Ranking malaria risk factors to guide malaria control efforts in African highlands

Chemical Exposure of Synthetic Pyrethroid on Deltamethrin Under the Selection Pressure over the Generations: A Reproductive Potential Study of Anopheles stephensi

Biochemical synthetic pyrethroids, deltamethrin are presently used insecticides for the control of mosquito vector-borne diseases in worldwide. Mosquito re-emergence with diseases becoming a serious problem due to development of insecticide resistance. The comprehensive knowledge on the underlying mechanisms of resistance against deltamethrin is required for implementation of an efficient vector control programme. The assessment of the biological fitness of a mosquito strain exposed to insecticide pressure is extremely vital because it provides information on the development of resistance. In the present study, the adult stage of malaria vector, Anopheles stephensi, was designated for the study of deltamethrin resistance (F40 generations). The non-blood-fed, laboratory-reared females to sub-lethal doses of deltamethrin (0.004%, 0.005%, 0.007%, or 0.01%) exposed to every generation for up to F40. The adult mosquito susceptibility was performed by WHO standard method for evaluation. After 24 h, mortality was recorded in both treated and control groups. Therefore, the biological fitness characteristics such as feeding, fecundity, hatchability, egg retention, immature duration, adult emergence, and adult life span were studied to assess the exposed deltamethrin under selection pressure as compared to the unexposed (control) population. The laboratory selection of An. stephensi exposed deltamethrin over the generations were diminished its biological fitness. Information on biological fitness including reproductive potential of mosquito strain under selection pressure against deltamethrin is incredibly necessary because it would facilitate in resistance management. Baseline information gives in this experiment will guide for future studies on the susceptibilities of wild malaria mosquito populations in India.

Predicting malaria risk considering vector control interventions under climate change scenarios

Many studies have projected malaria risks with climate change scenarios by modelling one or two environmental variables and without the consideration of malaria control interventions. We aimed to predict the risk of malaria with climate change considering the influence of rainfall, humidity, temperatures, vegetation, and vector control interventions (indoor residual spraying (IRS) and long-lasting insecticidal nets (LLIN)). We used negative binomial models based on weekly malaria data from six facility-based surveillance sites in Uganda from 2010-2018, to estimate associations between malaria, environmental variables and interventions, accounting for the non-linearity of environmental variables. Associations were applied to future climate scenarios to predict malaria distribution using an ensemble of Regional Climate Models under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Predictions including interaction effects between environmental variables and interventions were also explored. The results showed upward trends in the annual malaria cases by 25% to 30% by 2050s in the absence of intervention but there was great variability in the predictions (historical vs RCP 4.5 medians [Min-Max]: 16,785 [9,902-74,382] vs 21,289 [11,796-70,606]). The combination of IRS and LLIN, IRS alone, and LLIN alone would contribute to reducing the malaria burden by 76%, 63% and 35% respectively. Similar conclusions were drawn from the predictions of the models with and without interactions between environmental factors and interventions, suggesting that the interactions have no added value for the predictions. The results highlight the need for maintaining vector control interventions for malaria prevention and control in the context of climate change given the potential public health and economic implications of increasing malaria in Uganda.

Have there been efforts to integrate malaria and schistosomiasis prevention and control programs? A scoping review of the literature

Malaria and schistosomiasis are two important parasitic diseases that are a particular threat to young children and pregnant women in sub-Saharan Africa. Malaria and schistosomiasis prevention and control strategies primarily focus on the distribution of long-lasting insecticidal nets and the delivery of praziquantel tablets to at-risk populations in high burden settings through mass drug administration, respectively. The objective of this scoping review was to identify previous efforts to integrate malaria and schistosomiasis prevention and control programs in the literature and to summarize the strategies and approaches used in these programs following the PRISMA-ScR guidelines. We reviewed published and grey literature using a combination of keywords and search terms following themes surrounding "malaria", "Plasmodium falciparum", "Anopheles", "schistosomiasis", "Schistosoma haematobium", "Schistosoma mansoni", and "snails". Neither a date limit nor relevant terms for prevention and control were used. Out of 6374, eight articles were included in the scoping review-three articles investigated the integration of mass drug administration for schistosomiasis with the administration of antimalarials, four articles investigated the effect of administering antimalarials on malaria, schistosomiasis, and their co-infection, and one article assessed the impact of an educational intervention on malaria and schistosomiasis knowledge and preventative behaviors. Our findings suggest that there is an opportunity to link disease control programs to increase access and coverage of interventions to improve outcomes for malaria, schistosomiasis, and their co-infection. Further research is needed on the potential benefits, feasibility, and cost-effectiveness of integrating malaria and schistosomiasis prevention and control programs.

A machine learning approach to predict the glaucoma filtration surgery outcome

This study aimed at predicting the filtration surgery (FS) outcome using a machine learning (ML) approach. 102 glaucomatous patients undergoing FS were enrolled and underwent ocular surface clinical tests (OSCTs), determination of surgical site-related biometric parameters (SSPs) and conjunctival vascularization. Break-up-time, Schirmer test I, corneal fluorescein staining, Meibomian gland expressibility; conjunctival hyperemia, upper bulbar conjunctiva area of exposure, limbus to superior eyelid distance; and conjunctival epithelial and stromal (CET, CST) thickness and reflectivity (ECR, SCR) at AS-OCT were considered. Successful FS required a 30% baseline intraocular pressure reduction, with values ≤ 18 mmHg with or without medications. The classification tree (CT) was the ML algorithm used to analyze data. At the twelfth month, FS was successful in 60.8% of cases, whereas failed in 39.2%. At the variable importance ranking, CST and SCR were the predictors with the greater relative importance to the CART tree construction, followed by age. CET and ECR showed less relative importance, whereas OSCTs and SSPs were not important features. Within the CT, CST turned out the most important variable for discriminating success from failure, followed by SCR and age, with cut-off values of 75 µm, 169 on gray scale, and 62 years, respectively. The ROC curve for the classifier showed an AUC of 0.784 (0.692-0.860). In this ML approach, CT analysis found that conjunctival stroma thickness and reflectivity, along with age, can predict the FS outcome with good accuracy. A pre-operative thick and hyper-reflective stroma, and a younger age increase the risk of FS failure.

Comparing field-collected versus remotely-sensed variables to model malaria risk in the highlands of western Uganda

BACKGROUND

Malaria risk is not uniform across relatively small geographic areas, such as within a village. This heterogeneity in risk is associated with factors including demographic characteristics, individual behaviours, home construction, and environmental conditions, the importance of which varies by setting, making prediction difficult. This study attempted to compare the ability of statistical models to predict malaria risk at the household level using either (i) free easily-obtained remotely-sensed data or (ii) results from a resource-intensive household survey.

METHODS

The results of a household malaria survey conducted in 3 villages in western Uganda were combined with remotely-sensed environmental data to develop predictive models of two outcomes of interest (1) a positive ultrasensitive rapid diagnostic test (uRDT) and (2) inpatient admission for malaria within the last year. Generalized additive models were fit to each result using factors from the remotely-sensed data, the household survey, or a combination of both. Using a cross-validation approach, each model's ability to predict malaria risk for out-of-sample households (OOS) and villages (OOV) was evaluated.

RESULTS

Models fit using only environmental variables provided a better fit and higher OOS predictive power for uRDT result (AIC = 362, AUC = 0.736) and inpatient admission (AIC = 623, AUC = 0.672) compared to models using household variables (uRDT AIC = 376, Admission AIC = 644, uRDT AUC = 0.667, Admission AUC = 0.653). Combining the datasets did not result in a better fit or higher OOS predictive power for uRDT results (AIC = 367, AUC = 0.671), but did for inpatient admission (AIC = 615, AUC = 0.683). Household factors performed best when predicting OOV uRDT results (AUC = 0.596) and inpatient admission (AUC = 0.553), but not much better than a random classifier.

CONCLUSIONS

These results suggest that residual malaria risk is driven more by the external environment than home construction within the study area, possibly due to transmission regularly occurring outside of the home. Additionally, they suggest that when predicting malaria risk the benefit may not outweigh the high costs of attaining detailed information on household predictors. Instead, using remotely-sensed data provides an equally effective, cost-efficient alternative.

Enhancing Malaria Research, Surveillance, and Control in Endemic Areas of Kenya and Ethiopia

Malaria control programs in Africa encounter daunting challenges that hinder progressive steps toward elimination of the disease. These challenges include widespread insecticide resistance in mosquito vectors, increasing outdoor malaria transmission, lack of vector surveillance and control tools suitable for outdoor biting vectors, weakness in malaria surveillance, and an inadequate number of skilled healthcare personnel. Ecological and epidemiological changes induced by environmental modifications resulting from water resource development projects pose additional barriers to malaria control. Cognizant of these challenges, our International Center of Excellence for Malaria Research (ICEMR) works in close collaboration with relevant government ministries and agencies to align its research efforts with the objectives and strategies of the national malaria control and elimination programs for the benefit of local communities. Our overall goal is to assess the impact of water resource development projects, shifting agricultural practices, and vector interventions on Plasmodium falciparum and P. vivax malaria in Kenya and Ethiopia. From 2017 to date, the ICEMR has advanced knowledge of malaria epidemiology, transmission, immunology, and pathogenesis, and developed tools to enhance vector surveillance and control, improved clinical malaria surveillance and diagnostic methods, and strengthened the capacity of local healthcare providers. Research findings from the ICEMR will inform health policy and strategic planning by ministries of health in their quest to sustain malaria control and achieve elimination goals.

