population. The marginalised residents of subdivided ats often possess many health-related risk factors and socioeconomic disadvantages from having one or a mixture of low income, low education level, being elderly, migrant status, or rental in ation that outpace their salary increase (Li, 2001;Wong and Chan, 2019;Yau and Ho, 2017). Therefore, the building and occupant characteristics of subdivided ats are likely risk factors for the spread of bedbug infestations in Hong Kong (Cheung, 2017;Ting, 2019). Furthermore, reports suggests that bedbug infestations in subdivided ats are contributing to a wider social issue of their occupants sleeping at 24-hour fast food restaurant to avoid bedbug bites (NowTv, 2019;Ting, 2019).Bedbug infestations are largely neglected in Hong Kong despite being a public health threat due to 1) the perception that bedbugs pose an insigni cant health concern compared to other pests such as mosquitoes; 2) those affected by bedbugs being unlikely to report or seek help for several reasons such as shame, and the lack of means or know-how; and 3) the perception that bedbug infestations are a personal hygiene instead of a public health issue, assigning blame onto individuals and their households rather than addressing social disparities (Cheung, 2017;Ting, 2019).Although dilapidated housing features may manifest similarly in different countries, the unique features of Hong Kong's housing situation and the effect of the local context on socioeconomic disadvantages may affect Hong Kong's bedbug issue differently. Studies have been done previously in the US that identi ed lower income (

Bedbugs (Cimex spp.) are a nuisance public-health pest that is on the rise globally, particularly in crowded cities such as Hong Kong. To investigate the health impacts of bedbug infestations among bedbug victims, online surveys were distributed in Hong Kong between June 2019 to July 2020. Data on sociodemographics, self-rated health, average hours of sleep per day, and details of bedbug infestation were collected. Bivariate and multivariable analysis were performed using logistic regression. The survey identified 422 bedbug victims; among them, 223 (52.9%) experienced ≥five bites in the past month; most bites occurred on the arms (n = 202, 47.8%) and legs (n = 215, 51%), and the most common reaction to bites were itchiness (n = 322, 76.3%), redness, and swelling of the skin (n = 246, 58.1%), and difficulties sleeping or restlessness (n = 125, 29.6%). Bites usually occurred during sleep (n = 230, 54.5%). For impact on daily life in the past month, most bedbug victims reported moderate to severe impact on mental and emotional health (n = 223, 52.8%) and sleeping quality (n = 239, 56.6%). Lower self-rated health (aOR < 1) was independently associated with impact on physical appearance (p = 0.008), spending money on medication or doctor consultation (p = 0.04), number of bites in the past month (p = 0.023), and irregular time of bites (p = 0.003). Lower average hours of sleep per day (aOR < 1) was independently associated with impact on mental and emotional health (p = 0.016). This study brings attention to the neglected issue of bedbug infestation by considering bedbugs as an infectious agent instead of a vector and providing empirical evidence describing its health impacts.

