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Epidemiology of malaria and other diseases of public health importance and implications for interventions in high transmission settings in sub-saharan africa.

Leah Moriarty Follow

Date of Award

Degree type.

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Public Health

First Advisor

Gerardo Chowell

Second Advisor

Richard Rothenberg

Third Advisor

Mateusz Plucinski

Infectious diseases remain a major cause of disability of death in low-resource settings. Malaria alone was responsible for an estimated 405,000 deaths globally in 2018, with the 94% of these deaths occurring in sub-Saharan Africa. In Mozambique and Democratic Republic of the Congo (DRC), communicable diseases, including malaria, lower respiratory infections, and neonatal disorders are among the top causes of disability and death. Understanding malaria and co-endemic diseases in these two countries can aid the planning, evaluation, and targeting of public health interventions. Additionally, studying the efficacy of the drugs used to treat malaria will preserve the ability for malaria cases to be treated successfully.

The three studies in this dissertation describe the epidemiology of malaria and co-endemic diseases of public health importance in Mozambique and evaluate the efficacy of medicines used to treat malaria in DRC. The first study will describe the spatial epidemiology of malaria in two high-burden districts in northern Mozambique to explore the utility of exploration of local spatial heterogeneity in high-transmission settings. The second study will investigate patterns in antibody responses to several infectious pathogens of public health importance in Mozambique, providing an opportunity to understand common predictors of infectious diseases endemic in this region. The third study will examine the efficacy of three artemisinin-based combination therapies used to treat uncomplicated malaria and molecular markers of antimalarial resistance in five sites in DRC.

Collectively, the three studies in this dissertation describe factors that have implications for intervention planning and disease surveillance in areas with high malaria and other tropical disease burden and limited health resources. Careful consideration of transmission setting can support more efficient and higher quality data collection and may allow for intervention design tailored to the local realities that can target multiple diseases of public health importance.

Recommended Citation

Moriarty, Leah, "Epidemiology of Malaria and Other Diseases of Public Health Importance and Implications for Interventions in High Transmission Settings in Sub-Saharan Africa." Dissertation, Georgia State University, 2021. doi: https://doi.org/10.57709/20221191

https://doi.org/10.57709/20221191

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• Published: 23 July 2019

Modelling the relationship between malaria prevalence as a measure of transmission and mortality across age groups

• Sammy Khagayi 1 , 2 , 3 ,
• Meghna Desai 4 , 5 ,
• Nyaguara Amek 1 ,
• Vincent Were 1 ,
• Eric Donald Onyango 1 ,
• Christopher Odero 1 ,
• Kephas Otieno 1 ,
• Godfrey Bigogo 1 ,
• Stephen Munga 1 ,
• Frank Odhiambo 1 ,
• Mary J. Hamel 4 , 5 ,
• Simon Kariuki 1 ,
• Aaron M. Samuels 4 , 5 ,
• Laurence Slutsker 4 , 5 ,
• John Gimnig 4 , 5 &
• Penelope Vounatsou   ORCID: orcid.org/0000-0002-4904-5352 2 , 3  

Malaria Journal volume  18 , Article number:  247 ( 2019 ) Cite this article

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Parasite prevalence has been used widely as a measure of malaria transmission, especially in malaria endemic areas. However, its contribution and relationship to malaria mortality across different age groups has not been well investigated. Previous studies in a health and demographic surveillance systems (HDSS) platform in western Kenya quantified the contribution of incidence and entomological inoculation rates (EIR) to mortality. The study assessed the relationship between outcomes of malaria parasitaemia surveys and mortality across age groups.

Parasitological data from annual cross-sectional surveys from the Kisumu HDSS between 2007 and 2015 were used to determine malaria parasite prevalence (PP) and clinical malaria (parasites plus reported fever within 24 h or temperature above 37.5 °C). Household surveys and verbal autopsy (VA) were used to obtain data on all-cause and malaria-specific mortality. Bayesian negative binomial geo-statistical regression models were used to investigate the association of PP/clinical malaria with mortality across different age groups. Estimates based on yearly data were compared with those from aggregated data over 4 to 5-year periods, which is the typical period that mortality data are available from national demographic and health surveys.

Using 5-year aggregated data, associations were established between parasite prevalence and malaria-specific mortality in the whole population (RR malaria  = 1.66; 95% Bayesian Credible Intervals: 1.07–2.54) and children 1–4 years (RR malaria  = 2.29; 1.17–4.29). While clinical malaria was associated with both all-cause and malaria-specific mortality in combined ages (RR all-cause  = 1.32; 1.01–1.74); (RR malaria  = 2.50; 1.27–4.81), children 1–4 years (RR all-cause  = 1.89; 1.00–3.51); (RR malaria  = 3.37; 1.23–8.93) and in older children 5–14 years (RR all-cause  = 3.94; 1.34–11.10); (RR malaria  = 7.56; 1.20–39.54), no association was found among neonates, adults (15–59 years) and the elderly (60+ years). Distance to health facilities, socioeconomic status, elevation and survey year were important factors for all-cause and malaria-specific mortality.

Malaria parasitaemia from cross-sectional surveys was associated with mortality across age groups over 4 to 5 year periods with clinical malaria more strongly associated with mortality than parasite prevalence. This effect was stronger in children 5–14 years compared to other age-groups. Further analyses of data from other HDSS sites or similar platforms would be useful in investigating the relationship between malaria and mortality across different endemicity levels.

There has been a substantial reduction in malaria related mortality worldwide over the last decade, however, the burden is still disproportionately felt in sub-Saharan Africa (SSA) [ 1 ]. Due to the high burden in children and pregnant women [ 2 ], malaria control intervention resources in previous years have been targeted to these vulnerable populations. Increased quality data on malaria infection dynamics and mortality across all ages [ 3 ] has created an increased awareness of the burden of disease amongst the other populations, and policies have been expanded to ensure universal coverage with effective vector control methods (e.g. long-lasting insecticidal nets [LLIN]), availability of diagnostics (e.g. rapid diagnostic tests, RDT), and availability of appropriate treatments (e.g. artemisinin-based combination therapy, ACT) to all.

There is evidence that the malaria burden in older children and adults in terms of mortality and parasite prevalence [ 4 , 5 , 6 ] is higher than had been thought of previously. With data analysed from a health and demographic surveillance system (HDSS) in western Kenya run by the Kenya Medical Research Institute (KEMRI) and Centers for Disease Control and Prevention (CDC) showing that, largely due to increased malaria/HIV prevention and treatment interventions, malaria mortality rates decreased in young children and persons aged ≥ 15 years, but remained stable in 5–14 year olds [ 6 ]; suggesting that malaria control efforts should be intensified in this group. Furthermore, older children and adults have been shown to act as reservoirs of transmission due to high levels of asymptomatic infections [ 7 ], supporting the current policy of universal coverage of malaria control interventions.

Measuring malaria transmission intensity and its effect on mortality can be used to monitor disease burden and assess the impact of interventions and control programmes. This has been done previously using entomological inoculation rates (EIR) [ 8 , 9 ]; however, measuring EIR is expensive, time consuming and is often imprecise, particularly in low transmission settings. Other measures of malaria transmission include slide positivity rate (SPR), parasite prevalence, disease incidence, sporozoite rate, and vectorial capacity [ 10 , 11 , 12 , 13 ].

Malaria parasite prevalence (PP) surveys carried out mostly during peak transmission times through representative sampling of populations are a preferred method for measuring malaria burden because reporting from weak or non-existent health systems is inadequate to measure incidence [ 14 ], at the same time health facilities do not capture asymptomatic infections which are important for malaria transmission. Furthermore, PP survey data are easier to interpret and less prone to uncertainty compared to other measures [ 15 ]. These surveys are however limited in their ability to capture malaria morbidity, seasonality of transmission and monitor temporal trends from surveys that are not seasonally aligned [ 16 ]. With regular, consistent survey intervals, stringent methodology in sampling and diagnosis, PP surveys can provide measures of malaria transmission which are useful to policy makers.

