Spatial and Temporal Variation of Malaria Incidence in Children Under 10 Years in a Pyrethroid-Resistant Vector Area in Southern Benin

doi:10.21203/rs.3.rs-4808089/v1

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Spatial and Temporal Variation of Malaria Incidence in Children Under 10 Years in a Pyrethroid-Resistant Vector Area in Southern Benin

https://doi.org/10.21203/rs.3.rs-4808089/v1

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Background: Spatial and temporal identification of malaria-endemic areas is a key component of vector-borne disease control. Strategies to target the most vulnerable populations, the periods of high transmission and the most affected geographical areas, should make vector-borne disease control and prevention programmes more cost-effective. The present study focuses on the spatial and temporal dynamics of malaria cases and the exogenous factors influencing the transmission in an area with pyrethroid-resistant mosquito vector populations.

Methods: A prospective cohort study of 1,806 children under 10 years of age was conducted over 20 months to assess the risk of malaria incidence in the Cove-Zagnanado-Ouinhi (CoZO) health zone located in southern Benin. Childhood malaria data were used to identify malaria hotspots according to months of follow-up using the Kulldoff algorithm. Stability scores were calculated by season to assess incidence heterogeneity. Incidence values by month were aggregated with meteorological data; and demographic data were merged to detect cross-correlation between incidence and meteorological variables. Generalised equation estimators were used to identify the factors explaining the spatio-temporal heterogeneity of malaria incidence in the Cove-Zagnanado-Ouinhi (CoZO) health zone.

Results: We observed spatial heterogeneity in malaria transmission hotspots over the study period, with relative risks ranging from 1.59 (p-value=0.032) to 16.24 (p-value=0.002). Malaria incidence ranged from 1.41 (95% IC: 0.96-2.08) to 13.91 (95% IC: 12.22-15.84) cases per 100 child-months. We also found that there was a significant negative association (correlation coefficient =-0.56) between malaria incidence and temperature; and a slightly positive association (correlation coefficient = 0.58) between malaria incidence and rainfall.

Conclusion: Our results have shown that high-resolution satellite data can be used on a small scale to find the relationship with vector-borne diseases such as malaria.

Spatial and temporal heterogeneity

Malaria incidence

Hotspots

Benin

Despite the progress achieved in recent decades, malaria remains a major public health problem in sub-Saharan Africa (SSA). The World Health Organization (WHO) reported nearly 249 million cases in 2022, with 631,000 deaths in 85 endemic countries and areas [1]. Malaria control strategies mainly include use of insecticide-treated bed nets, indoor residual spraying, intermittent preventive treatment (IPT), and seasonal malaria chemoprevention[2]. The main factors associated with malaria-related deaths are often late diagnosis and failures to receive adequate treatments. Other major challenges include the spread of resistance in malaria vectors to insecticides and parasite resistance to antimalarial drugs [3, 4].

In Benin, approximately 38,122 children under 5 years old die, 37.8% of them from malaria, diarrhoea and acute respiratory infections every year. The Zou department accounts for a significant 47% of all deaths in Benin [5]. However, there are very few data available on the factors associated with malaria transmission in this area of Benin, as well as its spatial and temporal variations. Humidity, temperature and rainfall have been described as the common factors influencing malaria transmission in Benin [6–8]. Environmental and behavioural factors are also well-known to be associated with the spread of malaria [9]. Similarly, it is important not to underestimate the importance of other elements, such as vegetation, which can provide refuge and shelter for mosquitoes in adverse weather conditions, such as high summer temperatures, or impede mosquito movement.[10–14]. Larval development ponds in puddles used as mosquito breeding sites can be influenced by topography and soil type, depending on water retention + time [15–17]. The local malaria transmission risk is increased by these breeding sites, as the mosquitoes born from them develop and reproduce nearby [18]. The distance between larval habitats also affects the spread of malaria [13, 19, 20]. In SSA, there are very few studies with larger sample sizes using small-scale meteorological data to investigate the malaria transmission. Malaria incidence is quite heterogeneous in southern Benin, but temporal variations are not well-understood, particularly in areas with a high prevalence of malaria vector resistance to standard insecticides. Additional evidence is then needed to better inform the spatial and temporal transmission of malaria incidence in malaria control strategies in low-income countries such as Benin.

