Kim and Pak: Spatial risk factors of the 2016 to 2018 highly pathogenic avian influenza epidemics in the Republic of Korea

Abstract

The current study explored the epidemiological associations between the 2016/18 highly pathogenic avian influenza (HPAI) epidemics and spatial factors, including the distance from a poultry farm to the closest groundwater source, migratory bird habitat, eco-natural area, and poultry farm altitude. We included 14 spatial factors as independent variables. The variables were used in the original continuous measurement format. In total, 288 poultry farms (144 HPAI-confirmed and 144 non-confirmed) were used as the dependent variable. In addition, the variables’ continuous measurement was converted to a categorical measurement format by using a general additive model. For risk factor analysis based on the continuous measurements of spatial factors, the non-graded eco-natural area distance (odds ratio [OR]: 1.00) and the grade one eco-natural area distance (OR: 0.99) were statistically significant independent variables. However, in the risk factor analysis based on the categorical measurement format of the spatial factors, the non-graded eco-natural area distance (OR: 0.08) and poultry farm altitude (OR: 0.44) were statistically significant independent variables in both a univariate and multiple logistic regression model. In other words, when a poultry farm was located far from the non-graded eco-natural area or in a highland area, the likelihood of an HPAI epidemic would decrease. From an HPAI control perspective, it is recommended that the government apply increased levels of biosecurity measures, such as bird-nets, fences, intensive disinfection of equipment, and regular bird health monitoring, for poultry farms located near non-graded eco-natural areas or in a lowland area.

Introduction

Since the first outbreak of highly pathogenic avian influenza (HPAI) in 2003 in the Republic of Korea, there have been 12 HPA epidemic waves, one in 2003/2004, one in 2006/2007, one in 2008, one in 2010/2011, four in 2014/2016, one in 2016/2017, two in 2017, and one in 2017/2018 [15]. Several studies related to the HPAI epidemics, including molecular identification of the HPAI virus, have been performed in the Republic of Korea. For instance, Sun et al. [16] identified the genetic characteristics of the HPAI H5N6 virus isolated from migratory birds during the 2017 HPAI epidemics. Similarly, Jeong et al. [8] collected 4,403 migratory bird fecal samples during the 2017/2018 HPAI epidemics and performed a phylogenetic analysis. According to their study results, the virus sample shared >98% nucleotide identity with North American wild birds. Baek et al. [1] conducted a comparative phylogenetic analysis of HPAI H5N6 virus samples from migratory birds in the 2018 HPAI epidemic.
To date, however, there have been few risk factor analyses exploring the relationship between HPAI outbreaks and environmental factors such as poultry farm slope, poultry farm altitude, or location of migratory bird habitat near a poultry farm. A risk factor analysis can be used to determine spatial, environmental, or biosecurity factors that can increase the likelihood of HPAI outbreak. For instance, Gompo et al. [6] explored the risk factors of HPAI by performing a case-control study from 2018 to 2019 in Nepal. Based on the results, they suggested that a flock size greater than 1,500 birds (odds ratio (OR): 4.41), a farm did not apply the rule to wear boots for visitors inside the farm (OR: 4.32), and the presence of other commercial farms located within a one-kilometer distance (OR: 10.00) were factors that increased the risk of an HPAI outbreak. Liang et al. [11] also investigated the association between environmental factors and the 2015/17 HPAI epidemics in Taiwan. In their findings, high poultry farm density (17.46 of OR in 2015 and 8.23 of OR in 2017), poultry heterogeneity index (12.28 of OR in 2015 and 2.79 of OR in 2017), non-registered waterfowl flock density (6.8 of OR in 2015 and 9.17 of OR in 2017), and a high percentage of cropping land coverage (1.36 of OR in 2015 and 1.04 of OR in 2017) were associated with an increase in HPAI outbreak risk. Similarly, Wang et al. [17] performed a meta-analysis on the risk factors for avian influenza (AI) on poultry farms. They reported, based on their analysis of 15 AI risk factor studies, the following were associated with AI infections on poultry farms: visitors entering the farm (OR: 1.47), sharing of equipment (OR: 1.63), open-type water resource (OR: 2.89), nearby farm infection status (OR: 4.54), contact with backyard poultry (OR: 1.05), contact with other birds (OR: 1.14), and contact with other livestock on the farm (OR: 1.90).
It is important for the Korean government to allocate human and/or material resources on a high priority basis to control the risk factors associated with HPAI outbreaks because the national budget for HPAI control is limited. In the current study, a multiple factor logistic regression model was used to explore the relationship between spatial factors and the 2016/18 HPAI epidemics in the Republic of Korea. The distance from a poultry farm to the nearest migratory bird habitat, the distance from a poultry farm to the closest lake, and the distance from a poultry farm to the closest underground water source were the spatial factors examined.

