Spatial patterns and climate drivers of malaria in three border areas of Brazil, Venezuela and Guyana, 2016-2018

Factors associated with never treatment and acceptability of mass drug administration for the elimination of lymphatic filariasis in Guyana, 2021

Guyana remains one of four countries in the Americas endemic for lymphatic filariasis (LF). Elimination of LF requires repeated annual mass drug administration (MDA) with sufficient levels of coverage for success. This study assesses the acceptability and never treatment of LF MDA using data from a routine assessment survey in 2021. A subset of individuals, over 20 years of age (n = 2498), were selected to receive an expanded questionnaire to examine factors associated with acceptability and never treatment. Assessed factors include respondent demographics, knowledge, risk perceptions of LF, and opinions on the MDA programme. The majority (73%) of those with scores above the acceptability threshold (score ≥22.5) reported participating in MDA two or more times. Factors strongly and positively associated with scoring above the acceptability threshold include beliefs in importance of participation in MDA for their community (aOR = 2.8, 95%CI (1.1-7.2)), perception of importance of LF treatment (6.9 (3.2-14.7)), receiving treatment in 2021 (2.9 (1.5-5.4)), and the number of self-reported times taking treatment for LF (2.2 (1.1-4.4)). Ten percent of respondents participated in the MDA for the first time in 2021, while 15% reported never treatment during any round of LF MDA. Three factors were statistically associated with participation in MDA across the two levels of the models (level 1: took LF treatment once versus never, and level 2: took LF treatment twice versus never) included: 1) scoring above the acceptability threshold (aOR = 6.2, 95%CI(3.8-10.0)), 2) self-reported importance of participation in MDA for their community (7.1 (2.9-17.8)), and 3) personal beliefs that they should take LF treatment even if they are not sick (2.6 (1.7-3.9)). As Guyana moves closer to LF elimination, these results provide further insight and understanding into programmatic results and could inform further action following MDA activities-particularly if an approach is needed to address never treatment during MDA.

Comparison of SD Bioline Malaria Ag Pf/Pan and Acro Malaria P.f./P.v./Pan with Microscopy and Real Time PCR for the Diagnosis of Human Plasmodium Species

The early diagnosis of malaria is crucial to controlling morbidity and mortality. The World Health Organization (WHO) recommends diagnosing malaria either using light microscopy or a malaria rapid diagnostic test (RDT). Most RDTs use antibodies to detect two P. falciparum histidine-rich proteins named PfHRP2 and PfHRP3. However, false-negative results are known to occur due to the poor performance of RDTs depending on the species and the deletion of the Pfhrp2 and Pfhrp3 genes. This study evaluated new malaria RDTs for the detection of the human Plasmodium species. The Acro Malaria P.f./P.v./Pan Rapid Test Cassette allows the qualitative detection of parasite antigens, such as PfHRP2 specific to Plasmodium falciparum, PvLDH specific to Plasmodium vivax, and/or panLDH Plasmodium genus lactate dehydrogenase, in the blood of infected individuals. This RDT was assessed against 229 samples collected from imported malaria cases, mainly from Africa. The samples were previously diagnosed using light microscopy and RDT (SD Malaria Ag P.f./Pan, SD Bioline Alere Abbott), then confirmed using real time PCR. The two RDTs were evaluated using a comparison with real time PCR as the reference method, and their performances were compared with each other. Compared to SD RDT, the Acro RDT showed a better sensitivity to P. falciparum (96.8% vs. 89.8%), P. vivax (78.6% vs. 64.3%), P. ovale (73.7% vs. 5.3%), and P. malariae (20.0% vs. 0%). The respective specificities of the Acro RDT and SD RDT are 90.7% vs. 95.3% to P. falciparum, 100% to P. vivax, and 100% vs. 100% to Plasmodium genus. Therefore, Acro RDT showed better performance in the identification of P. ovale and low parasitaemia of P. falciparum. In addition, Acro RDT has the advantage of detecting PvLDH-specific antigens. The Acro Malaria RDT presents the benefits of detecting a P. falciparum antigen (PfHRP2) and a P. vivax antigen (PvLDH) with high sensitivity (96.8% and 73.7%, respectively) and specificity (90.7% and 100%, respectively). Acro Malaria P.f./P.v./Pan rapid diagnostic tests could be effectively used in endemic areas, especially when microscopic examination cannot be performed.

