Retrospective multidisciplinary analysis of human alveolar echinococcosis in Hungary using spatial epidemiology approaches

doi:10.21203/rs.3.rs-4829932/v1

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Retrospective multidisciplinary analysis of human alveolar echinococcosis in Hungary using spatial epidemiology approaches

https://doi.org/10.21203/rs.3.rs-4829932/v1

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Background Human alveolar echinococcosis (HAE), which is caused by Echinococcus multilocularis tapeworm, is an increasing healthcare issue in Hungary. Of the 40 known cases in the country, 25 were detected in the last five years. Our study aimed to reveal the epidemiological backgrounds of these cases.

MethodsWe investigated the spatial impact of potential risk factors of HAE by cluster analysis, and local and global regression models. This analysis was completed by a questionnaire survey on the patients’ lifestyle.

ResultsWe found two HAE hyperendemic foci in the country with very dissimilar biotic and climatic features, and controversial impact of different environmental factors. Only two factors, viz forest cover and socio-economic development, proved important countrywide. The most forested and the least developed districts showed the highest HAE risk. Among the patients, kitchen gardening and dog ownership seemed the most risky activities.

ConclusionsOur models detected an anomaly in one of the poorest regions of Hungary where all risk factors behaved contrary to that of the neighbouring areas. This phenomenon was supposed to be the result of under-detection of the disease, and it called attention to the urgent priority of knowledge dissemination to the public and the healthcare professionals.

human alveolar echinococcosis

Echinococcus multilocularis

spatial epidemiology

culture of poverty

Hungary

Human alveolar echinococcosis (HAE) is a zoonotic disease caused by Echinococcus multilocularis, an indirectly developing cestode. Its natural definitive host is red fox (Vulpes vulpes) while its intermediate hosts are rodents [1]. Humans can acquire infection by accidental uptake of parasite eggs with contaminated food or water, or during contact with infected animals [2, 3]. Although the distribution area of HAE extensively grows in the Holarctic realm [1], it does not get enough attention from neither the researchers nor the health care systems. Therefore HAE is considered a neglected disease, which affects the most vulnerable communities [4].

In Europe, outside its Alpine historic distribution area, HAE is hypothesised to be under-detected because of low disease awareness of both the public and the healthcare system [3, 4]. Therefore, determination of risk factors of HAE contributes to better disease awareness, which can enhance diagnostic and treatment success, and also preventive measures [5]. In Hungary, HAE was first described in 2008 [6]. A later retrospective study, which analysed country-wide HAE cases between 2003–2018, ascertained that the disease has low incidence but high case-fatality rate due to diagnostic delay and inappropriate treatment [7].

Epidemiological analyses based on conventional, non-spatial analysis often do not provide satisfactory answers during the investigation of disease occurrence, spread and root causes. This is especially true for rare diseases such as infections caused by E. multilocularis. In this case, the occurrence and transmission of the parasite in the final hosts or human populations could vary conspicuously on a wide spatial scale. The variations in trends of the disease may occur even globally and regionally, but it could also be characteristic locally [8–10]. The advancement of geographic information technology, sophisticated statistics and geospatial analytical software can improve the knowledge and understanding of diseases. This complex approach could contribute to the foundation of competent integrated preventive control programs against epidemics [11].

For investigation of spatial heterogeneity, the geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) models were introduced [12] (Oshan et al., 2019). In contrast to traditional linear regression models (e.g. ordinary least squares - OLS), these methods can borrow data from neighbouring locations over spatial weights. Thus they can manipulate spatially heterogeneous associations between the dependent and explanatory variables [11].

Our study aimed to analyse the epidemiological background of 40 HAE cases that were detected between April of 2003 and April of 2024, in Hungary. Therefore, we compared the cases by their geographical location in a district-based geospatial analysis, and by the patients’ life-style features based on a questionnaire survey. Besides natural biotic and climatic factors, a socio-economic variable was also involved to estimate the potential additional risk of vulnerable regions.

Data sources

This retrospective study enrolled 40 patients who were diagnosed with alveolar echinococcosis between 9 April 2003 and 12 April 2024. The Hungarian patients included in this study were those who had positive serology for Echinococcus multilocularis infection with one highly sensitive (ELISA or IHA) and one highly specific (Western blot) test and fulfilled the clinical and laboratory diagnostic criteria for probable or confirmed AE patient as proposed by the WHO-IWGE. A probable case was defined as any patient with typical organ lesions detected by imaging methods (CT/MRI/ultrasound), and positive serology for AE with two positive tests. A confirmed case was defined as the above, plus 1) histopathology compatible with AE and/ or 2) detection of E. multilocularis genetic material in a clinical specimen. Patients who were serologically negative or non-specific positive but E. multilocularis infection was unequivocally confirmed by histopathological methods were also included [7, 13, 14].