Materials and Methods

2016/18 HPAI epidemics

The 2016/18 HPAI epidemics data were compiled from the Official Veterinary bulletin published on the QIA website [15]. There were five HPAI epidemic waves during that period. The first HPAI epidemic wave was from March 23 in 2016 to April 5 in 2016 (13 days) and included two outbreaks. The second wave was from November 16 in 2016 to March 3 in 2017 (107 days) and included 343 outbreaks. The third wave was from February 2 in 2017 to April 4 in 2017 (57 days), and 40 poultry farms were confirmed to be infected with the HPAI virus. The fourth wave was from June 2 in 2017 and June 19 in 2017 (17 days), and the number of HPAI-infected poultry farms was 36. The fifth HPAI epidemic wave was from November 17 in 2017 to March 17 in 2018 (121 days), and 22 poultry farms were infected.

Risk factor analysis

A multiple logistic regression model was used to assess the spatial risk factors related to the likelihood of an HPAI outbreak. The dependent variable was set as the occurrence of HPAI during the 2016/18 HPAI epidemic period, and the independent variables were 14 spatial factors. The 14 spatial factors were: the distance from a poultry farm to the closest lake, the distance from a poultry farm to the closest underground water source, the distance from a poultry farm to the closest migratory bird habitat, marsh, forest, motorway, eco-natural area grade one, two, three, and non-graded, farmland, residential area, the altitude of the poultry farm, and the slope of poultry farm (Table 1). The eco-natural area, as indicated by the Ministry of Environment, defined a specific area according to its ecological or natural value as grade one, two, three, or non-graded. For example, a grade one eco-natural area is an area where endangered species live or in which the ecosystem is well developed. A grade two eco-natural area includes the area along the boundary of an eco-natural area grade one area. The eco-natural area grade three area included areas other than the grades one and two eco-natural areas, while non-graded eco-natural areas are as defined by other Korean regulations [10]. All information related to environmental factors was obtained from the Ministry of Land, Infrastructure and Transport, the Ministry of Environment [12], and the Korean Animal Health Integrated System [9]. Based on the availability of spatial factor information, 144 poultry farms confirmed to have an HPAI infection, and the same number of poultry farms that were not infected were included in the study group. In total, 288 poultry farms were included in the current multiple logistic regression model. The independent variable measurements were added to the multiple logistic regression model in two formats: continuous or categorical. A general additive model was applied to convert the continuous (or discrete) scale of the independent variable measurements to categorical scales.
Table 1.
Independent variables included in the multiple logistic regression model for the 2016/2018 highly pathogenic avian influenza epidemics in the Republic of Korea
Variable Description Unit Source
Lake distance Distance from a poultry farm to the closest lake Meter MOLIT, 2020 [12]
Groundwater distance Distance from a poultry farm to the closest underground water source Meter MOLIT, 2020 [12]
Migratory bird habitat distance Distance from a poultry farm to the closest migratory bird habitat Meter KAHIS, 2020 [9]
Marsh distance Distance from a poultry farm to the closest marsh Meter MOLIT, 2020 [12]
Forest distance Distance from a poultry farm to the closest forest Meter MOLIT, 2020 [12]
Motorway distance Distance from a poultry farm to the closest motorway Meter MOLIT, 2020 [12]
Grade one eco-natural area distance Distance from a poultry farm to the closest grade one eco-natural area Meter KLRI, 2020 [10]
Grade two eco-natural area distance Distance from a poultry farm to the closest grade two eco-natural area Meter KLRI, 2020 [10]
Grade three eco-natural area distance Distance from a poultry farm to the closest grade three eco-natural area Meter KLRI, 2020 [10]
Non-graded eco-natural area distance Distance from a poultry farm to the closest non-graded eco-natural area Meter KLRI, 2020 [10]
Farmland distance Distance from a poultry farm to the closest farmland Meter MOLIT, 2020 [12]
Residential area distance Distance from a poultry farm to the closest residential area Meter MOLIT, 2020 [12]
Altitude The altitude of a poultry farm Meter KAHIS, 2020 [9]
Slope The slope of a poultry farm Degree KAHIS, 2020 [9]
Forward stepwise multiple logistic regression analysis was performed for the datasets. First, a univariate logistic regression model was constructed for each of the independent variables. Second, the p-value of each independent variable from the univariate logistic regression model was recorded, and the variable was included in the multivariate logistic regression model when p < 0.05. Third, the estimated coefficients and odds ratios of the independent variables included in the multivariate logistic regression model were recorded. Lastly, the estimated Akaike information criterion (AIC) scores from both the univariate and multivariate logistic regression models were compared to determine the goodness of fit of the models. Statistical tests were performed using R software (version 3.6.1; R Development Core Team, Vienna, Austria) and its packages, including ‘gam’.

