High vector diversity and malaria transmission dynamics in five sentinel sites in Cameroon.

BACKGROUND: Malaria remains one of the main causes of morbidity and mortality in Cameroon. To inform vector control intervention decision making, malaria vector surveillance was conducted monthly from October 2018 to September 2020 in five selected sentinel sites (Gounougou and Simatou in the North, and Bonabéri, Mangoum and Nyabessang in the South). METHODS: Human landing catches (HLCs), U.S. Centers for Disease Control and Prevention (CDC) light traps, and pyrethrum spray catches (PSCs) were used to assess vector density, species composition, human biting rate (HBR), endophagic index, indoor resting density (IRD), parity, sporozoite infection rates, entomological inoculation rate (EIR), and Anopheles vectorial capacity. RESULTS: A total of 139,322 Anopheles mosquitoes from 18 species (or 21 including identified sub-species) were collected across all sites. Out of the 18 species, 12 were malaria vectors including Anopheles gambiae sensu lato (s.l.), Anopheles funestus s.l.., Anopheles nili, Anopheles moucheti, Anopheles paludis, Anopheles demeilloni, Anopheles. pharoensis, Anopheles ziemanni, Anopheles multicinctus, Anopheles tenebrosus, Anopheles rufipes, and Anopheles marshallii. Anopheles gambiae s.l. remains the major malaria vector (71% of the total Anopheles) collected, though An. moucheti and An. paludis had the highest sporozoite rates in Nyabessang. The mean indoor HBR of Anopheles ranged from 11.0 bites/human/night (b/h/n) in Bonabéri to 104.0 b/h/n in Simatou, while outdoors, it varied from 24.2 b/h/n in Mangoum to 98.7 b/h/n in Simatou. Anopheles gambiae s.l. and An. moucheti were actively biting until at least 8:00 a.m. The mean Anopheles IRD was 17.1 females/room, and the parity rate was 68.9%. The mean EIRs for each site were 55.4 infective bites/human/month (ib/h/m) in Gounougou, 99.0 ib/h/m in Simatou, 51.2 ib/h/m in Mangoum, 24.4 ib/h/m in Nyabessang, and 18.1 ib/h/m in Bonabéri. Anopheles gambiae s.l. was confirmed as the main malaria vector with the highest vectorial capacity in all sites based on sporozoite rate, except in Nyabessang. CONCLUSION: These findings highlight the high malaria transmission occurring in Cameroon and will support the National Malaria Control Program to design evidence-based malaria vector control strategies, and deployment of effective and integrated vector control interventions to reduce malaria transmission and burden in Cameroon, where several Anopheles species could potentially maintain year-round transmission.

Relationship between malaria, anaemia, nutritional and socio-economic status amongst under-ten children, in the North Region of Cameroon: A cross-sectional assessment.

BACKGROUND: Despite malaria, malnutrition and anaemia being major public-health challenges in Cameroon, very little has been reported on the interaction between these interconnected health determinants. This study therefore sought to investigate the relationship between malaria, anaemia, nutritional and socio-economic status amongst under-ten children living in six localities within two health districts in the North Region of Cameroon. METHODS: Accordingly, a cross- sectional survey was conducted during the peak malaria season in November 2014, in Pitoa and Mayo-Oulo Health Districts. Three hundred and sixty eight children aged 6months-10 years were enrolled. Structured questionnaires were used to assess socio-economic status. Anthropometric indices were taken using standard methods and nutritional status assessed by calculating Height for Age (HA), Weight for Age (WA) and Weight for Height (WH) z-scores to determine stunting, underweight and wasting respectively. Finger-prick blood samples were used to prepare thin and thick blood smears for microscopy. Whole blood was collected to determine the PCV and blood spots on filter paper were used to extract plasmodium DNA for speciation by PCR. RESULTS: Overall prevalence rates of malaria, malnutrition and anaemia were 32.9%, 54.1% and 20.6% respectively. Stunting, underweight and wasting were detected in 56.9%, 63.5% and 34.8% of the children respectively. There was a significant association between malaria and malnutrition [OR = 1.89, (95% CI: 1.12-3.19); (p = 0.017)]. Malnutrition was also strongly associated with malaria status [OR = 2.07, (95% CI: 1.22-3.53); (p = 0.007)]. The prevalence rates of mild, moderate and severe anaemia were 8.1%, 9.2% and 3.3% respectively. Both malaria status and anaemia correlated with development index [OR = 0.75, (95% CI: 0.58-0.99); (p = 0.042)] and [OR = 1.45, (95% CI: 1.05-2.00); (p = 0.023)] respectively. CONCLUSION: Our findings show a synergistic relationship between malaria and malnutrition. Effective collaboration between malaria control and nutrition intervention programmes is essential for proper case management and improved socio-economic status.