The period prevalence (PP) was calculated at the country and district levels. Regarding human infections, the index was expressed to 100,000 citizens and covered the interval between 2003 and 2024, and the case fatality rate was also counted. The human population size was obtained from the 2011 census data. The calculation of PP in the fox populations was based on the results of a previously shown study [15], in which 1612 animals were investigated. We counted the index by the following equation in both cases:

$$\:PP=\left(\frac{NIP}{NP}\right)$$

where

PP = period prevalence,

NIP = number of infected humans/animals,

NP = number of population in particular spatial unit (country or district).

Land cover categories were obtained from the CLC2018 database with a minimum mapping unit of 25 hectares (https://land.copernicus.eu/en/products/corine-land-cover). We classified the surface characteristics into six variables: URB (including urban and industrial areas and transport infrastructures), AGRO (including arable lands, permanent crops, grasslands, and heterogenous agricultural areas), FOR (including forests), and WETHABIT (including wetlands and water bodies) (Supplementary Information 1). Two climatic variables were chosen in our analysis. The mean earth skin temperature (TSMEAN) and the surface soil wetness (GWTOP) were adopted. These climatic components are well-known and substantial drivers in the viability of the infective E. multilocularis eggs in the environment [16].

Not only abiotic and biotic environmental factors may play a role in the spread of zoonotic diseases, but also the socio-economic status of the affected human population. Strong evidence supported that some socio-economic drivers (e.g. economic disparity, higher unemployment rate, poor living conditions) could also potentially provide a higher risk of infectious diseases, including HAE [3, 17, 18,]. For this reason, we implicated a socio-economic level variable (district development index = DDI). The data obtained from the Institute for Economic and Enterprise Research Hungarian Chamber of Commerce and Industry and calculated from 21 different indicators (https://gvi.hu/files/researches/608/GVI_JFM_2019_tables_en_190830.pdf) (Supplementary Information 2). All the datasets that were applied in this study are shown in Table 1.

Table 1

The involved data sources utilised in the retrospective analysis of Hungarian HAE (2003–2024).
source	data	period	link
Humanitarian Data Exchange	shapefile of Hungarian districts	2015–2022	https://data.humdata.org/dataset/cod-ab-hun
Hungarian Central Statistical Office	census	2011	https://www.ksh.hu/?lang=en
Copernicus Land Monitoring Service	land cover categories	2018	https://land.copernicus.eu/en/products/corine-land-cover
NASA Prediction Of Worldwide Energy Resources	meteorological data	1992–2022	https://power.larc.nasa.gov/data-access-viewer/
Institute for Economic and Enterprise Research Hungarian Chamber of Commerce and Industry	calculated from 22 socio-economic indicators	2017	https://gvi.hu/files/researches/608/GVI_JFM_2019_tables_en_190830.pdf
National Food Chain Safety Office, Budapest, Hungary	E. multilocularis infection in fox	hunting seasons of 2008 and 2012 years	https://www.sciencedirect.com/science/article/pii/S0304401713004986#sec0010

Data preparation and selection

The Hungarian districts (Local Administrative Units level 1) were chosen as the units of analysis (https://ec.europa.eu/eurostat/web/nuts/local-administrative-units).

There are currently 197 district-level administrative units in Hungary, of which 23 represent Budapest districts. Although two cases of HAE were reported in the Budapest area, the 23 city districts were excluded from the analysis. On the one hand, fox data were available in only seven districts of the data used (14 animals in total) therefore the proportion of the missing data was not tolerable. On the other hand, the chance of foxes occurring and the resulting human infections in particular districts (mainly downtown, e.g. 1st, 5th, 6th, 7th and 8th Districts) is omitted. Thus, the analysis could have given distorted results for the city. Six of the remaining 174 districts also had no fox data. Since sufficient data were available on these districts' neighbours, we used the neighbours' mean imputation method [19], applying the generally accepted 5% replacement limit [20].

We assigned each case to the administrative district of the patient's residence if the patient had lived there for at least ten years. If the patient has lived in their current residence for less than ten years, he or she has been assigned to the district, in which he or she had lived for ten years previously [21].

As a first step, we determined the period prevalence of the infection using the total number of cases and the number of inhabitants in each district. For mapping the results, the calculated ratio was expressed to 100,000 people. In the case of foxes, the prevalence in each district was given as the proportion of infected individuals among all animals tested for E. multilocularis. These crude proportions, calculated from small areas (districts) and small populations (humans and foxes) of different sizes, provide unstable and imprecise estimations. The estimation accuracy is linked to the size of the population at risk, which can cause a high variance in results (i.e. districts with a larger population size have lower variance than districts with a smaller population size). For this reason, adjusting the crude rates could improve the precision of the estimates. This stabilisation can be achieved by spatial rate smoothing [22] (Anselin et al. 2006). The calculated rate smoothing yield of human cases (HUMRISK) was used as a dependent variable, while the spatial rate smoothing yield of fox infection (FOXRISK) applied as independent variables (Supplementary Information 3). The GeoDa software (v1.22) was used to perform the smoothing process.

