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Map of Scotland showing population densities of the 11 mainland health boards. https://doi.org/10.1371/journal.pone.0253636.g001
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Background
There have been large regional differences in COVID-19 virus activity across the UK with many commentators suggesting that these are related to age, ethnicity and social class. There has also been a focus on cases, hospitalisations and deaths rather than on hospitalisation rates expressed per 100,000 population. The purpose of our study...
Context in source publication
Context 1
... population was 5,463,300 in mid-2019 and average population density in Scotland was 70/sq km that year. There are large differences in population density between mainland boards ranging from 10/sq km in Highland, the least densely populated board, to 1072/sq km in Greater Glasgow and Clyde, the most densely populated board [11] (Fig 1). For comparison, average population densities for the rest of the United Kingdom are as follows: England (432/sq km), Wales (152/sq km) and Northern Ireland (137/sq km) [12]. ...
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Coronavirus disease 2019 (COVID-19) mortality can be estimated based on reliable mortality data. Variable testing procedures and heterogeneous disease course suggest that a substantial number of COVID-19 deaths is undetected. To address this question, we screened an unselected autopsy cohort for the presence of SARS-CoV-2 and a panel of common resp...
Citations
... Namun demikian hubungan tersebut tidak bersifat konsisten. Sementara hubungan tingkat hospitalisasi atau kesembuhan ditunjukkan oleh studi di Skolandia yang menunjukkan ada korelasi kepadatan penduduk dengan tingkat hospitalisasi pada gelombang pertama COVID-19 (Rideout et al., 2021). ...
Tingkat keparahan wabah penyakit menular tidak dapat dipisahkan dengan faktor sosial dan demografis/penduduk (socio-demographics) di suatu wilayah, baik pada level lokal, regional, nasional, hingga global. Sebagaimana penelitian (Ling et al., 2022) tentang analisis spasial temporal wabah COVID-19 menunjukkan atribut-atribut dari faktor sosio-demografis merupakan salah satu determinan yang signifikan. Begitu pula hubungan persepsi masyarakat terhadap keterpaparan COVID-19 dengan moda transportasi ditentukan secara signifikan oleh faktor sosio-demografis penduduk (Ozbilen et al., 2021). Bahkan penularan COVID-19 pada tenaga kesehatan di Brazil juag dipengaruhi oleh faktor sosio-demografis (Zuma et al., 2023). Selain itu faktor geografis sebagai lingkungan fisik dan tempat tinggal manusia sangat berperan terhadap penularan penyakit menular. Seperti wabah monkeypox di beberapa negara yang diteliti oleh (Abu-Hammad et al., 2023) menunjukkan wabah penyakit ini pada tahun 2022 di bagian Barat global lebih berkaitan dengan hubungan seksual Lelaki Suka Lelaki. Sementara itu prevalensi rendah terjadi di bagian Timur disebabkan perilaku sosial masyarakat yang lebih konservatif terhadap homoseksual. Demikian pula pada wabah Kolera di wilayah rural Haiti oleh (Page et al., 2015), kasus infeksi dan kematian lebih banyak terjadi di wilayah yang sulit diakses (remote area) dan hampir 5% penduduk mengalami kematian pada bulan pertama epidemi. Faktor sosio-demografis dan geografis merupakan determinan penyakit menular yang saling berkaitan dan berkorelasi satu sama lain. Hal ini yang menyebabkan faktor penyebab terjadinya wabah penyakit menular sangat dinamis dan kompleks sehingga relatif memiliki tingkat kesulitan tinggi dalam penanganannya, seperti halnya pandemi COVOD-19. Pada artikel ini akan dibahas berdasarkan beberapa literatur penelitian tentang hubungan faktor sosio-demografis dan geografis dengan tingkat keparahan wabah COVID-19. Artikel ini adalah bagian dari laporan disertasi tentang model pengendalian wabah COVID-19 di Indonesia.