Case Definitions of Clinical Malaria in Children from Three Health Districts in the North Region of Cameroon.

Malaria endemicity in Cameroon greatly varies according to ecological environment. In such conditions, parasitaemia, which is associated with fever, may not always suffice to define an episode of clinical malaria. The evaluation of malaria control intervention strategies mostly consists of identifying cases of clinical malaria and is crucial to promote better diagnosis for accurate measurement of the impact of the intervention. We sought out to define and quantify clinical malaria cases in children from three health districts in the Northern region of Cameroon. A cohort study of 6,195 children aged between 6 and 120 months was carried out during the raining season (July to October) between 2013 and 2014. Differential diagnosis of clinical malaria was performed using the parasite density and axillary temperature. At recruitment, patients with malaria-related symptoms (fever [axillary temperature ≥ 37.5°C], chills, severe malaise, headache, or vomiting) and a malaria positive blood smear were classified under clinical malaria group. The malaria attributable fraction was calculated using logistic regression models. Plasmodium falciparum was responsible for over 91% of infections. Children from Pitoa health district had the highest number of asymptomatic infections (45.60%) compared to those from Garoua and Mayo Oulo. The most suitable cut-off for the association between parasite densities and fever was found among children less than 24 months. Overall, parasite densities that ranged above 3,200 parasites per µl of blood could be used to define the malaria attributable fever cases. In groups of children aged between 24 and 59 months and 60 and 94 months, the optimum cut-off parasite density was 6,400 parasites per µl of blood, while children aged between 95 and 120 months had a cut-off of 800 parasites per µl of blood. In the same ecoepidemiological zone, clinical malaria case definitions are influenced by age and location (health district) and this could be considered when evaluating malaria intervention strategies in endemic areas.

Implications of insecticide resistance for malaria vector control with long-lasting insecticidal nets: a WHO-coordinated, prospective, international, observational cohort study.

BACKGROUND: Scale-up of insecticide-based interventions has averted more than 500 million malaria cases since 2000. Increasing insecticide resistance could herald a rebound in disease and mortality. We aimed to investigate whether insecticide resistance was associated with loss of effectiveness of long-lasting insecticidal nets and increased malaria disease burden. METHODS: This WHO-coordinated, prospective, observational cohort study was done at 279 clusters (villages or groups of villages in which phenotypic resistance was measurable) in Benin, Cameroon, India, Kenya, and Sudan. Pyrethroid long-lasting insecticidal nets were the principal form of malaria vector control in all study areas; in Sudan this approach was supplemented by indoor residual spraying. Cohorts of children from randomly selected households in each cluster were recruited and followed up by community health workers to measure incidence of clinical malaria and prevalence of infection. Mosquitoes were assessed for susceptibility to pyrethroids using the standard WHO bioassay test. Country-specific results were combined using meta-analysis. FINDINGS: Between June 2, 2012, and Nov 4, 2016, 40 000 children were enrolled and assessed for clinical incidence during 1·4 million follow-up visits. 80 000 mosquitoes were assessed for insecticide resistance. Long-lasting insecticidal net users had lower infection prevalence (adjusted odds ratio [OR] 0·63, 95% CI 0·51-0·78) and disease incidence (adjusted rate ratio [RR] 0·62, 0·41-0·94) than did non-users across a range of resistance levels. We found no evidence of an association between insecticide resistance and infection prevalence (adjusted OR 0·86, 0·70-1·06) or incidence (adjusted RR 0·89, 0·72-1·10). Users of nets, although significantly better protected than non-users, were nevertheless subject to high malaria infection risk (ranging from an average incidence in net users of 0·023, [95% CI 0·016-0·033] per person-year in India, to 0·80 [0·65-0·97] per person year in Kenya; and an average infection prevalence in net users of 0·8% [0·5-1·3] in India to an average infection prevalence of 50·8% [43·4-58·2] in Benin). INTERPRETATION: Irrespective of resistance, populations in malaria endemic areas should continue to use long-lasting insecticidal nets to reduce their risk of infection. As nets provide only partial protection, the development of additional vector control tools should be prioritised to reduce the unacceptably high malaria burden. FUNDING: Bill & Melinda Gates Foundation, UK Medical Research Council, and UK Department for International Development.

