Introduction

Graphical abstract

In Republic of Korea, county authorities are responsible for managing many infectious diseases, including coronavirus disease 2019 (COVID-19) [1]. The statistics of outbreaks of infectious diseases, including COVID-19, are aggregated by county. In addition, epidemiological investigations on COVID-19 patients are conducted by county-level health authorities (public health centres) in the county where each case is confirmed. The county's public health centre is obligated to disinfect places visited by patients with confirmed infection and has the authority to order isolation of contacts of patients with confirmed infection. Infection transmission across county boundaries necessitates collaboration with public health centres in other counties, which can be complicated. If the incidence of extracounty infections is high, more attention needs to be paid to outbreaks in surrounding counties, which requires collaborative work with health authorities in other counties. If the source of infection crosses county borders, the responsibility for infection control is unclear, leading to an increased risk of poor infectious disease management [2]. Therefore, it is important to review the ratio of intracounty to extracounty transmission in each county. Furthermore, identifying characteristics associated with a low ratio of intracounty to extracounty transmission—implying a high level of extracounty transmission—could help health authorities in counties with similar characteristics manage the spread of infectious diseases.

This study investigated the correlations between the ratio of intracounty to extracounty transmissions and county characteristics, including the population size, population density, and proportion of older residents.

Materials and Methods

We used a database containing information on cases of COVID-19 in Gangwon Province confirmed using polymerase chain reaction tests. The database included findings from basic and in-depth epidemiological investigations, comprising demographic information (sex, age, and occupation, region in which the COVID-19 test was performed, and region of residence), epidemiological information (date of diagnosis, date of onset of COVID-19 symptoms, presence of symptoms, and the cycle threshold value), and information from the infection schematic created based on in-depth epidemiological investigations (relationship between the person who transmitted the infection and the infected person, suspected place in which transmission occurred, and suspected date and time of transmission). The database was managed and accessed by the Gangwon Provincial Office.

The region of the infectious contact was analysed for each COVID-19 case reported in Gangwon Province from February 22, 2020, when the first case occurred, to February 7, 2022. Cases of infections from overseas arrivals and those in detention centres, military units, and nursing homes were excluded. The region of the person who transmitted the infection (transmitter) was classified into (1) the same county/city (intracounty), (2) a different county/city (extracounty), and (3) cases with no information on the transmitter. The ratio of intracounty to extracounty infections was calculated.

The population, population density, area (size of the jurisdictional area), proportion of urban residents in the county, proportion of older adults (>65 years) in the county, financial independence, and the number of adjacent counties were assessed for all 18 counties in Gangwon Province. The correlation coefficient (CC) between each regional characteristic and the ratio of intracounty to extracounty infections was calculated, and p-values were used to test for statistical significance. A sensitivity analysis based on age was conducted to calculate the CCs between the county characteristics and the proportion of extracounty transmission. The analysis was performed using SAS ver. 9.4 (SAS Institute Inc.).

The study was approved by the Institutional Review Board (IRB) of Kangwon National University Hospital (IRB No: KNUH-2021-02-001).

Results

During the analysis period, 20,658 COVID-19 cases were reported in Gangwon Province, of which 19,645 cases satisfied the criteria for inclusion in the analysis. No information on the transmitter was available in 2,956 cases (25.2% of all cases). Table 1 shows the intracounty to extracounty transmission ratio in each county. Wonju County had the highest ratio (4.13), and Goseong County had the lowest ratio (0.62). Table 2 shows the correlations between the county characteristics and the ratio of intracounty to extracounty transmission. Population (CC, 0.57; p=0.01), population density (CC, 0.57; p=0.01), the proportion of older adults (CC, −0.77; p<0.01), and the proportion of urban residents (CC, 0.56; p=0.02) were significantly correlated with the ratio of intracounty to extracounty transmissions. Of the county characteristics, the proportion of older adults was the most strongly and significantly correlated. Therefore, the cases were divided according to age (younger and older than 65 years) to examine this correlation in more detail (Table 3). Both the total population and the proportion of older adults were significantly negatively correlated with the ratio of intracounty to extracounty transmissions. A sensitivity analysis based on age was conducted to evaluate the correlations between each regional characteristic and the proportion of extracounty transmission. The proportion of older adults and the proportion of urban residents were significantly correlated with the ratio of intracounty to extracounty transmissions, with CCs of −0.64 and 0.54, respectively. Figure 1 shows a scatterplot of significant correlations between the ratio of intracounty to extracounty transmission and county characteristics. The ratio of intracounty to extracounty transmission showed a stronger linear relationship to the proportion of older adults than to any other county characteristics.

