key: cord-1035085-mswxx8qs
authors: Raham, Tareef Fadhil
title: Influence of Malaria Endemicity and Tuberculosis Prevalence on COVID-19 Mortality
date: 2021-03-03
journal: Public Health
DOI: 10.1016/j.puhe.2021.02.018
sha: 0f68e92db1fee99a5b394e5a04061f956e4e2b6c
doc_id: 1035085
cord_uid: mswxx8qs

Objectives Regarding Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it is known that a substantial percentage of the adult population do not become infected when exposed to this novel coronavirus. Several studies provide an initial indication of the possible role of preexisting immunity, whether cross-immunity or not. The possible role of latent tuberculosis (TB) and malaria has been suggested to create innate cross heterogeneous immunity. In this study, we looked for the influence of these factors on coronavirus disease 2019 (COVID-19) mortality in malaria-endemic countries. Study design Eighty malaria-endemic countries were enrolled in this cross-sectional study. Data subjected to testing included TB prevalence, Bacillus Calmette–Guérin (BCG) vaccine coverage, malaria incidence, and COVID-19 mortality. Methods Hierarchical multiple regression type of analysis was used for data analyses. TB prevalence/100,000 population standardized to BCG coverage rates was taken as a direct factor in the test. Malaria incidence/1000 population was considered as an intermediate factor. The outcome was COVID-19 mortality/million (M) population. Results The results showed with robust statistical support that standardized TB prevalence was significantly associated with reduced COVID-19 mortality. Malaria had an additional effect in reducing COVID-19 mortality with a highly significant association. Conclusions Malaria and standardized TB prevalence are statistically significant factors associated negatively with COVID-19 mortality.

In 2011, Netea et al. proposed the term "trained immunity" to describe the ability of innate immune cells to nonspecifically adapt, protect and remember primary stimulation 1 .

Latent tuberculosis (TB) infection was suggested to create a heterogeneous immune response to the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral infection in different patterns and severity according to different Bacillus Calmette-Guérin (BCG) statuses 3,4 . Further studies have consolidated such a role for latent TB 5,6,7 .

In Italy, it has been suggested that the Black population is less affected by coronavirus disease 2019 (COVID-19) due to suggested previous exposure to malaria and the presence of anti-glycosylphosphatidylinositol antibodies, which have a possible protective effect against malaria re-infection and may give cross-protection against ( SARS-COV2 infection). 8,9 Furthermore, an epidemiological paradox between COVID-19 and malaria endemicity was noticed during the initial phase of the pandemic. 10 Adding to this evidence, further study showed that high endemicity of TB and malaria and universal BCG programs were suggested to have a cushioning effect on the proportion of the population affected by COVID-19. 6 Both BCG implementation and latent TB prevalence, later on, did not fully explain variances in COVID-19 mortalities across different countries. In South Africa, for example, the COVID-19 mortality/M population was 238/ M at the time of the study, while the TB prevalence/100,000 population was 520, which is very high. Moreover, these studies did not explain the low COVID-19 mortality rates in countries that have relatively low TB prevalence/100,000 population, such as in Togo, Benin and Mali, where the estimated TB prevalence is 36, 56 and 53, while COVID-19 mortality/M is 3, 3 and 6, respectively.

Our study's background hypothesis stands on the possible heterogeneous immunity generated by malaria in addition to possible heterogeneous immunity generated by TB. This study compared COVID-19 mortality in malaria-endemic countries against TB prevalence standardized by BCG coverage. Then, the mortality rate was tested again when the malaria incidence effect was added to the composite sample to look for statistical associations and significances.

This study addressed mortalities instead of morbidities because the real number of affected people was beyond counting, many confounders affect testing and because of the wide distribution of asymptomatic persons. Furthermore, this study considered standardized TB prevalence to BCG coverage instead of non-standardized TB prevalence values.

Eighty malaria-endemic countries were enrolled in this cross-sectional study. Data subjected to testing included TB prevalence, BCG coverage, malaria incidence, and COVID-19 mortalities as it were in August, 31, 2020 .

The direct factor reducing mortality rates concerning COVID-19 was the standardized TB/100,000 population by BCG vaccination coverage percentage in 2018 through dividing the factor of TB prevalence/100,000 population rates by the factor of BCG vaccination coverage in 2018. The indirect effect that reduced the mortality rates, named as an intermediate factor, was the malaria incidence for 2018/1000 population.