Due to their nature, HDSS sites can be used to collect data that are well aligned in space and time, so as to investigate variations in malaria transmission in relation to morbidity and mortality. They provide data on mortality across age groups, and in conjunction with PP surveys, offer a unique platform through which the relationship between malaria transmission and mortality can be investigated while taking into consideration spatio-temporal factors [ 16 , 17 , 18 ] and hence monitor the impact of interventions over time. One such project, the Malaria Transmission Intensity and Mortality Burden across Africa (MTIMBA) investigated the effect of EIR as a measure of exposure and its effect on mortality in several HDSS sites in Africa and showed that small changes in transmission dynamics as measured by EIR, impact greatly on mortality [ 8 , 9 ].

This study sought to understand how malaria parasite prevalence and clinical malaria translate into mortality and consequently help inform national control programmes on how to best use their survey data in estimating mortality. The relation between malaria prevalence and mortality was explored across all age-groups using Bayesian geostatistical models on data collected between 2007 and 2015 from the KEMRI and CDC HDSS (KHDSS) site in western Kenya. There have been no studies done to investigate the usefulness of PP and its association with mortality using data that are well aligned in space and time across different age groups in similar settings; hence enriching the knowledge of malaria transmission-mortality using high quality consistent data. Since estimation of malaria deaths is still not very clear, results of this study would clarify the potentials of PP surveys to reliably estimate malaria deaths and as its intensity reduces worldwide, help inform decisions on resource allocation and monitoring the impact of interventions.

Study area and population

The KHDSS located in Siaya County of western Kenya follows a population of over 240,000 people as of mid-2015 in an area of over 700 km 2 [ 19 ]. This HDSS is located in a malaria endemic zone with a high burden of HIV/AIDS compared to the rest of the country [ 20 , 21 ].

From the HDSS, data on an initial population at baseline was collected followed by subsequent 4 monthly cycles every year during which data were collected on births, deaths, in-migration and out-migrations. These data were used to estimate person-years of observation (pyo) that served as a denominator to calculate mortality rates. Verbal autopsy (VA) was used to determine malaria-specific mortality rates. The methods used for verbal autopsy have been described in detail elsewhere [ 19 , 22 ]; it involves capturing data on a deceased person’s last illness, signs, symptoms and medical history which is then used to determine the most probable cause of death using a computer-based Bayesian expert algorithm called InterVA [ 23 ].

Malaria prevalence

Annual all-age malaria and anaemia prevalence surveys were conducted by randomly sampling compounds within the HDSS, and testing all consenting members of the compound for malaria by blood smear microscopy, from the population during the peak malaria transmission period in July. Details of the sampling by year are shown in Additional file 1 . Trained interviewers then visited the compounds, administered a questionnaire to collect information on demographics, risk factors for malaria infection, healthcare-seeking, previous illness, socioeconomic status, LLIN ownership/use, and collected a blood sample to prepare thick and thin smears for microscopy. The blood slides were transported to a central laboratory, stained with 10% Giemsa and examined for malaria parasites by expert microscopists.

Two measures of transmission were considered; prevalence of malaria parasites and clinical malaria for comparative purposes. Parasite prevalence by age group, village, and year was defined as the proportion of participants in each village that had malaria by microscopy out of all the participants from the same village who were tested for malaria. Similarly, clinical malaria prevalence was defined as the proportion of participants in each village who had malaria parasites of any density by microscopy in combination with either a reported fever in the previous 24 h or a temperature of 37.5 °C and above out of all those tested.

Data management and statistical analysis

Rates of clinical malaria and PP were aggregated at village level and linked to mortality data by village, year of study and age group. The age groups were defined as: 0–28 days (neonates), 1–11 months (infants), 1–4 years (child), 5–14 years (older child), 15–59 (adults) and 60+ (elderly). Crude and age specific all-cause/malaria-specific mortality rates were calculated by dividing the deaths in each group with the total person-years observed (pyo) in that group.

A measure of socioeconomic status was constructed based on household asset ownership using a composite score, derived from multiple correspondence analysis (MCA) [ 24 ] and categorized into 3 levels as least poor for the well off, poor for the average and poorest for the lowest rank while LLIN coverage was calculated as the percentage of households in a village owning at least one net per two people in a given year. Distance to health facilities was calculated as the networked distance of each household from the nearest health facility, and classified into 3 categories as less than 1 km, 1 to 2 km and greater than 2 km. the elevation of each household was downloaded from the remote sensing United States geological survey (USGS) Earth Resources Observation and Science (EROS) website [ 25 ]. These variables were also aggregated at village level and linked to the parasitaemia and mortality data.

The analysis considered two approaches; in one approach, the data were aggregated on a yearly basis, hence 9 years of observation; the second approach was aggregating the data into two periods (2007–2010 and 2011–2015).

For each age group, Bayesian negative binomial geostatistical models were fitted to assess the relationship between PP and all-cause/malaria-specific mortality. Variable selection based on bivariate negative binomial models was used to identify potential confounders. Variables with a p-value below 0.1 were included in the final geostatistical models so as not leave out important variable whose effects would be missed when investigated alone, but become important if included in combination with other factors. Spatial correlation was taken into account by village specific random effects modelled via a Gaussian process with a mean of zero and an exponential correlation matrix of the distance between villages in the study [ 26 ]. Bayesian models were fitted in OpenBugs version 3.1.2 (Imperial College and Medical Research Council London, UK) using Markov Chain Monte Carlo (MCMC) simulation for parameter estimation. Regression coefficients from the Bayesian geostatistical model were exponentiated to obtain prevalence rate ratios (PRR) and summarized by their posterior median and 95% Bayesian Credible Intervals (BCI). Covariate effects were considered statistically important when the BCI of the corresponding regression coefficients on the log scale did not include zero. Due to the nature of Bayesian statistical inference, the terminology of statistically significant was replaced by statistically important effect when reporting results. In this paper, the results for the association between clinical malaria and all-cause mortality are presented; clinical malaria and malaria-specific mortality; and lastly PP and both all-cause/malaria-specific mortality in that order. Model formulation details are provided in Additional file 2 .

Descriptive statistics

Between the year 2007 and 2015, over 441,000 individuals were enrolled/monitored in the HDSS contributing a total of 2,114,223 pyo and 26,283 deaths, for an average crude death rate of 12.4 (95% confidence interval; 12.3–12.6) deaths per 1000 pyo as shown in Table  1 .

All-cause mortality during the study period rose from 15.5 (14.9–16.1) deaths per 1000 pyo in 2007 to 18.8 (18.2–19.3) in 2008 then dropped to a low of 9.4 (9.0–9.8) in the year 2015 with malaria-specific mortality following a similar trend; rising from 1.3 (1.2–1.5) deaths per 1000 pyo in 2007 to a high of 3.5 (3.3–3.7) in 2008, but eventually dropping to 0.9 (0.7–1.0) deaths per 1000 pyo in 2015 (Table  1 ). The average PP during the whole study period was 35.8% (35.2–36.5); ranging between 27.3% in 2008 to a high of 39.7% in 2010 but then dropped over the years to 29.8% in 2015. A further breakdown of the positive slides showed that 8.1% (7.7–10.4) of the respondents had clinical malaria (parasites and fever); on average one fifth of all the positives had clinical malaria (Fig.  1 ).

All-cause and malaria specific mortality rates versus malaria parasite and clinical malaria prevalence

The highest parasite prevalence was observed among older children aged 5–14 years, with an average PP of 56% (95% CI 54–57), followed by children aged 1–4 years at 40% (39–41), adults at 22% (21–24), and infants at 22% (19–25); the elderly at 14% (12–16) had the lowest rate. The age distribution of prevalence indicates an increase in parasite prevalence from infanthood to older children followed by a drop as the population ages (Fig.  2 a). However, by including the presence of fever, a rise in clinical malaria from infants to children aged 1 to 4 years was observed, after which it drops in the 5–14 age-group and in adults but rises slightly among the elderly. The highest prevalence of clinical malaria was in infants with a peak of 18.4% among those tested in the year 2007 (Fig.  2 b).

Malaria parasite prevalence ( a ) and clinical malaria ( b ) by age groups

Model-based results

Relationship between clinical malaria and all-cause mortality.