Regional heterogeneity of malaria prevalence and associated risk factors among children under five in Togo: evidence from a national malaria indicators survey

BACKGROUND

Malaria remains a major cause of morbidity and death among children less than 5 years of age. In Togo, despite intensification of malaria control interventions, malaria remained highly prevalent, with significant heterogeneity from one region to another. The aim of this study is to explore further such regional differences in malaria prevalence and to determine associated risk factors.

METHODS

Data from a 2017 cross-sectional nationally representative malaria indicator survey was used. Children aged 6-59 months in selected households were tested for malaria using a rapid diagnostic test (RDT), confirmed by microscopy. Univariate and multivariate logistic regression analysis were performed using Generalized Linear Models.

RESULTS

A total of 2131 children aged 6-59 months (1983 in rural areas, 989 in urban areas) were enrolled. Overall 28% of children tested positive for malaria, ranging from 7.0% in the Lomé Commune region to 4% 7.1 in the Plateaux region. In multivariate analysis, statistically significant differences between regions persisted. Independent risk factors identified were higher children aged (aOR = 1.46, 95% CI [1.13-1.88]) for those above 24 months compared to those below; households wealth quintile (aOR = 0.22, 95% CI [0.11-0.41]) for those richest compared to those poorest quintiles; residence in rural areas (aOR = 2.02, 95% CI [1.32-3.13]).

CONCLUSION

Interventions that target use of combined prevention measures should prioritise on older children living in poorest households in rural areas, particularly in the regions of high malaria prevalence.

Ranked determinants of telemedicine diabetic retinopathy screening performance in the United States primary care safety-net setting: an exploratory CART analysis

Background

Diabetic retinopathy (DR) is a leading cause of blindness worldwide, despite easy detection and effective treatment. Annual screening rates in the USA remain low, especially for the disadvantaged, which telemedicine-based DR screening (TDRS) during routine primary care has been shown to improve. Screening rates from such programs have varied, however, pointing to inconsistent implementation and unaddressed barriers. This work seeks to identify and prioritize modifiable barriers for targeted intervention.

Methods

In this final phase of an exploratory mixed-methods study, we developed, validated, and administered a 62-item survey to multilevel stakeholders involved with TDRS in primary care safety-net clinics. Survey items were aligned with previously identified determinants of clinic-level screening and mapped to the Consolidated Framework for Implementation Research (CFIR). Classification and Regression Tree (CART) analyses were used to identify and rank independent variables predictive of individual-level TDRS screening performance.

Results

Overall, 133 of the 341 invited professionals responded (39%), representing 20 safety-net clinics across 6 clinical systems. Respondents were predominately non-Hispanic White (77%), female (94%), and between 31 and 65 years of age (79%). Satisfaction with TDRS was high despite low self-reported screening rates. The most important screening determinants were: provider reinforcement of TDRS importance; explicit instructions by providers to staff; effective reminders; standing orders; high relative priority among routine diabetic measures; established TDRS workflows; performance feedback; effective TDRS champions; and leadership support.

Conclusions

In this survey of stakeholders involved with TDRS in safety-net clinics, screening was low despite high satisfaction with the intervention. The best predictors of screening performance mapped to the CFIR constructs Leadership Engagement, Compatibility, Goals & Feedback, Relative Priority, Champions, and Available Resources. These findings facilitate the prioritization of implementation strategies targeting determinants of TDRS performance, potentially increasing its public health impact.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-07915-5.

Risk Factors Analysis of Malaria Transmission at Cross-Boundaries Area in Menoreh Hills, Java, Indonesia

Background:

Risk factors of Malaria transmission at cross-boundaries area is important to be identified. This study aimed to identify the risk factors of Malaria transmission at cross-boundaries area in Menoreh Hills, Java, Indonesia.

Methods:

The design of the study was an observational study with a case-control design. Data on malaria cases and controls were obtained from the Primary Health Care in Menoreh. All malaria positive patients with clinical and laboratory examinations recorded in health services during 1 Jan 2015–31 Dec 2015. Overall, 138 cases and 138 controls were included. Several variables were collected such as altitude, night out behavior, the use of mosquito nets, nighttime bed, travel history, mosquito bite prevention activities, cattle ownership, distance to mosquito breeding site, etc. Data were obtained by structured questionnaires and observation. Data were analyzed by univariate, bivariate and multivariate

Results:

The altitude of house >500 m above sea level proved to be influential as a risk factor for Malaria (OR 3.62, 95% CI 1.61–8.16, P=0.002). Several variables were identified as a risk factor of Malaria such as the wall of the house from bamboo/wood, no insecticide and distance of house <100 m from mosquito breeding site.

Conclusion:

An awareness for the local health sector particularly to provide a recommendation for house construction to protect a community from Malaria transmission.

Abundance and Distribution of Malaria Vectors in Various Aquatic Habitats and Land Use Types in Kakamega County, Highlands of Western Kenya

Background

Management of malaria transmission relies heavily on vector control. Implementation and sustenance of effective control measures require regular monitoring of malaria vector occurrences, species abundance and distribution. The study assessed mosquito larval species composition, distribution and productivity in Kakamega County, western Kenya.

Methods

A cross-sectional survey of Anopheline larvae was conducted in various aquatic habitats and land use types in Kakamega County, highlands of western Kenya between the month of March and June 2019.

Results

One thousand, five hundred and seventy six aquatic habitats were sampled in various land use types. The mean densities of An. gambiae s.l (46.2), An. funestus (5.3), An. coustani (1.7), An. implexus (0.13) and An. squamosus (2.0) were observed in fish ponds, burrow pits, drainage ditches, and tire tracks, respectively. High mean densities of An. gambiae s.l was reported in farmland (20.4) while high mean abundance of An. funestus s.l (8.2) and An. coustani s.l (4.0) were observed in artificial forests.

Conclusion

The study revealed that the productivity of anopheles larvae varied across various habitat types and land use types. Therefore, treatment of potential breeding sites should be considered as an additional strategy for malaria vector control in Kakamega County, western Kenya.

A Systematic Review of the Effects of Temperature on Anopheles Mosquito Development and Survival: Implications for Malaria Control in a Future Warmer Climate

The rearing temperature of the immature stages can have a significant impact on the life-history traits and the ability of adult mosquitoes to transmit diseases. This review assessed published evidence of the effects of temperature on the immature stages, life-history traits, insecticide susceptibility, and expression of enzymes in the adult Anopheles mosquito. Original articles published through 31 March 2021 were systematically retrieved from Scopus, Google Scholar, Science Direct, PubMed, ProQuest, and Web of Science databases. After applying eligibility criteria, 29 studies were included. The review revealed that immature stages of An. arabiensis were more tolerant (in terms of survival) to a higher temperature than An. funestus and An. quadriannulatus. Higher temperatures resulted in smaller larval sizes and decreased hatching and pupation time. The development rate and survival of An. stephensi was significantly reduced at a higher temperature than a lower temperature. Increasing temperatures decreased the longevity, body size, length of the gonotrophic cycle, and fecundity of Anopheles mosquitoes. Higher rearing temperatures increased pyrethroid resistance in adults of the An. arabiensis SENN DDT strain, and increased pyrethroid tolerance in the An. arabiensis SENN strain. Increasing temperature also significantly increased Nitric Oxide Synthase (NOS) expression and decreased insecticide toxicity. Both extreme low and high temperatures affect Anopheles mosquito development and survival. Climate change could have diverse effects on Anopheles mosquitoes. The sensitivities of Anopeheles mosquitoes to temperature differ from species to species, even among the same complex. Notwithstanding, there seem to be limited studies on the effects of temperature on adult life-history traits of Anopheles mosquitoes, and more studies are needed to clarify this relationship.

The New Zoonotic Malaria: Plasmodium cynomolgi

Plasmodium cynomolgi is a simian malaria parasite that has been a central model parasite since it was first described in 1907. Recently it has made the zoonotic jump and started naturally infecting humans. In this paper, the interactions between Plasmodium cynomolgi and humans, the environment and the non-human animal intermediates or definitive host will be discussed, with a particular focus on the clinical implications of infection and approaches to management of this novel zoonotic parasite.

Enhanced Health Facility Surveys to Support Malaria Control and Elimination across Different Transmission Settings in the Philippines

Following substantial progress in malaria control in the Philippines, new surveillance approaches are needed to identify and target residual malaria transmission. This study evaluated an enhanced surveillance approach using rolling cross-sectional surveys of all health facility attendees augmented with molecular diagnostics and geolocation. Facility surveys were carried out in three sites representing different transmission intensities: Morong, Bataan (pre-elimination), Abra de Ilog, Occidental Mindoro (stable medium risk), and Rizal, Palawan (high risk, control). Only one rapid diagnostic test (RDT)–positive infection and no PCR confirmed infections were found in Bataan and Occidental Mindoro, suggesting the absence of transmission. In Palawan, the inclusion of all health facility attendees, regardless of symptoms, and use of molecular diagnostics identified 313 infected individuals in addition to 300 cases identified by routine screening of febrile patients with the RDT or microscopy. Of these, the majority (313/613) were subpatent infections and only detected using molecular methods. Simultaneous collection of GPS coordinates on tablet-based applications allowed real-time mapping of malaria infections. Risk factor analysis showed higher risks in children and indigenous groups, with bed net use having a protective effect. Subpatent infections were more common in men and older age-groups. Overall, malaria risks were not associated with participants’ classification, and some of the non-patient clinic attendees reported febrile illnesses (1.9%, 26/1,369), despite not seeking treatment, highlighting the widespread distribution of infection in communities. Together, these data illustrate the utility of health facility–based surveys to augment surveillance data to increase the probability of detecting infections in the wider community.