A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research

Advancements in Bayesian spatial and spatio-temporal modelling have been observed in recent years. Despite this, there are unresolved issues about the choice of appropriate spatial unit and adjacency matrix in disease mapping. There is limited systematic review evidence on this topic. This review aimed to address these problems. We searched seven databases to find published articles on this topic. A modified quality assessment tool was used to assess the quality of studies. A total of 52 studies were included, of which 26 (50.0%) were on infectious diseases, 10 (19.2%) on chronic diseases, 8 (15.5%) on maternal and child health, and 8 (15.5%) on other health-related outcomes. Only 6 studies reported the reasons for using the specified spatial unit, 8 (15.3%) studies conducted sensitivity analysis for prior selection, and 39 (75%) of the studies used Queen contiguity adjacency. This review highlights existing variation and limitations in the specification of Bayesian spatial and spatio-temporal models used in health research. We found that majority of the studies failed to report the rationale for the choice of spatial units, perform sensitivity analyses on the priors, or evaluate the choice of neighbourhood adjacency, all of which can potentially affect findings in their studies.

Climate variability, socio-economic conditions and vulnerability to malaria infections in Mozambique 2016-2018: a spatial temporal analysis

Background

Temperature, precipitation, relative humidity (RH), and Normalized Different Vegetation Index (NDVI), influence malaria transmission dynamics. However, an understanding of interactions between socioeconomic indicators, environmental factors and malaria incidence can help design interventions to alleviate the high burden of malaria infections on vulnerable populations. Our study thus aimed to investigate the socioeconomic and climatological factors influencing spatial and temporal variability of malaria infections in Mozambique.

Methods

We used monthly malaria cases from 2016 to 2018 at the district level. We developed an hierarchical spatial-temporal model in a Bayesian framework. Monthly malaria cases were assumed to follow a negative binomial distribution. We used integrated nested Laplace approximation (INLA) in R for Bayesian inference and distributed lag nonlinear modeling (DLNM) framework to explore exposure-response relationships between climate variables and risk of malaria infection in Mozambique, while adjusting for socioeconomic factors.

Results

A total of 19,948,295 malaria cases were reported between 2016 and 2018 in Mozambique. Malaria risk increased with higher monthly mean temperatures between 20 and 29°C, at mean temperature of 25°C, the risk of malaria was 3.45 times higher (RR 3.45 [95%CI: 2.37-5.03]). Malaria risk was greatest for NDVI above 0.22. The risk of malaria was 1.34 times higher (1.34 [1.01-1.79]) at monthly RH of 55%. Malaria risk reduced by 26.1%, for total monthly precipitation of 480 mm (0.739 [95%CI: 0.61-0.90]) at lag 2 months, while for lower total monthly precipitation of 10 mm, the risk of malaria was 1.87 times higher (1.87 [1.30-2.69]). After adjusting for climate variables, having lower level of education significantly increased malaria risk (1.034 [1.014-1.054]) and having electricity (0.979 [0.967-0.992]) and sharing toilet facilities (0.957 [0.924-0.991]) significantly reduced malaria risk.

Conclusion

Our current study identified lag patterns and association between climate variables and malaria incidence in Mozambique. Extremes in climate variables were associated with an increased risk of malaria transmission, peaks in transmission were varied. Our findings provide insights for designing early warning, prevention, and control strategies to minimize seasonal malaria surges and associated infections in Mozambique a region where Malaria causes substantial burden from illness and deaths.

Distribution and Risk Factors of Malaria in the Greater Accra Region in Ghana

Malaria remains a serious public health challenge in Ghana including the Greater Accra Region. This study aimed to quantify the spatial, temporal and spatio-temporal patterns of malaria in the Greater Accra Region to inform targeted allocation of health resources. Malaria cases data from 2015 to 2019 were obtained from the Ghanaian District Health Information and Management System and aggregated at a district and monthly level. Spatial analysis was conducted using the Global Moran's I, Getis-Ord Gi*, and local indicators of spatial autocorrelation. Kulldorff's space-time scan statistics were used to investigate space-time clustering. A negative binomial regression was used to find correlations between climatic factors and sociodemographic characteristics and the incidence of malaria. A total of 1,105,370 malaria cases were reported between 2015 and 2019. Significant seasonal variation was observed, with June and July being the peak months of reported malaria cases. The hotspots districts were Kpone-Katamanso Municipal District, Ashaiman Municipal Districts, Tema Municipal District, and La-Nkwantanang-Madina Municipal District. While La-Nkwantanang-Madina Municipal District was high-high cluster. The Spatio-temporal clusters occurred between February 2015 and July 2017 in the districts of Ningo-Prampram, Shai-Osudoku, Ashaiman Municipal, and Kpone-Katamanso Municipal with a radius of 26.63 km and an relative risk of 4.66 (p < 0.001). Malaria cases were positively associated with monthly rainfall (adjusted odds ratio [AOR] = 1.01; 95% confidence interval [CI] = 1.005, 1.016) and the previous month's cases (AOR = 1.064; 95% CI 1.062, 1.065) and negatively correlated with minimum temperature (AOR = 0.86, 95% CI = 0.823, 0.899) and population density (AOR = 0.996, 95% CI = 0.994, 0.998). Malaria control and prevention should be strengthened in hotspot districts in the appropriate months to improve program effectiveness.