Results

In the univariate logistic regression model, which used an independent variable in a continuous measurement format, migratory bird habitat distance, marsh distance, non-graded eco-natural area distance, grade one eco-natural area distance, and farm altitude were statistically significant factors (Table 2). In the multiple logistic regression model, which included the five independent variables just mentioned, the odds ratios were 0.999 for migratory bird habitat distance, 0.999 for marsh distance, 0.999 for non-graded eco-natural area distance, 1.000 for grade one eco-natural area distance, and 0.995 for altitude (Table 3). Among those independent variables, the non-graded eco-natural area distance and the grade one eco-natural area distance were statistically significant.
Table 2.
Results of univariate logistic regression model using data in continuous measurement format for the 2016/2018 highly pathogenic avian influenza epidemics in the Republic of Korea
Variable Estimated coefficient Standard error z-value p-value AIC
Lake distance -0.000 0.000 -0.190 0.849 403.22
Groundwater distance -0.000 0.000 -0.930 0.352 402.38
Migratory bird habitat distance -0.000 0.000 -2.800 0.005 394.92
Marsh distance -0.000 0.000 -2.049 0.040 398.98
Forest distance 0.000 0.000 0.511 0.609 402.99
Motorway distance 0.000 0.001 0.465 0.642 403.03
Grade one eco-natural area distance 0.000 0.000 2.670 0.007 395.65
Grade two eco-natural area distance 0.000 0.000 0.774 0.439 402.65
Grade three eco-natural area distance -0.000 0.000 -0.287 0.774 403.17
Non-graded eco-natural area distance -0.000 0.000 -4.771 0.000 374.54
Farmland distance -0.000 0.000 -1.014 0.311 400.42
Residential area distance -0.000 0.000 -1.014 0.311 400.42
Altitude -0.005 0.001 -3.278 0.001 389.81
Slope -0.040 0.028 -1.404 0.160 401.23

∗ AIC: Akaike information criterion

Table 3.
Results of multiple logistic regression model using data in continuous measurement formal for the 2016/2018 highly pathogenic avian influenza epidemics in the Republic of Korea
Variable Estimated coefficient Standard error z-value p-value Odds ratio AIC
Migratory bird habitat distance -0.000 0.000 -1.733 0.083 0.999 350.63
Marsh distance -0.000 0.000 -1.791 0.073 0.999 ?
Grade one eco-natural area distance -0.000 0.000 -5.600 0.000 0.999 ?
Non-graded eco-natural area distance 0.000 0.000 2.157 0.031 1.000 ?
Altitude -0.000 0.000 -1.876 0.060 0.995 ?

