Indoor residual spraying uptake and its effect on malaria morbidity in Ngoma district, Eastern province of Rwanda, 2018–2021

Abstract

Background

Indoor residual spraying (IRS) has been implemented in Rwanda in districts with high malaria transmission, including Ngoma District. The first IRS campaign (IRS-1) was conducted in March 2019, ahead of the peak malaria season, followed by a second campaign (IRS-2) in August 2020, targeting 89,331 structures. This study assessed factors influencing IRS uptake and evaluated the impact of IRS interventions on malaria morbidity in Ngoma District, Eastern Province, Rwanda.

Methods

A household survey employing multistage cluster sampling design was conducted in May 2021 to randomly select households. A structured questionnaire was administered to the head of household or a designated representative. Logistic regression, adjusted for the complex survey design and weighted for sampling, was used to identify factors associated with IRS uptake. Additionally, secondary data on malaria cases registered in the Rwanda Health Management Information System (RHMIS) from January 2015 to December 2022 were analyzed using interrupted time series analysis to evaluate the effect of IRS on malaria morbidity.

Results

A total of 636 households participated in the survey. Households headed by self-employed individuals (aOR = 0.07; 95% CI 0.01–0.55) and unemployed individuals (aOR = 0.18; 95% CI 0.03–0.99) were less likely to take up IRS compared to those headed by farmers. Households receiving IRS information through media channels (aOR = 0.01; 95% CI 0.00–0.17) were less likely to participate compared to those informed by community health workers. From the RHMIS data, 919,843 malaria cases were identified from January 2015 to December 2022. Interrupted time series analysis revealed that the baseline number of adjusted malaria cases was approximately 16,920. The first IRS intervention in March 2019 resulted in a significant reduction of 14,380 cases (p < 0.001), while the second intervention in August 2020 led to a reduction of 2495 cases, though this was not statistically significant (p = 0.098).

Conclusion

This study demonstrates the effectiveness of IRS in reducing malaria incidence in Ngoma District and highlights the role of socioeconomic factors and sources of information in influencing IRS uptake. To maximize the impact of IRS and ensure equitable benefits, targeted strategies, enhanced IRS education, and integrated malaria control approaches, including the use of bed nets, are crucial.

Background

Indoor residual spraying (IRS) involves the application of long-lasting residual insecticides to potential malaria vector resting surfaces, including internal walls, eaves, and ceilings of houses or structures. The primary purpose of IRS is to reduce malaria transmission by targeting and killing malaria-carrying mosquitoes that rest on treated surfaces, thereby interrupting the vector lifecycle and preventing disease transmission within households [1]. Along with long-lasting insecticidal nets (LLINs), IRS is a cornerstone vector control intervention for malaria prevention [2, 3]. Historically, IRS played a pivotal role in malaria elimination campaigns in Europe, particularly in Southern European countries such as Italy and Greece during the mid-twentieth century. These efforts, conducted after World War II, significantly reduced malaria incidence, underscoring the efficacy of IRS in diverse settings [4].

The effectiveness of IRS has been widely documented across regions with varying malaria prevalence [4, 5]. Studies conducted in sub-Saharan Africa, Asia, the Americas, and Europe consistently demonstrate that IRS, when combined with interventions such as LLINs, artemisinin-based combination therapy (ACT), and intermittent preventive treatment for pregnant women (IPTp), leads to substantial reductions in malaria transmission [6,7,8,9,10,11]. On the African continent, the scale-up of IRS protected 10 million individuals in 2005, a figure that rose to 124 million by 2013 [1]. The proportion of the population at risk decreased from 10.5% in 2010 to 5.7% in 2015, largely attributed to the combined implementation of IRS and other malaria control measures such as LLINs, ACT, and IPTp [1, 3, 12].

