Study Design
A retrospective review of data on LF cases presented at the Institute for Lassa fever Research and Control, Irrua, Edo State between 2008 and 2014.
Study Location
Edo State is one of the states in the South South geo-political zone of Nigeria; lying on 05’44 N and 07’34 N latitudes, 05’ 4 E and 06’45 E longitudes (Figure 1), and mostly tropical rain forest. Administratively, it has 3 senatorial zones (Edo North, Edo Central and Edo South), 18 LGAs and 192 wards. The 2018 projected human population for Edo State from the 2006 national population census was 4,600,000. Agriculture is the main occupation of the people (FGN 2019). Edo State has 472 health facilities with 55 private health facilities and the Institute for Lassa fever Research and Control situated at Irrua in Esan Central LGA.
Case Definition
LF case was established based on the World Health Organization (WHO) definition, 2004.
Suspected case
A suspected case was a person with acute illness of <3 weeks duration, with temperature of 38oC and above, showing no response to effective antimalarial treatment after the first dose and no response to chloramphenicol treatment afters 48 hours.
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.