Use of prospective hospital surveillance data to define spatiotemporal heterogeneity of malaria risk in coastal Kenya

Spatiotemporal dynamics of malaria in Zanzibar, 2015-2020

BACKGROUND

Despite high coverage of malaria interventions, malaria elimination in Zanzibar remains elusive, with the annual number of cases increasing gradually over the last 3 years.

OBJECTIVE

The aims of the study were to (1) assess the spatiotemporal dynamics of malaria in Zanzibar between 2015 and 2020 and (2) identify malaria hotspots that would allow Zanzibar to develop an epidemiological stratification for more effective and granular intervention targeting.

METHODS

In this study, we analysed data routinely collected by Zanzibar's Malaria Case Notification (MCN) system. The system collects sociodemographic and epidemiological data from all malaria cases. Cases are passively detected at health facilities (ie, primary index cases) and through case follow-up and reactive case detection (ie, secondary cases). Analyses were performed to identify the spatial heterogeneity of case reporting at shehia (ward) level during transmission seasons.

RESULTS

From 1 January 2015 to 30 April 2020, the MCN system reported 22 686 index cases. Number of cases reported showed a declining trends from 2015 to 2016, followed by an increase from 2017 to 2020. More than 40% of cases had a travel history outside Zanzibar in the month prior to testing positive for malaria. The proportion of followed up index cases was approximately 70% for all years. Out of 387 shehias, 79 (20.4%) were identified as malaria hotspots in any given year; these hotspots reported 52% of all index cases during the study period. Of the 79 hotspot shehias, 12 were hotspots in more than 4 years, that is, considered temporally stable, reporting 14.5% of all index cases.

CONCLUSIONS

Our findings confirm that the scale-up of malaria interventions has greatly reduced malaria transmission in Zanzibar since 2006. Analyses identified hotspots, some of which were stable across multiple years. Malaria efforts should progress from a universal intervention coverage approach to an approach that is more tailored to a select number of hotspot shehias.

Schistosomiasis, soil transmitted helminthiasis, and malaria co-infections among women of reproductive age in rural communities of Kwale County, coastal Kenya

BACKGROUND

Schistosoma haematobium, soil transmitted helminthes (STH), and malaria lead to a double burden in pregnancy that eventually leads to poor immunity, increased susceptibility to other infections, and poor pregnancy outcomes. Many studies have been carried out on pre-school and school aged children but very little has been done among the at risk adult population including women of reproductive age (WRA). Our current study sought to establish the risk factors and burden of co-infection with S. haematobium, STH, and Plasmodium sp. among WRA in Kwale County, Coastal Kenya.

METHODS

A total of 534 WRA between the ages of 15-50 were enrolled in this cross-sectional study from four villages; Bilashaka and Mwaluphamba in Matuga sub-County, and Mwachinga and Dumbule in Kinango sub-County. Socio-demographic information was collected using a pre-tested standardized questionnaire. Parasitological examination was done using urine filtration method for Schistosoma haematobium, Kato Katz for STH (Ascaris lumbricoides, Hookworm, Trichuris trichiura), and standard slide microscopy for Plasmodium sp. Statistical analyses were carried out using STATA version 15.1.

RESULTS

The overall prevalence of S. haematobium was 3.8% (95% CI: 2.6-5.4) while that for malaria was 4.9% (95% CI: 2.0-11.7). The prevalence of STH was 5.6% (95% CI: 2.8-11.3) with overall prevalence of 5.3% (95% CI: 2.5-10.9) for hookworm and 0.6% (95% CI: 0.2-1.9) for T. trichiura. The occurrence of co-infection was low and was recorded between S. haematobium and P. falciparum (0.6%), followed by S. haematobium and STH (0.4%). Among pregnant women, 2.6% had co-infection with S. haematobium and P. falciparum. Only 1.3% had co-infection with S. haematobium and hookworm or T. trichiura. Among non-pregnant women, co-infection with S. haematobium and P. falciparum was 0.2%. Similarly, co-infection with S. haematobium and hookworm or T. trichiura was 0.2%. Bed net ownership and usage among pregnant women was 87.8 and 96.6%, respectively. 66.3% of the women reported using improved water sources for drinking while 78.1% reported using improved sanitation facilities.

