Reducing malaria cases and vector populations represents a major milestone in malaria control efforts [1]. Vector control is a highly effective means of reducing malaria transmission and constitutes a cornerstone of malaria control and elimination strategies [2].

The World Health Organization (WHO) has raised concern by declaring that malaria control is at a crossroads [2]. The “High burden to high impact” response, launched in 2018, is intended to reinvigorate progress in malaria control [2][3]. Despite remarkable achievements, progress in malaria control worldwide has stalled in recent years, and many high-burden countries have experienced setbacks [1][3]. While the reduction in malaria cases has stagnated in recent years, incidence may have increased [4]. This alarming trend highlights the need for new tools with which to combat the disease, hence the introduction of next-generation insecticide-treated nets (LLINs) based on combined pyrethroids, either with a synergist such as piperonyl butoxide (PBO) or with other active ingredients such as chlorfenapyr and pyriproxyfen. These compounds are incorporated with pyrethroids during LLIN impregnation—chlorfenapyr combined with alpha-cypermethrin (Interceptor® G2) and pyriproxyfen combined with alphacypermethrin (Royal Guard®)—representing a timely innovation [5]-[8].

Globally, the incidence of malaria declined between 2000 and 2015, remained stable from 2015 to 2019, and showed a slight increase in 2020. Despite a target of 34.8 cases per 1000 individuals, a modest decline was observed in 2021, followed by stabilization through 2022 and a slight increase in 2023 to 60.4 cases per 1000 individuals in the population; this can be compared with the target of 21.3 cases per 1000 individuals in the population [4][8][9].

A similar situation has been observed in the Democratic Republic of the Congo (DRC), where malaria incidence trends showed a progressive increase between 2014 and 2020, followed by a slight decrease in 2021. Despite the slight year-to-year variations observed, malaria incidence remained high between 2020 and 2023, reaching 200, 185, 227, and 224 cases per 1000 population, respectively. Notably, incidence rates remained above the targets set by the National Malaria Control Programme (NMCP) from 2021 to 2023 [10]. These incidence trends suggest a decline in the effectiveness of the primary preventive strategy—campaign-based distribution of LLINs—despite its status as a high-impact intervention for malaria reduction in the DRC [5][10][11]. Long-lasting insecticidal nets (LLINs) have nevertheless played a critical role in reducing the incidence of malaria and corresponding vector populations in malaria-endemic countries [12].

The DRC remains among the countries most heavily affected by malaria despite decades of control efforts, including multiple campaign-based programs distributing insecticide-treated nets [13]. Since 2010, there have been between four and six LLIN distribution cycles across provinces, primarily using pyrethroid-treated nets. In 2023, next-generation LLINs (PBO and IG2) were introduced into campaign-based distribution campaigns, beginning in Kongo Central Province [14][15].

Kongo Central Province underwent its fifth mass LLIN distribution cycle, occur-ring in three-year intervals, in 2023, following cycles in 2011, 2014, 2017, and 2020. The first four cycles involved pyrethroid-treated LLINs, while next-generation LLINs were employed in the most recent cycle. Notably, during the most recent campaign, 17 health zones distributed pyrethroid-PBO LLINs, whereas 14 health zones distributed pyrethroid-chlorfenapyr (IG2) LLINs [16].

Several studies conducted in the DRC and internationally have identified malaria vectors’ increasing resistance to pyrethroids as a major contributor to the rising incidence of malaria. Other studies have identified variations in temperature and relative humidity as predictors of the incidence of malaria, alongside resistance to antimalarial drugs [17]-[20].

To date, no studies have examined the temporal evolution and determinants of the persistent increase in the incidence of malaria in Kongo Central Province within the context of regular LLIN distribution and the transition to next-generation LLINs. Given the continued burden of malaria in terms of cases and deaths, the province remains far from achieving the global targets of a 90% reduction in malaria incidence and corresponding mortality by 2030. This situation underscores the significance of under-standing how next-generation LLINs and other contributing factors influence the incidence of malaria in regard to developing effective strategies for reorienting malaria control efforts in this region [1][3][4]. The objective of this study is to assess temporal trends in the incidence of malaria and environmental determinants after the deployment of standard and next-generation LLINs (PBO/IG2) in Kongo Central Province.

The first four campaign-based distribution cycles of LLINs were conducted using standard LLINs (2011-2020), while the most recent cycle in 2023 involved PBO/IG2 LLINs. Overall, the trend of malaria incidence remained largely the same in this period, showing a continued increase [9]. This pattern is consistent with trends observed in Angola, Burundi, and Madagascar in 2022 relative to 2015 [26]. In contrast, studies conducted in other regions have demonstrated that mass LLIN distribution significantly reduce malaria incidence, including in Kenya, India, and in M’bé, central Côte d’Ivoire (with PBO LLINs) [20][27][28]. This is further evidenced by the 2024 target for the DRC, which aimed to reduce malaria incidence below 231 cases per 1000 individuals; however, Kongo Central exceeded this target with 296 cases per 1000 individuals [15][24].

Modeling of malaria incidence from 2024 to 2030, within the context of PBO/IG2 LLINs, showed similar temporal patterns of monthly incidence. In addition, a declining trend in the incidence rate was often observed between August and September each year, reflecting the influence of the dry season following the disappearance of temporary Anopheles breeding sites [22]. Incidence was higher among children under five years of age compared to those over five, aligning with national trends that suggest older individuals are more aware of and more likely to use LLINs [29]. Conversely, a study on Mali reported higher incidence among individuals over five years of age [15][30]. Furthermore, incidence was higher in urban health zones one year after PBO/IG2 LLIN distribution, likely due to improved case reporting in urban areas [15].