The following variables met the criteria for inclusion in the age-specific geostatistical mortality models: reported net usage, distance to health facilities, socioeconomic status, year of study and altitude. For comparability, these variables were included in the Bayesian models fitted by age group. Results in Table  2 show that the prevalence of confirmed malaria when aggregated over four and 5-year periods, was associated with all-cause mortality in the combined age groups (RR = 1.32; 95% BCI: 1.01–1.74), in the 1–4 year olds (RR = 1.89; 1.00–3.51) and in the 5–14 year olds (RR = 3.94; 1.34–11.1). Increase in distance to health facilities was associated with higher mortality among neonates, children aged 1–4 years and the combined age group analysis. Risk of all-cause mortality was higher in the period 2007–2010 compared to 2011–2015 in all ages except in neonates. Higher SES and increased elevation were both associated with lower mortality. The association between reported net use and mortality was not statistically important across most age groups save for the elderly. The minimum distance at which spatial correlation was below 5% ranged from 13.2 km to 50 km for all the age groups. The analyses of the yearly prevalence data did not show a statistically important relation between confirmed malaria and all-cause mortality (see Additional file 3 ).

Relationship between clinical malaria and malaria-specific mortality

The pattern of association between clinical malaria and malaria-specific mortality across all age groups was similar to that of clinical malaria and all-cause mortality, however, the magnitude of the estimates was higher. The effect of clinical malaria risk on malaria-specific mortality was statistically important and strong among children 5–14 years (RR = 7.56; 1.20–39.54) and 1–4 year olds (RR = 3.37; 1.23–8.93). Meanwhile in the overall population, malaria-mortality rate increases two and half times for every increase in the proportion of clinical malaria by 1% (RR = 2.50; 1.27–4.81) as shown in Table  3 . Similar to all-cause mortality analysis, statistically important variables were elevation, distance to health facilities, year of study and socioeconomic status. Reported net use was not statistically important for malaria-specific mortality in any ages except among the elderly (RR = 2.05; 1.04–4.34) in the yearly analysis, where an elevated risk with higher levels of net use was observed. The minimum distance at which spatial correlation was not important (< 5%) ranged from 13.4 to 50.42 km. The analyses of the yearly aggregated data did not show a statistically important relation between confirmed malaria risk and malaria-specific mortality (see Additional file 4 ).

Relationship between parasite prevalence and all-cause/malaria specific mortality

The relation between PP and all-cause mortality was not statistically important across all ages (Table  2 ). However, there was a statistically important association between PP and malaria-specific mortality (Table  3 ) among children aged 1–4 years (RR = 2.29; 95% BCI: 1.17–4.29), and in the combined age group (RR = 1.66; 95% BCI: 1.07–2.54) when data was aggregated over 5 to 4 year. Analyses of yearly data did not reveal statistically important associations except between PP and all-cause mortality among the adults (RR = 1.23; 95% BCI: 1.01–1.50) (see Additional file 3 ) and with malaria-specific mortality among the elderly (RR = 3.42; 95% CI: 1.39–8.63) (see Additional file 4 ).

Using data from community level cross-sectional surveys, it was shown that; parasite prevalence is associated with malaria-mortality in the overall population, while clinical malaria is associated with both all-cause and malaria-specific mortality more so in the age groups 1–4 years and 5–14 years. This relationship was established by fitting over 50 different Bayesian geo-statistical models across different age groups on large data from verbal autopsies, longitudinal household surveys, and cross-sectional malaria parasitaemia surveys carried out annually over 9 years in the HDSS located in a malaria endemic region of western Kenya. These data aggregated over four to 5-year periods showed statistically important relations between clinical malaria and mortality (all-cause and malaria-specific) in the overall population, in children 1–4, and older children aged 5–14 years old, while PP had a statistically important association with malaria-specific mortality in 1–4 year olds and in the overall population. Meanwhile, analyses of the same data, annually aggregated did not establish any association between prevalence of clinical malaria nor PP with either all-cause or malaria-specific mortality across most age groups except for all-cause mortality in adults aged 15–59 years and malaria-specific mortality in the elderly.

Studies in malaria-endemic areas have also shown that children above the age of 5 years are least affected by the malaria burden in terms of confirmed symptomatic malaria and mortality compared to other age groups, even though they remain the biggest reservoir of the malaria parasites [ 5 , 27 ]. However, the long-term effects of declining transmission on mortality in this age group have not been well explored. This study showed a sevenfold increase in malaria-specific mortality for every 1% increase in clinical malaria prevalence, which was more than twice the effect in children 1–4 year old. This finding could be attributed to low utilization of ITNs by older children compared to other age groups in this study, as well as from previous studies [ 6 ] as well as poor health care-seeking behaviour among the same age group [ 28 ], resulting in higher mortality rates when data is captured at household level compared to sentinel health facilities. This reinforces the importance of universal coverage of malaria control interventions particularly in high transmission areas.

The absence of an association between PP and all-cause mortality could be due to several factors. First, parasite prevalence from the community might capture more asymptomatic carriers who have acquired immunity from malaria disease, eventually recover without adverse outcomes and hence survive. Second, malaria mortality is usually preceded by severe illness and, therefore, the PP data may be biased, as most of those who were severely ill may have gone to the hospital or succumbed to the disease prior to the time of the survey. Furthermore, an increase or decrease in mortality could be also due to other unmeasured factors that are unrelated to parasite prevalence; an example was shown by the influence of political instability on mortality in the year 2008 in Kisumu [ 29 ] that resulted in massive disruption of health delivery.

The lack of association between PP or clinical malaria and mortality in the 15–49 age groups may be an indicator of misclassification of malaria as a cause of death by verbal autopsy. This weakness of verbal autopsy in identifying malaria as a cause of death among adults [ 30 ] could result in fewer deaths being classified as malaria than there really are in the population. Evidence suggests that people with HIV have more frequent episodes of symptomatic malaria [ 31 ] and that malaria increases HIV plasma viral load and decreases CD4+ T cells [ 32 ]. Therefore, an alternative explanation could be that malaria specific mortality among adults may be classified by verbal autopsy as HIV/AIDS-related rather than malaria related.

The estimated effects of PP and clinical malaria were higher for malaria-specific mortality compared to all-cause. Furthermore, clinical malaria was a better predictor of mortality than PP. In fact, some of the asymptomatic infections may neither lead to severe disease nor death and therefore prevalence of clinical malaria is a better indicator for monitoring the disease burden at the population level. The stronger effect of clinical malaria and PP on malaria-specific mortality compared to all-cause mortality indicates that an increase in malaria transmission measures results in more malaria deaths which in turn inflate overall mortality. The stronger effect of prevalence on malaria specific mortality is because there is a clear biological cause and effect [ 33 ] and malaria infection can and does lead to mortality, however, the relationship between prevalence and all-cause mortality is diluted by other causes of mortality.

From these findings, it is worth noting that prevalence as a measure of transmission shows more stability in determining mortality over longer periods of time (4–5 years) compared to annual measures. Comparing estimates of the relation between mortality and malaria transmission measured by prevalence (of parasitaemia and confirmed malaria) in the current study, incidence measured as slide positivity rate (SPR) [ 10 ] and the log of EIR from capture better the relationship between malaria transmission and mortality (Table  4 ). Confirmed malaria prevalence averaged over 4–5 years is likely to be more stable in areas of high transmission and therefore a useful measure of transmission over a long period while incidence and EIR capture the malaria-mortality relationship better over shorter periods [ 10 , 34 ]. These differences could be due to the fact that PP one-off estimates can be misleading indicators of long-term transmission potential, since they vary markedly with season [ 35 ]. These short-term fluctuation would then make it harder to associate yearly PP measures with mortality occurring all year round; suggesting that population based prevalence surveys do represent long term transmissions as opposed to short term changes.