... The COVID-19 pandemic overwhelmed global healthcare systems and exposed social and geographic inequities. Regional variations were observed, attributed to population density and characteristics, impacting infection and hospitalization rates [1][2][3]. Racial disparities and social inequalities were evident in infections, hospitalizations, and mortality rates. Prevention strategies, such as lockdowns, mask-wearing, and vaccination, played a role in limiting the virus spread and hospitalizations [2,[4][5][6]. ...
... The common perception is that higher population density increases social contacts resulting in increased transmission of infection and resultant adverse events like hospitalization. Nevertheless, while some studies have a found a positive correlation between COVID-19 hospitalisation and population density others have found that population density was not an important factor in COVID-19 spread [3,22]. Infection control measures like social distancing, mask wearing and contact tracing which were introduced in England, may have impacted the relationship between population density and infection rates [23]. ...
Background
This study explores regional variations in COVID-19 hospitalization rates, in-hospital mortality, and acute kidney injury (AKI) in England. We investigated the influence of population demographic characteristics, viral strain changes, and therapeutic advances on clinical outcomes.
Methods
Using hospital episode statistics, we conducted a retrospective cohort study with 749,844 admissions in 337,029 adult patients with laboratory-confirmed COVID-19 infection (March 1, 2020, to March 31, 2021). Multivariable logistic regression identified factors predicting AKI and mortality in COVID-19 hospitalized patients.
Results
London had the highest number of COVID-19 admissions (131,338, 18%), followed by the North-west region (122,683, 16%). The North-west had the highest population incidence of COVID-19 hospital admissions (21,167 per million population, pmp), while the South-west had the lowest (9,292 admissions pmp). Patients in London were relatively younger (67.0 ± 17.7 years) than those in the East of England (72.2 ± 16.8 years). The shortest length of stay was in the North-east (12.2 ± 14.9 days), while the longest was in the North-west (15.2 ± 17.9 days). All eight regions had higher odds of death compared to London, ranging from OR 1.04 (95% CI 1.00, 1.07) in the South-west to OR 1.24 (95% CI 1.21, 1.28) in the North-west. Older age, Asian ethnicity, emergency admission, transfers from other hospitals, AKI presence, ITU admission, social deprivation, and comorbidity were associated with higher odds of death. AKI incidence was 30.3%, and all regions had lower odds of developing AKI compared to London. Increasing age, mixed and black ethnicity, emergency admission, transfers from other providers, ITU care, and different levels of comorbidity were associated with higher odds of developing AKI.
Conclusions
London exhibited higher hospital admission numbers and AKI incidence, but lower odds of death compared to other regions in England.
Trial registration
Registered on National Library of Medicine website (www.clinicaltrials.gov) with registration number NCT04579562 on 8/10/2020.
... In our study, we found that population density is positively associated with COVID-19 severity. Other studies have reported similar patterns [78][79][80]. Altogether, our study shows that densely populated communities suffer less COVID-19 infection but higher COVID-19 severity. Densely populated communities are generally poor communities characterized by overcrowded human settlements, unhygienic environment, limited access to quality healthcare, and unhealthy dietary behaviour, all of which may predispose to severe COVID-19. ...
The outbreak of COVID-19 quickly spread around the globe, with staggering infection and mortality rates. Given the reported benefits of greenness to human health, we hypothesized that greener neighbourhoods would suffer less from COVID-19 infection and severity than not. We modelled COVID-19 infection and severity in South Africa by fitting Generalized Linear Mixed Effects models to various socio-demographic and greenness data (EVI, i.e., Enhanced Vegetation Index, and tree and grass covers) collected from January 2020 to June 2022 at local municipalities, Wards, and districts scales. We found that income level showed a significant negative association with COVID-19 prevalence at the Ward level, suggesting that poorer neighbourhoods are more susceptible to COVID-19 infection. We also found that EVI and tree cover consistently showed a significant negative correlation with COVID-19 infection rate, irrespective of the scale of analysis, whereas grass cover showed a similar pattern but only at the Ward scale. Finally, we found that neither EVI, grass cover, nor tree cover showed a significant association with COVID-19 severity. Overall, greener neighbourhoods show greater resilience to the spread of COVID-19, suggesting that exposure to greenness may boost natural killer cells in the human body, thus reinvigorating our defence system against infections. Our results re-enforced the need to establish and preserve greenspaces particularly in the developing world, given their ability to buffer the spread of COVID-19.