Association of the rs562556 PCSK9 Gene Polymorphism with Reduced Mortality in Severe Malaria among Malian Children

Recent evidence suggests that proprotein convertase subtilisin/kexin type 9 (PCSK9), a downmodulator of cellular uptake of blood cholesterol, also negatively impacts host immune response to microbial infection. In this study, we investigated whether carrying the loss-of-function (LOF) rs562556 (c.1420 A > G; p.I474 V) PCSK9 single nucleotide polymorphism (SNP) affected the outcome of severe malaria in children. Archival DNA of a cohort of 207 Malian children suffering from severe malaria was genotyped for the rs562556 SNP. Sixty-four children were either heterozygous or homozygous for the minor G allele (carriers); 143 children were homozygous for the common A allele (noncarriers). Among carriers, there was one mortality case (1.6%), compared to 15 cases (10.5%) among noncarriers (p=0.0251), suggesting that the G allele is associated with better survival in severe malaria. Intriguingly, this allele did not negatively segregate with any of the clinical symptoms linked to mortality in this cohort. Studies are needed to determine whether PCSK9 inactivation promotes a protective immune response to malaria infection.

Mapping the Structure of Social Vulnerability Systems for Malaria in East Africa

Millions of people fall ill with malaria every year—most of them are located in sub-Saharan Africa. The weight of the burden of malaria on a society is determined by a complex interplay of environmental and social factors, including poverty, awareness and education, among others. A substantial share of the affected population is characterized by a general lack of anticipation and coping capacities, which renders them particularly vulnerable to the disease and its adverse side effects. This work aims at identifying interdependencies and feedback mechanisms in the malaria social vulnerability system and their variations in space by combining concepts, methods and tools from Climate Change Adaptation, Spatial Analysis, and Statistics and System Dynamics. The developed workflow is applied to a selected set of social, economic and biological vulnerability indicators covering five East-African Nations. As the study areas’ local conditions vary in a multitude of aspects, the social vulnerability system is assumed to vary accordingly throughout space. The study areas’ spatial entities were therefore aggregated into three system-regions using correlation-based clustering. Their respective correlation structures are displayed as Causal Loop Diagrams (CLDs). While the three resulting CLDs do not necessarily display causal relations (as the set of social vulnerability indicators are likely linked through third variables and parts of the data are proxies), they give a good overview of the data, can be used as basis for discussions in participatory settings and can potentially enhance the understanding the malaria vulnerability system.

Targeting remaining pockets of malaria transmission in Kenya to hasten progress towards national elimination goals: an assessment of prevalence and risk factors in children from the Lake endemic region

BACKGROUND

With an overall decline of malaria incidence, elimination of malaria is gradually becoming the next target for many of countries affected by the disease. In Kenya the national malaria control strategy is aiming to reach pre-elimination for most parts of the country. However, considerable heterogeneity in prevalence of the disease within the country and especially the remaining high prevalent region of the Lake endemic region is likely to slow progress towards this target. To achieve a sustained control and an eventual elimination, a clear understanding of drivers of ongoing malaria transmission in remaining hotspots is needed.

METHODS

Data from the 2015 Malaria Indicator Survey (MIS) were analysed for prevalence of malaria parasitaemia in children (6 months to 14 years) of different countries within the highly endemic Lake region. Univariate and multivariate logistic regression analysis were preformed to explore associations between selected risk factors and being parasitaemic. A predictive model was built for the association between malaria and the risk factors with the aim of identifying heterogeneities of the disease at the lower administrative levels.

RESULTS

Overall, 604/2253 (27%, 95% CI 21.8-32.2) children were parasitaemic. The highest prevalence was observed in Busia County (37%) and lowest in Bungoma County (18%). Multivariate logistic regression analysis showed that the 10-14 years age group (OR = 3.0, 95% CI 2.3-4.1), households in the poorest socio-economic class (OR = 2.1, 95% CI 1.3-3.3), farming (OR = 1.4, 95% CI 1.2-2.5) and residence in Busia (OR = 4.6, 95% CI 2.1-8.2), Kakamega (OR = 2.6, 95% CI 1.3-5.4), and Migori counties (OR = 4.6 95% CI 2.1-10.3) were associated with higher risk of parasitaemia. Having slept under a long-lasting insecticide-treated bed net (LLIN) was associated with a lower risk (OR = 0.7, 95% CI 0.6-0.9). No association were found between malaria infection and the gender of the child, the household head, and the education status of the household head.

DISCUSSION AND CONCLUSION

Detailed analysis of malaria prevalence data in a hotspot area can identify new threats and avail opportunities for directing intervention. In the Lake endemic region of Kenya, interventions should be focused more on counties with the highest prevalence, and should target older children as well as children from the lower socio-economic strata. Precisely targeting interventions in remaining hotspots and high-risk populations will likely make impact and accelerate progress towards pre-elimination targets.

Melanoma survival with Classification and Regression Trees Analysis: a complement for the communication of prognosis to patients

BACKGROUND

Prognostic factors in cutaneous melanoma are commonly evaluated by the Cox proportional hazard model. However, the interpretation of the effect of multiple variables is not straightforward. Classification and Regression Trees Analysis (CART), which allows a more friendly data evaluation, could be a valid integration of the message from Cox model.

METHODS

The CART algorithm splits up data, creating a "tree" of groups of patients with different profiles for the risk of death. Results are easy to interpret in clinical practice. A total of 2692 patients with invasive cutaneous melanoma registered in Romagna (northern Italy) between 1993-2012 and followed-up until the end of 2013 were included. The Cox model and CART analysis were applied to sex, patient age, histological subtype, Breslow's tumor thickness, ulceration, site of disease, and Clark level.

RESULTS

The CART analysis identified 15 categories which were collapsed into five classes with statistically different survival. The best prognostic group (10-year observed survival, 99.1%) included subjects with Breslow thickness ≤0.78 mm and age 16-81 years. The worst prognostic group (10-year observed survival, 35.8%) comprised subjects with thickness ≥3.75 mm and age 16-96 years. According to the Cox model, patient age, histological subtype, Breslow thickness, ulceration, and site of disease had a significant independent prognostic value.

CONCLUSIONS

CART and Cox models provided consistent results. CART seemed friendlier in its interpretation and it could facilitate the communication of risk.

Achieving explanatory depth and spatial breadth in infectious disease modelling: Integrating active and passive case surveillance

Ideally, the data used for robust spatial prediction of disease distribution should be both high-resolution and spatially expansive. However, such in-depth and geographically broad data are rarely available in practice. Instead, researchers usually acquire either detailed epidemiological data with high resolution at a small number of active sampling sites, or more broad-ranging but less precise data from passive case surveillance. We propose a novel inferential framework, capable of simultaneously drawing insights from both passive and active data types. We developed a Bayesian latent point process approach, combining active data collection in a limited set of points, where in-depth covariates are measured, with passive case detection, where error-prone, large-scale disease data are accompanied only by coarse or remotely-sensed covariate layers. Using the example of malaria, we tested our method's efficiency under several hypothetical scenarios of reported incidence in different combinations of imperfect detection and spatial complexity of the environmental variables. We provide a simple solution to a widespread problem in spatial epidemiology, combining latent process modelling and spatially autoregressive modelling. By using active sampling and passive case detection in a complementary way, we achieved the best-of-both-worlds, in effect, a formal calibration of spatially extensive, error-prone data by localised, high-quality data.

Regional variation in life history traits and plastic responses to temperature of the major malaria vector Nyssorhynchus darlingi in Brazil

The primary Brazilian malaria vector, Nyssorhynchus darlingi (formerly Anopheles darlingi), ranges from 0°S–23°S across three biomes (Amazonia, Cerrado, Mata Atlântica). Rising temperatures will increase mosquito developmental rates, and models predict future malaria transmission by Ny. darlingi in Brazil will shift southward. We reared F₁Ny. darlingi (progeny of field-collected females from 4 state populations across Brazil) at three temperatures (20, 24, 28 °C) and measured key life-history traits. Our results reveal geographic variation due to both genetic differences among localities and plastic responses to temperature differences. Temperature significantly altered all traits: faster larval development, shorter adult life and overall lifespan, and smaller body sizes were seen at 28 °C versus 20 °C. Low-latitude Amazonia mosquitoes had the fastest larval development at all temperatures, but at 28 °C, average development rate of high-latitude Mata Atlântica mosquitoes was accelerated and equivalent to low-latitude Amazonia. Body size of adult mosquitoes from the Mata Atlântica remained larger at all temperatures. We detected genetic variation in the plastic responses among mosquitoes from different localities, with implications for malaria transmission under climate change. Faster development combined with larger body size, without a tradeoff in adult longevity, suggests vectorial capacities of some Mata Atlântica populations may significantly increase under warming climates.

Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging

Background

There is a need for comprehensive evaluations of the underlying local factors that contribute to residual malaria in sub-Saharan Africa. However, it is difficult to compare the wide array of demographic, socio-economic, and environmental variables associated with malaria transmission using standard statistical approaches while accounting for seasonal differences and nonlinear relationships. This article uses a Bayesian model averaging (BMA) approach for identifying and comparing potential risk and protective factors associated with residual malaria.