Higher socioeconomic status, shorter distance to health facilities and increasing altitude are known protective factors that were statistically important for both, all-cause and malaria-specific mortality. Individuals at a higher social status are more likely to live in well-constructed houses that offer better protection against endophagic/endophilic malaria vectors that transmit malaria in sub-Saharan Africa, afford better nutrition and pay for superior treatment [ 36 ]. Increasing elevation is associated with lower temperatures which increase the development time of both vector and parasite [ 37 ], resulting in lower transmission. Similarly, it has been shown that distance to health facilities influences mortality [ 38 ].

Lack of association between net use and mortality across ages except for yearly data among the elderly could be due to data aggregation at village level which diminished the expected individual level protection associated with net use reported in earlier studies during the 90′s and early 2000′s in the same region [ 34 , 39 ]. This change from earlier years could have been due to a number of factors among them ITN’s having achieved maximum benefits, compromised effectiveness due to misuse/pyrethroid resistance or other unmeasured factors which countered their protective effect (Hamel et al. pers.commun.). The diminished effect of net use might also be due to use of self-reported net use information which could lead to bias as it does not measure constant use. The negative effect of net use on malaria-specific mortality among the elderly, a group that has not been well researched in malaria cannot be explained adequately, and requires further investigation. However, it could be hypothesized that since mortality is generally high in this age-group, at the same time society considers them vulnerable, issuance and use of ITNs could be higher and hence their protective effect is masked.

There are inherent limitations in survey data and in estimating malaria mortality using verbal autopsy that could influence the study results. First, the surveys were conducted in specific months (i.e. in April, just before the rains (or just as they were starting) or June/July after the rains were ending.); therefore, the prevalence estimates of may be biased by unexpected changes in climatic and environmental factors in other. Use of verbal autopsy as a tool for determining cause of death has been criticized [ 30 ], even though recent improvements in the InterVA coding have been said to reduce classification errors, especially at population level [ 40 ]. Despite these limitations, the 9-year data in the study have been collected consistently in the same area using rigorous data collection methods and strict quality control measures. These data are thus unique in studying the relation between malaria prevalence and mortality across all groups in this population within a high endemic area.

From yearly cross-sectional malaria prevalence surveys, the study showed that; (i) Clinical malaria at population level best captures the association with mortality among children aged 1–4 years and 5–14 year olds. It can also be used as a marker of malaria mortality in the general population. (ii) Prevalence as a measure of transmission is more stable over longer periods of time (4 to 5 years) compared to incidence or EIR which better capture the malaria-mortality relationship on a yearly basis. However, its lower size effect compared to clinical malaria may underestimate malaria deaths. Analyses of data from other HDSS sites or similar platforms with differing levels of malaria endemicity different socio-economic status, or different access to effective anti-malarial drugs would be useful in understanding the contribution of parasite prevalence to mortality across age groups.

Data availability

Data were obtained with permission of the Kisumu HDSS and Malaria branch steering committee. Any data requests may be sent to the respective steering committees, through Dr. Simon Kariuki ([email protected]) or Dr. Stephen Munga ([email protected]).

Abbreviations

artemisinin-based combination therapy

acquired immune deficiency syndrome

Bayesian Credible Interval

Centers for Disease Control and Prevention

Credible Intervals

Health and Demographic Surveillance Systems

entomological inoculation rate

human immunodeficiency virus

Kenya Medical Research Institute

long lasting insecticide-treated nets

multiple correspondence analysis

Markov Chain Monte Carlo

Malaria Transmission Intensity and Mortality Burden across Africa

parasite prevalence

person years of observation

rate ratios

socio-economic status

slide positivity rate

Sub-Saharan Africa

verbal autopsy

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Acknowledgements

We thank the HDSS community in Siaya County for the many years of support and participation in surveillance activities; the HDSS and Malaria branch staff of the KEMRI-CGHR for assisting in managing the platforms, data collection and management.

Penelope Vounatsou (PV) acknowledges financial support from the H2020 European Research Council (ERC) for advanced grant project no. 323180. The authors also acknowledge support from the Swiss National Science Foundation (SNSF) Swiss Programme for Research on Global Issues for Development (R4D) project no. IZ01Z0-147286. The funders did not have any role in the study or its outcome.

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SK, MD and PV conceived the study; SK analysed the data and drafted the initial manuscript; PV supported data analysis; SK, MD, NA, VW, EO, CO, KO, LS, JG, FO, KL, AMS, SK, GB, SM, and MH designed the initial surveillance system, participated in data collection, processing and management. All authors gave inputs in revision of the first drafts, read and approved the final manuscript. The findings and conclusions in this paper are those of the authors and do not necessarily represent the official position of the U.S. Centers for Disease Control and Prevention. All authors read and approved the final mansuscript.

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The study protocols for the KHDSS and parasite prevalence surveys were approved by the KEMRI Scientific and Ethics Review Unit (SERU) and the CDC Institutional Review Board (IRB). Informed written consent was obtained from the heads of compounds in conducting the household interviews while malaria prevalence data was collected from individuals who gave consent or from their parents/guardians if the participants were children.

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Study designs for the malaria survey data during 2007-2015.

Additional file 2.

Bayesian model formulation.

Additional file 3.

Posterior estimates of the effects of prevalence on all-cause mortality aggregated annually.

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Posterior estimates of the effects of prevalence on malaria-specific mortality aggregated annually.

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Khagayi, S., Desai, M., Amek, N. et al. Modelling the relationship between malaria prevalence as a measure of transmission and mortality across age groups. Malar J 18 , 247 (2019). https://doi.org/10.1186/s12936-019-2869-9

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Malaria Journal

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Malaria diagnosis based on a machine learning system