BACKGROUND Studies have shown increasing COVID-19 vaccination hesitancy among migrant populations in certain settings compared to the general population. Hong Kong has a growing migrant population with diverse ethnic backgrounds. Apart from individual-level factors, little is known about the migrants’ preference related to COVID-19 vaccines. OBJECTIVE This study aims to investigate which COVID-19 vaccine–related attributes combined with individual factors may lead to vaccine acceptance or refusal among the migrant population in Hong Kong. METHODS An online discrete choice experiment (DCE) was conducted among adults, including Chinese people, non-Chinese Asian migrants (South, Southeast and Northeast Asians), and non-Asian migrants (Europeans, Americans, and Africans) in Hong Kong from February 26 to April 26, 2021. The participants were recruited using quota sampling and sent a link to a web survey. The vaccination attributes included in 8 choice sets in each of the 4 blocks were vaccine brand, safety and efficacy, vaccine uptake by people around, professionals’ recommendation, vaccination venue, and quarantine exemption for vaccinated travelers. A nested logistic model (NLM) and a latent-class logit (LCL) model were used for statistical analysis. RESULTS A total of 208 (response rate 62.1%) migrant participants were included. Among the migrants, those with longer local residential years (n=31, 27.7%, for ≥10 years, n=7, 20.6%, for 7-9 years, n=2, 6.7%, for 4-6 years, and n=3, 9.7%, for ≤3 years; <i>P</i>=.03), lower education level (n=28, 28.3%, vs n=15, 13.9%, <i>P</i>=.01), and lower income (n=33, 25.2%, vs n=10, 13.2%, <i>P</i>=.04) were more likely to refuse COVID-19 vaccination irrespective of vaccination attributes. The BioNTech vaccine compared with Sinovac (adjusted odds ratio [AOR]=1.75, 95% CI 1.14-2.68), vaccine with 90% (AOR=1.44, 95% CI 1.09-1.91) and 70% efficacy (AOR=1.21, 95% CI 1.03-1.44) compared with 50% efficacy, vaccine with fewer serious adverse events (1/100,000 compared with 1/10,000; AOR=1.12, 95% CI 1.00-1.24), and quarantine exemption for cross-border travelers (AOR=1.14, 95% CI 1.01-1.30) were the vaccine attributes that could increase the likelihood of vaccination among migrants. For individual-level factors, full-time homemakers (AOR=0.44, 95% CI 0.29-0.66), those with chronic conditions (AOR=0.61, 95% CI 0.41-0.91) and more children, and those who frequently received vaccine-related information from the workplace (AOR=0.42, 95% CI 0.31-0.57) were found to be reluctant to accept the vaccine. Those with a higher income (AOR=1.79, 95% CI 1.26-2.52), those knowing anyone infected with COVID-19 (AOR=1.73, 95% CI 1.25-2.38), those having greater perceived susceptibility of COVID-19 infection (AOR=3.42, 95% CI 2.52-4.64), those who received the influenza vaccine (AOR=2.15, 95% CI 1.45-3.19), and those who frequently received information from social media (AOR=1.52, 95% CI 1.12-2.05) were more likely to accept the vaccine. CONCLUSIONS This study implies that migrants have COVID-19 vaccination preference heterogeneity and that more targeted and tailored approaches are needed to promote vaccine acceptance for different subgroups of the migrant population in Hong Kong. Vaccination promotion strategies are needed for low-education and low-income migrant groups, migrants with chronic diseases, the working migrant population, homemakers, and parents.

Background Studies have shown increasing COVID-19 vaccination hesitancy among migrant populations in certain settings compared to the general population. Hong Kong has a growing migrant population with diverse ethnic backgrounds. Apart from individual-level factors, little is known about the migrants’ preference related to COVID-19 vaccines. Objective This study aims to investigate which COVID-19 vaccine–related attributes combined with individual factors may lead to vaccine acceptance or refusal among the migrant population in Hong Kong. Methods An online discrete choice experiment (DCE) was conducted among adults, including Chinese people, non-Chinese Asian migrants (South, Southeast and Northeast Asians), and non-Asian migrants (Europeans, Americans, and Africans) in Hong Kong from February 26 to April 26, 2021. The participants were recruited using quota sampling and sent a link to a web survey. The vaccination attributes included in 8 choice sets in each of the 4 blocks were vaccine brand, safety and efficacy, vaccine uptake by people around, professionals’ recommendation, vaccination venue, and quarantine exemption for vaccinated travelers. A nested logistic model (NLM) and a latent-class logit (LCL) model were used for