Spatial analysis of confirmed Lassa fever cases in Edo State, Nigeria, 2008 – 2014

Spatial analysis of confirmed . Background: Lassa fever (LF) is endemic and poses public health threats in Edo State. Identification of primary clusters will help prioritize public health interventions in the state. We investigated retrospective cases of LF to identify primary cluster of the disease for household exposure management. Method: We reviewed retrospective data (n = 1400) of LF case-patients at a referral hospital in Edo State from 2008 to 2014 based on World Health Organization case definition for LF. We determined primary cluster of confirmed cases on Bernoulli model and evaluated environmental factors in the primary cluster: presence of rodent deterrents, proximity of households at 2 km radius to rice farm, rice post-harvest storage facility, refuse dump, forest, hospital and main road using Multi-criteria analysis at p < 0.05. Results : Of the reviewed cases, 171 (12.2%) were confirmed case-patients. The median age of confirmed case-patients was 30 years (IQR: 27.5). Of the confirmed case-patients, 101 (59.1%) were male. A primary spatial cluster (4.45 km radius; geographic centre at 6.717900 o N, 6.243500 o E) was identified in Esan West LGA. Associated environmental factors included presence of rodent deterrents (p < 0.001), proximities of households to refuse dump (p < 0.001), rice post-harvest storage facility (p = 0.01) and rice farm (p = 0.03). Conclusion : Esan West LGA was identified as a primary cluster for Lassa fever. Presence of rodent deterrents, household proximity to refuse dump sites, rice storage facilities and rice farms were associated environmental factors. We recommend improved rice post-harvest storage and use of rodent deterrents in Edo State, Nigeria.


Introduction
Lassa fever (LF) is a viral haemorrhagic zoonotic disease caused by an arenavirus. Humans get infected with the virus primarily through ingestion of food or food substances contaminated with the excreta of Mastomys natalensis rodent (commonly known as the Multimammate rat), which is the natural reservoir for the virus. Human to human transmission is also possible through contact with secretions and excretions of infected persons (1). LF is predominant in West Africa including Sierra Leone, Liberia and Nigeria (2). It affects 100,000 to 300,000 people every year in this region (3) There have been several LF outbreaks in various parts of Nigeria and the largest outbreak ever reported was in 2018 which shows an increasing trend in the number of cases and deaths (4). In 2018, a total of 3016 suspected LF cases were reported from 22 states. Of these cases, 559 were confirmed positive, 17 probable and 2440 negative. The case fatality rate among confirmed cases was 25.6 % and 100 % among probable cases. All the affected 22 states had at least one confirmed case spreading through 90 Local Government Areas (LGAs). Three of the 22 affected states constituted 83.0% of the confirmed cases: Edo (46%), Ondo (24%) and Ebonyi (13%) states (4).
Edo State is one of the states in Nigeria with high burden of LF cases with occurrence all through the year. The first outbreak of LF in Edo State occurred in Esan West LGA in 1989 (3) (5). Identification of primary spatial clusters have contributed immensely to understanding disease risk behavior as well as help guide and inform prioritization of public health intervention strategies (6). Studies have indicated that habitat suitability such as agricultural intensification associated with post-harvest grain, storage density on residential areas could significantly influence Mastomys breeding and transmission of the Lassa virus to humans (7)(1). Geographic information system (GIS) and spatial models have been used to gain better understanding about the risk distribution of LF along West Africa sub-region (8

Probable case
A probable case was a person with clinical illness, not laboratory confirmed and was not epidemiologically linked to a confirmed case but with appropriate exposure history.

Confirmed case of LF
A confirmed case of LFwas a person that was confirmed in the Laboratory, or that met the clinical illness case defined by the WHO and was not laboratory confirmed case but epidemiological linked to a confirmed case.

Controls
Suspected cases that were tested negative in corresponding locations in which confirmed cases were located.

Data Collection
A total of 1,400 case records were reviewed. Data on the age, sex, house location and laboratory status were extracted for all laboratory confirmed cases.
We visited house locations of confirmed LF case-patients and controls and geographic coordinates of their house locations were obtained using the handheld geographic positioning system (GPS). Where the house locations of case-patients could not be reached and majority of locations of controls, geographic coordinates were captured using Google Earth Pro images for geo-positioning. Data on age, sex, number of cases and geo-coordinates in each LGA were line listed on Microsoft Excel spreadsheet. After determining primary spatial cluster for LF cases, an observational check-list was used to assess environmental risk factors of case-patients' households in the primary spatial cluster.
We considered environmental risk factors that could favor the breeding and access of mastomys rat such as proximity of household to rice farms, rice post-harvest storage facilities, refuse dump sites and presence of rodent deterrents [7]; and other environmental factors such as proximity of households to forest, main road and hospital facilities. By presence of rodent deterrents: we observed the houses of LF case-patients for absence of cracks and crevices in the windows, doors and walls. All proximities were considered at 2 km radius of the house locations of confirmed LF case-patients to any of the environmental factors considered.