... This discrepancy may reflect a testing drive that was done after an early outbreak in a rural northern area of Manitoba [29]. Differences in SARS-CoV-2 positivity between urban and rural areas could be explained by factors associated with population density [15,30], such as differing levels of congestion in living conditions, occupational exposures, use of public transportation, and opportunity for social distancing. Socioeconomic factors and SDH were found to play a significant role in SARS-CoV-2 testing and positivity. ...
Background
The effects of the COVID-19 pandemic have been more pronounced for socially disadvantaged populations. We sought to determine how access to SARS-CoV-2 testing and the likelihood of testing positive for COVID-19 were associated with demographic factors, socioeconomic status (SES) and social determinants of health (SDH) in three Canadian provinces.
Methods
An observational population-based cross-sectional study was conducted for the provinces of Ontario, Manitoba and New Brunswick between March 1, 2020 and April 27, 2021, using provincial health administrative data. After excluding residents of long-term care homes, those without current provincial health insurance and those who were tested for COVID-19 out of province, records from provincial healthcare administrative databases were reviewed for 16,900,661 healthcare users. Data was modelled separately for each province in accordance to a prespecified protocol and follow-up consultations among provincial statisticians and collaborators. We employed univariate and multivariate regression models to examine determinants of testing and test results.
Results
After adjustment for other variables, female sex and urban residency were positively associated with testing, while female sex was negatively associated with test positivity. In New Brunswick and Ontario, individuals living in higher income areas were more likely to be tested, whereas in Manitoba higher income was negatively associated with both testing and positivity. High ethnocultural composition was associated with lower testing rates. Both high ethnocultural composition and high situational vulnerability increased the odds of testing positive for SARS-CoV-2.
Discussion
We observed that multiple demographic, income and SDH factors were associated with SARS-CoV-2 testing and test positivity. Barriers to healthcare access identified in this study specifically relate to COVID-19 testing but may reflect broader inequities for certain at-risk groups.
... They found large heterogeneity with higher hospitalization rates in the initial epicenter. Similar results for the first wave were found in Italy [33], Saudi Arabia [34], Scotland [35] or New York City [36]. In terms of LoS, an early systematic review conducted in April 2020 found a median of 14 days in China and 5 days in the rest of the world [37]. ...
Background
One of the main challenges of the COVID-19 pandemic is to make sense of available, but often heterogeneous and noisy data. This contribution presents a data-driven methodology that allows exploring the hospitalization dynamics of COVID-19, exemplified with a study of 17 autonomous regions in Spain from summer 2020 to summer 2021.
Methods
We use data on new daily cases and hospitalizations reported by the Spanish Ministry of Health to implement a Bayesian inference method that allows making short-term predictions of bed occupancy of COVID-19 patients in each of the autonomous regions of the country.
Results
We show how to use the temporal series for the number of daily admissions and discharges from hospital to reproduce the hospitalization dynamics of COVID-19 patients. For the case-study of the region of Aragon, we estimate that the probability of being admitted to hospital care upon infection is 0.090 [0.086-0.094], (95% C.I.), with the distribution governing hospital admission yielding a median interval of 3.5 days and an IQR of 7 days. Likewise, the distribution on the length of stay produces estimates of 12 days for the median and 10 days for the IQR. A comparison between model parameters for the regions analyzed allows to detect differences and changes in policies of the health authorities.
Conclusions
We observe important regional differences, signaling that to properly compare very different populations, it is paramount to acknowledge all the diversity in terms of culture, socio-economic status, and resource availability. To better understand the impact of this pandemic, much more data, disaggregated and properly annotated, should be made available.