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• Maduako, Chidinma
• Petra Rohrbach (Supervisor2)
• Timothy Geary (Supervisor1)
• SUMMARYIntroduction: The latest World Malaria Report released in November 2017 estimated that 219 million cases of malaria occurred and deaths due to malaria reached 435,000 in 2017(1). The WHO considers microscopy to be the gold standard for clinical diagnosis of malaria due to its ready availability. However, microscopy has many shortcomings, including inter-user variability and inconsistency, due to the fact that many microscopy technicians do not assess the standard number of high-power fields, are not adequately trained on recognizing all forms of malaria and the high disparity associated with the quality of manual Giemsa slide production (4) To remedy the mis-use of empiric (symptom-guided) treatment, malaria testing is required by many governmental health organizations before commencing antimalarial drug therapy, thereby resulting in increased demand for up to 500 million malaria tests in 2012 (10). Understanding the diagnostic expertise necessary and representing it by specifically tailored image processing, analysis and pattern recognition algorithms can help in designing an automated diagnosis system. Although it is not yet a widespread research topic, automated diagnosis of malaria directly addresses several current gaps (11). My research aims to develop a machine learning system that can identify the stage and number of Plasmodium falciparum in cultured erythrocytes based on their morphology using thin film slides, the second objective is to develop a machine learning system that can identify the number of ring-stage parasites in samples of cultured erythrocytes diluted with fresh whole blood.Methods: Giemsa stained thin blood smears were made from synchronized cultures of the 3D7 strain of plasmodium falciparum stored in an incubator with shaking at 37oC, 5% CO2, 3 % O2, 92 % N2. Thin blood smears were viewed with an EVOS microscope and digital images were acquired, saved as tiff format and stored in a memory stick. The images were transferred as files to a computer, then the images further pre-processed, segmented, and the parasites and the stages of the life cycle detected. The algorithm formulated with the MATLAB programme was trained using 109 images. For the first objective, 397 images were used and for the second objective 163 images were used.The Otsu algorithm was used for this study, gray level images were reduced to binary images. The algorithm assumes that the images contain foreground and background pixels. Results: This study showed a relatively strong, positive linear association/ correlation between automated count and manual counts. The correlation between the manual count and the automated count was 0.85. The Pearson correlation between the automated and manual count was 0.7. The diagnostic tool showed a sensitivity of 94.6% for rings, 96.5% for trophozoites and 98.2% for schizont. Moreso, it showed a specificity of 96.5% for rings, 88.9% for trophozoites and 81.8% for schizonts. The R and G channels of the RGB color scheme had clear features which were used to identify objects containing chromatin in Giemsa-stained blood films. The input images transformed to grayscale highlighted parasites containing chromatin.Conclusion: This study developed an automated system that could enhance the diagnosis and therefore treatment of malaria. The automated method detected more trophozoites and schizonts than the ring stage parasites as seen with a correlation value of 0.83, 0.86 and 0.94 for the ring, trophozoite and ring stages respectively
• SOMMAIREIntroduction : Le dernier Rapport sur la Malaria dans le Monde, publié en novembre 2017, estimait à 219 millions le nombre de cas de Malaria et à 435 000 le nombre de décès dus à celle-ci pour l'année 2017 (1). L'OMS considère que la microscopie est la référence en matière de diagnostic clinique de la Malaria en raison de sa disponibilité immédiate. Cependant, la microscopie présente de nombreuses lacunes, notamment la variabilité et l'incohérence entre les utilisateurs, dû au fait que de nombreux techniciens en microscopie n'analysent pas le nombre standard de champs à forte puissance (HPF), ne sont pas formés de manière adéquate afin de pouvoir reconnaitre toutes les variétés de Malaria et à la grande disparité associée avec la qualité de la production manuelle de lames Giemsa (4) Afin de remédier à la mauvaise utilisation d'un traitement empirique (guidé par les symptômes), de nombreux organismes de santé gouvernementaux exigent un test de dépistage de la malaria avant de commencer un traitement antipaludique, ce qui a entraîné une demande accrue de 500 millions de tests de dépistage de la malaria en 2012 (10). Comprendre le savoir-faire nécessaire en matière de diagnostic et le représenter au moyen de traitement d'image, d'analyse et de reconnaissance d'algorithmes modèles spécialement adaptés peut aider afin de concevoir un système de diagnostic automatisé. Bien que le sujet de la recherche ne soit pas encore très répandu, le diagnostic automatisé de la malaria traite directement plusieurs lacunes actuelles (11). Ma recherche vise à développer un système d'apprentissage automatique capable d'identifier le stade et le nombre de Plasmodium falciparum dans des érythrocytes en culture, sur la base de leur morphologie en utilisant des lames minces. Le second objectif est de développer un système d'apprentissage automatique permettant d'identifier le nombre de stades anneau parasites dans des érythrocytes en culture dilués avec du sang total frais.Méthode : Des frottis sanguins minces colorés au Giemsa ont été réalisés à partir de cultures synchronisées de la souche 3D7 de plasmodium falciparum conservée dans un incubateur sous agitation à 37 °C, 5% de CO2, 3 % de O2, 92 % de N2. Des frottis sanguins minces ont été observés à l'aide d'un microscope EVOS et des images numériques ont été acquises, enregistrées au format tiff et stockées sur une clé USB. Les images ont été transférées en tant que fichiers sur un ordinateur, prétraitées et segmentées puis les parasites et les diverses étapes du cycle de vie y ont été détectés. L'algorithme formulé à l'aide du programme MATLAB a été formé en utilisant 109 images. 397 images ont été utilisées pour le premier objectif et 163 pour le deuxième objectif. L'algorithme Otsu a été utilisé pour cette étude, les images en niveaux de gris ont été réduites à des images binaires. Résultats : Cette étude a montré une association/corrélation linéaire positive relativement forte entre le comptage automatisé et le comptage manuel. La corrélation entre le nombre manuel et le nombre automatisé était de 0,85. La corrélation de Pearson entre le comptage automatisé et manuel était de 0,7. L'outil de diagnostic a montré une sensibilité de 94,6% pour les anneaux, de 96,5% pour les trophozoïtes et de 98,2% pour les schizontes. De plus, une spécificité de 96,5% pour les anneaux, de 88,9% pour les trophozoïtes et 81,8% pour les schizontes a été démontrée. Les images d'entrée transformées en niveaux de gris ont mis en évidence des parasites contenant de la chromatine.Conclusion : Cette étude a développé un système automatisé pouvant améliorer le diagnostic et donc le traitement de la malaria. La méthode automatisée a détecté plus de trophozoïtes et de schizontes que de stades anneau parasites, avec une valeur de corrélation de 0,83, 0,86 et 0,94 respectivement pour l'anneau, le trophozoïte et les stades anneau
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• Institute of Parasitology
• Master of Science
• Theses & Dissertations

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P. vivax Malaria and Dengue Fever Co-infection: A Cross-Sectional Study in the Brazilian Amazon

Belisa m. l. magalhães.

1 Universidade do Estado do Amazonas, Manaus, Brazil

2 Fundação de Medicina Tropical Dr. Heitor Vieira Dourado, Manaus, Brazil

André M. Siqueira

Márcia a. a. alexandre, marcela s. souza, joão b. gimaque, michele s. bastos, regina m. p. figueiredo, gisely c. melo, marcus v. g. lacerda, maria p. g. mourão.

Conceived and designed the experiments: BMLM AMS MAAA MVGL MPGM. Performed the experiments: BMLM AMS MAAA MSS. Analyzed the data: BMLM AMS. Contributed reagents/materials/analysis tools: MSS JBG MSB RMPF GCM. Wrote the paper: BMLM AMS MVGL MPGM.

Associated Data

The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its Supporting Information files.

Malaria and dengue are the most prevalent vector-borne diseases worldwide and represent major public health problems. Both are endemic in tropical regions, propitiating co-infection. Only few co-infection cases have been reported around the world, with insufficient data so far to enhance the understanding of the effects of co-infection in the clinical presentation and severity.

Methodology/Principal Findings

A cross-sectional study was conducted (2009 to 2011) in hospitalized patients with acute febrile syndrome in the Brazilian Amazon. All patients were submitted to thick blood smear and PCR for Plasmodium sp. detection, ELISA, PCR and NS1 tests for dengue, viral hepatitis, HIV and leptospirosis. In total, 1,578 patients were recruited. Among them, 176 (11.1%) presented P. vivax malaria mono-infection, 584 (37%) dengue fever mono-infection, and 44 (2.8%) were co-infected. Co-infected patients had a higher chance of presenting severe disease (vs. dengue mono-infected), deep bleeding (vs. P. vivax mono-infected), hepatomegaly, and jaundice (vs. dengue mono-infected).

Conclusions/Significance

In endemic areas for dengue and malaria, jaundice (in dengue patients) and spontaneous bleeding (in malaria patients) should raise the suspicion of co-infection. Besides, whenever co-infection is confirmed, we recommend careful monitoring for bleeding and hepatic complications, which may result in a higher chance of severity, despite of the fact that no increased fatality rate was seen in this group.

Author Summary

Malaria and dengue fever are typical diseases in tropical regions of developing countries; such as the Brazilian Amazon. They become serious problems in public health as they mostly affect vulnerable populations. Both diseases are mosquito-borne. These diseases present similar signs and symptoms. Brazil registers most of the malaria cases in the Amazon. The four dengue serotypes also circulate in this region. Similar to malaria, there are records of dengue outbreaks during the first months of the year, and isolated cases in the remaining months. Official records of malaria and dengue co-infection are infrequent in Brazil; however, we believe that this event is more frequent than usually reported. Our study detected high prevalence of the co-infection in the hospitalized patients infected with malaria or dengue in a tertiary health care unit, reference in the treatment of tropical and infectious diseases in Manaus, Amazonas, Brazil. We highlight the high likelihood of co-infected patients to present clinical complications. Besides, we observed that the presence of jaundice in dengue patients, and bleeding in malaria patients, are possible indications of co-infection. Therefore, this paper is useful to physicians working in the tropics, enabling the clinical suspicion of a not so rare condition.

Introduction

Malaria and dengue fever are the most prevalent vector-borne diseases worldwide and represent major public health problems. Dengue epidemics have been reported in several countries; 500,000 people with severe dengue require hospitalization each year, and 2.5% of those affected die. Similarly, malaria is a life-threatening disease which was responsible for 627,000 deaths in 2012 [1] , [2] . However, the occurrence of dengue and malaria co-infected patients is not well reported.

The dengue virus (DENV) is the major arbovirus responsible for human disease in Brazil. The four serotypes cause a variety of clinical presentation in humans, ranging from acute self-limited febrile illness to severe and fatal forms [3] , [4] . Regarding malaria, the Brazilian Amazon reports 50% of episodes in the Americas [5] . In 2012, 241,806 cases were reported, with 86.9% of them due to P. vivax [6] .