CONCLUSION

The use of improved WASH activities might have contributed to the low prevalence of STHs and S. haematobium infections. Further, bed net ownership and usage might have resulted in the low prevalence of Plasmodium sp. infections observed.

Spatial and spatio-temporal methods for mapping malaria risk: a systematic review

Background

Approaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this review aimed to systematically retrieve, summarise methods and examine covariates that have been used for mapping malaria risk in sub-Saharan Africa (SSA).

Methods

A systematic search of malaria risk mapping studies was conducted using PubMed, EBSCOhost, Web of Science and Scopus databases. The search was restricted to refereed studies published in English from January 1968 to April 2020. To ensure completeness, a manual search through the reference lists of selected studies was also undertaken. Two independent reviewers completed each of the review phases namely: identification of relevant studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, data extraction and methodological quality assessment using a validated scoring criterion.

Results

One hundred and seven studies met the inclusion criteria. The median quality score across studies was 12/16 (range: 7–16). Approximately half (44%) of the studies employed variable selection techniques prior to mapping with rainfall and temperature selected in over 50% of the studies. Malaria incidence (47%) and prevalence (35%) were the most commonly mapped outcomes, with Bayesian geostatistical models often (31%) the preferred approach to risk mapping. Additionally, 29% of the studies employed various spatial clustering methods to explore the geographical variation of malaria patterns, with Kulldorf scan statistic being the most common. Model validation was specified in 53 (50%) studies, with partitioning data into training and validation sets being the common approach.

Conclusions

Our review highlights the methodological diversity prominent in malaria risk mapping across SSA. To ensure reproducibility and quality science, best practices and transparent approaches should be adopted when selecting the statistical framework and covariates for malaria risk mapping. Findings underscore the need to periodically assess methods and covariates used in malaria risk mapping; to accommodate changes in data availability, data quality and innovation in statistical methodology.

Spatial-temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast

BACKGROUND

The over-distributed pattern of malaria transmission has led to attempts to define malaria "hotspots" that could be targeted for purposes of malaria control in Africa. However, few studies have investigated the use of routine health facility data in the more stable, endemic areas of Africa as a low-cost strategy to identify hotspots. Here the objective was to explore the spatial and temporal dynamics of fever positive rapid diagnostic test (RDT) malaria cases routinely collected along the Kenyan Coast.

METHODS

Data on fever positive RDT cases between March 2018 and February 2019 were obtained from patients presenting to six out-patients health-facilities in a rural area of Kilifi County on the Kenyan Coast. To quantify spatial clustering, homestead level geocoded addresses were used as well as aggregated homesteads level data at enumeration zone. Data were sub-divided into quarterly intervals. Kulldorff's spatial scan statistics using Bernoulli probability model was used to detect hotspots of fever positive RDTs across all ages, where cases were febrile individuals with a positive test and controls were individuals with a negative test.

RESULTS

Across 12 months of surveillance, there were nine significant clusters that were identified using the spatial scan statistics among RDT positive fevers. These clusters included 52% of all fever positive RDT cases detected in 29% of the geocoded homesteads in the study area. When the resolution of the data was aggregated at enumeration zone (village) level the hotspots identified were located in the same areas. Only two of the nine hotspots were temporally stable accounting for 2.7% of the homesteads and included 10.8% of all fever positive RDT cases detected.

CONCLUSION

Taking together the temporal instability of spatial hotspots and the relatively modest fraction of the malaria cases that they account for; it would seem inadvisable to re-design the sub-county control strategies around targeting hotspots.

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.