Results

The relative influence of a comprehensive set of demographic, socio-economic, environmental, and malaria intervention variables on malaria prevalence were modelled using BMA for variable selection. Data were collected in Bunkpurugu-Yunyoo, a rural district in northeast Ghana that experiences holoendemic seasonal malaria transmission, over six biannual surveys from 2010 to 2013. A total of 10,022 children between the ages 6 to 59 months were used in the analysis. Multiple models were developed to identify important risk and protective factors, accounting for seasonal patterns and nonlinear relationships. These models revealed pronounced nonlinear associations between malaria risk and distance from the nearest urban centre and health facility. Furthermore, the association between malaria risk and age and some ethnic groups was significantly different in the rainy and dry seasons. BMA outperformed other commonly used regression approaches in out-of-sample predictive ability using a season-to-season validation approach.

Conclusions

This modelling framework offers an alternative approach to disease risk factor analysis that generates interpretable models, can reveal complex, nonlinear relationships, incorporates uncertainty in model selection, and produces accurate predictions. Certain modelling applications, such as designing targeted local interventions, require more sophisticated statistical methods which are capable of handling a wide range of relevant data while maintaining interpretability and predictive performance, and directly characterize uncertainty. To this end, BMA represents a valuable tool for constructing more informative models for understanding risk factors for malaria, as well as other vector-borne and environmentally mediated diseases.

Electronic supplementary material

The online version of this article (10.1186/s12936-018-2491-2) contains supplementary material, which is available to authorized users.

Influence of Climatic Factors on Malaria Epidemic in Gulu District, Northern Uganda: A 10-Year Retrospective Study

Background

Globally, 15 countries, mainly in Sub-Saharan Africa, account for 80% of malaria cases and 78% of malaria related deaths. In Uganda, malaria is endemic and the mortality and morbidity due to malaria cause significant negative impact on the economy. In Gulu district, malaria is the leading killer disease among children <5 years. In 2015, the high intensity of malaria infection in Northern Uganda revealed a possible link between malaria and rainfall. However, available information on the influence of climatic factors on malaria are scarce, conflicting, and highly contextualized and therefore one cannot reference such information to malaria control policy in Northern Uganda, thus the need for this study.

Methods and Results

During the 10 year's retrospective study period a total of 2,304,537 people suffered from malaria in Gulu district. Malaria infection was generally stable with biannual peaks during the months of June-July and September-October but showed a declining trend after introduction of indoor residual spraying. Analysis of the departure of mean monthly malaria cases from the long-term mean monthly malaria cases revealed biannual seasonal outbreaks before and during the first year of introduction of indoor residual spraying. However, there were two major malaria epidemics in 2015 following discontinuation of indoor residual spraying in the late 2014. Children <5 years of age were disproportionally affected by malaria and accounted for 47.6% of the total malaria cases. Both rainfall (P=0.04) and relative humidity (P=0.003) had significant positive correlations with malaria. Meanwhile, maximum temperature had significant negative correlation with malaria (P=0.02) but minimum temperature had no correlation with malaria (P=0.29).

Conclusion

Malaria in Gulu disproportionately affects children under 5 years and shows seasonality with a generally stable trend influenced by rainfall and relative humidity. However, indoor residual spraying is a very promising method to achieve a sustained malaria control in this population.

Interactions between climatic changes and intervention effects on malaria spatio-temporal dynamics in Uganda

Background

Although malaria burden in Uganda has declined since 2009 following the scale-up of interventions, the disease is still the leading cause of hospitalization and death. Transmission remains high and is driven by suitable weather conditions. There is a real concern that intervention gains may be reversed by climatic changes in the country. In this study, we investigate the effects of climate on the spatio-temporal trends of malaria incidence in Uganda during 2013–2017.

Methods

Bayesian spatio-temporal negative binomial models were fitted on district-aggregated monthly malaria cases, reported by two age groups, defined by a cut-off age of 5 years. Weather data was obtained from remote sensing sources including rainfall, day land surface temperature (LSTD) and night land surface temperature (LSTN), Normalized Difference Vegetation Index (NDVI), altitude, land cover, and distance to water bodies. Spatial and temporal correlations were taken into account by assuming a conditional autoregressive and a first-order autoregressive process on district and monthly specific random effects, respectively. Fourier trigonometric functions modeled seasonal fluctuations in malaria transmission. The effects of climatic changes on the malaria incidence changes between 2013 and 2017 were estimated by modeling the difference in time varying climatic conditions at the two time points and adjusting for the effects of intervention coverage, socio-economic status and health seeking behavior.

Results

Malaria incidence declined steadily from 2013 to 2015 and then increased in 2016. The decrease was by over 38% and 20% in children <5 years and individuals ≥5 years, respectively. Temporal trends depict a strong bi-annual seasonal pattern with two peaks during April–June and October–December. The annual average of rainfall, LSTD and LSTN increased by 3.7 mm, 2.2 °C and 1.0 °C, respectively, between 2013 and 2017, whereas NDVI decreased by 6.8%. On the one hand, the increase in LSTD and decrease in NDVI were associated with a reduction in the incidence decline. On the other hand, malaria interventions and treatment seeking behavior had reverse effects, that were stronger compared to the effects of climatic changes. Important interactions between interventions with NDVI and LSTD suggest a varying impact of interventions on malaria burden in different climatic conditions.

Conclusion

Climatic changes in Uganda during the last five years contributed to a favorable environment for malaria transmission, and had a detrimental effect on malaria reduction gains achieved through interventions scale-up efforts. The NMCP should create synergies with the National Meteorological Authority with an ultimate goal of developing a Malaria Early Warning System to mitigate adverse climatic change effects on malaria risk in the country.

Evaluating Efficacy of Landsat-Derived Environmental Covariates for Predicting Malaria Distribution in Rural Villages of Vhembe District, South Africa

Malaria in South Africa is still a problem despite existing efforts to eradicate the disease. In the Vhembe District Municipality, malaria prevalence is still high, with a mean incidence rate of 328.2 per 100,0000 persons/year. This study aimed at evaluating environmental covariates, such as vegetation moisture and vegetation greenness, associated with malaria vector distribution for better predictability towards rapid and efficient disease management and control. The 2005 malaria incidence data combined with Landsat 5 ETM were used in this study. A total of nine remotely sensed covariates were derived, while pseudo-absences in the ratio of 1:2 (presence/absence) were generated at buffer distances of 0.5-20 km from known presence locations. A stepwise logistic regression model was applied to analyse the spatial distribution of malaria in the area. A buffer distance of 10 km yielded the highest classification accuracy of 82% at a threshold of 0.9. This model was significant (ρ < 0.05) and yielded a deviance (D²) of 36%. The significantly positive relationship (ρ < 0.05) between the soil-adjusted vegetation index and malaria distribution at all buffer distances suggests that malaria vector (Anopheles arabiensis) prefer productive and greener vegetation. The significant negative relationship between water/moisture index (a₁ index) and malaria distribution in buffer distances of 0.5, 10, and 20 km suggest that malaria distribution increases with a decrease in shortwave reflectance signal. The study has shown that suitable habitats of malaria vectors are generally found within a radius of 10 km in semi-arid environments, and this insight can be useful to aid efforts aimed at putting in place evidence-based preventative measures against malaria infections. Furthermore, this result is important in understanding malaria dynamics under the current climate and environmental changes. The study has also demonstrated the use of Landsat data and the ability to extract environmental conditions which favour the distribution of malaria vector (An. arabiensis) such as the canopy moisture content in vegetation, which serves as a surrogate for rainfall.

The contribution of malaria control interventions on spatio-temporal changes of parasitaemia risk in Uganda during 2009-2014

BACKGROUND

In Uganda, malaria vector control interventions and case management with Artemisinin Combination Therapies (ACTs) have been scaled up over the last few years as a result of increased funding. Data on parasitaemia prevalence among children less than 5 years old and coverage of interventions was collected during the first two Malaria Indicator Surveys (MIS) conducted in 2009 and 2014, respectively. In this study, we quantify the effects of control interventions on parasitaemia risk changes between the two MIS in a spatio-temporal analysis.

METHODS

Bayesian geostatistical and temporal models were fitted on the MIS data of 2009 and 2014. The models took into account geographical misalignment in the locations of the two surveys and adjusted for climatic changes and socio-economic differentials. Parasitaemia risk was predicted over a 2 × 2 km2 grid and the number of infected children less than 5 years old was estimated. Geostatistical variable selection was applied to identify the most important ITN coverage indicators. A spatially varying coefficient model was used to estimate intervention effects at sub-national level.

RESULTS

The coverage of Insecticide Treated Nets (ITNs) and ACTs more than doubled at country and sub-national levels during the period 2009-2014. The coverage of Indoor Residual Spraying (IRS) remained static at all levels. ITNs, IRS, and ACTs were associated with a reduction in parasitaemia odds of 19% (95% BCI: 18-29%), 78% (95% BCI: 67-84%), and 34% (95% BCI: 28-66%), respectively. Intervention effects varied with region. Higher socio-economic status and living in urban areas were associated with parasitaemia odds reduction of 46% (95% BCI: 0.51-0.57) and 57% (95% BCI: 0.40-0.53), respectively. The probability of parasitaemia risk decline in the country was 85% and varied from 70% in the North-East region to 100% in Kampala region. The estimated number of children infected with malaria declined from 2,480,373 in 2009 to 825,636 in 2014.

CONCLUSIONS

Interventions have had a strong effect on the decline of parasitaemia risk in Uganda during 2009-2014, albeit with varying magnitude in the regions. This success should be sustained by optimizing ITN coverage to achieve universal coverage.