This study aimed to assess the meteorological, socio-demographic, epidemiological, entomological and environmental factors that might explain the spatial and temporal variation of malaria incidence in a high-risk region of southern Benin. Knowledge of the spatio-temporal distribution of the malaria incidence at fine scales is now essential for improving malaria control interventions.

Study area

The study was conducted in the Cove Zagnanado and Ouinhi (CoZO) health area in the Zou department of Benin. The site is located 155 km north of Cotonou, the economic capital of Benin. It has a savannah climate with an average annual temperature of 27.1°C and an average annual rainfall of 1,003.4 mm. There are four seasons, two of which are rainy (from March to July and from October to November). The CoZO health area comprises 123 villages spread over 18 sub-districts in 3 districts. According to the 2019 census conducted as part of the New Nets project [21], the health zone had a population of 216,289 inhabitants, including 66,341 children under the age of 10. In terms of health infrastructure, the health zone has 15 district health centres, a district hospital, 2 outposts and an isolated maternity unit [22].

Data source and population

This study was embedded in the New Nets cluster-randomised control trial, which aimed to evaluate the efficacy of two long-lasting insecticidal nets (LLINs) with dual insecticides (pyrethroid-chlorfenapyr(Py-CFP) and pyrethroid-pyriproxyfen (Py-PPF)) compared to the standard LLIN (pyrethroid-only) for the control of malaria transmitted by pyrethroid-resistant vectors. The cluster-randomised control trial comprised 60 clusters with 20 each randomly assigned to 3 arms. The trial methodology has been fully described elsewhere [23]. A prospective cohort of 1805 children under 10 years, randomly selected from 60 clusters (30 children per cluster), was followed up for 20 months after the LLIN distribution.

Data collection

Children were visited at home monthly during the dry season and twice a month during the rainy season. During these household visits, children with a temperature above 37.5°C and/or a history of fever in the previous 48 hours were systematically tested for malaria using a rapid diagnostic test (RDT). Positive children were treated with artemisinin-based combination therapies (ACTs) according to the Ministry of Health guidelines. Data were collected using digital forms through the ODK (Open data kits) collect. The meteorological data were based on data downloaded from the Copernicus database " ERA5-Land monthly averaged data from 1950 to present" [24].

The cross-sectional baseline survey of the New Net project was also used to assess living standards by cluster. Household wealth data were used to calculate Social Economic Scores (SES), which were then split in two to create two categories of different living standards for different households. The modal class of these two categories is used to determine the standard of living of the cluster.

Data processing and analysis

Hotspot analysis

GPS coordinates of households with malaria-positive children over the 20-month follow-up period were used for monthly spatial analyses using Kulldorff’s method with SaTScan V10.1.3 software[25, 26]. This method consists of an elliptical window of variable size, centre and rotation to group adjacent clusters into clusters of similar incidence. A Monte Carlo algorithm (9999 replicates) was used to test the Kulldorff statistic based on the likelihood ratio (Poisson model with purely spatial analysis). Areas were classified as hotspots the malaria case incidence within the window was significantly higher than the incidence outside the window (p < 0.05). If at least one child in a study cluster was part of a significant cluster detected by SaTScan for a target period, this cluster was considered a hotspot.

Stability analysis

Monthly maps of hotspot clusters were generated and stability scores were calculated based on the percentage of months in which a cluster was a hotspot[27, 28]. Stability maps of malaria hotspots using these percentages were generated for the study period, by season, study arm and soil type using ArcGis V10.3 software.