∗ AIC: Akaike information criterion

According to the general additive model results, the cut-off points that divided the independent variable measurements into two categories were as follows: 1,000 m for lake distance, 5,000 m for groundwater distance, 1,000 m for migratory bird habitat distance, 3,000 m for marsh distance, 1000 m for forest distance, 100 m for motorway distance, 4,000 m for grade one eco-natural area distance, 500 m for grade two eco-natural area distance, 1,500 m for grade three eco-natural area distance, 100,000 m for non-graded eco-natural area distance, 500 m for farmland distance, 500 m for residential area distance, 100 m for altitude, and seven degrees for slope (Fig. 1).
Fig. 1.
Results of the general additive model for the 14 spatial factors associated with the 2016/18 highly pathogenic avian influenza epidemics in the Republic of Korea
jpvm-2020-44-4-186f1.gif
The univariate logistic regression model, which used the independent variable as a categorical term, the non-graded eco-natural area distance and the altitude variables were statistically significant (Table 4). In the multiple logistic regression model, which included the previous two independent variables, the odds ratios were 0.087 for the non-graded eco-natural area distance and 0.448 for the altitude (Table 5); both of those independent variables were statistically significant.
Table 4.
Results of univariate logistic regression model using data converted to a categorical measurement format for the 2016/2018 highly pathogenic avian influenza epidemics in the Republic of Korea
Variable Estimated coefficient Standard error t-value p-value AIC
Lake distance -0.125 0.500 -0.250 0.803 403.19
Groundwater distance 0.083 0.235 0.354 0.724 403.13
Migratory bird habitat distance 0.498 0.337 1.478 0.139 401.02
Marsh distance -0.055 0.236 -0.236 0.813 403.20
Forest distance 0.237 0.489 0.486 0.627 403.02
Motorway distance 0.149 0.386 0.386 0.700 403.10
Grade one eco-natural area distance 0.467 0.260 1.797 0.072 399.99
Grade two eco-natural area distance -0.087 0.418 -0.209 0.834 403.21
Grade three eco-natural area distance -0.072 0.269 -0.270 0.787 403.18
Non-graded eco-natural area distance -2.406 0.291 -8.258 0.000 319.88
Farmland distance -0.714 0.717 -0.996 0.319 402.20
Residential area distance -0.714 0.717 -0.996 0.319 42.20
Altitude -0.725 0.254 -2.847 0.004 394.97
Slope -0.374 0.262 -1.428 0.153 401.20

∗ AIC: Akaike information criterion

Table 5.
Results of multiple logistic regression model using data converted to a categorical measurement format for the 2016/2018 highly pathogenic avian influenza epidemics in the Republic of Korea
Variable Estimated coefficient Standard error z-value p-value Odds ratio AIC
Non-graded eco-natural area distance -2.431 0.297 -8.188 0.000 0.087 314.51
Altitude -0.801 0.298 -2.686 0.007 0.448 ?

∗ AIC: Akaike information criterion

Discussion

The current study explored the association between spatial factors and the 2016/18 HPAI epidemics in the Republic of Korea by applying a multiple logistic regression model. The AIC score of the multivariate logistic model for the 2016/18 HPAI epidemics was lower than that of the univariate logistic model for the HPAI epidemics. In other words, the multiple logistic model provided a better indication of the factors associated with the 2016/18 HPAI epidemics than that from the univariate logistic model. Based on our research, when the distance from a poultry farm to the non-graded eco-natural area was greater than 100,000 m, the likelihood of an HPAI outbreak decreased. Given the definition of the non-graded eco-natural area, i.e., a valuable natural area to be protected [10], those areas may not be easily monitored or quarantined. Thus, if a poultry farm was located far (>10,000 m) from those non-graded eco-natural areas, the likelihood of HPAI