Systems thinking in combating infectious diseases

The transmission of infectious diseases is a dynamic process determined by multiple factors originating from disease pathogens and/or parasites, vector species, and human populations. These factors interact with each other and demonstrate the intrinsic mechanisms of the disease transmission temporally, spatially, and socially. In this article, we provide a comprehensive perspective, named as systems thinking, for investigating disease dynamics and associated impact factors, by means of emphasizing the entirety of a system’s components and the complexity of their interrelated behaviors. We further develop the general steps for performing systems approach to tackling infectious diseases in the real-world settings, so as to expand our abilities to understand, predict, and mitigate infectious diseases.

Schistosoma mansoni reinfection: Analysis of risk factors by classification and regression tree (CART) modeling

Praziquantel (PZQ) is an effective chemotherapy for schistosomiasis mansoni and a mainstay for its control and potential elimination. However, it does not prevent against reinfection, which can occur rapidly in areas with active transmission. A guide to ranking the risk factors for Schistosoma mansoni reinfection would greatly contribute to prioritizing resources and focusing prevention and control measures to prevent rapid reinfection. The objective of the current study was to explore the relationship among the socioeconomic, demographic, and epidemiological factors that can influence reinfection by S. mansoni one year after successful treatment with PZQ in school-aged children in Northeastern Minas Gerais state Brazil. Parasitological, socioeconomic, demographic, and water contact information were surveyed in 506 S. mansoni-infected individuals, aged 6 to 15 years, resident in these endemic areas. Eligible individuals were treated with PZQ until they were determined to be negative by the absence of S. mansoni eggs in the feces on two consecutive days of Kato-Katz fecal thick smear. These individuals were surveyed again 12 months from the date of successful treatment with PZQ. A classification and regression tree modeling (CART) was then used to explore the relationship between socioeconomic, demographic, and epidemiological variables and their reinfection status. The most important risk factor identified for S. mansoni reinfection was their “heavy” infection at baseline. Additional analyses, excluding heavy infection status, showed that lower socioeconomic status and a lower level of education of the household head were also most important risk factors for S. mansoni reinfection. Our results provide an important contribution toward the control and possible elimination of schistosomiasis by identifying three major risk factors that can be used for targeted treatment and monitoring of reinfection. We suggest that control measures that target heavily infected children in the most economically disadvantaged households would be most beneficial to maintain the success of mass chemotherapy campaigns.

Geostatistical modelling of malaria indicator survey data to assess the effects of interventions on the geographical distribution of malaria prevalence in children less than 5 years in Uganda

BACKGROUND

Malaria burden in Uganda has declined disproportionately among regions despite overall high intervention coverage across all regions. The Uganda Malaria Indicator Survey (MIS) 2014-15 was the second nationally representative survey conducted to provide estimates of malaria prevalence among children less than 5 years, and to track the progress of control interventions in the country. In this present study, 2014-15 MIS data were analysed to assess intervention effects on malaria prevalence in Uganda among children less than 5 years, assess intervention effects at regional level, and estimate geographical distribution of malaria prevalence in the country.

METHODS

Bayesian geostatistical models with spatially varying coefficients were used to determine the effect of interventions on malaria prevalence at national and regional levels. Spike-and-slab variable selection was used to identify the most important predictors and forms. Bayesian kriging was used to predict malaria prevalence at unsampled locations.

RESULTS

Indoor Residual Spraying (IRS) and Insecticide Treated Nets (ITN) ownership had a significant but varying protective effect on malaria prevalence. However, no effect was observed for Artemisinin Combination-based Therapies (ACTs). Environmental factors, namely, land cover, rainfall, day and night land surface temperature, and area type were significantly associated with malaria prevalence. Malaria prevalence was higher in rural areas, increased with the child's age, and decreased with higher household socioeconomic status and higher level of mother's education. The highest prevalence of malaria in children less than 5 years was predicted for regions of East Central, North East and West Nile, whereas the lowest was predicted in Kampala and South Western regions, and in the mountainous areas in Mid-Western and Mid-Eastern regions.

CONCLUSIONS

IRS and ITN ownership are important interventions against malaria prevalence in children less than 5 years in Uganda. The varying effects of the interventions calls for selective implementation of control tools suitable to regional ecological settings. To further reduce malaria burden and sustain malaria control in Uganda, current tools should be supplemented by health system strengthening, and socio-economic development.

Assessing the Risk Factors Associated with Malaria in the Highlands of Ethiopia: What Do We Need to Know?

Malaria has been Ethiopia's predominant communicable disease for decades. Following the catastrophic malaria outbreak in 2003⁻2004, the Federal Ministry of Health (FMoH) took drastic public health actions to lower the burden of malaria. The FMoH achieved significant declines in malaria mortality and incidence, and recently declared its objective to achieve malaria elimination in low malaria transmission areas of Ethiopia by 2020. However, while the overall malaria prevalence has decreased, unpredictable outbreaks increasingly occur irregularly in regions previously considered "malaria-free". Such outbreaks have disastrous consequences on populations of these regions as they have no immunity against malaria. The Amhara Region accounts for 31% of Ethiopia's malaria burden and is targeted for malaria elimination by the FMoH. Amhara's epidemiological surveillance system faces many challenges to detect in a timely manner the unpredictable and irregular malaria outbreaks that occur in areas of otherwise low transmission. Despite the evidence of a shift in malaria transmission patterns, Amhara's malaria control interventions remain constrained to areas that are historically known to have stable malaria transmission. This paper discusses the influence of temperature and precipitation variability, entomological parameters, and human population mobility on malaria transmission patterns across the Amhara Region, and in particular, in areas of unstable transmission. We argue that malaria epidemiological surveillance systems can be improved by accounting for population movements in addition to environmental and entomological factors. However, to date, no study has statistically analyzed the interplay of population dynamics on environmental and entomological drivers of malaria transmission.

Assessing the effect of indoor residual spraying (IRS) on malaria morbidity in Northern Uganda: a before and after study

Background

Indoor residual spraying (IRS) is known to reduce malaria transmission. In northern Uganda, a high endemic area, IRS has been implemented since 2006. Limited data however, exists on the effect of IRS on the malaria burden. This study sought to assess the effect of IRS on malaria morbidity in the high intensity area of northern Uganda. Retrospective routine data from ten health facilities in three districts which had received at least five rounds of IRS in northern Uganda was analysed. The primary outcome of interest was malaria morbidity, measured by the slide positivity rate (SPR). Descriptive statistics were used to describe the malaria morbidity stratified by age and sex. The average change in the malaria morbidity, measured by the SPR was assessed according to time, measured as calendar months. A fixed-effects linear regression model was used which included a polynomial function of time and controlled for malaria seasonality and variations between districts/facilities.

Results

The total out-patient department attendance in the ten health facilities for the study period was 2,779,246, of which 736,034 (26.5%) malaria cases were diagnosed with 374,826 (50.9%) cases of under 5 years and an overall SPR of 37.5%. The percentage point (p.p.) changes in SPR according to time measured as calendar months following IRS, revealed a decreasing trend in malaria morbidity in the first 3 months following each round of IRS. The highest percentage point decrease in the SPR was observed in the second month following IRS (9.5 p.p., CI −17.85 to −1.16, p = 0.026), among patients above 5 years. The SPR decline however waned by the fourth month following IRS, with an increase in the SPR of 8.4 p.p. at district level by the sixth month, p = 0.510.

Conclusion

The study results show that IRS was associated with a significant reduction in malaria morbidity in northern Uganda in the first 3 months following IRS. The malaria reduction however waned by the fourth month following IRS.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-016-1652-4) contains supplementary material, which is available to authorized users.

An integrated risk and vulnerability assessment framework for climate change and malaria transmission in East Africa

BACKGROUND

Malaria is one of the key research concerns in climate change-health relationships. Numerous risk assessments and modelling studies provide evidence that the transmission range of malaria will expand with rising temperatures, adversely impacting on vulnerable communities in the East African highlands. While there exist multiple lines of evidence for the influence of climate change on malaria transmission, there is insufficient understanding of the complex and interdependent factors that determine the risk and vulnerability of human populations at the community level. Moreover, existing studies have had limited focus on the nature of the impacts on vulnerable communities or how well they are prepared to cope. In order to address these gaps, a systems approach was used to present an integrated risk and vulnerability assessment framework for studies of community level risk and vulnerability to malaria due to climate change.

RESULTS

Drawing upon published literature on existing frameworks, a systems approach was applied to characterize the factors influencing the interactions between climate change and malaria transmission. This involved structural analysis to determine influential, relay, dependent and autonomous variables in order to construct a detailed causal loop conceptual model that illustrates the relationships among key variables. An integrated assessment framework that considers indicators of both biophysical and social vulnerability was proposed based on the conceptual model.

CONCLUSIONS

A major conclusion was that this integrated assessment framework can be implemented using Bayesian Belief Networks, and applied at a community level using both quantitative and qualitative methods with stakeholder engagement. The approach enables a robust assessment of community level risk and vulnerability to malaria, along with contextually relevant and targeted adaptation strategies for dealing with malaria transmission that incorporate both scientific and community perspectives.

Development and validation of a rapid assessment tool for malaria prevention

BACKGROUND

Insecticide-treated bed nets (ITN) have been shown to be efficacious in reducing malaria morbidity and mortality in many regions. Unfortunately in some areas, malaria has persisted despite the scale up of ITNs. Recent reports indicate that human behaviour and mosquito behaviour are potential threats to the efficacy of ITNs. However, these concerns are likely highly heterogeneous even at very small scales. This study aimed at developing, testing and validating a rapid assessment tool to collect actionable information at local levels for a quick evaluation of potential barriers to malaria prevention.