In Rwanda, malaria cases increased from 2.5 million in 2015 to 4.2 million in 2018, representing a 68% surge [11]. This increase has been linked to factors such as climatic changes favorable to mosquito breeding (e.g., increased rainfall and fluctuating temperatures), insecticide resistance among malaria vectors, and gaps in malaria prevention strategies, including inconsistent IRS and LLIN usage [11]. The entire population is at risk, with 1.8 million children under the age of five and 450,000 pregnant women identified as highly vulnerable [13]. By 2016, the malaria incidence rate rose from 112 per 1000 in 2013–2014 to 308 per 1000 in 2015–2016, with over 70% of cases occurring in the Eastern and Southern Provinces [13, 14]. Malaria transmission in Rwanda is perennial, peaking during the rainy seasons of May–June and November–December. Contributing factors include climatic conditions such as rainfall and altitude, as well as socioeconomic and environmental determinants like high population density, irrigation schemes, population movements, and cross-border interactions [15, 16].

To address this burden, Rwanda employs an integrated approach to malaria control, encompassing vector control measures such as LLINs and IRS, alongside case management strategies that include home-based management (HBM) and health facility-based treatment. IRS has been implemented in Rwanda since 2007 through the Africa Indoor Residual Spraying (AIRS Rwanda) Project, supported by the United States President’s Malaria Initiative (PMI). The program transitioned to PMI VectorLink Rwanda in 2018, focusing on two Eastern Province districts (Nyagatare and Kirehe) [15]. The Rwandan government, with support from the Global Fund, extended IRS coverage to additional high-burden districts, including Gatsibo, Rwamagana, Kayonza, Bugesera, Ngoma, Nyanza, Huye, and Gisagara.

Ngoma District, located in the Eastern Province, is highly endemic for malaria due to its tropical savanna climate and proximity to high-burden districts like Kayonza and Bugesera. Two IRS campaigns were implemented in Ngoma District: the first in March 2019 and the second in August 2020. This study aimed to analyze malaria morbidity trends before, during, and after IRS interventions and to assess factors influencing IRS uptake in the district.

Methods

Study design and setting

This cross-sectional study employed a multistage cluster sampling method to assess factors associated with IRS uptake and its impact on malaria morbidity in Ngoma District, Rwanda (Fig. 1). The sampling frame included all households in Ngoma District. Four of the district’s 15 sectors (Remera, Gashanda, Mutenderi, and Rukumberi) were randomly selected as study clusters. Within each sector, two cells and two villages per cell were randomly selected. In each village, a minimum of 40 households were randomly chosen, resulting in approximately 159 households per cluster and a total of 636 households participating in the study.

Fig. 1

Map showing sectors in Ngoma, a district in Rwanda

Ngoma District benefits from various malaria control interventions, including LLINs, home-based malaria case management (HBM), health facility-based services, and IRS. The first IRS round (IRS-1) was conducted in March 2019, targeting 357,058 individuals before the high transmission season (May–June). The second IRS round (IRS-2) was implemented in August 2020 during a twenty-day campaign, targeting 89,331 structures.

This study analyzed data from the National Health Management Information System (HMIS) covering January 2015 to December 2022 to address two objectives: (1) evaluate changes in malaria incidence rates before, during, and after the two IRS interventions and (2) identify factors associated with IRS uptake among households using a household survey conducted in May 2021.

Data sources and collection

Malaria case data were obtained from HMIS, which compiles information on patients diagnosed at the community and health facility levels in Ngoma District. Household-level data were collected using a structured questionnaire developed based on previously validated instruments. The questionnaire was reviewed by epidemiologists and experts from the Malaria and Other Parasitic Diseases Unit at the Rwanda Biomedical Centre (RBC). It underwent pilot testing in two villages with 20 households to ensure clarity and completeness. Adjustments were made based on feedback to improve question phrasing and response options.

The final questionnaire was administered by trained healthcare workers to the head of household or, if unavailable, another adult member (aged 18 or older) residing in the household during IRS. Interviewers were trained extensively, and each selected sector had a designated supervisor to oversee data collection and ensure accuracy.

Dependent and independent variables

To assess the factors influencing IRS uptake, the dependent variable was defined as whether a household participated in IRS. Independent variables included various sociodemographic and household characteristics, such as the head of household’s sex, age, occupation, marital status, religion, education level, mosquito net ownership, perceived benefits of IRS, and dissatisfaction with the intervention. Additional independent variables included socioeconomic status, categorized by Rwanda’s “Ubudehe” classification, which divides households into four categories based on income and vulnerability.