Prevalence and risk factors associated with asymptomatic Plasmodium falciparum infection and anemia among pregnant women at the first antenatal care visit: A hospital based cross-sectional study in Kwale County, Kenya

Background

Prevalence of Prevalence of malaria in pregnancy (MiP) in Kenya ranges from 9% to 18%. We estimated the prevalence and factors associated with MiP and anemia in pregnancy (AiP) among asymptomatic women attending antenatal care (ANC) visits.

Methods

We performed a cross-sectional study among pregnant women attending ANC at Msambweni Hospital, between September 2018 and February 2019. Data was collected and analyzed in Epi Info 7. Descriptive statistics were calculated and we compared MiP and AiP in asymptomatic cases to those without either condition. Adjusted prevalence Odds odds ratios (aPOR) and 95% confidence intervals (CI) were calculated to identify factors associated with asymptomatic MiP and AiP.

Results

We interviewed 308 study participants; their mean age was 26.6 years (± 5.8 years), mean gestational age was 21.8 weeks (± 6.0 weeks), 173 (56.2%) were in the second trimester of pregnancy, 12.9% (40/308) had MiP and 62.7% had AiP. Women who were aged ≤ 20 years had three times likelihood of developing MiP (aPOR = 3.1 Cl: 1.3–7.35) compared to those aged >20 years old. The likelihood of AiP was higher among women with gestational age ≥ 16 weeks (aPOR = 3.9, CI: 1.96–7.75), those with parasitemia (aPOR = 3.3, 95% CI: 1.31–8.18), those in third trimester of pregnancy (aPOR = 2.6, 95% CI:1.40–4.96) and those who reported eating soil as a craving during pregnancy (aPOR = 1.9, 95%CI:1.15–3.29).

Conclusions

Majority of the women had asymptomatic MiP and AiP. MiP was observed in one tenth of all study participants. Asymptomatic MiP was associated with younger age while AiP was associated with gestational age parasitemia, and soil consumption as a craving during pregnancy.

Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

The United Nations' Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used-Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino-southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.

Predicting Risk of Imported Disease with Demographics: Geospatial Analysis of Imported Malaria in Minnesota, 2010-2014

Although immigrants who visit friends and relatives (VFRs) account for most of the travel-acquired malaria cases in the United States, there is limited evidence on community-level risk factors and best practices for prevention appropriate for various VFR groups. Using 2010–2014 malaria case reports, sociodemographic census data, and health services data, we explored and mapped community-level characteristics to understand who is at risk and where imported malaria infections occur in Minnesota. We examined associations with malaria incidence using Poisson and negative binomial regression. Overall, mean incidence was 0.4 cases per 1,000 sub-Saharan African (SSA)–born in communities reporting malaria, with cases concentrated in two areas of Minneapolis–St. Paul. We found moderate and positive associations between imported malaria and counts of SSA- and Asian-born populations, respectively. Our findings may inform future studies to understand the knowledge, attitudes, and practices of VFR travelers and facilitate and focus intervention strategies to reduce imported malaria in the United States.

Malaria smear positivity among Kenyan children peaks at intermediate temperatures as predicted by ecological models

Background

Ambient temperature is an important determinant of malaria transmission and suitability, affecting the life-cycle of the Plasmodium parasite and Anopheles vector. Early models predicted a thermal malaria transmission optimum of 31 °C, later revised to 25 °C using experimental data from mosquito and parasite biology. However, the link between ambient temperature and human malaria incidence remains poorly resolved.

Methods

To evaluate the relationship between ambient temperature and malaria risk, 5833 febrile children (<18 years-old) with an acute, non-localizing febrile illness were enrolled from four heterogenous outpatient clinic sites in Kenya (Chulaimbo, Kisumu, Msambweni and Ukunda). Thick and thin blood smears were evaluated for the presence of malaria parasites. Daily temperature estimates were obtained from land logger data, and rainfall from National Oceanic and Atmospheric Administration (NOAA)’s Africa Rainfall Climatology (ARC) data. Thirty-day mean temperature and 30-day cumulative rainfall were estimated and each lagged by 30 days, relative to the febrile visit. A generalized linear mixed model was used to assess relationships between malaria smear positivity and predictors including temperature, rainfall, age, sex, mosquito exposure and socioeconomic status.