Spatial cluster analysis

During the spatial cluster, analysis districts where PP > 0 was observed in either humans or foxes were considered infected units. However, we evaluated them as non-infected since the calculation gave a PP = 0 result.

The scan statistic was performed using SaTScan software (v9.6.1) [23]. We applied in both cases a Bernoulli-based model with Gini coefficient to assess the true human and fox clusters’ pattern. The maximum size of the clusters was determined to be a maximum 50% of the population. We did not allow the geographical overlap between the clusters and boosted the statistical power of our analysis with Monte Carlo iterations (n = 999).

Clusters with a p-value < 0.05 were considered statistically significant.

Global and local regression models

The strong correlation between some predictors can weaken the power of coefficients and

make the p-value vague. To avoid this problem, before the model building, we checked the multicollinearity between the explanatory variables, and only explanatory variables with a variance inflation factor (VIF) value of less than five were accepted [24].

In the first step of the spatial regression analysis, we used the conventionally accepted ordinary least squares (OLS) approach to evaluate the global (stationary) distribution of HUMRISK. This model evaluates the effects of explanatory variables in a global sense using the following equation:

$$\:{Y}_{i}={\beta\:}_{0}+{\sum\:}_{}^{}{\beta\:}_{k}{X}_{ik}+{\epsilon\:}_{i}$$

where:

Y_i= the dependent variable at the i^th sample point,

β₀ = the estimated parameter (coefficient) of the linear regression equation,

β_k = the estimated parameter (coefficient) of the k^th independent variable,

X_ik = the k^th independent variable at the i^th sample point,

ε_i = the random error.

The global effect through space may be unrealistic [25]. Some explanatory variables do not fluctuate from sample point to sample point, but many are space-dependent (e.g. mean temperature, land cover categories). If stationary and non-stationary relationships can be observed, the assumptions of OLS may misestimate the coefficients. This problem could be eliminated by local regression models, such as geographically weighted regression or multiscale geographically weighted regression since these models are capable of solving the problem of spatial heterogeneity [26]. The GWR model is an ameliorated traditional linear regression model with the power to apply a spatial weight matrix. The model determines a fixed bandwidth (neighbourhood range) because it is supposed that the independent variables alter in the same spatial scale. The regression coefficients of spatial units are analysed separately and could give a finer explanation of spatial effects. The GWR model is based on the following equation:

$$\:{Y}_{i}={\beta\:}_{0}\left({u}_{i},{v}_{i}\right)+{\sum\:}_{}^{}{\beta\:}_{k}{\left({u}_{i},{v}_{i}\right)X}_{ik}+{\epsilon\:}_{i}$$

where:

Y_i= the dependent variable,

β₀(u_i, v_i) = the estimated parameter (regression coefficient) of the linear regression equation at (u_i, v_i),

β_k(u_i, v_i) = the estimated parameter (regression coefficient) of the k^th independent variable at (u_i, v_i),

X_ik = the k^th independent variable at the i^th sample point,

ε_i = the random error.

The MGWR model is an extended GWR model. In this model, the different explanatory variables have diverse bandwidths, so each independent variable has its own bandwidth in the MGWR models compared to the GWR. This scheme could interpret the non-stationary action of different independent variables better, giving an improved model estimation [26]. The model is given as follows:

$$\:{Y}_{i}={\beta\:}_{0}\left({u}_{i},{v}_{i}\right)+{\sum\:}_{}^{}{\beta\:}_{bw\cdot\:k}{\left({u}_{i},{v}_{i}\right)X}_{ik}+{\epsilon\:}_{i}$$

where:

Y_i= the dependent variable,

β₀(u_i, v_i) = the estimated parameter (regression coefficient) of the linear regression equation at (u_i, v_i),

β_bw×k(u_i, v_i) = the differential bandwidth of the k^th independent variable at (u_i, v_i),

X_ik = the k^th independent variable at the i^th sample point,

ε_i = the random error.

Four indices were used to evaluate the different models. Firstly we compared the residual sum of squares (RSS), the Akaike Information Criterion (AIC), and the adjusted coefficient of the model (adj. R²). The lower value of RSS and AIC, while a higher adj. R² points out a better model fit. The calculation was performed using MGWR (v2.2) software with the following settings: projected coordinate type, adaptive spatial kernel, golden selection bandwidth search and Monte Carlo test with 1000 iterations.

In the case of RSS, we used Moran’s I Z-value to estimate the spatial autocorrelation. If the RSS has significant positive autocorrelation, the standard errors of the parameter estimates are not accurate enough [25]. GeoDa software (v1.22) was used to map the results and detect spatial autocorrelation.

