No Difference in the Incidence of Malaria in Human-Landing Mosquito Catch Collectors and Non-Collectors in a Senegalese Village with Endemic Malaria

International audienceBackground The human landing catches is the gold standard method used to study the vectors of malaria and to estimate their aggressiveness. However, this method has raised safety concerns due to a possible increased risk of malaria or other mosquito-borne diseases among the mosquito collectors. The aim of this study was to evaluate the incidence of malaria attacks among mosquito collectors and to compare these results with those of non-collectors in a Senegalese village. Methods From July 1990 to December 2011, a longitudinal malaria study involving mosquito collectors and non-collectors was performed in Dielmo village, Senegal. During the study period, 4 drugs were successively used to treat clinical malaria, and long-lasting insecticide-treated nets were offered to all villagers in July 2008. No malaria chemoprophylaxis was given to mosquito collectors. Incidence of uncomplicated clinical malaria and asymptomatic malaria infection were analyzed among these two groups while controlling for confounding factors associated with malaria risk in random effects negative binomial and logistic regression models, respectively. Results A total of 3,812 person-trimester observations of 199 adults at least 15 years of age were analyzed. Clinical malaria attacks accounted for 6.3% both in collectors and non-collectors, and asymptomatic malaria infections accounted for 21% and 20% in collectors and non-collectors, respectively. A non-significant lower risk of malaria was observed in the collector group in comparison with the non-collector group after adjusting for other risk factors of malaria and endemicity level (Clinical malaria: adjusted incidence rate ratio = 0.89; 95% confidence interval = 0.65-1.22; p= 0.47). Conclusion Being a mosquito collector in Dielmo was not significantly associated with an increased risk of malaria both under holoendemic, mesoendemic and hypoendemic conditions of malaria epidemiology. This result supports the view that HLC, the most accurate method for evaluating malaria transmission, may be used without health concerns in Dielmo

Another challenge in malaria elimination efforts: the increase of malaria among adults after the implementation of long-lasting insecticide-treated nets (LLINs) in Dielmo, Senegal

Abstract Background The widespread use of artemisinin-based combination therapy (ACT) and long-lasting insecticide-treated nets (LLINs) has led to an impressive decrease of malaria burden these recent years in Africa. However, some new challenges about the future of malaria control and elimination efforts have appeared. Among these challenges, the loss and—or—the only partial acquisition of anti-Plasmodium immunity among exposed populations lead to an increase of the age at risk of malaria. Indeed, older children and adults may become more vulnerable to malaria. Studies about malaria among adults seemed, therefore, important. This study investigated the evolution of malaria morbidity in adults of Dielmo (Senegal) before and after the implementation of LLINs. Methods From August 2007 to July 2015, a longitudinal study involving adults above 15 years old was carried out in Dielmo, where ACT was introduced in June 2006 and LLINs in July 2008. In July 2011 and August 2014, all LLINs were renewed. The presence of each person in the village was monitored daily. Thick smears associated lately with rapid diagnosis test (RDT) and quantitative polymerase chain reaction methods were performed for all cases of fever. To assess malaria prevalence, thick smears and RDT were performed quarterly in all individuals. Malaria risks factors were assessed using negative binomial regression mixed-model based on person-trimester observations. Results Malaria morbidity among adults has decreased significantly since the implementation of LLINs in Dielmo. However, malaria resurgences have occurred twice during the 7 years of LLINs use. During these malaria resurgences, the overall incidence of malaria among adults was similar to the incidence during the year before the implementation of LLINs (adjusted incidence rate ratio [95% CI] aIRR = 1.04 [0.66–1.64], p = 0.88 and aIRR = 1.16 [0.74–1.80], p = 0.52 during the first and the second malaria resurgence period, respectively). Younger adults were most vulnerable during these malaria upsurges as the incidence of malaria increased significantly among them (χ2 = 5.2; p = 0.02). Conclusion Malaria among adults especially younger adults should deserve more attention in the areas where malaria was previously endemic as they became vulnerable probably because of the partial acquisition and—or—the loss of anti-Plasmodium relative immunity and the non regular use of LLINs

Random-effect logistic regression models exploring factors associated with asymptomatic malaria infection (n = 3,812).

<p>OR: Odds ratio; aOR: adjusted odds ratio.</p><p>Random-effect logistic regression models exploring factors associated with asymptomatic malaria infection (n = 3,812).</p

Malaria incidence among mosquito collectors and non-mosquito collectors according to different periods in Dielmo.

<p>Malaria incidence among mosquito collectors and non-mosquito collectors according to different periods in Dielmo.</p

Socio-demographic and others characteristics among mosquito collectors and non-collectors in Dielmo (n = 3,812) using a random-effect logistic regression.

<p>OR: Odds ratio.</p><p>Socio-demographic and others characteristics among mosquito collectors and non-collectors in Dielmo (n = 3,812) using a random-effect logistic regression.</p

Socio-demographic and biological characteristics according to malaria attacks and results of random-effect negative binomial regression models exploring factors associated with clinical malaria cases (n = 3,812).

<p>IRR: Incidence rate ratio; aIRR: adjusted incidence rate ratio.</p><p>Socio-demographic and biological characteristics according to malaria attacks and results of random-effect negative binomial regression models exploring factors associated with clinical malaria cases (n = 3,812).</p