Discussion

The proportion of older adults was the county characteristic that showed the highest correlation with the ratio of intracounty to extracounty infections. In the age-stratified analysis, the negative correlation between the proportion of older adults in the county and the ratio of intracounty to extracounty transmissions was significant in all age groups. These findings suggest that population ageing in a region affects not only the characteristics of infections in older adults, but also the characteristics of infections in younger individuals living in the same region.

COVID-19 is a respiratory infectious disease closely related to social gatherings [3]. Counties with ageing populations could be expected to have fewer social gatherings with people from other counties than counties with younger populations. Older adults tend to have smaller networks and travel shorter distances, and counties with ageing populations often have poor transportation options [4]. Therefore, we expected that counties with ageing populations would have less interaction with other counties and less disease transmission from individuals in other counties. However, the observed trend was contrary to our expectations. Therefore, in counties with ageing populations, it is necessary to carefully observe the trends in infectious disease outbreaks in other counties and prepare adequate measures to prevent transmission.

Our study has some limitations. First, we only investigated COVID-19 cases in Gangwon Province. Second, approximately 25.2% of cases did not have information on the transmitter, so the results may have been biased. Third, cases of transmission from Gangwon Province to regions outside Gangwon Province were not investigated.

Of the county characteristics assessed, population, population density, the proportion of older adults, and the proportion of urban residents were significantly related to the ratio of intracounty to extracounty transmission. Of these factors, the proportion of older adults was of particular importance. Based on the findings of this study, counties with ageing populations should carefully observe trends in infectious disease outbreaks in nearby counties to prevent possible transmission. Additional research is needed to determine how county characteristics, such as the proportion of older adults in the county population, affect the transmission of infectious diseases. In addition, a nationwide study should be conducted to enable an in-depth analysis of regional characteristics and the spread of infectious diseases between regions.

HIGHLIGHTS

• In the counties of Gangwon Province in Republic of Korea, the population, population density, proportion of older adults, and proportion of urban residents were significantly correlated with the ratio of intracounty to extracounty COVID-19 infections.

• The proportion of older adults had the highest negative correlation with the ratio of intracounty to extracounty transmissions, and this correlation remained significant in the younger age group.

• Counties with ageing populations need to carefully observe trends in infectious disease outbreaks in other counties and prepare adequate measures to prevent transmission.

Article information

Ethics Approval

This study was approved by the Institutional Review Board of Kangwon National University Hospital (IRB No: KNUH-2021-02-001) and performed in accordance with the principles of the Declaration of Helsinki. The requirement for informed consent was waived because of the retrospective nature of this study.

Conflicts of Interest

The authors have no conflicts of interest to declare.

Funding

None.

Availability of Data

All data generated or analysed during this study are included in this published article. Other data may be requested through the corresponding author.

Authors’ Contributions

Conceptualization: SJ, SB; Data curation: CL, YN, EK, MK, SK, YK; Formal analysis: SJ, SB; Methodology: SJ, CL, SB; Validation: SJ, SB; Writing–original draft: SJ, CL; Writing–review & editing: all authors. All authors read and approved the final manuscript.

Figure 1.

Correlations between the regional characteristics of counties and the ratio of intracounty and extracounty transmission (factors with p<0.05).

References

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• 2. Jones T, Hedberg C. Coordination of multiple states and federal agencies [Internet]. Centers for Disease Control and Prevention; 2019 [cited 2023 May 11]. Available from: https://www.cdc.gov/eis/field-epi-manual/chapters/Coordinating-Agencies.html.
• 3. Zhang Y, Tao Y, Shyu ML, et al. Simulating COVID19 transmission from observed movement. Sci Rep 2022;12:3044. ArticlePubMedPMCPDF
• 4. Toepoel V. Ageing, leisure, and social connectedness: how could leisure help reduce social isolation of older people? Soc Indic Res 2013;113:355−72.ArticlePubMedPMC

Figure & Data

References

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