We investigated the validity of the assumptions of the studied model that adopted the results of the quantitative measurements. Table 1 shows the results of the multiple linear model fitness test resulting from the regression analysis of variance. The effectiveness of the model's fitness was observed in quality when the intermediate factor was present. The level of significance was greatly reduced compared to the case of the model's quality in the absence of the intermediate factor (Table 1) . Table 2 

In this study, the prevalence of exposure to mycobacterium spp. (standardized to BCG vaccine coverage) by populations was negatively associated with COVID-19 deaths / M population. This supports the previously mentioned studies. 3,4,5,6,7 TB prevalence standardization for BCG coverage was an important factor regarding studying countries currently implementing BCG programs, as long as the coverage was reflecting the degree of benefits added to the factor (latent TB prevalence) that the coverage does. Likewise, the influence of time duration of cessation of the BCG vaccination program was a factor in determining COVID-19 mortality in countries that ceased implementing this vaccine, which we concluded in our previous study 11 . Malaria can induce an immunological response that is significantly associated with a reduction in COVID-19 mortality. This association needs confirmatory immunological and clinical control studies to establish causation. This finding can explain the variances in COVID-19 mortality among different countries much deeper than latent TB and BCG vaccination. Differences in BCG vaccination policies were of concern earlier than for latent TB, which later became a more prominent concern. Previous studies were conflicting and were criticized because of the possible confounding factors. In this study, all countries were implementing national BCG programs, but TB countries' prevalence/100,00 population normalized by BCG coverage rates showed a significant association with the reduction in COVID-19 mortality. The supportive evidence for TB prevalence and malaria incidence in this study was conducted with a robust statistical method-Hierarchical multiple regression analysis. Hierarchical multiple regression analysis is a subset of regression methods that we chose to prove our theory using collected evidence for a proposed role of variables entered in blocks 12 . TB prevalence was in the 1st block in this study, the malaria incidence was in the 2nd block, and the reduction of COVID-19 mortality was the effect. This test allowed us to look at the R² change and F-statistic change between the two models, in addition to reporting the level of significance for each predictor variable that were entered into the model in pre-determined iterations. Table 2 shows that R square was 0.091 and increased to 0.171 when the intermediate factor was added to the composite. Furthermore, these results show that constant parameters constitute a significant proportion causing COVID-19 mortality measured (152.38). TB prevalence, when standardized to a BCG coverage rate, made a -22.11 change, and when malaria incidence was included in the regression model (constants), made a -0.331 further change. Constants constituted a considerable number not included in the regression model (Table 3) . Based on the benefits of heterogeneous immunity, possibly, two important questions may need to be answered: Does a potent malaria vaccine need to be considered for malaria eradication? Does latent TB management in the future need to be more conservative? Limitations: Possible confounding variables have not been evaluated, such as population density, ethnicity, life expectancy, comorbidities, lifestyle, the pandemic phase, data accuracy and, health services.

Conclusions: Though confounding variables have not been evaluated, results of this study suggested that malaria incidence and TB prevalence are possible determining factors for COVID-19 mortality. Further research is needed for exploring such findings. Ethics and dissemination:

Ethical permission is not necessary as this study analyzed publically published data and patients were not involved.

There was no conflict of interest.

Funding: No specific source of funding was utilized for the current study. J o u r n a l P r e -p r o o f

Role of latent tuberculosis infections in reduced COVID-19 mortality: Evidence from an instrumental variable method analysis. Medical Hypotheses

Finding Tentative Causes for the Reduced Impact of Covid-19 on the Health Systems of Poorer and Developing Nations: An Ecological Study of the Effect of Demographic, Climatological and Health Related Factors on the Global Spread of Covid-19

Trained immunity" from Mycobacterium spp. exposure or BCG vaccination and COVID-19 outcomes

Coronavirus disease 2019 (COVID 19) and Malaria: Have anti glycoprotein antibodies a role?

Global Spread of Coronavirus Disease 2019 and Malaria: An Epidemiological Paradox in the Early Stage of A Pandemic

Impact of Duration of Cessation of Mass BCG Vaccination Programs on Covid -19

Hierarchical Multiple Regression Analysis Using at Least Two Sets of Variables