Results

Malaria smear positivity varied between 42–83% across four clinic sites in western and coastal Kenya, with highest smear positivity in the rural, western site. The temperature ranges were cooler in the western sites and warmer in the coastal sites. In multivariate analysis controlling for socioeconomic status, age, sex, rainfall and bednet use, malaria smear positivity peaked near 25 °C at all four sites, as predicted a priori from an ecological model.

Conclusions

This study provides direct field evidence of a unimodal relationship between ambient temperature and human malaria incidence with a peak in malaria transmission occurring at lower temperatures than previously recognized clinically. This nonlinear relationship with an intermediate optimal temperature implies that future climate warming could expand malaria incidence in cooler, highland regions while decreasing incidence in already warm regions with average temperatures above 25 °C. These findings support efforts to further understand the nonlinear association between ambient temperature and vector-borne diseases to better allocate resources and respond to disease threats in a future, warmer world.

Electronic supplementary material

The online version of this article (10.1186/s13071-019-3547-z) contains supplementary material, which is available to authorized users.

Parasitic infections during pregnancy need not affect infant antibody responses to early vaccination against Streptococcus pneumoniae, diphtheria, or Haemophilus influenzae type B

BACKGROUND

Globally, vaccine-preventable diseases remain a significant cause of early childhood mortality despite concerted efforts to improve vaccine coverage. One reason for impaired protection may be the influence of prenatal exposure to parasitic antigens on the developing immune system. Prior research had shown a decrease in infant vaccine response after in utero parasite exposure among a maternal cohort without aggressive preventive treatment. This study investigated the effect of maternal parasitic infections on infant vaccination in a more recent setting of active anti-parasitic therapy.

METHODOLOGY/PRINCIPAL FINDINGS

From 2013-2015, 576 Kenyan women were tested in pregnancy for malaria, soil-transmitted helminths, filaria, and S. haematobium, with both acute and prophylactic antiparasitic therapies given. After birth, 567 infants received 10-valent S. pneumoniae conjugate vaccine and pentavalent vaccine for hepatitis B, pertussis, tetanus, H. influenzae type B (Hib) and C. diphtheriae toxoid (Dp-t) at 6, 10, and 14 weeks. Infant serum samples from birth, 10 and 14 weeks, and every six months until age three years, were analyzed using a multiplex bead assay to quantify IgG for Hib, Dp-t, and the ten pneumococcal serotypes. Antenatal parasitic prevalence was high; 461 women (80%) had at least one and 252 (43.6%) had two or more infections during their pregnancy, with the most common being malaria (44.6%), S. haematobium (43.9%), and hookworm (29.2%). Mixed models comparing influence of infection on antibody concentration revealed no effect of prenatal infection status for most vaccine outcomes. Prevalences of protective antibody concentrations after vaccination were similar among the prenatal exposure groups.

CONCLUSIONS/SIGNIFICANCE

These findings are in contrast with results from our prior cohort study performed when preventive anti-parasite treatment was less frequently given. The results suggest that the treatment of maternal infections in pregnancy may be able to moderate the previously observed effect of antenatal maternal infections on infant vaccine responses.

malERA: An updated research agenda for characterising the reservoir and measuring transmission in malaria elimination and eradication