outbreak was lower than if the poultry farm was located near (<10,000 m) those areas. This result is consistent other those in previous environmental risk factor research reports. Biswas et al. [2] reported that when an untreated pond or river was present in the area nearby a poultry farm, it would be a risk factor for an HPAI outbreak. In the present study, when a poultry farm was located at an elevation greater than 100 m, the likelihood of an HPAI outbreak was lower than if it was located at an elevation below 100 m. Several other studies have reported an increased HPAI risk in lowland areas [5, 14, 18]. Overall, the results indicate that a poultry farm located near a non-graded eco-natural area or in a lowland area should increase its biosecurity level to prevent an HPAI outbreak.
The results of this study did not directly indicate that those two spatial factors, a non-graded eco-natural area distance or an altitude, were causative factors of HPAI outbreaks. In logistic regression analysis, statistically significant factors are not always causative factors. If the relationship between a dependent variable and one or more independent variables have a biologically based plausibility, then the statistically significant independent variables can be causative. In addition, the two statistically significant independent variables (non-graded eco-natural area distance and farm altitude) in the current study were not the entire list of potential HPAI risk factors but only part of such risks. Several risk factors can influence the probability of an HPAI outbreak. For instance, biosecurity measures, including the hygiene of farmworkers, disinfection of vehicles and farms, disposal of dead poultry, registering visitors, and sharing of equipment, are also risk factors [3, 4, 7, 13]. Therefore, a case-control study or a cohort-based study to explore the causative relationship between poultry farm biosecurity measures and the likelihood of an HPAI outbreak is needed to obtain more conclusive results.
This study had some limitations. First, the period of the current analysis model was limited to the three years (2016 to 2018) since spatial information on poultry farms became available. If the study period could include the whole HPAI epidemic period (2003 to 2018), the analysis results could provide insights that would be more meaningful to national HPAI surveillance and monitoring programs. Second, during the study period (2016 to 2018) there were five HPAI epidemic waves; thus, a separate multiple logistic regression model could be developed for each epidemic. However, the HPAI epidemic wave from March 23 in 2016 to April 5 in 2016 (13 days) had only two outbreak cases. In addition, the number of HPAI-confirmed cases of the HPAI epidemic wave from June 2 in 2017 and June 19 in 2017 was 36, and that of the HPAI epidemic wave from November 17 in 2017 to March 17 in 2018 was 22. Those HPAI epidemic waves were unsuitable for multiple logistic regression model analyses due to their small sample sizes; thus, we merged all HPAI outbreaks into a single model. Lastly, we used a general additive model to convert a continuous measurement spatial factor to a categorical scale factor. The cut-off point from the general additive model is subjective as it was based on a visual evaluation. However, we applied a multiple logistic regression model with data in both continuous and categorical measurement formats to determine the consistency of the results obtained from those two approaches. For instance, the non-graded eco-natural area distance spatial factor was statistically significant in both multiple logistic regression models. Therefore, the subjectivity of the cut-off points developed from a general additive model was consistent with the results from a multiple logistic regression model with continuous measurements.