Challenges of DHS and MIS to capture the entire pattern of malaria parasite risk and intervention effects in countries with different ecological zones: the case of Cameroon.

BACKGROUND: In 2011, the demographic and health survey (DHS) in Cameroon was combined with the multiple indicator cluster survey. Malaria parasitological data were collected, but the survey period did not overlap with the high malaria transmission season. A malaria indicator survey (MIS) was also conducted during the same year, within the malaria peak transmission season. This study compares estimates of the geographical distribution of malaria parasite risk and of the effects of interventions obtained from the DHS and MIS survey data. METHODS: Bayesian geostatistical models were applied on DHS and MIS data to obtain georeferenced estimates of the malaria parasite prevalence and to assess the effects of interventions. Climatic predictors were retrieved from satellite sources. Geostatistical variable selection was used to identify the most important climatic predictors and indicators of malaria interventions. RESULTS: The overall observed malaria parasite risk among children was 33 and 30% in the DHS and MIS data, respectively. Both datasets identified the Normalized Difference Vegetation Index and the altitude as important predictors of the geographical distribution of the disease. However, MIS selected additional climatic factors as important disease predictors. The magnitude of the estimated malaria parasite risk at national level was similar in both surveys. Nevertheless, DHS estimates lower risk in the North and Coastal areas. MIS did not find any important intervention effects, although DHS revealed that the proportion of population with an insecticide-treated nets access in their household was statistically important. An important negative relationship between malaria parasitaemia and socioeconomic factors, such as the level of mother's education, place of residence and the household welfare were captured by both surveys. CONCLUSION: Timing of the malaria survey influences estimates of the geographical distribution of disease risk, especially in settings with seasonal transmission. In countries with different ecological zones and thus different seasonal patterns, a single survey may not be able to identify all high risk areas. A continuous MIS or a combination of MIS, health information system data and data from sentinel sites may be able to capture the disease risk distribution in space across different seasons.

Design of a study to determine the impact of insecticide resistance on malaria vector control: a multi-country investigation.

BACKGROUND: Progress in reducing the malaria disease burden through the substantial scale up of insecticide-based vector control in recent years could be reversed by the widespread emergence of insecticide resistance. The impact of insecticide resistance on the protective effectiveness of insecticide-treated nets (ITN) and indoor residual spraying (IRS) is not known. A multi-country study was undertaken in Sudan, Kenya, India, Cameroon and Benin to quantify the potential loss of epidemiological effectiveness of ITNs and IRS due to decreased susceptibility of malaria vectors to insecticides. The design of the study is described in this paper. METHODS: Malaria disease incidence rates by active case detection in cohorts of children, and indicators of insecticide resistance in local vectors were monitored in each of approximately 300 separate locations (clusters) with high coverage of malaria vector control over multiple malaria seasons. Phenotypic and genotypic resistance was assessed annually. In two countries, Sudan and India, clusters were randomly assigned to receive universal coverage of ITNs only, or universal coverage of ITNs combined with high coverage of IRS. Association between malaria incidence and insecticide resistance, and protective effectiveness of vector control methods and insecticide resistance were estimated, respectively. RESULTS: Cohorts have been set up in all five countries, and phenotypic resistance data have been collected in all clusters. In Sudan, Kenya, Cameroon and Benin data collection is due to be completed in 2015. In India data collection will be completed in 2016. DISCUSSION: The paper discusses challenges faced in the design and execution of the study, the analysis plan, the strengths and weaknesses, and the possible alternatives to the chosen study design.