... For instance, Dasgupta et al. [39] investigated the association of socioeconomic variables with the risk of becoming a hotspot across US counties based on incidence rates. Two studies researched COVID-19 hospitalizations over time using governmental data on hospitalizations and primary care data from national healthcare records [64,65]. ...
... Studies that considered hospitalizations (n = 2) found persistent inequalities to the disadvantage of socioeconomically disadvantaged populations. While Rideout et al. did not find statistically significant socioeconomic inequalities in hospitalization rates, Simpson et al. identified a higher risk for socioeconomically disadvantaged populations at all time points during the observation period [64,65]. Figure 3 shows the temporal dynamics of socioeconomic inequalities in COVID-19 mortality by socioeconomic indicators. ...
Objectives: International evidence of socioeconomic inequalities in COVID-19 outcomes is extensive and growing, but less is known about the temporal dynamics of these inequalities over the course of the pandemic.
Methods: We systematically searched the Embase and Scopus databases. Additionally, several relevant journals and the reference lists of all included articles were hand-searched. This study follows the PRISMA guidelines for scoping reviews.
Results: Forty-six studies were included. Of all analyses, 91.4% showed stable or increasing socioeconomic inequalities in COVID-19 outcomes over the course of the pandemic, with socioeconomically disadvantaged populations being most affected. Furthermore, the study results showed temporal dynamics in socioeconomic inequalities in COVID-19, frequently initiated through higher COVID-19 incidence and mortality rates in better-off populations and subsequent crossover dynamics to higher rates in socioeconomically disadvantaged populations (41.9% of all analyses).
Conclusion: The identified temporal dynamics of socioeconomic inequalities in COVID-19 outcomes have relevant public health implications. Socioeconomic inequalities should be monitored over time to enable the adaption of prevention and interventions according to the social particularities of specific pandemic phases.
... As population density is easier to calculate, most of them used this variable instead of other types of density. Based on our review, many studies conducted in countries such as the US (Sarmadi et al., 2021;White and Hébert-Dufresne, 2020), Italy (Ilardi et al., 2020), Iran (Ahmadi et al., 2020), Bangladesh (Sharif et al., 2021;Alam, 2021;Rahman et al., 2021), Oman (Al Kindi et al., 2021), France (Tchicaya et al., 2021), Nigeria (Bayode et al., 2022) and Scotland (Rideout et al., 2021) found population density as a positive and significant predictor of COVID-19 cases on state scale. However, about 40 % of studies reported that population density is not a significant factor to explain the difference in COVID-19 cases in different states (Ramírez-Aldana et al., 2020;Gupta et al., 2020;Perone, 2021;Gargiulo et al., 2020;Basellini and Camarda, 2021;Pilkington et al., 2021;Sen-Crowe et al., 2021). ...
Soon after its emergence, COVID-19 became a global problem. While different types of vaccines and treatments are now available, still non-pharmacological policies play a critical role in managing the pandemic. The literature is enriched enough to provide comprehensive, practical, and scientific insights to better deal with the pandemic. This research aims to find out how the built environment and human factors have affected the transmission of COVID-19 on different scales. This is done through a systematic literature review of papers indexed on the Web of Science and Scopus. Initially, these databases returned 3811 papers, and after different stages of screening, we found 166 relevant papers and reviewed them. The empirical papers that had at least one case study and analyzed the effects of at least one built environment factor on the spread of COVID-19 were selected. Results showed that the driving forces can be divided into seven main categories: density, land use, transportation and mobility, housing conditions, demographic factors, socio-economic factors, and health-related factors. We found that among other things, overcrowding, public transport use, proximity to public spaces, the share of health and services workers, levels of poverty, and the share of minorities and vulnerable populations are major predictors of the spread of the pandemic. As the most studied factor, density was associated with mixed results on different scales, but about 58 % of the papers reported that it is linked with a higher number of cases. This study provides insights for policymakers and academics to better understand the dynamic roles of the non-pharmacological driving forces of COVID-19 at different levels.
