Risk factors for Lyme disease resulting from residential exposure amidst emerging Ixodes scapularis populations: A neighbourhood-level analysis of Ottawa, Ontario

Lyme disease is an emerging health threat in Canada due to the continued northward expansion of the main tick vector, Ixodes scapularis. It is of particular concern to populations living in expanding peri-urban areas where residential development and municipal climate change response impact neighbourhood structure and composition. The objective of this study was to estimate associations of socio-ecological characteristics with residential Lyme disease risk at the neighbourhood scale. We used Lyme disease case data for 2017–2020 reported for Ottawa, Ontario to determine where patients’ residential property, or elsewhere within their neighbourhood, was the suspected site of tick exposure. Cases meeting this exposure definition (n = 118) were aggregated and linked to neighbourhood boundaries. We calculated landscape characteristics from composited and classified August 2018 PlanetScope satellite imagery. Negative binomial generalized linear models guided by a priori hypothesized relationships explored the association between hypothesized interactions of landscape structure and the outcome. Increases in median household income, the number of forest patches, the proportion of forested area, forest edge density, and mean forest patch size were associated with higher residential Lyme disease incidence at the neighbourhood scale, while increases in forest shape complexity and average distance to forest edge were associated with reduced incidence (P<0.001). Among Ottawa neighbourhoods, the combined effect of forest shape complexity and average forest patch size was associated with higher residential Lyme disease incidence (P<0.001). These findings suggest that Lyme disease risk in residential settings is associated with urban design elements. This is particularly relevant in urban centres where local ecological changes may impact the presence of emerging tick populations and how residents interact with tick habitat. Further research into the mechanistic underpinnings of these associations would be an asset to both urban development planning and public health management

The effect of treatment disruption on model-simulated point malaria prevalence (all ages) on Oct 1, 2020.

Several aspects of treatment disruptions were simulated in a factorial manner, including duration of disruption (1–6 months, along x-axes), extent of disruption (0–100%, colored lines), and onset of disruption relative to the beginning of the pandemic (0–3 month offset from March 1, 2020, panels A-D). Points represent individual simulations, solid color lines represent the regression line of malaria prevalence on duration of treatment disruption grouped by severity of disruption, and light color bands represent the 95% confidence interval of the regression line.</p

Quotes related to the impact of COVID-19 on malaria control in French, presented in order of inclusion in the main text.

Quotes related to the impact of COVID-19 on malaria control in French, presented in order of inclusion in the main text.</p

Key informant interview guide.

The COVID-19 pandemic has sent ripple effects across health systems and impacted the burden of many other diseases, such as malaria in sub-Saharan Africa. This study takes a mixed method approach to assess the impact of COVID-19 on malaria control programs in three rural communes in Benin. We conducted individual semi-structured interviews with key informants who play important roles in malaria control in Benin at three levels of the health system–national, health zone, and commune. Using a purposive sampling technique, informants were interviewed regarding their roles in malaria control, the impact of the pandemic on their activities, and the mitigation strategies adopted. Relevant themes were identified by content analysis. We then formulated an agent-based model of malaria epidemiology to assess the impacts of treatment disruption on malaria burden. The key informant interviews revealed that essential aspects of malaria control were upheld in Benin due to the close collaboration of public health practitioners and health care providers at all levels of the health system. There were some disruptions to case management services for malaria at the start of the pandemic due to the public avoiding health centers and a brief shortage of malaria treatment that may not be entirely attributable to the pandemic. Results from the agent-based model suggest that duration, severity, and timing of treatment disruption can impact malaria burden in a synergistic manner, though the effects are small given the relatively mild disruptions observed. This study highlights the importance of top-down leadership in health emergencies, as well as the critical role of community health workers in preventing negative health outcomes for their communities. We also showcased the integration of qualitative research and mathematical models–an underappreciated form of mixed methods research that offer immense value in the continued evaluation of rapidly evolving health emergencies.</div

Inclusivity in global research questionnaire.

The COVID-19 pandemic has sent ripple effects across health systems and impacted the burden of many other diseases, such as malaria in sub-Saharan Africa. This study takes a mixed method approach to assess the impact of COVID-19 on malaria control programs in three rural communes in Benin. We conducted individual semi-structured interviews with key informants who play important roles in malaria control in Benin at three levels of the health system–national, health zone, and commune. Using a purposive sampling technique, informants were interviewed regarding their roles in malaria control, the impact of the pandemic on their activities, and the mitigation strategies adopted. Relevant themes were identified by content analysis. We then formulated an agent-based model of malaria epidemiology to assess the impacts of treatment disruption on malaria burden. The key informant interviews revealed that essential aspects of malaria control were upheld in Benin due to the close collaboration of public health practitioners and health care providers at all levels of the health system. There were some disruptions to case management services for malaria at the start of the pandemic due to the public avoiding health centers and a brief shortage of malaria treatment that may not be entirely attributable to the pandemic. Results from the agent-based model suggest that duration, severity, and timing of treatment disruption can impact malaria burden in a synergistic manner, though the effects are small given the relatively mild disruptions observed. This study highlights the importance of top-down leadership in health emergencies, as well as the critical role of community health workers in preventing negative health outcomes for their communities. We also showcased the integration of qualitative research and mathematical models–an underappreciated form of mixed methods research that offer immense value in the continued evaluation of rapidly evolving health emergencies.</div

Summary of key informant interview results, organized based on whether the effect (or response) was expected, whether the effect on malaria control was positive or negative, and the level of the health system at which the effect was observed.

Summary of key informant interview results, organized based on whether the effect (or response) was expected, whether the effect on malaria control was positive or negative, and the level of the health system at which the effect was observed.</p

Fig 1 -

Maps depicting (A) health zones in Benin, (B) communes within, and the (C) three communes (Covè, Ouinhi, and Zagnanando) within the Zou health zone where this study took place. Map content was produced with Esri ArcGIS software using data provided by GADM [28] and Natural Earth [29].</p

Time series of malaria prevalence (all ages) in the baseline model, 2019–2020.

Solid dark-red line represents the mean malaria prevalence of the simulated replicates, and the light red band around it captures the range of simulated values. Dashed gray line marks the digitized LLIN distribution campaign (March 17–23, 2020) as well as the first reported case of COVID-19 in Benin (March 16, 2020).</p

Fig 3 -

Simulated malaria dynamics for the year 2020 with A) varying severity of treatment disruption, where disruption began on March 1 and lasted 6 months, B) varying duration of treatment disruption, where there was 100% disruption that began on March 1, and C) varying timing of treatment disruption onset, where treatment was 100% disrupted for 6 months. Solid color lines represent the mean malaria prevalence of the simulated replicates, and the light color bands around them capture the range of simulated values. Dashed gray line indicates Oct 1, 2020, and the red cross indicates the observed malaria prevalence in the NPP study reference group (28%) [26].</p