Knowledge of malaria prevention and transmission was also assessed using a 10-point scoring system. Respondents earned one point for each correct answer on questions about awareness of IRS, ITNs, malaria symptoms, and transmission mechanisms. Knowledge levels were categorized as low (0–3 points), intermediate (4–6 points), and high (7–10 points).

For malaria morbidity analysis, the dependent variable was the monthly number of new malaria cases, while the independent variable was the timing of IRS interventions, categorized as periods before, during, and after each of the two campaigns.

Sample size and sampling

All data registered in the HMIS during this study period in the Ngoma district were used in this study. The sample size (n) for the household survey to estimate the proportion of IRS uptake in the Ngoma district with a given precision was determined using the formula below:

$$n = \frac{{z^{2} p\left( {1 - p} \right)}}{{d^{2} }}$$

where n = sample size, d = precision/standard error: a precision of 5% was used, and Z = the z score associated with the confidence level. For a 95% confidence level, z is typically 1.96, p = proportion, and p was assumed to be 50% since there was no information on IRS uptake in the Ngoma district.

Therefore, the sample size n = [(1.962)² × 0.5 (1–0.5)]/(0.05)² was estimated to be 385. By adjusting for the design effect of 1.5, the adjusted sample size was (n × 1.5), which equals 578. After accounting for a nonresponse rate of 10%, the sample size was 642.

Statistical analysis

The data were collected using EPI Info 7.2.1.0, exported into Microsoft Excel for cleaning and then imported into Stata version 15 for analysis. Frequency and medians with ranges were calculated.

In this study, to assess the effect of IRS interventions on malaria morbidity, the dependent variable was monthly new malaria cases, while the main independent variable was the IRS intervention (periods before and after the two IRS interventions) at two different intervention calendar time points. Potential confounders, such as other malaria control interventions (e.g., LLIN distribution and case management strategies) and seasonal variations, were controlled for using interrupted time series analysis. Seasonal decomposition was applied to isolate the effects of seasonality, and the study assumed a uniform distribution of other control interventions across the district represented by the following equation:

$$Y_{t} = T_{t} + S_{t} + R_{t}$$

where Yt is the observed number of malaria cases at time t, decomposed into its trend component (Tt), seasonal component (St), and residual component (Rt). Next, we adjusted the data for seasonality to isolate the impact of the interventions. The seasonally adjusted malaria cases (Yt, adj) were calculated as follows:

$$Y_{{t,{\text{adj}}}} = Y_{t} - S_{t}$$

This adjustment allowed for a clearer analysis of trends and levels unrelated to seasonal fluctuations. For the ITS analysis, A segmented regression model was employed to evaluate the changes in malaria cases before and after the interventions. The model is specified as follows:

$$Y_{{t,{\text{adj}}}} = \beta_{0} + \beta_{t} \cdot I_{t1} + \beta_{2} \cdot I_{t2} + \varepsilon_{t}$$

In this model, β₀ represents the baseline level of adjusted malaria cases before any intervention. β₁ and β₂ are coefficients estimating the immediate change in malaria cases following the first and second interventions, respectively. I_t1 and I_t2 are dummy variables indicating the occurrence of the first (March 2019) and second (August 2020) interventions, respectively. ε_t is the error term. This approach provided a robust framework for assessing the effects of public health interventions on malaria morbidity, accounting for both the trend and seasonality in the data.

The IRS coverage among households was calculated as the proportion of sprayed households to the total number of households participating in the study. This study focused on the uptake of IRS by households and their associated factors. Thus, a sprayed household was considered a positive outcome during the data analysis to determine factors associated with IRS uptake. Factors associated with sprayed households were assessed using a logistic regression model adjusted for the complex survey design and weighted to account for the complex sampling design. Variables with a p-value < 0.5 in the bivariate analysis were included in the multivariable model. The final model included the occupation of household heads and the source of information about IRS as these variables showed significant associations with IRS uptake.

Ethical consideration

Participation in the IRS uptake study was voluntary, a consent form was administered to each participant before the beginning of an interview, and the study was ethically approved by the Institutional Review Board (IRB) of the University of Rwanda, College of Medicine, and Health Sciences. However, for confidentiality reasons, no personally identifiable information was collected or managed by the study team.