Malaria and dengue are endemic in similar tropical regions, and therefore, may result in the possibility of co-infection. Urban demographic expansion, deforestation and agricultural settlements in peri-urban areas, are known causes of the increase in the probability of concurrent infection of these two diseases [7] .

Considering the endemicity of dengue and malaria in the Amazon [8] , it is reasonable to envisage that the occurrence of concurrent infections would not be rare [9] , [10] . However, due to non-systematic investigation of both diseases, only a few cases of malaria and dengue co-infection have been reported [11] , [12] . In Brazil, for instance, a study performed in 2009 with 132 patients with vivax malaria found 11 co-infection episodes, all confirmed by molecular tests. These patients demonstrated severe manifestations, in particular hepatic injury [10] . The objective of the present study was to understand the interplay of both infections in a higher sample, and the impact on the clinical severity.

Ethical Statement

The study was approved by the Ethics Review Board of Fundação de Medicina Tropical Dr. Heitor Vieira Dourado (FMT-HVD, 2009/15243), Manaus, Brazil. All participants signed an informed consent.

Study Design and Site

The study design was a cross-sectional study of patients hospitalized with acute febrile syndrome (history of fever in the past 10 days) from 2009 to 2011. The study was carried out in FMT-HVD, Manaus, capital of Amazonas State, Northern Brazil, where all four dengue serotypes co-circulate since 2008 and 95% of malaria cases result from P. vivax infection. FMT-HVD is a tertiary health care facility and a teaching and research center, which is the reference for infectious diseases in the region. Around 30% of all malaria cases reported in Manaus are assisted in this institution. During the study period, 14,884 cases of malaria and 6,302 cases of dengue fever were diagnosed in the hospital, from which 505 and 1,127, respectively, were hospitalized. In 2011, a dengue outbreak resulted in 5,400 cases reported at the FMT-HVD (∼10% of all reported cases in the state, based on data of surveillance system).

Patients and Data Collection

During the study period, all hospitalized patients with acute febrile syndrome were considered eligible. If they signed the informed consent, they were included and submitted to malaria and dengue investigation. They were also searched for hepatitis A, B and C, HIV and leptospirosis. Abdominal ultrasound and chest X-rays were also performed when indicated. Other tests were requested at physicians' discretion.

Patients with P. vivax infection with primaquine-induced hemolysis (hemoglobin <10 g/dL and reticulocytes >1.5% or increased indirect bilirubin after starting primaquine) were also excluded from the analysis.

The diagnosis of vivax malaria was confirmed by real-time PCR. The diagnosis of dengue was made either by a positive serology (IgM) or a positive NS1 protein or a positive molecular test (RT-PCR), considering that every patient was tested by all the three methods. The Group C was defined as patients co-infected with both dengue and P. vivax . They were compared to two different groups: malaria mono-infection (Group A), and dengue mono-infection (Group B). Severity was classified and managed according to the World Health Organization (WHO) guidelines for dengue and malaria [2] , [13] .

Laboratory Testing

Automatized blood biochemistry and whole blood count were performed systematically in all patients. The continuous variables used for analysis were the most altered throughout hospitalization. Walker's technique was used for thick blood smear [14] . The number of asexual parasites was counted in high magnification fields per 500 leukocytes and expressed as parasites per mm 3 . Real time polymerase chain reaction (qPCR) was performed to confirm P. vivax mono-infection. In brief, the extraction of total DNA from whole blood was performed using the QIAamp DNA Blood Mini Kit (Qiagen, USA), according to the manufacturer's protocol. The DNA was amplified in an Applied Biosystems 7500 Fast System (Applied Biosystems, USA) using primers and TaqMan fluorescence labeled probes for RT-PCR [15] .

The DENV diagnosis was based on three methods: a) IgM antibodies (MAC-ELISA) detection [16] ; b) detection of NS1 protein by Platelia Dengue NS1 Ag kit (Bio-Rad, France), and c) molecular diagnostics with the identification of viral serotype from the RT-PCR [17] . For extraction of viral RNA, mini kit QIAamp viral RNA (Qiagen, USA) was used, following the manufacturer's instructions. For the production of complementary DNA copy (cDNA) from RNA, AccessQuick kit RT-PCR System (Promega, USA) was used, according to the manufacturer's recommendations. The genomic region of dengue virus (DENV) was amplified by semi-nested PCR included genes C/prM.

Serological tests for leptospirosis (IgM) [18] , HIV-1/HIV-2 [19] , hepatitis A (anti-HAV IgM), hepatitis B (HBsAg), hepatitis C (anti-HCV), and hepatitis D (total anti-HDV), were based on commercial kits from Diasorin (Italy) and Bioeasy (Korea), following the manufacturers' instructions.

Statistical Analysis

Demographics, clinical and laboratorial characteristics from the group of patients co-infected with dengue and malaria vivax were compared to the group of patients mono-infected with dengue and the group of patients mono-infected with malaria vivax. The association between categorical variables and the risk of co-infection (as the outcome variable) was performed by means of univariable logistic regression with the presentation of the odds ratios and 95% confidence intervals. The 95% confidence intervals (95% CI) are presented. Means and standard deviation (SD) of continuous variables with normal distributions were compared using the Student's T test; those variables with non-normal distribution (as assessed by the Kolmogorov-Smirnov test) were described using median and interquartile range (IQR) and compared using the Kruskal-Wallis test. All the analyses were performed using Stata v.11 (College Station, Texas, USA) [20] .

From 2009 to 2011, 1,578 patients with acute febrile syndrome were hospitalized at the FMT-HVD. Among them, 176 (11.1%, 95% CI 9.6–12.7%) had vivax malaria mono-infection (Group A), 584 (37%, 95% CI 34.6–39.4%) had dengue fever mono-infection (Group B) and 44 (2.8%, 95% CI 2.0–3.6%) were co-infected with malaria and dengue (Group C). The prevalence of co-infected patients was 20% among patients with malaria and 7% among those with dengue ( Figure 1 ).

Gray boxes represent patients included in the analyses; Dashed boxes represent patients excluded.

As shown in Table 1 , the characteristics across the groups are homogenous. It is important to highlight though that fewer children and pregnant women were included in the co-infected group.

Tables 2 and ​ and3 3 compare clinical and laboratorial data between Group C and Groups A and B. Patients with the co-infection had a higher chance of presenting severe disease (OR 4.71, 95% CI: 2.37–9.34) according to WHO's criteria than those mono-infected with dengue ( Table 2 ). Conversely, those with malaria mono-infection had less frequently severe disease than co-infected patients, but this was not statistically significant.

Compared to P. vivax mono-infected patients, the increased odds of deep bleeding in co-infected patients (OR 12.5, 95% CI: 4.7–33.3) was statistically significant (p<0.001), although platelet count was not different ( Tables 2 and ​ and3). 3 ). When compared with dengue mono-infected patients, co-infected patients had a higher chance of presenting deep bleeding (OR 3.5, 95% CI: 1.8–6.8). Conversely, superficial bleeding was more frequent among dengue mono-infected patients. The overall bleeding, however, was more frequent on co-infected patients, despite significant reduction in platelet counts ( Tables 2 and ​ and3 3 ).

Regarding hepatic injury, co-infected patients had a higher chance of having hepatomegaly and clinical jaundice compared to those with malaria mono-infection, although this was not statistically significant ( Table 2 ), despite significant increase in bilirubin levels ( Table 3 ). When compared to dengue mono-infected patients, co-infected patients had a higher chance of presenting hepatomegaly (OR 35.28, 95% CI: 13.4–92.6) and jaundice (OR 138.5, 95% CI: 37.8–507.7), which was paralleled by significantly increased in bilirubin and AST levels ( Tables 2 and ​ and3 3 ).

Co-infected patients also had prolonged fever when compared to dengue mono-infected patients. Finally, other dengue warning signs [2] , such as abdominal pain and vomiting, as well as dyspnea, were significantly more frequent among co-infected patients. Noteworthy, all four co-infected pregnant women had severe disease.