Questionnaire

To discover the risk factors, an interview was conducted with 28 of the 40 patients. We could not reach the total number of patients to complete the questionnaire because several went unnoticed by the health care system. The patients were interviewed concerning their gender, age, residence, education level, pet ownership, outdoor activity regarding the potential environmental exposure, and travel history in the historical E. multilocularis countries (Supplementary Information 4). Majority of the potential risk factors to analyse were chosen from a previously conducted large scale European study supported by EFSA [27]. If the percentage of answers to a question exceeded 50%, it was considered to be a primary risk factor.

Descriptive findings

At the beginning of the investigation period, period prevalence was low but has risen sharply in recent years (Fig. 1).

The countrywide overall PP in humans was 0.4025/period/10⁵ with a broad range, 0.0/period/10⁵ and 23.07period/10⁵ (Fig. 2). During the study period, 4 out of 40 patients died directly from alveolar echinococcosis. Thus the case fatality proved 10%. The two districts with the highest PP had six and three human patients of whom three and three were detected during the last five years, respectively. Due to Budapest's exclusion, 38 cases out of 40 confirmed cases were included in the spatial analysis which were aggregated in 29 districts. Based on the raw data, the most infected districts were mainly observed in the country's northern, northeastern, and southwest regions.

After the smoothing, we obtained an extended risk area, which covered 87 districts. On the other hand, owing to smoothing, the areas between the affected local endemics (N-NE and SW) were also estimated to be at higher risk. After smoothing the raw data, it was found that the areas most affected by HAE were the North Hungarian Mountains (north), a part of South Transdanubia (south west), and the Little Hungarian Plain (north west) region. The least endangered area is the eastern part of the country, thus the Great Hungarian Plain (Fig. 3, Supplementary Information 5).

Spatial clustering

In the case of HAE, the high-rated cluster (central coordinates: 47.929150 N, 19.137118 E) involved 16 districts, of which 11 had human cases. The relative risk was 6.03, representing that the probability of HAE occurring within this window was more than six times higher than in areas outside the cluster. The low-rated cluster (central coordinates: 47.024704 N, 21.172044 E) consisted of 44 districts, in which no human cases were confirmed (relative risk = 0).

Three significant clusters were confirmed for foxes, of which two highly overlapped with human clusters (Fig. 4).

The most conspicuous difference in the clustering of human and fox cases was observed in the Transdanubian region, which proved to be a high risk area for fox infection contrary to the lower number of human cases (Table 2).

Table 2

Spatial clusters of *E. multilocularis* infection were detected in the human and fox populations in Hungary.
human clusters
cluster type	central coordinates	number of districts	radius (km)	relative risk	p-value
high-rated*	7.929150 N 19.137118 E	16	53.28	6.03	0.001
low-rated	47.024704 N 21.172044 E	44	109.72	0	0.012
indicated with 1 on Fig. 3 (A), *indicated with 2 on Fig. 3 (A)
fox clusters
cluster type	central coordinates	number of districts	radius (km)	relative risk	p-value
high-rated 2*	48.102382 N 19.805404 E	15	59.72	2.41	0.0016
high-rated 3**	47.235514 N 16.622270 E	63	178.04	1.99	0.024
low-rated	46.709441 N 20.143731 E	39	94.16	0.13	< 0.0001
on Fig. 3 (B) this cluster signed with 2, *on Fig. 3 (B) this cluster signed with 3

Global (OLS) and local (GWR and MGWR) regression models

During the multicollinearity checking, we excluded one variable (AGRO) because its VIF value exceeded the threshold level (5 < VIF). The final result of our OLS model was relatively poor (R² = 0.237). Based on the standard t-value threshold (± 1.96), four independent variables (GWTOP, FOR, DDI, FOXRISK) had a stationary statistically verifiable effect on HUMRSK. When the t-value was greater than 1.96, we accepted it as a positive significant relationship. At the same time, the t-test generated a lesser number than − 1.96, and the association was evaluated as negatively significant. The examination of the residuals suggested a strong autocorrelation (Moran’s I = 0.496, p = 0.0002).

The GWR and MGWR models showed considerable improvement, and revealed the spatial relationships between the variables (Table 3). The results made it possible to conclude that the best model was MGWR. This statement was reinforced by the fact that spatial autocorrelation could no longer be detected in the case of the residuals of the model (Moran’s I = 0.042, p = 0.134).