Statistical analysis

Incidence data aggregated by cluster and month were used to find the statistical risk factor within R V4.3.2 software. The number of new malaria cases per cluster and the size of the dependent population were cumulated, to which we added the aggregated meteorological data (temperature and rainfall), the soil type, the average altitude of the children's houses and the standard of living of the clusters (obtained by principal component analysis of the assets available in households within the clusters). The stationarity of the obtained incidence and meteorological time series were determined using the Kwiatkowski-Phillips-Schmidt-Shin method. Cross-correlation functions were used to determine the lags between the stationary time series for incidence and each meteorological factor. The most significant lags near the origin were taken into account, as well as the value at the origin if it was significant at a threshold of 5%. The lag from the origin that is most correlated with incidence is included in the univariate and varied multiple analyses. Generalised estimating equations were then used to assess the impact of the different meteorological and environmental factors on malaria incidence.

Ethical statement

This study was approved by the Ethics Committee of the Ministry of Health of Benin (Reference N°6/30/MS/DC/SGM/DRFMT/CNERS/SA) and by the Institutional Ethics Committee of the London School of Hygiene and Tropical Medicine (N°16237). All participation was voluntary. Written informed consent was obtained from an adult guardian in the household or given by the participant if over 18 years of age. Consent was obtained for children over 10 years of age.

In July 2020, 2283 households were randomly selected to form the base sample for the enrolment of children for the post-intervention cohort follow-up. There were 3129 eligible children, of whom 1829 were randomly selected and 1806 gave their consent, with 52.2% children under 5 years of age and a sex ratio of 1.07. In the study area, 36.7% of the surface was fine medium soil (Table 1).

Table 1: Baseline characteristics of the children enrolled in the study at the recruitment

Characteristics	All	Py-PPF-LLIN	Py-CFP-LLIN	Standard Py-LLIN
Number of clusters	60	20	20	20
Number of children	1806	604	601	601
Proportion of female children	868/1806(48.1%)	285/604 (47.2%)	291/601 (48.4%)	293/601(48.8%)
Proportion of children under 5 years	942/1806 (52.2%)	316/604 (52.3%)	304/601 (50.6%)	322/601(53.6%)
Proportion of <50% household with high SL)	32/60 (53.3%)	12/20 (60.0%)	10/20 (50.0%)	10/20 (50.0%)
Net usage the night before	1787/1806 (98.9%)	598/604 (99.0%)	593/601 (98.7%)	596/601 (99.2%)
Abbreviations: Py-PPF: pyrethroid-pyriproxyfen; Py-CFP: pyrethroid-chlorfenapyr; SL: standard of living

Between August 2020 and March 2022, there were 2112 malaria cases in cohort children in the CoZo health zone (491, 732, and 889 cases in the Py-CFP LLIN arm, Py-PPF LLIN arm, and standard LLIN arm, respectively). The highest number of malaria cases (228) occurred in August 2021 and the lowest (23) in January 2022. The monthly average malaria incidence rate was 6.65 (95% CI: 06.37-06.94) malaria cases per 100 child-month at risk for the period. The lowest malaria incidence rate of 01.41 (95% CI: 00.96-02.08) cases per 100 child-month was reached in April 2021. This may be due to the deworming treatment administered to children in March 2021. In the CFP-LLIN arm, we observed a relatively low malaria incidence rate compared with the other arms, with an average of 04.61 (95% CI: 04.22-05.03) cases per 100 child-month (Table S2-appendix).

Hotspot analysis

Malaria incidence-related maps, classified by cluster and month, were used to identify the hotspots of households most affected by malaria in the study region. We observed 13 significant hotspots (P-value < 0.05) out of a total of 113 malaria-susceptible hotspots over the whole study period (Figure 1).

Malaria incidence in the significant hotspots varied between 0.86 and 11.37 cases per 100 child-months, with a relative risk (RR) of 6.05 (P-value<0.001) and 7.78 (P-value=0.05) by the estimated risk outside the cluster, respectively (Table S1-appendix).

Stability analysis

Heterogeneity by season and by trial arm

Stability analysis of the significant clusters showed that the northern clusters were the most malaria-prone, with a malaria stability score of at least 20%, compared with some of the southern clusters, which maintained a stability score of less than 20% throughout the study period. We also observed variability in levels of stability across seasons. There was a decrease in the number of clusters with a stability score of at least 20% between the first and second rainy and dry seasons. It should also be noted that there was a change in the areas showing stability in malaria incidence between the two dry seasons (Figure 2). Similarly, northern clusters showed high stability (>50%) in malaria incidence during the two rainy seasons and the first dry season. In addition, the degree of stability in the incidence of malaria was distributed across the study arms.