The predominant dengue serotypes in the co-infected group were DENV 2 and DENV 4, both with nine patients (33.3%). These serotypes were the most common among the dengue mono-infection group, 127 (49.6%) and 80 (31.2%), respectively.

No patient required hospitalization in the intensive care unit, and fatality rate was zero in our casuistic.

In an endemic area of dengue fever and vivax malaria, we found a high prevalence of the co-infection, mainly among those with malaria. In Brazil, a prospective study performed in 2009 on 132 patients with vivax malaria found 11 co-infected and the prevalence was 8.3% [10] . During a dengue outbreak in India, the prevalence of co-infection was 5.8% among all cases of fever (77 of 546) [12] . In the French Guiana, the prevalence of co-infection was 7.1% (17 of 238) among patients with dengue [11] , which is similar to our results. In Pakistan, however, the prevalence found was as high as 23.2% [21] . Thus, the prevalence of co-infection may fluctuate, depending on local endemicity. In these studies, the prevalence was estimated on hospitalized patients, therefore it could not be extrapolated to the community-based level.

In our study, being co-infected resulted in a much higher chance of presenting deep bleeding as compared to both groups of mono-infected patients, suggesting a possible synergistic pathogenic mechanism, which could be related to both capillary fragility and coagulation disorders, but not the low platelet count. Bleeding is reported as an infrequent finding in malaria, despite common platelet depletion [22] , [23] . Conversely, bleeding is the most feared complication of dengue fever, where in addition to platelet depletion, virus-induced endothelial and liver injury concur to the risk of coagulopathy [24] , [25] , [26] . In our casuistic, although bleeding was more frequent among co-infected patients, it was also frequent among mono-infected patients in both groups.

Hepatic injury was also a concern in the co-infected group, which, together with bleeding, resulted in a higher chance of dengue severity according to WHO criteria. Jaundice in malaria is mostly a result of cholestasis or intravascular hemolysis [27] , while in dengue fever it has been associated with fulminant liver failure [28] , [29] . Interestingly, like bleeding, jaundice is no longer considered to be a malaria severity criteria according to WHO [13] . A prospective study performed during a dengue outbreak in India, reported more frequent bleeding on co-infected patients, as well as thrombocytopenia and hepatic injury [12] . On the other hand, in the French Guiana, although co-infected patients presented more hematologic complications and hepatic injury, bleeding was uncommon [11] .

A warning sign commonly used to describe severe dengue is hemoconcentration (increase in the basal hematocrit ≥20%) [30] . However, even with more severe dengue cases, our co-infected patients presented a low mean of hematocrit. An explanation for this fact can be attributed to malaria-induced anemia, a common complication in vivax malaria [31] . For this reason, the malaria clinical manifestation may be a confounder for health care professionals during the interpretation and application of dengue severity criteria, in areas where both diseases occur. The proper clinical management of co-infected patients may be compromised due to diagnostic delays or misinterpretation, and inappropriate treatment may result in fatal complications [32] , [33] .

Dengue warning signs, such as vomiting, abdominal pain and hepatomegaly, were very frequent in the co-infection cases. The cautious detection of these signs is of extreme importance as they characterize potential dengue severity [2] . Our findings were similar to the results reported by the study performed in the French Guiana [11] , although they did not use the dengue severity criteria from WHO [2] ; in both cases, the co-infected patients presented a higher frequency of warning signs and the sample had more severe cases.

In addition to classical warning signs and symptoms, dyspnea was also frequent in all groups, particularly in co-infected patients. Dyspnea is an early clinical feature of plasma leakage and, in dengue, may be the evidence of fluid accumulation of in the pleural cavity [2] , [34] . In malaria, dyspnea may be an evidence of acute lung edema [35] , which is one of the severity criteria for falciparum malaria [13] . In a study conducted in Timor East, one patient co-infected with falciparum malaria and dengue presented respiratory distress with radiographic findings compatible with the presentation of acute lung edema [33] . The clinical management of these cases may be difficult, as the inadequate fluid therapy for dengue treatment may induce fluid overload and large fluid effusion to the lungs.

The pregnant women had a more complicated presentation, although we could not follow up them until the end of their pregnancy. In a case series of co-infected patients from the Amazon region, pregnant women (2 of 11) presented severe acute lung edema and anemia [10] . Dengue is known to cause obstetric complications and to increase the risk of dengue severity among pregnant women [36] . In malaria, on the other hand, this association is not clear, because reported studies on the impact of P. vivax on pregnancy are scarce [37] .

Co-infected patients presented similar days of fever as compared to malaria patients. That means that a patient with the diagnosis of dengue presenting with prolonged evolution should raise the suspicion of malaria co-infection. Our findings corroborate the results of a long case series in Pakistan, which presented longer disease duration on patients co-infected with vivax malaria and dengue [21] .

No specific dengue serotype was associated to the co-infected patients, however the number of cases was not big enough to test that hypothesis.

Our study has some limitations. It was not possible to confirm dengue infection by PCR in all patients due to the time of the disease presentation and possible non-viremic periods. On the other hand, a positive IgM in patients with malaria could also reflect recent dengue infection or recent yellow fever vaccination. In addition, our results are not extendable to other healthcare settings or to community basis, since we only included hospitalized patients.

On the other hand, this study has also some strengths. This is one of few studies addressing malaria and dengue co-infection, with a considerable amount of cases diagnosed by molecular tests. Besides, this work has been conducted by the same health care team, who applied consistent selection and severity criteria throughout the duration of the study. Furthermore, the majority of the existing works are case series reports and retrospective studies, which may produce low evidence level.

Malaria and dengue co-infection is a relatively common event. Being malaria the disease with easier and faster diagnosis, in areas with known endemicity, it is recommended the systematic testing for Plasmodium sp. on cases with acute febrile syndrome. At last, the patients with parasitological malaria diagnosis which present spontaneous bleeding must be systematically investigated for dengue, and likewise, in suspected and confirmed dengue patients presenting jaundice, Plasmodium sp. investigation must be performed. Besides, whenever co-infection is confirmed, we recommend a carefully monitoring for bleeding and hepatic complications, which may result in a higher chance of severity, regardless of WHO criteria.

Supporting Information

Checklist s1.

STROBE checklist.

Acknowledgments

As part of her PhD thesis, BMLM dedicates this manuscript to her son, Eduardo Magalhães Valentin. The authors would like to thank the staff of Fundação de Medicina Tropical Doutor Heitor Vieira Dourado ; the Universidade do Estado do Amazonas ; Mônica Costa, for contributing with malaria diagnosis; Márcia Castilho, for contributing with dengue diagnosis; Marcelo Cordeiro, Anette Trajman and Eduardo Valentin for reviewing the text. We are also thankful to Carlos Morel, coordinator of the National Institute of Science and Technology on Neglected Diseases Innovation, and to Cláudio Tadeu Daniel-Ribeiro, coordinator of the Laveran & Deane Seminar on Malaria.

Funding Statement

The authors received no specific funding for this work.

Data Availability

Mathematical modelling of malaria transmission and pathogenesis

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The ecology and behaviour of insecticide resistant malaria vectors and implications for control in Burkina Faso

Sanou, Antoine (2020) The ecology and behaviour of insecticide resistant malaria vectors and implications for control in Burkina Faso. PhD thesis, University of Glasgow.