Results

Distribution of respondents’ characteristics and households with reported malaria cases within 6 months after IRS between in Ngoma district

Table 1 outlines respondent characteristics and malaria case reports from households within 6 months post-IRS implementation. Among the 636 households surveyed, the median age of participants was 39 years (range: 18–99), with most respondents being female (57.4%), married (53.9%), and having a primary level of education (71.9%). Farmers comprised the majority of household heads (56.8%). The predominant religion was Catholicism (51.9%), and most households were in the second Ubudehe socioeconomic category (53.8%).

Table 1 Characteristics of the respondents and households with reported malaria cases within 6 months after IRS between 2019 and 2020 in the Ngoma District, Rwanda

The study revealed that 87.0% of households were willing to participate in future IRS campaigns, and the same proportion reported owning at least one mosquito net, a critical malaria prevention measure. Knowledge of malaria prevention and transmission was intermediate among 68.2% of respondents, highlighting a need for further educational initiatives. Approximately 14.6% of households reported at least one malaria case within the 6 months preceding the survey.

Malaria prevalence was notably higher in male-headed households (21.3%) compared to female-headed households (9.4%). By age group, malaria prevalence was similar among the 18–30 years (13.4%) and 31–60 years (16.6%) age groups, but lower in households headed by individuals aged over 60 years (11.7%). Households headed by married individuals had a slightly higher malaria prevalence (16.7%) compared to those headed by unmarried individuals (12.4%). Interestingly, households with higher education levels tended to have lower prevalence rates compared to those with unemployed heads, which reported the lowest prevalence at 4.9%.

Furthermore, households that expressed willingness to participate in future IRS rounds demonstrated a lower malaria prevalence (11.5%) compared to households that were unwilling (34.4%).

Trends of new malaria cases in the Ngoma district from january 2015 to december 2022

Malaria case data from January 2015 to December 2022 demonstrated a seasonal pattern from 2015 to 2018, followed by a gradual decline starting in 2019. By 2022, malaria cases were consistently maintained at significantly lower levels compared to pre-IRS years, reflecting the impact of IRS interventions (Fig. 2).

Fig. 2

Trends of monthly new malaria cases in Ngoma district, Rwanda (January 2015–December 2022)

Bivariate analysis of the factors associated with IRS uptake among households in Ngoma district

The results of the bivariate analysis, summarized in Table 2, highlight key associations between IRS uptake and various factors. Participants who received information about IRS activities through media sources (radio or TV) or other informal sources were significantly less likely to participate in IRS compared to those informed by community health workers. The odds ratios (ORs) were 0.01 (95% CI 0.00–0.21) and 0.02 (95% CI 0.00–0.20), with p-values of 0.017 and 0.013, respectively, indicating statistically significant differences.

Table 2 Bivariate analysis of the factors associated with IRS uptake among households in 2019–2020 in Ngoma district, Rwanda

Households headed by individuals employed in government, self-employed individuals, and unemployed individuals were also less likely to take up IRS compared to households headed by farmers. The ORs for these groups were 0.24 (95% CI 0.08–0.76), 0.06 (95% CI 0.01–0.40), and 0.17 (95% CI 0.04–0.73), with p-values of 0.029, 0.018, and 0.030, respectively.

Multivariate analysis of the factors associated with IRS uptake among households in Ngoma district

Multivariate analysis was performed to identify the factors associated with IRS uptake among households (Table 3). The final multivariable model included two variables: the occupation of household heads and the source of information about IRS, selected based on a cut-off p-value of < 0. In the bivariate analysis. Households headed by self-employed individuals were significantly less likely to use IRS compared to those headed by farmers, with an adjusted odds ratio (AOR) of 0.07 (95% CI 0.01–0.55) and a p-value of 0.026. Similarly, households headed by unemployed individuals were less likely to use IRS, with an AOR of 0.18 (95% CI 0.03–0.99) and a p-value of 0.050.

Table 3 Multivariate analysis of the factors associated with IRS uptake among households in 2019–2020 in Ngoma district, Rwanda

Information sources also influenced IRS uptake. Participants who received IRS information via media (radio or TV) or other informal sources were significantly less likely to participate in IRS campaigns compared to those informed by community health workers, with AORs of 0.01 (95% CI 0.00–0.17) and 0.02 (95% CI 0.00–0.15), respectively.

