have considered this model (Table 3 & Figure 2).
Model
Effect size and 95% confidence interval
Test of null
Heterogeneity
Tau-squared
Number studies
Point estimate
Standard Error
Variance
Lower limit
Upper limit
Z-value
P-value
Q- value
df(Q)
P-value
l-squared
Tau
SquaredStandard Error
Variance
Tau
Fixed
9
0.036
0.005
0.00
0.026
0.046
7.378
0.00
551.347
8
0.00
98.549
0.686
0.839
0.704
0.828
Random
9
-1.244
0.302
0.091
-1.836
-0.653
-4.125
0.00
Table 2: This table was shown fixed effect model and Random effect model according to information of this study.
author
Year and place of publication
Type of study
Male/femal
Place of study
Median age
Malaria diagnostic method
Covid-19 diagnostic method
Plasmodium spp
Study time
Negative number in terms of malaria and covid-19
No. of patients with COVID19
Yahaya11
2020/nigeria
Cross sectional
-
Isolation center Dutse, Jigawa Nigeria
-
Stained with Giemsa; and examined through a microscope
RT-PCR
-
March, 2020 to July2020.
20
54
Michael12
2021/uganda
Cross sectional
-
This is an ecological study which compared population variables of 195 countries
31
-
-
-
-
-
-
Marissa13
2020/hamburg
Cohort study
24/6
hospital
55
With Giemsa; and examined through a microscope
RT-PCR
Falciparum
-
13
20
Junior14
2020/congo
Cohort study
82/78
hospital
54
With Giemsa; and examined through a microscope
RT-PCR
-
Mars 11th to July 22th 2020
-
160
Niraj15
2020/india
Cross sectional
267/224
hospital
32
Microscopy or RDT
RT-PCR
Plasmodium vivax
April 18th to October 31st 2020
-
491
Jane16
2021/london
Cohort study
502/95
hospital
36
Microscopy and PCR
RT-PCR
Falciparum
April 15, to 10/30/2020
-
600
Amoo17
2020/nigeria
Cross sectional
358/259
COVID- 19 Drivethrough testing center
34
RDT
RT-PCR
P Falciparum.
April and May 2020
489
121
Jane18
2020/sanfrancisco
Cross sectional
-
Malaria reference center
23
RDT
-
-
April 2020- March 2021
-
-
Vanroye19
2021/belgium
Cross sectional
79/91
Institute of Tropical Medicine
42.5
RT-PCR and microscopy
Ab Rapid Test RT-PCR
Falciparum
-
-
196
Table 3: Extracts additional information from the articles reviewed in this study.
Author
No. of
malaria as a
co-infectionTreatment for COVID-19
Symptoms
Co-morbidities
Hospitalization duration
Yahaya11
34
NS
NS
NS
NS
Michael12
-
NS
NS
NS
NS
Marissa13
-
One COVID-19 patient had received treatment with Rituximab
NS
Hypertension, Diabetes, Coronary heart disease, Lung disease, Cancer
The COVID-19 patients spent 9.9 days and the malaria patients spent 4.9 days in the hospital.
Junior14
1
Hydroxychloroquine or
chloroquine phosphateFever, cough, fatigue,
Hypertension, diabetes, obesity, heart disease, asthma/ chronic pulmonary disease
15 (4–20)
Niraj15
27
Hydroxychloroquine
Fever , dry cough, and sore throat
Hypertension (11%), diabetes
(8%), bronchial asthma (4%),
hypothyroidism (2.8%),
tuberculosis (1.1%), ischemic
heart disease (1.3%), other
comorbidity (1.7%), more than
1 comorbidity (6.5%)21 (14–35)
Jane16
70
Vitamin D, zinc, paracetamol, azithromycin oxygen therapy, dexamethasone, heparin and oral warfarin,
Cough, runny nose, fever,
headache, shortness of breathDiabetes and heart disease
NS
Amoo17
2
As chloroquine (CQ), doxycycline, quinine, mefloquine, atovaquone/proguanil (Malarone),
Fever, chills, headaches, nausea, fatigue
NS
NS
Jane18
-
NS
NS
NS
NS
Vanroye19
170
NS
NS
NS
NS
Table 4: extracts additional clinical information from the articles reviewed in this study.
Additional Result of Study
Additional information of studies performed: In this study, in addition to obtaining a statistical analysis about the impact of the COVID-19 epidemic on malaria patients, other factors are separated and examined (Table 3). Factors that was considered as additional results including year and place of publication, number of men and women separately in each study, exact locations of each study separately, methods of diagnosis of malaria and COVID-19 in each study separately, evaluation of the type of malaria in patients with this disease, negative number in terms of malaria and COVID-19, number of patients with COVID19. Please refer to Table 3.