Table 3

The goodness of fit statistics of ordinary least regression (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) models.
	OLS	GWR	MGWR
adj. R²	0.237	0.519	0.751
AIC	455.546	397.242	289.085
RSS	127.396	68.056	32.966
(Note: adj. R² = adjusted coefficient of the model, AIC = Akaike Information Criterion,RSS = residual sum of squares)

When the OLS and MGWR models were compared, it was found that the effect of certain variables varied significantly from model to model.The OLS model indicated that the forest cover (FOR) and the smoothed fox infection rate (FOXRISK) had a positive correlation with the smoothed HAE risk. The district development (DDI) and, interestingly, the wetness of the ground surface (GWTOP) were negatively associated with HUMRISK. The MGWR results confirmed that two of the four above-mentioned variables were not considered stationary (GWTOP and FOXRISK), as their significant local effects were also confirmed. The soil surface temperature (TSMEAN) estimate also showed a non-stationary effect. The p-value of DDI and the FOR in the MGWR proved that these variables had a universal relationship with HAE risk across Hungary (Table 4). The bandwidth in the MGWR model denoted the side effect of the influencing determinants. The smaller the bandwidth, the larger the spatial heterogeneity over the study area and vice versa. If bandwidth is higher, the impact of a particular variable may not be local but regional or global [28].

Table 4

Comparison of the standardised regression coefficient in OLS and MGWR models.
variable	ꞵ (OLS)	mean ꞵ (MGWR)	SD of ꞵ (MGWR)	min ꞵ (MGWR)	median ꞵ (MGWR)	max ꞵ (MGWR)	Monte Carlo p-value (MGWR)	bandwidth (CI95%)^# MGWR
TSMEAN	0.035	-0.121	± 0.314	–0.545	0.175	0.619	0.040	43 (43–49)
GWTOP	-0.157*	-0.248	± 0.138	-0.452	-0.198	-0.089	0.033	114 (92–143)
URB	0.099	0.086	± 0.005	0.075	0.088	0.093	0.855	173 (124, 173)
FOR	0.291***	0.142	± 0.221	-0.204	0.070	0.939	0.108	43 (43, 47)
WETHABIT	-0.068	-0.056	± 0.015	-0.081	-0.052	-0.036	0.544	173 (124, 173)
DDI	-0.216**	-0.172	± 0.130	-0.491	-0.184	0.078	0.318	43 (43–49)
FOXRISK	0.369***	0.294	± 0.322	-0.225	0.261	1.049	0.000	43 (43, 47)
p < 0.05, p < 0.01, **p < 0.0001, # confidence interval 95%

Spatial non-stationarity in HAE patterns

The local R² map indicated that the total variance of the MGWR model ranged between 0.459 and 0.866. The best-fitting values were observed in Hungary's central, northern and southwestern parts (Fig. 5). The local model did not fit as firmly with data from the southernmost part of the Great Plain, so the local R² value in these areas was only moderate.

Figure 6 demonstrates the spatial fluctuation between TSMEAN and HUMRISK. The regression coefficient varied widely (from − 0.611 to 0.511). Significant relationships were detected in the north-south direction in the central zone of the country and the western region. The local standard t-value thresholds indicated that both lower and higher mean ground surface temperatures influenced human infection rates. The TSMEAN estimate in the OLS was very weak and non-significant (see Table 4).

The GWTOP showed a homogeneous impact on the districts. The ground surface wetness had a negative non-stationary association with HUMRISK on a large portion of the country (mainly the total Transdanubian region). This variable's effect was insignificant in the dryer regions (e.g. Great Hungarian Plain) (Fig. 7).

The effect of fox infection rate (FOXRISK) showed a similar spatial aggregation pattern to GWTOP. However, in this case, the positive, significant spatial relationship was confirmed in Hungary's central, northern and eastern regions. On the other hand, two districts in the South Transdanubia showed a provable negative correlation (Fig. 8).

Questionnaire

During the study period (2003–2024), 40 patients were diagnosed with HAE in Hungary (female, n = 22; male, n = 18), thus the sex ratio of the patients did not differ (female: 55%; male: 45%;p = 0.5304 ). The median age of patients was 56.6 years (CI95%=51–67), with the youngest and the oldest patient being 12 and 80 years old, respectively. The median age of the two sexes was statistically not different, p = 0.341 (female: 60.5 years, CI95%=51–71; male: 53 years, CI95%=32–62).

Our questionnaire consisted of 13 decidable questions, mainly related to pet keeping, outdoor activity, and some hygiene habits related to fruit and vegetable consumption (Supplementary Information 3). Education levels were sorted into four categories. Among 18 of the 28 respondents who gave education level, two third of them reached at least secondary level (Supplementary Information 3). Based on the responses, it was possible to conclude that there were four risk factors with a proportion exceeding 50%. Owning and playing with dogs occurred in 66.86% and 57.14% of respondents. The other two factors were related to outdoor activities (kitchen garden and regular forest walk), accounting for more than 60%. One of the factors next in the ranking (agricultural activity) was also related to outdoor activities. At the same time, the consumption of unwashed fruits, vegetables, and forest products was higher than 46% of respondents. Direct contact with foxes had the lowest proportion of responses (Fig. 9)

Human alveolar echinococcosis is an increasing health problem in Hungary. Between 2003 April and 2024 April, 40 cases were detected, of which 25 entered the health care system during the last 5 years. Two hyperendemic focuses are supposed to form in the country, one in the Northern Hungarian Mountains, and another in the south western part of the country, the South Transdanubia. The period prevalences for these focus areas, 9.46 and 23.07 (Fig. 2), are similar to those, which were detected in the previously identified highly endemic regions close to the historical Alpine focus area [2].