Conclusions

This study described the relationships between the 2016/18 HPAI epidemics and 14 spatial factors such as the distance from a poultry farm to the closest migratory bird habitat, eco-natural area grade one, two, three, non-graded, or the altitude of a poultry farm. According to the multiple logistic regression model, two spatial factors, the non-graded eco-natural area distance from the poultry farm and the poultry farm's altitude, were significant risk factors for an HPAI epidemic. It is recommended that biosecurity measures, including bird-nets, fences, intensive disinfection of equipment, and regular monitoring of bird health, should be applied in poultry farms that are located close to a non-graded eco-natural area or in a lowland area. However, biosecurity factors should be included in future risk factor analyses to obtain more conclusive results.

Acknowledgement

This study was financially supported by the Institute of Planning and Evaluation for Technology in Food, Agriculture, and Forestry (IPET) grant funded by the Korea government (No.318040-3).

References

1.Baek YG., Lee YN., Lee DH., Cheon SH., Kye SJ., Park YR., Si YJ., Lee MH., Lee YJ. A novel reassortant clade 2.3.4.4 highly pathogenic avian influenza H5N6 virus identified in South Korea in 2018. Infect Genet Evol. 2020. 78:104056.
[CrossRef] [Google Scholar]
2.Biswas PK., Christensen JP., Ahmed SSU., Das A., Rahman MH., Barua H., Giasuddin M., Hannan ASMA., Habib MA., Debnath NC. Risk for Infection with Highly Pathogenic Avian Influenza Virus (H5N1) in Backyard Chickens, Bangladesh. Emerg Infect Dis. 2009. 15:1931–1936.
[CrossRef] [Google Scholar]
3.Desvaux S., Grosbois V., Pham TTH., Fenwick S., Tollis S., Pham NH., Tran A., Roger F. Risk Factors of Highly Pathogenic Avian Influenza H5N1 Occurrence at the Village and Farm Levels in the Red River Delta Region in Vietnam. Transbound Emerg Dis. 2011. 58:492–502.
[CrossRef] [Google Scholar]
4.Fasina FO., Rivas AL., Bisschop SPR., Stegeman AJ., Hernandez JA. Identification of risk factors associated with highly pathogenic avian influenza H5N1 virus infection in poultry farms, in Nigeria during the epidemic of 2006-2007. Prev Vet Med. 2011. 98:204–208.
[CrossRef] [Google Scholar]
5.Gilbert M., Xiao X., Pfeiffer DU., Epprecht M., Boles S., Czarnecki C., Chaitaweesub P., Kalpravidh W., Minh PQ., Otte MJ., Martin V., Slingenbergh J. Mapping H5N1 highly pathogenic avian influenza risk in Southeast Asia. Proc Natl Acad Sci USA. 2008. 105:4769–4774.
[CrossRef] [Google Scholar]
6.Gompo TR., Shah BR., Karki S., Koirala P., Maharjan M., Bhatt DD. Risk factors associated with Avian Influenza subtype H9 outbreaks in poultry farms in Kathmandu valley, Nepal. PLoS One. 2020. 15:e0223550.
[CrossRef] [Google Scholar]
7.Henning J., Pfeiffer DU., Vu LT. Risk factors and characteristics of H5N1 Highly Pathogenic Avian Influenza (HPAI) post-vaccination outbreaks. Vet Res. 2009. 40(3):15.
[CrossRef] [Google Scholar]
8.Jeong S., Lee DH., Kim YJ., Lee SH., Cho AY., Noh JY., Tseren-Ochir EO., Jeong JH., Song CS. Introduction of Avian Influenza A(H6N5) Virus into Asia from North America by Wild Birds. Emerg Infect Dis. 2019. 25(11):2138–2140.
[CrossRef] [Google Scholar]
9.KAHIS. Korea Animal Health Integrated System. Animal and Plant Quarantine Agency;2020. https://www.kahis.go.kr/home/intrcn/intrcn_m1_01.do.
10.KLRI. Korea Legislation Research Institute. Natural Environment Conservation Act 32. 2020. https://elaw.klri.re.kr/kor_service/lawView.do?hseq=49087&lang=ENG.
[Google Scholar]
11.Liang WS., He YC., Wu HD., Li YT., Shih TH., Kao GS., Guo HY., Chao DY. Ecological factors associated with persistent circulation of multiple highly pathogenic avian influenza viruses among poultry farms in Taiwan during 2015-17. PLoS One. 2020. 15:e0236581.
[CrossRef] [Google Scholar]
12.MOLIT. Ministry of Land. Infrastructure and Transport Public Data Promotion. 2020. http://www.molit.go.kr/USR/WPGE0201/m_35445/DTL.jsp.
[Google Scholar]
13.Paul M., Wongnarkpet S., Gasqui P., Poolkhet C., Thongratsakul S., Ducrot C., Roger F. Risk factors for highly pathogenic avian influenza (HPAI) H5N1 infection in backyard chicken farms, Thailand. Acta Tropica. 2011. 118:209–216.
[CrossRef] [Google Scholar]
14.Pfeiffer DU., Minh PQ., Martin V., Epprecht M., Otte MJ. An analysis of the spatial and temporal patterns of highly pathogenic avian influenza occurrence in Vietnam using national surveillance data. Vet J. 2007. 174:302–309.
[CrossRef] [Google Scholar]
15.QIA. Animal and Plant Quarantine Agency. Epidemiological investigation results of the 2017/18 highly pathogenic avian influenza. 2018. http://ebook.qia.go.kr/20181219_100247.
[Google Scholar]
16.Sun J., Zhao L., Li X., Meng W., Chu D., Yang X., Peng P., Zhi M., Qin S., Fu T., Li J., Lu S., Wang W., He X., Yu M., Lv X., Ma W., Liao M., Liu Z., Zhang G., Wang Y., Li Y., Chai H., Lu J., Hua Y. Novel H5N6 avian influenza virus reassortants with European H5N8 isolated in migratory birds, China. Transbound Emerg Dis. 2020. 67(2):648–660.
[CrossRef] [Google Scholar]
17.Wang XX., Cheng W., Yu Z., Liu SL., Mao HY., Chen EF. Risk factors for avian influenza virus in backyard poultry flocks and environments in Zhejiang Province, China: a cross-sectional study. Infect Dis Poverty. 2018. 7(1):65–74.
[CrossRef] [Google Scholar]
18.Williams RA., Peterson AT. Ecology and geography of avian influenza (HPAI H5N1) transmission in the Middle East and northeastern Africa. Int J Health Geogr. 2009. 8:47–57.
[CrossRef] [Google Scholar]

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