Discussion
Summary of Evidence
One of the latest crises that have happened in human societies is the outbreak of the Corona virus (2019-nCOV) [11]. Although, COVID-19 infection that first started in China and then it later became a widespread pandemic in the worldwide. Patients infected with COVID-19 may exhibit symptoms ranging from asymptomatic to severe and fatal respiratory complications. Pandemics of infectious diseases such as COVID-19 can cause irreparable damage to health systems, especially when there are similarities in clinical manifestations with other infectious diseases such as the parasitic infection malaria [12]. In addition, malaria is still one of the significant infectious diseases in endemic areas, which significantly affect people's health, medical and health systems, and the health economy, and its diagnosis requires a detailed examination of the previous history [13]. Significantly, during the coronavirus pandemic, the diagnosis and treatment of malaria was affected by the COVID-19 infection, so that in malaria-endemic areas, there were reports of disruptions in the process of detection, diagnosis and control of malaria [14]. Also, Quarantine in endemic areas during the corona virus epidemic limited the access to medical and health services and for a long time the chemical prevention of malaria and the distribution of nets impregnated with insecticides were suspended, as a result the spread of malaria and its mortality increased [15].
Our study reveals the fact that the co-infection of malaria and covid-19 in some people may be misdiagnosed and the late diagnosis of these two diseases and their differentiation from each other can affect the mortality and complications caused by them. Our analysis shows the prevalence of different Plasmodium species during the coronavirus pandemic, characteristics, organism characteristics and diagnostic methods of COVID-19 in patients with co-infection of malaria and COVID-19 [20,21]. However, the prevalence of co-infection of covid-19 and malaria seems to be heterogeneous and the lowest prevalence of Plasmodium spp was observed among patients with covid-19, our results are very similar to those reported by Matangila et all [14]. Matangila et al believe that patients who were admitted to hospitals due to malaria infection and received anti-malarial drugs have a recovery process in the infection caused by COVID-19 [14]. Mahajan and et al demonstrated a low prevalence of co-infection of malaria and COVID-19 in India (5%), suggesting that co-infection may enhance recovery from COVID -19. Also, Mahajan et al reported a low prevalence of co-infection in India (5%), suggesting that co-infection with COVID-19 and malaria may enhance recovery from the coronavirus by clearing the virus through glycosylphosphatidylinositol antibodies (GPI) against different Plasmodium species that cross-react with SARS-CoV-2 antibodies. Also, in their study, they reported that a population exposed to malaria has a naturally selected genotype of ACE2 rs2106806 TT/T, which leads to the down-regulation of ACE2 in suppressing and reducing the chance of coronavirus entering the epithelial cells in the lung [15]. In contrast to these two studies, there is another study that reported a high prevalence (63% to 100%) in Nigeria. The results of Muhammad et al show that in patients with co-infection, the worsening of the disease is due to inflammatory responses caused by high oxidative stress against SARS-COV-2 infection [22]. In addition, Many reasons can have a direct impact on our current study, such as asymptomatic infection in patients in malaria endemic areas, especially in young people who are infected with COVID-19 without any symptoms, the lack of access to diagnostic tests with high sensitivity such as RT-PCR, the lack of health care workers who have received training based on the separation of these two diseases, the lack of similarity in the age characteristics of patients infected with malaria (children) and Covid-19 (adults) [23].
In this study, the systematic review is highlights and necessary the importance of screening and diagnosing for other diseases and does not focus only on COVID-19, and it is also important to consider the differential diagnosis in patients with overlapping symptoms [24]. The presence of some infectious diseases with symptoms similar to the coronavirus may lead to severe COVID-19 infection. Malaria can be life-threatening in untreated patients. Therefore, screening and segregation of diseases in such conditions, especially in endemic areas, is absolutely necessary [25]. In this systematic review, one of our goals was to investigate factors that could have detrimental effects on malaria control achievements, and we evaluated factors such as malaria and covid-19 co-infection and aspects such as symptoms, diagnosis and mortality.
Limitation
Nevertheless, the present study has limitations that include: in meta-analysis studies, sample size has many challenges. Also, a small number of countries have published reports of co-infection with malaria and COVID-19 from their regions. A number of studies have ambiguous data and enigmatic results. Extracting data from these studies is very difficult. In addition, in malaria endemic areas such as Africa, there are very limited and vague reports of co-infection with malaria and COVID-19. Eventually, the present study helps to develop management strategies in people with malaria and COVID-19 co-infection. Nevertheless, Extensive studies in this field seem essential and necessary.
Author Statements
Competing Interests
The authors declare no conflict of interest, financial or otherwise.
Funding
The current study was supported by Hamadan University of Medical Sciences, Hamadan, Iran.
Authors' Contributions
M.J - FS.A - H.A: wrote the manuscript, S.S – A.NZ: edited the manuscript and designed Figure, M.S: design and Supervision.
All authors read and approved the final manuscript.
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