Cluster analysis by SaTScan software could detect a high-risk and a low-risk HAE cluster in the eastern part of the country while the South Transdanubian case accumulation remained non-detected by this method. The Bernoulli-based approach uses the presence of cases in a district as a quality regardless of the prevalence in the concerned district. The South Transdanubian case accumulation involved only four districts as a contiguous area, and three districts further with plenty of negative districts around these spots, therefore the software identified this phenomenon as a coincidence.

After spatial rate smoothing of HAE, the two separate spots in the South Transdanubia became interconnected with a high risk in the west, which turned intermediate eastwardly. Moreover, the low risk classification of the Great Plain districts turned into moderate risk. This approach could consider the isolated cases on the Great Plain.

Spatial rate smoothing of infection risk in foxes displayed a pattern that agrees with the strong hypothesis that E. multilocularis spread to Hungary from west-northwest direction [29]. Smoothing risk in foxes also removed the least risky classification from the Great Plain districts. The GeoDa software can calculate smoothed risk from the true data of a concerned district completed with data of its neighbourhood. This procedure can provide a more appropriate estimation of risk than use of crude proportions, especially when the sample is not optimal to represent the whole population at risk. Therefore, increased smoothed risk within the apparently free area reflects the possibility of disease occurrence.

For analysing those variables that could influence the smoothed risk of HAE in a district, we applied the MGWR model, which could handle the heterogeneity of the candidate explanatory variables. The model provided very good R² values for almost the whole territory of the country, except the southernmost areas of the Great Plain where the fitness of the model was moderate. The model detected two factors, which affect HUMRISK globally. The socio-economic factor (DDI) showed negative influence on HUMRISK, thus less developed districts represented higher risk for HAE. Forest cover proportion of the district (FOR) also prevailed globally but with a positive influence, whereas the more the forest cover caused the higher the risk of HAE.

Those variables, which were involved as previously proven drivers of E. multilocularis endemics, such as earth skin temperature, ground surface wetness, and infection prevalence in red foxes [2, 30], showed various impacts on HAE by regions. In the northern part of the country, which was reached by the parasite for the first time [29], both the soil temperature (TSMEAN) and the prevalence in foxes (FOXRISK) behave very similar as it was detected within the historical focus of E. mutilocularis [30]. On the other hand, the moisture content of the ground did not show a significant impact on HUMRISK here.

The Northern Hungarian Mountains are under a strong influence of continental climate, which is modified by the microclimatic effect of the relief. This complex climatic impact causes harsh winters but less hot and dry summers in the mountains than in the surrounding plain areas.

Although the North Hungarian Mountains have a more humid climate than of the Great Plain, both showed the same pattern in correlation between ground surface wetness and HAE risk. The bandwidth for GWTOP proved large, thus this variable prevails in large areas. Besides the direct linkage between the two regions, a remarkable west-to-east change to a more continental climate can explain the similar GWTOP effect of the North Hungarian Mountains and the Great Plain. The controversial relationship between GWTOP and HUMRISK apparently questioned the previously proven impact of humidity on HAE risk. Nevertheless, it is worth noting that our study investigated district level values of different variables, therefore it was not suitable to detect microfoci within the concerned district. However, these small habitat spots can be the keys in the transmission of E. multilocularis [31].

The South Transdanubian Region of Hungary is affected by sub-Mediterranean climatic impact. Hot summers and mild winters are very different from the climatic features of the historic focus of E. multilocularis. In spite of this difference, a remarkable case accumulation could be observed in this area, especially during the last five years. Moreover, neither the comprehensive surveillance that was carried out in the red fox population of the region could provide an explanation for the phenomenon. In the western part of this area both TSMEAN and GWTOP showed a negative effect on HUMRISK, thus the warmer the temperature and the lower the humidity resulted in the higher the risk of HAE. Moreover, on a spot inside this area, a HAE case accumulation in humans accompanied by a low prevalence in red foxes (Fig. 8).

It is less probable that the E. multilocularis population in the southern part of Hungary differs this rate in its environmental demands from the northern populations. Though, genetic studies proved that the Hungarian E. multilocularis population has a higher genetic diversity than of Slovakian or Czech populations, the origin of all Central Eastern European populations are proved to be the historical focus in the Alps [32, 33].

A possible explanation for the parasite’s apparent preference for unsuitable district level environmental conditions could be a compensatory mechanism by other factors. These factors can also prevail in microfoci, in which the optimal conditions of survival and transmission exist in spite of the generally existing suboptimal climatic conditions. These factors can be detected by micro-epidemiological investigations, which can disclose local risk factors [10, 31]. Another reason for the parasite’s success in less appropriate conditions is the presence of regionally prevailing factors, which compensate for the risk mitigation effect of the suboptimal climate.