METHODS

The study was conducted at the Webuye Health and Demographic Surveillance Site in Bungoma East Sub-County, Kenya. Based on the findings from the case-control study, 12 primary surveillance components that encompass the major impediments to successful prevention were identified and used to develop a rapid assessment tool. Twenty community health volunteers were trained to identify patients with laboratory-confirmed malaria in six peripheral health facilities located within six sub locations and subsequently followed them up to their homes to conduct a rapid assessment. Sampling and analysis of the results of the survey are based on Lot Quality Assurance.

RESULTS

The tool was able to detect local heterogeneity in bed net coverage, bed net use and larval site abundance in the six health facility catchment areas. Nearly all the catchment areas met the action threshold for incomplete household coverage (i.e. not all household members not using a net the previous night) except the peri-urban area. Although the threshold for nets not in good condition was set very high (≥50%), only two catchment areas failed to meet the action threshold. On the indicator for "Net not used every day last week", half of the areas failed, while for net ownership, only two areas met the action threshold.

CONCLUSION

The rapid assessment tool was able to detect marked heterogeneity in key indicators for malaria prevention between patients attending health facilities, and can distinguish between priority areas for intervention. There is need to validate it for use in other contexts.

Assessing temporal associations between environmental factors and malaria morbidity at varying transmission settings in Uganda

Background

Environmental factors play a major role in transmission of malaria given their relationship to both the development and survival of the mosquito and parasite. The associations between environmental factors and malaria can be used to inform the development of early warning systems for increases in malaria burden. The objective of this study was to assess temporal relationships between rainfall, temperature and vegetation with malaria morbidity across three different transmission settings in Uganda.

Methods

Temporal relationships between environmental factors (weekly total rainfall, mean day time temperature and enhanced vegetation index series) and malaria morbidity (weekly malaria case count data and test positivity rate series) over the period January 2010–May 2013 in three sites located in varying malaria transmission settings in Uganda was explored using cross-correlation with pre-whitening. Sites included Kamwezi (low transmission), Kasambya (moderate transmission) and Nagongera (high transmission).

Results

Nagongera received the most rain (30.6 mm) and experienced, on average, the highest daytime temperatures (29.8 °C) per week. In the study period, weekly TPR and number of malaria cases were highest at Kasambya and lowest at Kamwezi. The largest cross-correlation coefficients between environmental factors and malaria morbidity for each site was 0.27 for Kamwezi (rainfall and cases), 0.21 for Kasambya (vegetation and TPR), and −0.27 for Nagongera (daytime temperature and TPR). Temporal associations between environmental factors (rainfall, temperature and vegetation) with malaria morbidity (number of malaria cases and TPR) varied by transmission setting. Longer time lags were observed at Kamwezi and Kasambya compared to Nagongera in the relationship between rainfall and number of malaria cases. Comparable time lags were observed at Kasambya and Nagongera in the relationship between temperature and malaria morbidity. Temporal analysis of vegetation with malaria morbidity revealed longer lags at Kasambya compared to those observed at the other two sites.

Conclusions

This study showed that temporal associations between environmental factors with malaria morbidity vary by transmission setting in Uganda. This suggests the need to incorporate local transmission differences when developing malaria early warning systems that have environmental predictors in Uganda. This will result in development of more accurate early warning systems, which are a prerequisite for effective malaria control in such a setting.

El Niño-based malaria epidemic warning for Oromia, Ethiopia, from August 2016 to July 2017

OBJECTIVE

Tropical highland malaria intensifies and shifts to higher altitudes during exceptionally warm years. Above-normal temperatures associated with El Niño during boreal winter months (December-March) may intensify malaria in East African highlands. We assessed the malaria risk for Oromia, the largest region of Ethiopia with around 30 million inhabitants.

METHODS

Simple linear regression and spatial analyses were used to associate sea surface temperatures (SST) in the Pacific and surface temperatures in Ethiopia with annual malaria risk in Oromia, based on confirmed cases of malaria between 1982 and 2005.

RESULTS

A strong association (R² = 0.6, P < 0.001) was identified between malaria and sea surface temperatures in the Pacific, anticipating a 70% increase in malaria risk for the period from August 2016 to July 2017. This forecast was quantitatively supported by elevated land surface temperatures (+1.6 °C) in December 2015. When more station data become available and mean March 2016 temperatures from meteorological stations can be taken into account, a more robust prediction can be issued.

CONCLUSION

An epidemic warning is issued for Oromia, Ethiopia, between August 2016 and July 2017 and may include the pre-July short malaria season. Similar relationships reported for Madagascar point to an epidemic risk for all East African highlands with around 150 million people. Preparedness for this high risk period would include pre-emptive intradomestic spraying with insecticides, adequate stocking of antimalarials, and spatial extension of diagnostic capacity and more frequent reporting to enable a rapid public health response when and where required.

Integrated paediatric fever management and antibiotic over-treatment in Malawi health facilities: data mining a national facility census

Background

There are growing concerns about irrational antibiotic prescription practices in the era of test-based malaria case management. This study assessed integrated paediatric fever management using malaria rapid diagnostic tests (RDT) and Integrated Management of Childhood Illness (IMCI) guidelines, including the relationship between RDT-negative results and antibiotic over-treatment in Malawi health facilities in 2013–2014.

Methods

A Malawi national facility census included 1981 observed sick children aged 2–59 months with fever complaints. Weighted frequencies were tabulated for other complaints, assessments and prescriptions for RDT-confirmed malaria, IMCI-classified non-severe pneumonia, and clinical diarrhoea. Classification trees using model-based recursive partitioning estimated the association between RDT results and antibiotic over-treatment and learned the influence of 38 other input variables at patient-, provider- and facility-levels.

Results

Among 1981 clients, 72 % were tested or referred for malaria diagnosis and 85 % with RDT-confirmed malaria were prescribed first-line anti-malarials. Twenty-eight percent with IMCI-pneumonia were not prescribed antibiotics (under-treatment) and 59 % ‘without antibiotic need’ were prescribed antibiotics (over-treatment). Few clients had respiratory rates counted to identify antibiotic need for IMCI-pneumonia (18 %). RDT-negative children had 16.8 (95 % CI 8.6–32.7) times higher antibiotic over-treatment odds compared to RDT-positive cases conditioned by cough or difficult breathing complaints.

Conclusions

Integrated paediatric fever management was sub-optimal for completed assessments and antibiotic targeting despite common compliance to malaria treatment guidelines. RDT-negative results were strongly associated with antibiotic over-treatment conditioned by cough or difficult breathing complaints. A shift from malaria-focused ‘test and treat’ strategies toward ‘IMCI with testing’ is needed to improve quality fever care and rational use of both anti-malarials and antibiotics in line with recent global commitments to combat resistance.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-016-1439-7) contains supplementary material, which is available to authorized users.

Seasonal and Geographic Variation of Pediatric Malaria in Burundi: 2011 to 2012

We analyzed hospitalization records from 2011 to 2012 to examine the spatial patterns of pediatric malaria in Burundi. Malaria case data for those below the age of five years were categorized according to the four principal seasons of Burundi, which are two rainy seasons (February to May; September to November) and two dry seasons (June to August; December to January). The Getis-Ord Gi* statistic was used to examine seasonal spatial patterns of pediatric malaria, whereas geographically weighted regression (GWR) were used to examine the potential role of environmental variables on the spatial patterns of cases. There were a total of 19,890 pediatric malaria cases reported during the study period. The incidence among males was higher than that among females; and it was higher in rural districts. The seasonal incidence peaks occurred in the northern half of the country during the wet season while during the dry season, incidence was higher in southern Burundi. Elevation played a greater role in explaining variance in the prevalence of pediatric malaria during seasonal peaks than rainfall. The counterintuitive finding in northern Burundi confirms previous findings and suggests other factors (e.g., land cover/land use) facilitate the persistence of the mosquito population in the highlands of Africa.

Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia

INTRODUCTION

Haemophilia is a rare genetic haemorrhagic disease characterized by partial or complete deficiency of coagulation factor VIII, for haemophilia A, or IX, for haemophilia B. As in any other medical research domain, the field of haemophilia research is increasingly concerned with finding factors associated with binary or continuous outcomes through multivariable models. Traditional models include multiple logistic regressions, for binary outcomes, and multiple linear regressions for continuous outcomes. Yet these regression models are at times difficult to implement, especially for non-statisticians, and can be difficult to interpret.

AIMS

The present paper sought to didactically explain how, why, and when to use classification and regression tree (CART) analysis for haemophilia research.

MATERIALS & METHODS

The CART method is non-parametric and non-linear, based on the repeated partitioning of a sample into subgroups based on a certain criterion. Breiman developed this method in 1984. Classification trees (CTs) are used to analyse categorical outcomes and regression trees (RTs) to analyse continuous ones.

RESULTS

The CART methodology has become increasingly popular in the medical field, yet only a few examples of studies using this methodology specifically in haemophilia have to date been published. Two examples using CART analysis and previously published in this field are didactically explained in details.

CONCLUSION

There is increasing interest in using CART analysis in the health domain, primarily due to its ease of implementation, use, and interpretation, thus facilitating medical decision-making. This method should be promoted for analysing continuous or categorical outcomes in haemophilia, when applicable.