Interrupted time series (ITS) analysis: effect of indoor residual spraying interventions on malaria morbidity

Seasonal decomposition

The seasonal decomposition of malaria case data revealed three critical components essential for understanding transmission patterns in Ngoma District. The trend component reflected the overall direction of malaria cases over time, independent of seasonal variations, providing insights into the long-term trajectory of malaria transmission. The seasonal component identified predictable fluctuations aligned with specific times of the year, such as the rainy seasons, influenced by environmental factors like rainfall and humidity. Lastly, the residual component captured irregular or unexpected deviations from the trend and seasonal patterns, which could result from unique events such as disease outbreaks or public health interventions. This comprehensive decomposition provided a nuanced understanding of malaria transmission dynamics (Fig. 3).

Fig. 3

Seasonal decomposition of the new malaria cases in Ngoma District (January 2015 to December 2022)

Adjustment for seasonality

Adjusting for seasonality allowed the isolation of non-seasonal trends in malaria cases, which improved the reliability of assessing the impact of IRS interventions. By controlling for inherent seasonal fluctuations, the analysis provided a clearer view of the overall trends and irregularities in the data. The adjusted R-squared value of 0.729 demonstrated that the model accounted for 72.9% of the variability in malaria cases, underscoring its robustness and the effectiveness of the analytical approach.

ITS analysis using the segmented regression model

The segmented regression analysis of the seasonally adjusted malaria cases in the Ngoma District provided significant insights into the impact of the two IRS interventions, specifically the first round conducted in March 2019 and the second round conducted in August 2020 (Fig. 4). The model's coefficients elucidated the following:

Baseline New Malaria Cases (Constant): The analysis estimated a baseline level of approximately 16,920 malaria cases, adjusted for seasonality. This figure represents the expected number of patients in the absence of the interventions studied.

First IRS Intervention (March 2019): A marked and statistically significant reduction in malaria cases was observed following the first intervention in March 2019. The model estimated a decrease of approximately 14,380 cases, a substantial decline with a p-value less than 0.001, indicating a high level of statistical significance. This result indicates that the first intervention had a considerable and measurable impact on reducing malaria morbidity.

Second Intervention (August 2020): The effect of the second intervention, implemented in August 2020, resulted in a reduction of approximately 2495 malaria cases. However, this change was not statistically significant (p-value = 0.098), suggesting that the impact of the second intervention on reducing malaria cases, while apparent, was not as pronounced or certain as that of the first intervention.

Overall Model Significance: The F-statistics for the model were significant, underscoring the statistical validity of the model as a whole in evaluating the effects of these interventions.

Fig. 4

Interrupted time series output comparing the periods before and after the first and second IRS interventions in Ngoma District

Discussion

This study evaluated the uptake of indoor residual spraying (IRS) for malaria control in Ngoma District, identified factors influencing its uptake, and assessed its impact on malaria morbidity. The findings revealed an IRS coverage of 96.4% among households in Ngoma District, slightly lower than the 98.3% coverage achieved during PMI/VectorLink-supported interventions in Rwanda but higher than rates reported in Uganda (89.3% in Lira District [17], 77.5% in Mulanda Subcounty, Tororo District [18]) and Zimbabwe (87% in Chiredzi District [19]). Multivariate analysis showed that households headed by self-employed or unemployed individuals were less likely to take up IRS compared to those headed by farmers. Moreover, receiving information about IRS from community health workers significantly increased uptake compared to other sources. Interrupted time series analysis revealed a significant reduction in malaria cases following the first IRS round, with a smaller, non-significant reduction after the second round. Sustained reductions in malaria incidence beyond the IRS campaign periods were likely supported by complementary malaria control measures, including LLINs and effective case management interventions.

The slightly lower IRS coverage in Ngoma District than anticipated is concerning but remains high enough to have a significant impact on malaria control in the area [20]. Studies in sub-Saharan Africa, including one in Zambia, have shown that actual IRS coverage often falls below target rates [20]. Similarly, in Uganda, while IRS reduced malaria transmission, inadequate coverage limited its impact [21]. These findings underscore the importance of enhancing IRS coverage to achieve optimal malaria control.