In South Transdanubia, this risk raising factor can be the composition of the predator community in this region, which is characterised by a large density of golden jackal (Canis aureus) populations. This wild canine can carry E. multilocularis and can significantly increase the risk of HAE in smaller geographical habitats. Nowadays, the presence of golden jackals can be detected all over Hungary but the south western part of the country was the place where this mesopredator entered the country during the 1990s. Based on the hunting bag data, South Transdanubia still possesses the densest golden jackal population of Hungary (Fig. 10). The other, district level, factors that could affect HUMRISK are DDI and FOR, which have the same impact in both hyperendemic focuses of HAE.

In South Transdanubia, the stable presence of golden jackals modifies the risk caused by foxes. These two mesocarnivores strongly compete for sources, thus resulting in increased occurrence of foxes around human settlements because they can tolerate humans better than golden jackals do [34, 35]. Besides competition and spatial distribution of these two canids, dissimilar host suitability might enhance each others’ role in disease transmission [1, 36].

It is curious that both the socio-economic factor (DDI) and forest coverage level had homologous effects in each investigated district, though with opposite signs. Between DDI and FOR variables, the statistical analysis could not identify multicollinearity, therefore both factors have their own impact on HAE risk in humans.

In the case of FOR, other research resulted in various conclusions. Most studies concluded that mature forest stands with high biodiversity provide less suitable habitat for E. multilocularis spread than meadows or shrublands, which are generally agreed as the most hazardous habitats for maintenance of E. multilocularis [2, 5, 37]. In our research, the applied data source did not distinguish between different types of forests, e.g. shrublands or woody pastures, thus the exact risk of different sylvatic environments could not be identified.

In Hungary, extended grasslands are characteristic exclusively for the Great Plain [38], which proved quite the less risky area of the country. On the other hand, smaller patches of grasslands are very important natural habitats in both the Northern Mountains and in Transdanubia [39]. In Hungary, the proportion of true grassland area decreased for two reasons. On the one hand, human demands for area, such as urbanisation and intensification of agriculture, reduced the area of grasslands. On the other hand, the end of extensive animal farming resulted in reduced pasturing and mowing activity, which led to afforestation of pastures and meadows [38]. For this reason, we presumed that a high proportion of forests at a district level meant a higher proportion of natural habitats compared to urban areas and large-scale arable lands. Our finding that FOR was identified as a general risk of HAE agreed with the early findings within the historical focus, thus mainly rural environments carried high infection risk [2].

For infection with HAE, a higher risk of low-income regions and low education level was investigated in China [3, 40] and Kyrgyzstan [4]. All studies emphasise the importance of individual knowledge on prevention. In our study, the education level groups of the patients were exactly equal, whereas one third of the patients had low, intermediate, and higher education levels, respectively. Therefore, the individual’s educational level apparently did not affect the chance of infection. However, the socio-economic environment seemed to have a strong impact on the patients’ risk for HAE acquisition. A District’s Development Index depends on a list of factors (see in Supplementary Information 2), which determine the access to information, healthcare, and services. The central role of an appropriately functioning and achievable healthcare system in prevention and early diagnosis could be well illustrated in Kyrgyzstan studies. After the end of the Soviet Union, the collapse of the local healthcare system led to the dramatic increase of both cystic echinococcosis, which is caused by E. granulosus, and HAE cases [4].

The questionnaire survey revealed that 66.67% of the HAE patients engaged in kitchen gardening. A European large-scale study on food self-provisioning revealed that the central motivation for gardening is financial as the poorest social groups produced a part of their food demand [41]. The questionnaire did not ask directly about the financial background of the respondents, therefore gardening as a socio-economic indicator of limited income could not be proven by this study. Moreover, less developed districts are characterised by countryside abandonment all over Europe [42]. This phenomenon creates plenty of appropriate, undisturbed shelters and feeding sites for mesocarnivores, such as red foxes and golden jackals, in the surroundings and inside the human settlements. Less sufficient waste management, which is also common in poor regions, provides a huge amount of anthropogenic sources for survival and successful breeding of wildlife [43]. In densely populated fox habitats, even lower prevalence of E. multilocularis can generate a higher risk as it could be observed in Switzerland between urbanised foxes [2].

The presence of golden jackals in the less developed districts in South Transdanubia highlights an anomaly, which could be experienced in the TSMEAN-HUMRISK relationship in the eastern part of the region. In spite of very similar characteristics to the western part of the region, the eastern half seemed free from HAE (Fig. 6). Therefore, only spatial rate smoothing suggested suspicion of risk eastward in South Transdanubia. However, the complete lack of cases in the southernmost districts of the east reversed the positive association between soil temperature and HAE risk, which was identified in the west.