Long-Lasting Insecticide-Treated Nets (LLINs) and Indoor Residual Spraying (IRS) are the most common and successful methods for malaria vector control in Africa. There is growing evidence of shifts in mosquito vector biting and resting behaviours in several African settings where high LLIN coverage has been achieved. These changes, combined with growing insecticide resistance, may reduce intervention success by decreasing the contact between vectors and insecticide-treated surfaces. While insecticide resistance in malaria vectors has been widely investigated, less is known about the implications of mosquito behavioural changes to malaria control. In recent years, LLIN programmes appear to have a reducing impact in a small number of high burden African countries including Burkina Faso. This reducing effectiveness is hypothesized to be the result of insecticide resistance, but the potential additional contribution of mosquito behavioural avoidance strategies has not yet been investigated in Burkina Faso. The aim of this PhD was to investigate the contribution of insecticide resistance and mosquito behaviours to the persistence of malaria transmission in southwestern Burkina Faso following a national LLIN-distribution campaign. Specific objectives were to (i) evaluate the performance of a new mosquito sampling method, the Mosquito Electrocuting Trap (MET) to measure spatial and temporal variation in human exposure to malaria vectors; and characterize the spatial, seasonal and longer-term trends in (ii) vector ecology and behaviours, (iii) insecticide resistance within Anopheles gambiae sensu lato (s.l.) and (iv) malaria vector survival and transmission potential in rural Burkina Faso. A two-year programme of longitudinal mosquito vector surveillance was initiated within 12 villages of south-western Burkina Faso in 2016, shortly after completion of a mass LLIN distribution. Host seeking malaria vectors were sampled monthly using Human Landing Catches (HLC) and METs conducted inside houses and in the surrounding outdoor area (911 households in total). Resting bucket traps (RBTs) were used to sample indoor and outdoor resting vectors. In an initial study (Chapter 2), I evaluated the performance of the MET relative to the HLC for sampling host-seeking malaria vectors over 15 months in 12 villages. Overall, the MET caught proportionately fewer An. gambiae s.l. than the HLC (mean estimated number of 0.78 versus 1.82 indoors, and 1.05 versus 2.04 outdoors). However provided a consistent representation of vector species composition, seasonal and spatial dynamics, biting behaviour (e.g. location and time) and malaria infection rates relative. The MET slightly underestimated the proportion of bites that could be prevented by LLINs relative to the HLC (5%). However, given the major advantage of the MET of reducing human infection risk during sampling, I conclude these limitations are acceptable and that the MET presents a promising and safer alternative for monitoring human exposure to malaria vectors in outdoor environments. Vector sampling was extended (using HLCs and RBTs) to investigate longer-term temporal changes in vector ecology and behaviour (Chapter 3). Analysis of a subset (20%) of the An. gambiae s.l. (N= 7852) indicated that An. coluzzii (53.82%) and An. gambiae (45.9%) were the main vector species. There was substantial variation in vector abundance between sites and seasons, with a predicted ~23% reduction in An. gambiae s.l. biting density from start to end of study. A higher proportion of outdoor biting (~54%) was detected than expected from previous studies; but there was no evidence of spatial, seasonal or longer-term changes in exophagy. Species level analyses indicated that revealed moderate but statistically significant different in the exophagy and biting time between An. coluzzii and An. gambiae. Combining information on biting times and location (indoors versus outdoors), I estimated that ~85% of exposure could be prevented using good quality and effective LLINs during standard sleeping hours (10 pm – 5 am). Bioassays were conducted on the An. gambiae s.l. population at 9 out of the original 12 study villages to estimate spatial, seasonal and longer-term variation in insecticide resistance (IR) over the study period. Overall, only 23% of An. gambiae s.l. exposed to a diagnostic dose of deltamethrin were killed within 24 hours; indicating that all surveyed populations are resistant. Furthermore, IR increased over the study period, with significant reduction in mortality after exposure to deltamethrin in bioassays. There was no evidence of variation in IR between An. gambiae and An. coluzzii. Finally, the transmission potential of An. gambiae s.l. in this area was investigated through assessment of mosquito parity rates (a proxy of survival), malaria infection rates and estimation of annual Entomological Inoculation Rates (EIR; Chapter 5). The daily survival rate of malaria vectors in this area was > 90%), but with variation between villages and seasons. After controlling for this spatial and seasonal variation, there was evidence of a longer-term increase in vector survival over the study period. In contrast, both mosquito vector biting densities and their malaria infection rates declined over the study period. This resulted in a drop in the predicted EIR from 320 to 105 infective bites per person/year respectively in year 1 and 2. Considering the proportion of exposure estimated to be preventable by effective LLIN use (~85%, Chapter 2 &3), I estimated that residents in this area are still exposed to ~32 infective bites per person per year even when this intervention is used. This confirms that even with 100% coverage and usage of highly effective LLINs, high levels of transmission will persist in this setting. Taking the case of Burkina Faso as an example, results obtained here confirm that both IR and outdoor biting by malaria vectors are contributing to the persistence of transmission in high burden African countries. Consequently, a successful vector control programme in this context need a clear insecticide resistance management plan and supplementary tools that target vectors feeding and resting outdoors.

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Current Topics and Emerging Issues in Malaria Elimination

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Published 21 July 2021

Doi 10.5772/intechopen.87323

ISBN 978-1-83968-484-5

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Copyright year 2021

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Malaria is one of the most important tropical diseases in the history of the world. This vector-borne disease has been a significant cause of morbidity and mortality in tropical countries of Africa, Asia, and Latin America. As such, this book provides updated information on epidemiological and public health research of malaria conducted in the last decade. Over four sections, chapters discuss such...

Malaria is one of the most important tropical diseases in the history of the world. This vector-borne disease has been a significant cause of morbidity and mortality in tropical countries of Africa, Asia, and Latin America. As such, this book provides updated information on epidemiological and public health research of malaria conducted in the last decade. Over four sections, chapters discuss such topics as diagnosis, epidemiology and surveillance, policy and prevention, and vector control and vaccines.

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Contact Us | Employment

Boosting Malaria Research in Southeast Asia

August 14, 2024 by Chloe Wang

Dr .  Daibin  Zhong Leads Grant Program to Study Dynamic Malaria Landscapes and Vector Biology

Source: UCI Public Health

July 12, 2024

In a boost to malaria research in Southeast Asia (SEA), a grant of more than $1.25 million has been awarded to  Daibin Zhong, PhD , associate project scientist of population health and disease prevention and a member of the   Yan Lab   at UC Irvine Wen School of Population and Public Health, to study the changing landscape of human and zoonotic malaria in this region.

The award is part of a five-year, $4 million grant given to Mahidol University in Thailand. Zhong will collaborate with Dr. Jetsumon Prachumsri, the primary researcher in Thailand and the Southeast Asia lead of the  International Centers of Excellence for Malaria Research (ICEMR) program , which is a global network of independent research centers organized by the National Institute of Allergy & Infectious Diseases.

Zhong leads Project Two of this research program that features two different projects that seek to understand the underlying relationship between malaria and epidemiology and vector biology in Southeast Asia.

As a result of modifications to the environment and human factors, the epidemiology of malaria in SEA is very dynamic and rapidly changing with several unique challenges affecting countries’ efforts to eradicate malaria. Additionally, the emergence and rapid spread of human  P. knowlesi , a malaria causing parasite, infections in Malaysia and Thailand pose serious public health concerns that warrant research to inform mitigation and elimination efforts.

The program’s primary goal is to comprehend the intricate interplay between malaria epidemiology and vector biology in SEA, with a particular emphasis on the dynamics of human and zoonotic malaria transmission, to inform strategies for malaria elimination.” – Zhong, PhD

Selected research sites across Thailand and Malaysia will encompass diverse landscapes and malaria transmission patterns that would aid in this research initiative. The research project is composed of two interdependent projects that will run concurrently.

Project One will focus on malaria surveillance in Thailand and Malaysia, while also performing population genetics analyses of plasmodium parasites. On the other hand, Project Two will take charge of vectorial systems and other ecological investigations in the field sites, conducting population genetics analyses of  Anopheles  mosquitoes to determine the effects of environmental changes on vector population structure, vector-parasite interactions, and vector competence for Plasmodium infection. This project will also examine the mechanisms of insecticide resistance in mosquitoes and test their response against spatial repellents to evaluate the efficacy of auto-dissemination techniques for mosquito control.

“The outcomes of this program will inform policy decisions related to malaria control and elimination, ultimately contributing to efforts to reduce the burden of this disease in the region,” Zhong added.

The ICEMR features a diverse team of research experts from Thailand, Malaysia, Japan, and the United States who strive to solve the problems of border malaria and zoonotic malaria that are relevant to both regional and global malaria elimination. Additional collaborating Institutions of this program include the University of Malaya and Ehime University in Japan.

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