This paper summarises key advances in defining the infectious reservoir for malaria and the measurement of transmission for research and programmatic use since the Malaria Eradication Research Agenda (malERA) publication in 2011. Rapid and effective progress towards elimination requires an improved understanding of the sources of transmission as well as those at risk of infection. Characterising the transmission reservoir in different settings will enable the most appropriate choice, delivery, and evaluation of interventions. Since 2011, progress has been made in a number of areas. The extent of submicroscopic and asymptomatic infections is better understood, as are the biological parameters governing transmission of sexual stage parasites. Limitations of existing transmission measures have been documented, and proof-of-concept has been established for new innovative serological and molecular methods to better characterise transmission. Finally, there now exists a concerted effort towards the use of ensemble datasets across the spectrum of metrics, from passive and active sources, to develop more accurate risk maps of transmission. These can be used to better target interventions and effectively monitor progress toward elimination. The success of interventions depends not only on the level of endemicity but also on how rapidly or recently an area has undergone changes in transmission. Improved understanding of the biology of mosquito-human and human-mosquito transmission is needed particularly in low-endemic settings, where heterogeneity of infection is pronounced and local vector ecology is variable. New and improved measures of transmission need to be operationally feasible for the malaria programmes. Outputs from these research priorities should allow the development of a set of approaches (applicable to both research and control programmes) that address the unique challenges of measuring and monitoring transmission in near-elimination settings and defining the absence of transmission.

Ecoinformatics (Big Data) for Agricultural Entomology: Pitfalls, Progress, and Promise

Ecoinformatics, as defined in this review, is the use of preexisting data sets to address questions in ecology. We provide the first review of ecoinformatics methods in agricultural entomology. Ecoinformatics methods have been used to address the full range of questions studied by agricultural entomologists, enabled by the special opportunities associated with data sets, nearly all of which have been observational, that are larger and more diverse and that embrace larger spatial and temporal scales than most experimental studies do. We argue that ecoinformatics research methods and traditional, experimental research methods have strengths and weaknesses that are largely complementary. We address the important interpretational challenges associated with observational data sets, highlight common pitfalls, and propose some best practices for researchers using these methods. Ecoinformatics methods hold great promise as a vehicle for capitalizing on the explosion of data emanating from farmers, researchers, and the public, as novel sampling and sensing techniques are developed and digital data sharing becomes more widespread.

Analysis of asymptomatic and clinical malaria in urban and suburban settings of southwestern Ethiopia in the context of sustaining malaria control and approaching elimination

BACKGROUND

Malaria intervention in Ethiopia has been strengthened significantly in the past decade. The Ethiopian government recently stratified the country based upon annual parasite incidence into malaria free, low, moderate and high transmission strata. Districts with low transmission were targeted for indigenous transmission elimination. Surveillance on malaria disease incidence is needed for planning control and elimination efforts.

METHODS

Clinical malaria was monitored prospectively in health facilities in Jimma town, Oromia Region, southwestern Ethiopia from July 2014 to June 2015. Seasonal cross-sectional parasite prevalence surveys in local communities were conducted in 2014 and 2015 in eight kebeles. Case report forms were administered to obtain sociodemographic and epidemiological information from patients.

RESULTS

A total of 1434 suspected malaria cases were examined from the health facilities and 428 confirmed malaria cases were found. Among them, 327 (76.4 %) cases were Plasmodium vivax, 97 (22.7 %) were Plasmodium falciparum, and 4 (0.9 %) were mixed infection of P. vivax and P. falciparum. The annual malaria incidence rate was 1.7 cases per 1000 people at risk. Parasite prevalence in the community was less than 3 %. Household ownership of insecticide-treated nets (ITNs) was 47.3 % (1173/2479) and ITN usage was 37.9 %. All ITNs were long-lasting insecticidal nets, and repellent use was not found in the study area. Being male and traveling were the significant risk factors for P. falciparum malaria. For P. vivax malaria, risk factors included occupation and history of malaria illness during the preceding 30 days.

CONCLUSION

Epidemiological evidence suggested low clinical malaria incidence and prevalence in Jimma town. More aggressive measures may be needed to further suppress vivax transmission. Strategies should be planned targeting sustained control and elimination.