Data Analysis
Kulldorff Sat Scan software was used to estimate primary clustered location. Associated environmental factors of LF cases in the primary cluster were determined using spatial regression with Geoda software. Eight spatial features were individually tested as predictors of LF cases in the primary clustered area through multi-criterial analysis (MCA)(1). Spatially lag independent variables were grouped into two subsets to reveal the effects of groups 1 and 2 characteristics separately.
Group one examined the effects of environmental variables related to residential areas at proximity of 2 km, while group 2 examined the rodent dynamics related variables. Variables were subjected to regression equation: y = ρwy+xB+ε8)where: y is an N by 1 Lassa fever case, N is referred to as a spatial weights matrix. For each location in the system, it specifies which of the other locations in the system affect the value at that location. This is necessary, since in contrast to the unambiguous notion of a "shift" along the time axis (such as yt−1 in an autoregressive model), there is no corresponding concept in the spatial domain, especially when observations are located irregularly in space. Instead of the notion of shift, a spatial lag operator is used, which is a weighted average of random variables at "neighboring" locations, in conclusion N is a spatial weighted matrix of neighboring locations of the regression model, ρ is the scalar spatial coefficient, wy is an N by 1 weighted matrix of Lassa fever cases, X is an N by k matrix of explanatory variables (X1 forest proximity, X2 Household proximity to main road, X3 Hospital proximity and X4 is human population density). B is a k by 1 vector of parameters, ε is an N by 1 vector of random error terms. Data were analyzed at p < 0.05 (9) Ethical Approval We obtained ethical approval with reference number HM.1208/188 from Edo State Ministry of Health.

Results
Of the 1,400 case records for LF reviewed; 171 (12.2 %) were laboratory confirmed. Of the 171 confirmed cases, 101 (59.1 %) were male. Figure 2 showed the spatial distribution of cases by sex.
The median age of the confirmed case-patients was 30 years (IOQ: 27.5). Most (32.8%) affected age group was within 25-34 years and most (43.2 %) of the confirmed case-patients were from Esan West LGA (Table 1). A primary spatial cluster (4.45 km radius; geographic centre at 6.717900 o N, 6.243500 o E) for LF cases was identified in Esan West LGA ( Figure 3) and was significant at p = 0.04 (Table 2). Environmental variables related to rodent dynamics that significantly characterized house locations of LF case-patients were presence of rodent deterrents (p < 0.001), proximities of households to refuse dump (p < 0.001), rice post-harvest storage facility (p = 0.01) and rice farm (p = 0.03). However, other environmental variables such as proximity of households to forest, main road and hospital facilities were not significantly associated with house locations of LF case-patients (Table   3). Our model also revealed that the presence of rodent deterrents in households was found to be a significant protective factor associated with LF case-patients. Absence of cracks in walls, spaces either through the windows or doors, presence of wire mesh have been reported to be effective in breaking the rodent-human interface needed to provide disease transmission pathways (24). (25) found the presence of rodent burrows and external hygiene around houses to be directly associated with a history of LF in the household in Sierra Leone. Though, a study in Nigeria found no significant difference in households of LF positive cases and controls with respect to housing quality (26). Other significant protective environmental variables in our model included rice farms and refuse dumps in proximities of 2 km radius to the house locations of confirmed LF case-patients. Rice farms and refuse dumps sites have been reported to harbour rodents especially Mastomys natalensis because of the availability of food at these sites (31) (32) (33). The continuous presence of food to rodents at these sites and at distance of 2 km possibly served as deterrents to rodent visiting human dwellings thus preventing the human-rodent interface needed for disease transmission in the human space. The preponderance of Mastomys natalensis in houses in rural settlements in East African countries (24) and Rattus rattus in some urban areas in sub-Saharan Africa. However, Karan (2019) reported a preponderance of Mastomys natalensis in residential areas in Urban areas in Guinea. The distance of the food sources-rice farms and refuse dumps to human dwellings could explain the reduction in LF cases in those households despite their preponderances.
Other environmental variables such as proximity of households to forest, main road and hospital facilities were not significantly associated with house locations of LF case-patients. This is similar to

Availability of data and materials
The data that support the findings of this study are available from Edo State Ministry of Health but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the me upon reasonable request and with permission of Edo State Ministry of Health.

Competing interests
The authors declare that they have no competing interests.