Bivariate analysis of the incidence of malaria from 2011 to 2020 according to the type of standard LLIN used, compared to PBO/IG2 LLINs in 2023, indicated that standard LLINs initially provided protective effects by reducing the incidence or number of malaria episodes relative to PBO/IG2 LLINs (especially in 2011 with deltamethrin 55 mg/m²). However, no protective effects were observed during the second distribution, and in 2017, the higher insecticide dose in LLINs (deltamethrin 80 mg/m²) was associated with exposure, likely due to pyrethroid resistance [31]. In contrast, in Tanzania in 2023, IG2 LLINs were more effective than PBO or standard LLINs, while pyrethroid-PBO nets were superior to pyrethroid-only nets [32][33].

Repeated measures ANOVA revealed that one year after each mass LLIN distribution (conducted in December), malaria incidence significantly decreased for both PBO/IG2 and standard LLINs, but this reduction was not significant in the second-year post-distribution. These findings are consistent with observations made in Rwanda in 2023 and the Amhara region of Ethiopia from 2019 to 2023, where PBO and standard LLINs had similar effects [34][35]. Studies of other areas, such as in Ebonyi State, Nigeria, in 2023, showed that PBO LLINs remained effective for up to two years [36].

Multivariate panel analysis identified two factors—rising temperature and deforestation—as major contributors to increased malaria incidence. Specifically, a 1˚C increase in temperature was associated with an increase of 10.8 cases per 1000 individuals, at a mean temperature of 25.00˚C and a median of 25.32˚C. These results are consistent with studies reporting that higher temperatures shorten the sporogonic cycle of Anopheles mosquitoes, thereby increasing malaria transmission risk, as observed in studies conducted in Lubumbashi and ecological analyses conducted in Burkina Faso [37]-[40]. Most health zones are at low altitudes, experiencing high temperatures ranging from 20.0˚C to 30.0˚C [37].

A 1% increase in deforestation was associated with an increase of 7.9 cases per 1000 individuals. Approximately one-third of health zones are forested, while nearly two-thirds are covered by wooded and grassy savannas, both of which are subjected to ongoing deforestation (e.g., for timber and charcoal production), creating stagnant water habitats that facilitate Anopheles breeding and increases in malaria incidence. Similar observations have been reported in the Lao People’s Democratic Republic and in the Brazilian Amazon [41]-[43]. However, studies conducted on Colombia, Latin America, and the Caribbean have reported both positive and negative correlations between deforestation and malaria incidence [44].

An increase of one inhabitant per km² was associated with a decrease of 0.09 cases per 1000 inhabitants, likely because greater population density, typical of urban areas, reduces vegetation and mosquito breeding sites, thereby limiting malaria transmission [45]. Conversely, in rural areas, a reduction in forest edges may lower chronic malaria exposure, leading to faster acquisition of temporary partial immunity [41].

Relative humidity, precipitation, and primary forest cover were not associated with reductions in malaria incidence. The same was observed for household LLIN coverage and the proportion of LLINs distributed, for both standard and next-generation LLINs, a result consistent with findings from Burkina Faso, where precipitation was not associated with malaria incidence [39]. Some studies, however, have found associations between malaria cases and minimum/maximum temperatures and relative humidity, too [27][28][39].

Study limitations: Secondary data from two sources were used instead of a single source: the national Excel database (2010-2016) and DHIS2 (2017-2024). Consequently, some variables, such as the number of reporting health facilities, were missing, pre-venting assessment of their influence on malaria case increases. Additionally, monthly data on deforestation, household LLIN coverage post-campaign, and the proportion of LLINs distributed were unavailable, limiting our ability to conduct monthly panel analyses across the 31 health zones.

This study identified a persistent upward trend in malaria incidence, driven primarily by environmental changes. Subsequently, incidence increased after each distribution cycle. Among all predictive factors analyzed using the developed panel model, only rising temperature and deforestation were associated with an increase in malaria incidence.

LLIN distribution should be guided by integrated findings from studies on Anopheles vector behavior and insecticide resistance to stratify health zones and in-form the choice of LLIN type. Consequently, qualitative studies on LLIN usage, as well as controlled comparative trials, could provide further insights into unidentified factors. Malaria control, beyond LLIN distribution, should rely on a combination of interventions across the health sector, as well as other sectors, including environmental management.

ECMWF: European Centre for Medium-Range Weather Forecasts, ERA5: Fifth generation atmospheric reanalysis, IG2: Second generation interceptor, LLINs: long-lasting insecticidal nets, WHO: World Health Organization, NMCP: National Malaria Control Program, PBO: Piperonyl butoxide, DRC: Democratic Republic of the Congo, NHIS: National Health Information System, UNILU: University of Lubumbashi; HZ: Health Zone.

Coverage of long-lasting insecticidal nets (LLINs) (%), proportion of LLINs distributed (%), deforestation rate (%), primary forest cover (%), temperature (˚C), precipitation (mm), population density (persons/km²), and relative humidity (%).

We thank all authors who contributed to this article.

Conceptualization, NKN and EMS; methodology, NKN, ANK, and EMS; software, NKN; validation, NKN, EMS, ANK, NMM, TBL, GMN, and TBL; formal analysis, NKN, ANK, and EMS; resources, NKN; data curation, NKN; writing—original draft preparation, NKN; writing—review and editing, NKN, EMS, ANK, NMM, TBL, GMN, and TBL; visualization, NKN; supervision, EMS; project administration, NKN and EMS; funding acquisition, NKN. All authors have read and agreed to the published version of the manuscript.

We used DHIS2, District Health Information Software 2, and the ERA5 dataset, which represents the fifth generation of global atmospheric re-analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF).

We obtained approval from the Medical Ethics Committee No. UNILU/CEM/ 013/2024, and then authorization from the Kongo Central provincial division for the use of malaria control data.