Mapping malaria risk and vulnerability in the United Republic of Tanzania: a spatial explicit model

Background

Outbreaks of vector-borne diseases (VBDs) impose a heavy burden on vulnerable populations. Despite recent progress in eradication and control, malaria remains the most prevalent VBD. Integrative approaches that take into account environmental, socioeconomic, demographic, biological, cultural, and political factors contributing to malaria risk and vulnerability are needed to effectively reduce malaria burden. Although the focus on malaria risk has increasingly gained ground, little emphasis has been given to develop quantitative methods for assessing malaria risk including malaria vulnerability in a spatial explicit manner.

Methods

Building on a conceptual risk and vulnerability framework, we propose a spatial explicit approach for modeling relative levels of malaria risk - as a function of hazard, exposure, and vulnerability - in the United Republic of Tanzania. A logistic regression model was employed to identify a final set of risk factors and their contribution to malaria endemicity based on multidisciplinary geospatial information. We utilized a Geographic Information System for the construction and visualization of a malaria vulnerability index and its integration into a spatially explicit malaria risk map.

Results

The spatial pattern of malaria risk was very heterogeneous across the country. Malaria risk was higher in Mainland areas than in Zanzibar, which is a result of differences in both malaria entomological inoculation rate and prevailing vulnerabilities. Areas of high malaria risk were identified in the southeastern part of the country, as well as in two distinct “hotspots” in the northwestern part of the country bordering Lake Victoria, while concentrations of high malaria vulnerability seem to occur in the northwestern, western, and southeastern parts of the mainland. Results were visualized using both 10×10 km² grids and subnational administrative units.

Conclusions

The presented approach makes an important contribution toward a decision support tool. By decomposing malaria risk into its components, the approach offers evidence on which factors could be targeted for reducing malaria risk and vulnerability to the disease. Ultimately, results offer relevant information for place-based intervention planning and more effective spatial allocation of resources.

Electronic supplementary material

The online version of this article (doi:10.1186/s12963-015-0036-2) contains supplementary material, which is available to authorized users.

Assessing the social vulnerability to malaria in Rwanda

Background

Since 2004, malaria interventions in Rwanda have resulted in substantial decline of malaria incidence. However, this achievement is fragile as potentials for local malaria transmissions remain. The risk of getting malaria infection is partially explained by social conditions of vulnerable populations. Since vulnerability to malaria is both influenced by social and environmental factors, its complexity cannot be measured by a single value. The aim of this paper is, therefore, to apply a composite indicator approach for assessing social vulnerability to malaria in Rwanda. This assessment informs the decision-makers in targeting malaria interventions and allocating limited resources to reduce malaria burden in Rwanda.

Methods

A literature review was used to conceptualize the social vulnerability to malaria and to select the appropriate vulnerability indicators. Indicators used in the index creation were classified into susceptibility and lack of resilience vulnerability domains. The main steps followed include selection of indicators and datasets, imputation of missing values, descriptive statistics, normalization and weighting of indicators, local sensitivity analysis and indicators aggregation. Correlation analysis helped to empirically evidence the association between the indicators and malaria incidence.

Results

The high values of social vulnerability to malaria are found in Gicumbi, Rusizi, Nyaruguru and Gisagara, and low values in Muhanga, Nyarugenge, Kicukiro and Nyanza. The most influential susceptibility indicators to increase malaria are population change (r = 0.729), average number of persons per bedroom (r = 0.531), number of households affected by droughts and famines (r = 0.591), and area used for irrigation (r = 0.611). The bed net ownership (r = −0.398) and poor housing wall materials (0.378) are the lack of resilience indicators that significantly correlate with malaria incidence.

Conclusions

The developed composite index social vulnerability to malaria indicates which indicators need to be addressed and in which districts. The results from this study are salient for public health policy- and decision makers in malaria control in Rwanda and timely support the national integrated malaria initiative. Future research development should focus on spatial explicit vulnerability assessment by combining environmental and social drivers to achieve an integrated and complete assessment of vulnerability to malaria.

Study and ranking of determinants of Taenia solium infections by classification tree models

Taenia solium taeniasis/cysticercosis is an important public health problem occurring mainly in developing countries. This work aimed to study the determinants of human T. solium infections in the Eastern province of Zambia and rank them in order of importance. A household (HH)-level questionnaire was administered to 680 HHs from 53 villages in two rural districts and the taeniasis and cysticercosis status determined. A classification tree model (CART) was used to define the relative importance and interactions between different predictor variables in their effect on taeniasis and cysticercosis. The Katete study area had a significantly higher taeniasis and cysticercosis prevalence than the Petauke area. The CART analysis for Katete showed that the most important determinant for cysticercosis infections was the number of HH inhabitants (6 to 10) and for taeniasis was the number of HH inhabitants > 6. The most important determinant in Petauke for cysticercosis was the age of head of household > 32 years and for taeniasis it was age < 55 years. The CART analysis showed that the most important determinant for both taeniasis and cysticercosis infections was the number of HH inhabitants (6 to 10) in Katete district and age in Petauke. The results suggest that control measures should target HHs with a high number of inhabitants and older individuals.

Spatial-explicit modeling of social vulnerability to malaria in East Africa

Background

Despite efforts in eradication and control, malaria remains a global challenge, particularly affecting vulnerable groups. Despite the recession in malaria cases, previously malaria free areas are increasingly confronted with epidemics as a result of changing environmental and socioeconomic conditions. Next to modeling transmission intensities and probabilities, integrated spatial methods targeting the complex interplay of factors that contribute to social vulnerability are required to effectively reduce malaria burden. We propose an integrative method for mapping relative levels of social vulnerability in a spatially explicit manner to support the identification of intervention measures.

Methods

Based on a literature review, a holistic risk and vulnerability framework has been developed to guide the assessment of social vulnerability to water-related vector-borne diseases (VBDs) in the context of changing environmental and societal conditions. Building on the framework, this paper applies spatially explicit modeling for delineating homogeneous regions of social vulnerability to malaria in eastern Africa, while taking into account expert knowledge for weighting the single vulnerability indicators. To assess the influence of the selected indicators on the final index a local sensitivity analysis is carried out.

Results

Results indicate that high levels of malaria vulnerability are concentrated in the highlands, where immunity within the population is currently low. Additionally, regions with a lack of access to education and health services aggravate vulnerability. Lower values can be found in regions with relatively low poverty, low population pressure, low conflict density and reduced contributions from the biological susceptibility domain. Overall, the factors characterizing vulnerability vary spatially in the region. The vulnerability index reveals a high level of robustness in regard to the final choice of input datasets, with the exception of the immunity indicator which has a marked impact on the composite vulnerability index.

Conclusions

We introduce a conceptual framework for modeling risk and vulnerability to VBDs. Drawing on the framework we modeled social vulnerability to malaria in the context of global change using a spatially explicit approach. The results provide decision makers with place-specific options for targeting interventions that aim at reducing the burden of the disease amongst the different vulnerable population groups.

Towards seasonal forecasting of malaria in India

Background

Malaria presents public health challenge despite extensive intervention campaigns. A 30-year hindcast of the climatic suitability for malaria transmission in India is presented, using meteorological variables from a state of the art seasonal forecast model to drive a process-based, dynamic disease model.

Methods

The spatial distribution and seasonal cycles of temperature and precipitation from the forecast model are compared to three observationally-based meteorological datasets. These time series are then used to drive the disease model, producing a simulated forecast of malaria and three synthetic malaria time series that are qualitatively compared to contemporary and pre-intervention malaria estimates. The area under the Relative Operator Characteristic (ROC) curve is calculated as a quantitative metric of forecast skill, comparing the forecast to the meteorologically-driven synthetic malaria time series.

Results and discussion

The forecast shows probabilistic skill in predicting the spatial distribution of Plasmodium falciparum incidence when compared to the simulated meteorologically-driven malaria time series, particularly where modelled incidence shows high seasonal and interannual variability such as in Orissa, West Bengal, and Jharkhand (North-east India), and Gujarat, Rajastan, Madhya Pradesh and Maharashtra (North-west India). Focusing on these two regions, the malaria forecast is able to distinguish between years of “high”, “above average” and “low” malaria incidence in the peak malaria transmission seasons, with more than 70% sensitivity and a statistically significant area under the ROC curve. These results are encouraging given that the three month forecast lead time used is well in excess of the target for early warning systems adopted by the World Health Organization. This approach could form the basis of an operational system to identify the probability of regional malaria epidemics, allowing advanced and targeted allocation of resources for combatting malaria in India.

Agro-ecology, household economics and malaria in Uganda: empirical correlations between agricultural and health outcomes

BACKGROUND

This paper establishes empirical evidence relating the agriculture and health sectors in Uganda. The analysis explores linkages between agricultural management, malaria and implications for improving community health outcomes in rural Uganda. The goal of this exploratory work is to expand the evidence-base for collaboration between the agricultural and health sectors in Uganda.

METHODS

The paper presents an analysis of data from the 2006 Uganda National Household Survey using a parametric multivariate Two-Limit Tobit model to identify correlations between agro-ecological variables including geographically joined daily seasonal precipitation records and household level malaria risk. The analysis of agricultural and environmental factors as they affect household malaria rates, disaggregated by age-group, is inspired by a complimentary review of existing agricultural malaria literature indicating a gap in evidence with respect to agricultural management as a form of malaria vector management. Crop choices and agricultural management practices may contribute to vector control through the simultaneous effects of reducing malaria transmission, improving housing and nutrition through income gains, and reducing insecticide resistance in both malaria vectors and agricultural pests.