Heterogeneity by season and soil type

Evaluation of the stability score by soil type shows a variation in the score depending on the soil type in the cluster. It was observed that clusters with the same soil type have almost the same characteristics in terms of malaria incidence stability. Clusters with the same geographical characteristics (soil type=medium fine) tend to have a high incidence area, regardless of the season. The opposite effect is observed in clusters geographically differentiated by fine soil type (Figure 3).

Temporal and risk factors analysis

Regarding the type of LLIN used, clusters in the Py-CFP LLIN arm had the lowest incidence rates compared to the other arms, regardless of the month. However, we observed a similar trend in the incidence rate in the other study LLIN groups (Py-PPF and standard LLIN groups) (Figure 4).

Figure 5 presents the effect of meteorological variables on the average malaria incidence. We observed a moderate positive correlation between rainfall and malaria incidence (correlation coefficient =0.58) and a moderate negative correlation with temperature (correlation coefficient =-0.56). Significant shifts related to malaria incidence were observed with temperature (p < 0.001) and rainfall (p < 0.001). We observed significant lags of up to two months for rainfall and one month for temperature.

We observed a significant association between average house altitude, soil type, temperature, and type of bed net used and malaria incidence (Table 2).

Table 2. Factors associated with malaria incidence in cohort children living in the Cove Zagnanado and Ouinhi (CoZO) health area, Benin, 2020-2022

Variables		Univariate analysis		Multivariate analysis
Variables		IRR (IC: 95%)	P-value	IRR (IC: 95%)	P-value
Soil type	Medium Fine	Reference		Reference
Soil type	Fine	0.78 (0.61 - 1)	0.048	0.54 (0.39 - 0.75)	p<0.001
Standard of living	<50% HH have a high SL	Reference		Reference
Standard of living	> 50% HH have a high SL	0.80 (0.62 - 1.04)	0.094	0.91 (0.70 -1.20)	0.516
Study LLIN	Standard-LLIN	Reference		Reference
	Py-PPF LLIN	0.81 (0.62 - 1.07)	0.15	0.77 (0.60 - 1)	0.048
	Py-CFP LLIN	0.54 (0.42 - 0.70)	p<0.001	0.56 (0.45 - 0.71)	p<0.001
Temperature (°)		0.69 (0.66-0.73)	p<0.001	0.91 (0.84 - 0.98)	0.013
Rainfall (mm)		1.14 (1.12 - 1.15)	p<0.001	1.09 (1.05 - 1.12)	P<0.001
Altitude of Household		1 (1 -1)	0.6	1 (0.99 - 1)	P<0.001
Abbreviations: HH: Household; SL: Standard of living, Py-PPF: pyrethroid-pyriproxyfen; Py-CFP: pyrethroid-chlorfenapyr

This study has highlighted the spatial and temporal variation of malaria incidence and the impact of meteorological factors in southern Benin, an area where malaria vectors are resistant to pyrethroid. It showed that there was a heterogeneous variability in malaria incidence on small scales, depending on several factors such as soil type, season, temperature, and rainfall. Spatial and temporal analysis or the identification of risk factors associated with malaria is a key component to better understanding malaria transmission and accelerating malaria control and elimination [29–32]. Some studies have shown the importance and impact of fine-scale results in informing national and local programme decisions regarding the control of waterborne, foodborne, vector-borne and other diseases [33,34]. We found a spatio-temporal variation in the distribution of malaria case hotspots. This variation depended on the season, or the type of bed net used. This suggests that malaria transmission is heterogeneous and confirms the findings that have identified a relationship between variation in malaria prevalence and use of mosquito nets (less than 17% malaria prevalence with insecticide-treated mosquito nets compared with no nets) and seasonal variation[35–39].