statistical analysis. Results A total of 208 (response rate 62.1%) migrant participants were included. Among the migrants, those with longer local residential years (n=31, 27.7%, for ≥10 years, n=7, 20.6%, for 7-9 years, n=2, 6.7%, for 4-6 years, and n=3, 9.7%, for ≤3 years; P=.03), lower education level (n=28, 28.3%, vs n=15, 13.9%, P=.01), and lower income (n=33, 25.2%, vs n=10, 13.2%, P=.04) were more likely to refuse COVID-19 vaccination irrespective of vaccination attributes. The BioNTech vaccine compared with Sinovac (adjusted odds ratio [AOR]=1.75, 95% CI 1.14-2.68), vaccine with 90% (AOR=1.44, 95% CI 1.09-1.91) and 70% efficacy (AOR=1.21, 95% CI 1.03-1.44) compared with 50% efficacy, vaccine with fewer serious adverse events (1/100,000 compared with 1/10,000; AOR=1.12, 95% CI 1.00-1.24), and quarantine exemption for cross-border travelers (AOR=1.14, 95% CI 1.01-1.30) were the vaccine attributes that could increase the likelihood of vaccination among migrants. For individual-level factors, full-time homemakers (AOR=0.44, 95% CI 0.29-0.66), those with chronic conditions (AOR=0.61, 95% CI 0.41-0.91) and more children, and those who frequently received vaccine-related information from the workplace (AOR=0.42, 95% CI 0.31-0.57) were found to be reluctant to accept the vaccine. Those with a higher income (AOR=1.79, 95% CI 1.26-2.52), those knowing anyone infected with COVID-19 (AOR=1.73, 95% CI 1.25-2.38), those having greater perceived susceptibility of COVID-19 infection (AOR=3.42, 95% CI 2.52-4.64), those who received the influenza vaccine (AOR=2.15, 95% CI 1.45-3.19), and those who frequently received information from social media (AOR=1.52, 95% CI 1.12-2.05) were more likely to accept the vaccine. Conclusions This study implies that migrants have COVID-19 vaccination preference heterogeneity and that more targeted and tailored approaches are needed to promote vaccine acceptance for different subgroups of the migrant population in Hong Kong. Vaccination promotion strategies are needed for low-education and low-income migrant groups, migrants with chronic diseases, the working migrant population, homemakers, and parents.

Background: Bedbugs have been a neglected issue globally, disproportionately affecting low-income households. The features of many deprived housing units in Hong Kong provide suitable habitats for bedbug infestations. This study aims to identify the housing risk factors for bedbug infestations in Hong Kong.Methods: Using a cross-sectional study design, online self-reported questionnaires in Chinese were distributed between June 2019 to July 2020. Data collected were socio-demographics, crowded household condition, housing type, dilapidated housing features, and frequency of noticing bedbugs in the participant’s place of residence in the past year. The latter was transformed into a dichotomous dependent variable, “bedbug infestation”. Weighted bivariate and multivariate analysis using binary logistic regression were performed on SPSS 24.Results: The study sampled N=696 participants, 63.7% have had bedbug infestations. Bivariate analysis shows a positive correlation between the number of dilapidated housing features and bedbug infestation (OR=1.28, 95% CI 1.18-1.39, p<0.001). N=663 were included in the multivariate analysis. Those aged 45-64 (OR=2.53, 95% CI 1.30-4.91, p=0.006) and have primary education or below (OR=9.43, 95% CI 3.12-28.44, p<0.001) have significantly greater odds of bedbug infestation compared to their respective reference groups, ≥65 and tertiary education. Having monthly household income ≤HKD30,000 (OR=1.69, 95% CI 1.15-2.5, p=0.008) and living in subdivided flats (OR=16.53, 95% CI 1.01-269.72, p=0.049) and crowded household (OR=1.55, 95% CI 1.06-2.28, p=0.024) increases the odds of bedbug infestation. Dilapidated housing features that significantly increase the odds of bedbug infestation are having second-hand furniture (OR=2.97, 95% CI 1.16-7.58, p=0.023), housing cleanliness issues (OR=2.66, 95% CI 1.13-6.25, p=0.024), presence of bedbugs in neighbouring residential units (OR=3.32, 95% CI 1.57-7.04, p=0.002), and presence of bedbugs on the streets (OR=1.9, 95% CI 1.12-3.23, p=0.018).Conclusions: Crowded household, subdivided flats, and dilapidated housing are risk factors for bedbug infestations. To better control bedbug infestations, there needs to be a shift from viewing infestations as a personal hygiene to a public health issue. Efforts and policies should focus on addressing the housing risk factors identified in this study and prioritise vulnerable groups such as the elderly, low education level, low-income groups, and occupants of subdivided flats.