RESULTS

The econometric results show the existence of statistically significant correlations between crops, such as sweet potatoes/yams, beans, millet and sorghum, with household malaria risk. Local environmental factors are also influential- daily maximum temperature is negatively correlated with malaria, while daily minimum temperature is positively correlated with malaria, confirming trends in the broader literature are applicable to the Ugandan context.

CONCLUSIONS

Although not necessarily causative, the findings provide sufficient evidence to warrant purposefully designed work to test for agriculture health causation in vector management. A key constraint to modeling the agricultural basis of malaria transmission is the lack of data integrating both the health and agricultural information necessary to satisfy the differing methodologies used by the two sectors. A national platform for collaboration between the agricultural and health sectors could help align programs to achieve better measurements of agricultural interactions with vector reproduction and evaluate the potential for agricultural policy and programs to support rural malaria control.

Exploring the relationship between access to water, sanitation and hygiene and soil-transmitted helminth infection: a demonstration of two recursive partitioning tools

BACKGROUND

Soil-transmitted helminths (STH) - a class of parasites that affect billions of people - can be mitigated using mass drug administration, though reinfection following treatment occurs within a few months. Improvements to water, sanitation and hygiene (WASH) likely provide sustained benefit, but few rigorous studies have evaluated the specific WASH components most influential in reducing infection. There is a need for alternative analytic approaches to help identify, characterize and further refine the WASH components that are most important to STH reinfection. Traditional epidemiological approaches are not well-suited for assessing the complex and highly correlated relationships commonly seen in WASH.

METHODOLOGY

We introduce two recursive partitioning approaches: classification and regression trees (C&RT) and conditional inference trees (CIT), which can be used to identify complex interactions between WASH indicators and identify sub-populations that may be susceptible to STH reinfection. We illustrate the advantages and disadvantages of these approaches utilizing school- and household-level WASH indicators gathered as part of a school-based randomized control trial in Kenya that measured STH reinfection of pupils 10 months following deworming treatment.

PRINCIPAL FINDINGS

C&RT and CIT analyses resulted in strikingly different decision trees. C&RT may be the preferred approach if interest lies in using WASH indicators to classify individuals or communities as STH infected or uninfected, whereas CIT is most appropriate for identifying WASH indicators that may be causally associated with STH infection. Both tools are well-suited for identifying complex interactions among WASH indicators.

CONCLUSIONS/SIGNIFICANCE

C&RT and CIT are two analytic approaches that may offer valuable insight regarding the identification, selection and refinement of WASH indicators and their interactions with regards to STH control programs; however, they represent solutions to two distinct research questions and careful consideration should be made before deciding which approach is most appropriate.

Testing a multi-malaria-model ensemble against 30 years of data in the Kenyan highlands

Background

Multi-model ensembles could overcome challenges resulting from uncertainties in models’ initial conditions, parameterization and structural imperfections. They could also quantify in a probabilistic way uncertainties in future climatic conditions and their impacts.

Methods

A four-malaria-model ensemble was implemented to assess the impact of long-term changes in climatic conditions on Plasmodium falciparum malaria morbidity observed in Kericho, in the highlands of Western Kenya, over the period 1979–2009. Input data included quality controlled temperature and rainfall records gathered at a nearby weather station over the historical periods 1979–2009 and 1980–2009, respectively. Simulations included models’ sensitivities to changes in sets of parameters and analysis of non-linear changes in the mean duration of host’s infectivity to vectors due to increased resistance to anti-malarial drugs.

Results

The ensemble explained from 32 to 38% of the variance of the observed P. falciparum malaria incidence. Obtained R²-values were above the results achieved with individual model simulation outputs. Up to 18.6% of the variance of malaria incidence could be attributed to the +0.19 to +0.25°C per decade significant long-term linear trend in near-surface air temperatures. On top of this 18.6%, at least 6% of the variance of malaria incidence could be related to the increased resistance to anti-malarial drugs. Ensemble simulations also suggest that climatic conditions have likely been less favourable to malaria transmission in Kericho in recent years.

Conclusions

Long-term changes in climatic conditions and non-linear changes in the mean duration of host’s infectivity are synergistically driving the increasing incidence of P. falciparum malaria in the Kenyan highlands. User-friendly, online-downloadable, open source mathematical tools, such as the one presented here, could improve decision-making processes of local and regional health authorities.

Impact of land-use on malaria transmission in the Plateau region, southeastern Benin

BACKGROUND

The goal of the study is to investigate if local agricultural practices have an impact on malaria transmission in four villages located in the same geographical area within a radius of 15 kilometers. Among the villages, one (Itassoumba) is characterized by the presence of a large market garden and fishpond basins, the three others (Itakpako, Djohounkollé and Ko-koumolou) are characterized by traditional food-producing agriculture.

METHODS

Malaria transmission was evaluated using human-landing catches, both indoors and outdoors, two nights per month for 12 months. Field collected females An. gambiae s.l. were exposed for 1 hour to 0.75% permethrin and 0.05% deltamethrin using WHO insecticide susceptibility test kits and procedures. The presence of the kdr mutation was analyzed by PCR.

RESULTS

Anopheles gambiae s.s form M (93.65%), was identified as the main malaria vector. Its susceptibility level to pyrethroids was the same (p > 0.05) in all villages. kdr mutation frequencies are 81.08 in Itakpako, 85 in Itassoumba, 79.73 in Djohounkollé and 86.84 in Ko-Koumolou (p = 0.63). The entomological inoculation rate ranged from 9.62 to 21.65 infected bites of An. gambiae per human per year in Djohounkollé, Itakpako and Ko-Koumolou against 1159.62 in Itassoumba (p < 0.0001).

CONCLUSION

The level of resistance of An. gambiae to pyrethroids was the same in the four villages. The heterogeneous character of malaria epidemiology was confirmed. The creation of fishponds basins and the development of market-gardening activities increased drastically the malaria transmission in Itassoumba.

The process and utility of classification and regression tree methodology in nursing research

Aim

This paper presents a discussion of classification and regression tree analysis and its utility in nursing research.

Background

Classification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams.

Design

Discussion paper.

Data sources

English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984–2013.

Discussion

Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered.

Implications for Nursing Research

Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method.

Conclusion

Classification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions.

Outdoor malaria transmission in forested villages of Cambodia

Background

Despite progress in malaria control, malaria remains an important public health concern in Cambodia, mostly linked to forested areas. Large-scale vector control interventions in Cambodia are based on the free distribution of long-lasting insecticidal nets (LLINs), targeting indoor- and late-biting malaria vectors only. The present study evaluated the vector density, early biting activity and malaria transmission of outdoor-biting malaria vectors in two forested regions in Cambodia.

Methods

In 2005 two entomological surveys were conducted in 12 villages and their related forest plots in the east and west of Cambodia. Mosquitoes were collected outdoors by human landing collections and subjected to enzyme-linked immunosorbent assay (ELISA) to detect Plasmodium sporozoites after morphological identification. Blood samples were collected in the same villages for serological analyses. Collected data were analysed by the classification and regression tree (CART) method and linear regression analysis.

Results

A total of 11,826 anophelines were recorded landing in 787 man-night collections. The majority (82.9%) were the known primary and secondary vectors. Most of the variability in vector densities and early biting rates was explained by geographical factors, mainly at village level. Vector densities were similar between forest and village sites. Based on ELISA results, 29% out of 17 Plasmodium-positive bites occurred before sleeping time, and 65% in the forest plots. The entomological inoculation rates of survey 1 were important predictors of the respective seroconversion rates in survey 2, whereas the mosquito densities were not.

Discussion

In Cambodia, outdoor malaria transmission in villages and forest plots is important. In this context, deforestation might result in lower densities of the primary vectors, but also in higher densities of secondary vectors invading deforested areas. Moreover, higher accessibility of the forest could result in a higher man-vector contact. Therefore, additional vector control measures should be developed to target outdoor- and early-biting vectors.

The effect of dams and seasons on malaria incidence and anopheles abundance in Ethiopia

Background

Reservoirs created by damming rivers are often believed to increase malaria incidence risk and/or stretch the period of malaria transmission. In this paper, we report the effects of a mega hydropower dam on P. falciparum malaria incidence in Ethiopia.

Methods

A longitudinal cohort study was conducted over a period of 2 years to determine Plasmodium falciparum malaria incidence among children less than 10 years of age living near a mega hydropower dam in Ethiopia. A total of 2080 children from 16 villages located at different distances from a hydropower dam were followed up from 2008 to 2010 using active detection of cases based on weekly house to house visits. Of this cohort of children, 951 (48.09%) were females and 1059 (51.91%) were males, with a median age of 5 years. Malaria vectors were simultaneously surveyed in all the 16 study villages. Frailty models were used to explore associations between time-to-malaria and potential risk factors, whereas, mixed-effects Poisson regression models were used to assess the effect of different covariates on anopheline abundance.

Results

Overall, 548 (26.86%) children experienced at least one clinical malaria episode during the follow up period with mean incidence rate of 14.26 cases/1000 child-months at risk (95% CI: 12.16 - 16.36). P. falciparum malaria incidence showed no statistically significant association with distance from the dam reservoir (p = 0.32). However, P. falciparum incidence varied significantly between seasons (p < 0.01). The malaria vector, Anopheles arabiensis, was however more abundant in villages nearer to the dam reservoir.

Conclusions

P. falciparum malaria incidence dynamics were more influenced by seasonal drivers than by the dam reservoir itself. The findings could have implications in timing optimal malaria control interventions and in developing an early warning system in Ethiopia.