We observed that meteorological factors (rainfall and temperature) influenced malaria incidence. This finding has also been observed in many West and East African countries [40–43]. Our study also showed a negative significant effect of mean monthly temperature on malaria cases, which is consistent with a study conducted in Asembo-Siaya County, Kenya[44]. According to previous studies, rainfall and temperature play a crucial role in the spread of malaria vectors [45]. Above a certain threshold, they favour or hinder the reproduction of Anopheles mosquitoes, the vector of malaria transmission[46–48], as well as the developmental environment for larvae, the incubation period for parasites and the survival of mosquitoes [49,50].

In addition to meteorological data, other factors can influence the risk of malaria transmission, such as the geography of the regions (altitude), seasonal variations (rainy or dry season), the environment (land cover), the standard of living and the type of bed nets. Our results have confirmed the impact of these different factors on the malaria incidence. The effect of the average altitude of the houses where the children in the cohort live on the incidence of malaria is shown by studies conducted in northwestern Ethiopia and West Africa[51–53]. Environmental factors such as land cover and land use have a significant impact on the density and geographical distribution of malaria vectors.

We did not find a correlation between living standards and malaria incidence as observed by [54,55]. This result may be explained by the number of living standard classes we considered after our principal component analysis or by attributing the modal standard of living to the cluster, rather than an individual assessment of the components associated with malaria incidence.

The main limitation of our study is the lack of entomological data (collected at the local level) that could confirm and highlight the interactions between entomology, epidemiology, climatology and meteorology to better explain the variation in malaria incidence.

Knowledge of the distribution of malaria incidence rates at fine scales is essential for control interventions. The use of new technologies such as remote sensing (satellite imagery) and geographic information systems is enabling a better understanding of the environmental, meteorological and geographical factors that influence the distribution of vector-borne diseases. This study has enabled us to identify the factors associated with transmission risk and the epidemiological areas most at risk from malaria. It also shows that these new tools can be used to improve malaria control strategies and better target intervention areas. The role of the environment and human behaviour is crucial in determining susceptibility to malaria transmission or vector-borne diseases.

ACTs: artemisinin-based combination therapies

CI: confidence interval

CoZO: Cove-Zagnanado-Ouinhi

GEE: Generalised estimating equations

GIS: geographic information systems

HH: Household

HZ: health zone

INC_100: incidence per 100 children-month

IPT: intermittent preventive treatment

LLIN (pyrethroid-only)

LLINs: long-lasting insecticidal nets

NC: number of malaria new cases

NF: exposure Population

ODK: open data kit

Py-CFP: Pyrethroid-chlorfenapyr

Py-PPF: Pyrethroid-Pyriproxyfen

RDT: rapid diagnostic test

RR: relative risk

SL: standard of living

sd: Standard deviation

WHO: World Health Organization

Consent for publication: Not applicable

Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.

Competing interests: The authors declare no competing interests

Funding: UNITAID, The Global Fund

Authors' contributions: ED, MA and MNA designed the study and refined the research question and possible analysis methods. ED and MA developed the data analysis plan, performed the analyses, interpreted the results and wrote the main text of the manuscript. All analysis methods were reviewed by MA, MNA, JC, NP and MAK. ED, MA, HA and LA were involved in conduct and field supervision. MCA and MNH supervised the work. All authors reviewed the manuscript, read and approved the final version of the manuscript.

Acknowledgements: Our sincere thanks go to the team at the Cotonou Entomological Research Centre, made up of the research assistants who carried out the child monitoring activity and the very dynamic administrative staff. We would also like to thank all those involved in the project who contributed to the success of the New Nets activities. We would also like to thank the residents, local authorities and community leaders of the CoZO health zone for their cooperation during this activity and the project. We also thank the trial steering committee, the data safety monitoring board, and Charles Thickstun (School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada) for supporting the mapping and cluster delineation.

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Spatial and Temporal Variation of Malaria Incidence in Children Under 10 Years in a Pyrethroid-Resistant Vector Area in Southern Benin

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Abstract

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Introduction

Materials and methods

Study area

Data source and population

Data collection

Data processing and analysis

Hotspot analysis

Stability analysis

Statistical analysis

Results

Discussion

Conclusion

Abbreviations

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References

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