The anomalous districts are part of Ormánság (see…), one of the poorest regions in Hungary. The average inhabitants are characterised by low education and consequential low income, without vision and ability of self-care [44]. Considering these facts and the biotic and climatic similarity of this region with the neighbouring HAE focus, it cannot be excluded that the apparent freedom of this area is due to the locals’ inattention. During the disease course, initially, the clinical signs of HAE are lacking or less severe [13], therefore most patients do not suspect a life-threatening disease. In addition, disease prevention and health screening tests do not fit the culture of poverty, which characterise the small villages of Ormánság [44]. Our lack of confidence in the disease free status of Ormánság was supported by the study of Balen Topic et al. [45], which detected a HAE case accumulation in Croatia, not far from the southern border of Ormánság. The contradictory finding about the south eastern part of Transdanubia highlighted the paramount importance of knowledge dissemination not only in known hyperendemic focuses but areas with uncertain status.

In this study, we investigated the interdependence of potential influencing factors and E. multilocularis infection in humans and foxes. For statistical analysis, we applied geospatial methods to determine the risk of HAE and its background at a district level. As a result, we revealed that two, very different HAE foci have been localised in the country, yet. However, the driving forces of HAE in the two foci proved remarkably different. The Northern Hungarian Mountain focus was found to be similar to the historic focus of E. multilocularis in the Alps. In the northern focus, cool soil temperature and higher parasite prevalence in red foxes seemed to increase the risk of human infection at district level. Curiously, humidity as a risk factor could not be justified.

The South Transdanubian focus showed a very different risk pattern. Warm and dry soil surfaces appeared to have higher HAE risk than a humid and cool environment. Based on this apparent contradiction to known environmental demand of E. multilocularis, we concluded that other, not investigated factors enhanced the disease transmission within this area, in spite of the suboptimal climatic features. This finding calls attention to the significance of micro-epidemiological investigations, which can identify microfoci and their risk factors contributing to HAE transmission even in seemingly less appropriate conditions.

Our study recognised an area in the country, which behaved in the model as an anomaly. This area is one of the poorest zones of Hungary. The climatic and biotic characteristics of this area is very similar to the neighbouring South Transdanubian focus and a Croatian area with HAE accumulation. In spite of that, HAE has never been detected here. We concluded that without comprehensive knowledge dissemination and health screening of the inhabitants, this anomaly cannot be evaluated correctly and the status of this area remains uncertain.

This study highlighted the weight of multidisciplinary approach to E. multilocularis. Our investigation revealed some biological, climatic and socio-economic factors of HAE. On the other hand, the analysis resulted in contradictory findings, which need micro-epidemiological research to clarify. This study aimed to determine the most important risk factors for human infection with E. multilocularis, therefore it concentrated on districts and could not analyse microfoci. These small natural spots are very important in disease transmission [31] but are too small for large-scale analysis. However, the results of large-scale risk analysis could point at those areas, which need more detailed investigation to determine true risk of HAE.

AE alveolar echinococcosis

AIC Akaike Information Criterion

CLC2018 CORINE (Coordination of Information on the Environment) Land Cover 2018

CT computed tomography

ELISA enzyme-linked immunosorbent assay

GWR geographically weighted regression

HAE human alveolar echinococcosis

IHA indirect hemagglutination test

MGWR multiscale geographically weighted regression

MRI magnetic resonance imaging

NIP number of infected humans/animals,

NP number of population in particular spatial unit

OLS ordinary least squares

PP periode prevalence

RSS residual sum of squares

VIF variance inflation factor

WHO-IWGE World Health Organization Informal Working Groups on Echinococcosis

Acknowledgments

The study was supported by the Flagship Research Groups Programme of the Hungarian University of Agriculture and Life Sciences.

Author contributions

All authors contributed to the study’s conception and design. Balázs Dezsényi and Eszter Csulak implemented the conceptualization, supervision, manuscript reviewing and final editing. Tamás Sréter, József Danka and Zsolt Káposztás performed the data collection, curation, and project administration. Gábor Nagy and Norbert Solymosi designed and carried out the analytical strategy and interpretation of the findings. Ágnes Csivincsik wrote and edited the manuscript. The authors declare that they have read and approved the final manuscript.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Available of data and materials The personal questionnaire data are not public. The datasets and supplementary information generated and analysed during the current study are available from the following link: https://zenodo.org/records/13134457.

Ethics approval and consent to participate

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Committee for Scientific and Research Ethics of the Medical Research Council (07-03-2024/BM/2130-3/2024).

Consent for publication Not applicable.

Competing interest

The authors have no relevant financial or non-financial interests to disclose.

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Retrospective multidisciplinary analysis of human alveolar echinococcosis in Hungary using spatial epidemiology approaches

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Background

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Data preparation and selection

Spatial cluster analysis

Global and local regression models

Questionnaire

Results

Descriptive findings

Spatial clustering

Global (OLS) and local (GWR and MGWR) regression models

Spatial non-stationarity in HAE patterns

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Discussion

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