In contrast, the higher IRS coverage in Ngoma District compared to other studies in sub-Saharan Africa reflects the prioritization of malaria control efforts in Rwanda and the strong collaboration between the government and external partners. The community-based approach, involving active participation by community health workers in sensitizing and mobilizing households, also contributed significantly to the high coverage [22].

The study demonstrated a significant decline in malaria incidence after the first IRS round, with a continued but slower decrease over time. These findings align with studies from Ethiopia and Zambia, which reported reductions in malaria incidence of 62% and 90%, respectively, after two rounds of IRS [10, 22]. However, the effectiveness of IRS can be influenced by factors such as insecticide resistance, coverage, spraying quality, and population compliance. Continuous monitoring and integration of complementary malaria control measures are crucial to sustain IRS gains, especially in high-burden areas.

The second IRS intervention in August 2020 successfully sustained the gains achieved by the first intervention in March 2019, contributing to a continued decline in malaria incidence. Evidence from Mozambique supports this, showing sustained reductions in malaria incidence following successive IRS rounds. However, the effectiveness of sustained IRS depends on factors such as timing, chemical choice, and quality of implementation. While our study did not investigate these factors, the observed sustained reductions suggest a well-executed intervention strategy.

IRS primarily targets indoor-biting mosquitoes and does not address outdoor-biting vectors. The sustained reduction in malaria incidence beyond the effective duration of the last IRS campaign (August 2020) suggests the contribution of complementary malaria control measures. These include the distribution of LLINs and effective case management strategies implemented under national malaria control programs. Although not explicitly evaluated in this study, these measures are consistent with findings from similar settings where integrated approaches have been effective. Future studies should investigate the relative contributions of these interventions to inform malaria control strategies.

The study results indicated that households headed by self-employed or unemployed individuals were significantly less likely to take up IRS compared to those headed by farmers. Interestingly, households with lower socioeconomic status showed higher IRS uptake in Ngoma District, likely due to effective mobilization by community health workers. These findings highlight the importance of context-specific strategies, such as tailored health education campaigns and financial incentives, to address barriers to IRS uptake among self-employed and unemployed households.

This study had limitations, including the absence of a control group for direct comparison with non-IRS districts. Additionally, it was assumed that other malaria control interventions were evenly distributed across households in Ngoma District. Despite these limitations, the findings provide valuable insights into IRS implementation and its impact on malaria morbidity, offering guidance for designing sustainable IRS programs in high-burden districts.

Conclusion and recommendations

This study underscored the significant role of Indoor Residual Spraying (IRS) in reducing malaria incidence in Ngoma District between 2018 and 2021. The findings highlighted key factors influencing IRS uptake, including socioeconomic status, the source of IRS information, and the occupation of household heads. IRS proved to be a highly effective intervention, with marked reductions in malaria incidence following each IRS campaign. However, disparities in IRS uptake were observed, particularly among households in lower socioeconomic categories and rural areas. These findings emphasized the need for targeted efforts to ensure equitable access to and benefits from IRS interventions.

To enhance IRS uptake and its effectiveness, several strategies are recommended. Strengthening community engagement is vital, particularly through tailored educational campaigns that utilize trusted community health workers and local leaders. These efforts should aim to address misinformation and raise awareness about the benefits of IRS.

Integrating IRS with other malaria control strategies is also crucial. This includes promoting the widespread use of long-lasting insecticidal nets (LLINs) and maintaining their high coverage. Complementary measures, such as larviciding and environmental management, should be implemented to target outdoor-biting mosquitoes and address residual malaria transmission effectively.

Improving IRS planning and implementation is essential to sustain its impact. Regular monitoring and evaluation of IRS campaigns should be conducted to identify and address challenges, such as insecticide resistance, and to ensure high-quality spraying. Moreover, expanding IRS coverage to additional high-burden areas while optimizing resource allocation based on malaria transmission patterns is necessary to maximize its effectiveness.

Finally, future research should focus on understanding the interaction and relative contributions of IRS and complementary interventions in reducing malaria incidence. Additionally, studies should assess the long-term cost-effectiveness and sustainability of IRS within integrated malaria control programs to guide future policy and strategy development.

Disclaimer

The findings and conclusions in this paper are those of the author(s) and do not necessarily represent the official position of the funding agencies.