Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 201
Filter
1.
Zhonghua Liu Xing Bing Xue Za Zhi ; 43(11): 1699-1704, 2022 Nov 10.
Article in Chinese | MEDLINE | ID: covidwho-2143855

ABSTRACT

Objective: To clarify the epidemiological characteristics and spatiotemporal clustering dynamics of COVID-19 in Shanghai in 2022. Methods: The COVID-19 data presented on the official websites of Municipal Health Commissions of Shanghai during March 1, 2022 and May 31, 2022 were collected for a spatial autocorrelation analysis by GeoDa software. A logistic growth model was used to fit the epidemic situation and make a comparison with the actual infection situation. Results: Pudong district had the highest number of symptomatic and asymptomatic infectants, accounting for 29.30% and 35.58% of the total infectants. Differences in cumulative attack rates and infection rates among 16 districts (P<0.001) were significant. The rates were significantly higher in Huangpu district than in other districts. The attack rate of COVID-19 from March 1, 2022 to May 31, 2022 had a global spatial positive correlation (P<0.05). Spatial distribution of COVID-19 attack rate was different at different periods. The global autocorrelation coefficient from March 16 to March 29, April 6 to April 12 and May 18 to May 24 had no statistical significance (P>0.05). Our local autocorrelation analysis showed that 22 high-high clustering areas were detected in eight periods.The high-risk hot-spot areas have experienced a "less-more-less" change process. The growth model fitting results were consistent with the actual infection situation. Conclusion: There was a clear spatiotemporal correlation in the distribution of COVID-19 in Shanghai. The comprehensive prevention and control measures of COVID-19 epidemic in Shanghai have effectively prohibited the growth of the epidemic, not only curbing the spatially spread of high-risk epidemic areas, but also reducing the risk of transmission to other cities.


Subject(s)
COVID-19 , Epidemics , Humans , COVID-19/epidemiology , China/epidemiology , Disease Outbreaks , Spatial Analysis
2.
JMIR Public Health Surveill ; 7(8): e29205, 2021 08 05.
Article in English | MEDLINE | ID: covidwho-2141332

ABSTRACT

BACKGROUND: Previous studies have shown that various social determinants of health (SDOH) may have contributed to the disparities in COVID-19 incidence and mortality among minorities and underserved populations at the county or zip code level. OBJECTIVE: This analysis was carried out at a granular spatial resolution of census tracts to explore the spatial patterns and contextual SDOH associated with COVID-19 incidence from a Hispanic population mostly consisting of a Mexican American population living in Cameron County, Texas on the border of the United States and Mexico. We performed age-stratified analysis to identify different contributing SDOH and quantify their effects by age groups. METHODS: We included all reported COVID-19-positive cases confirmed by reverse transcription-polymerase chain reaction testing between March 18 (first case reported) and December 16, 2020, in Cameron County, Texas. Confirmed COVID-19 cases were aggregated to weekly counts by census tracts. We adopted a Bayesian spatiotemporal negative binomial model to investigate the COVID-19 incidence rate in relation to census tract demographics and SDOH obtained from the American Community Survey. Moreover, we investigated the impact of local mitigation policy on COVID-19 by creating the binary variable "shelter-in-place." The analysis was performed on all COVID-19-confirmed cases and age-stratified subgroups. RESULTS: Our analysis revealed that the relative incidence risk (RR) of COVID-19 was higher among census tracts with a higher percentage of single-parent households (RR=1.016, 95% posterior credible intervals [CIs] 1.005, 1.027) and a higher percentage of the population with limited English proficiency (RR=1.015, 95% CI 1.003, 1.028). Lower RR was associated with lower income (RR=0.972, 95% CI 0.953, 0.993) and the percentage of the population younger than 18 years (RR=0.976, 95% CI 0.959, 0.993). The most significant association was related to the "shelter-in-place" variable, where the incidence risk of COVID-19 was reduced by over 50%, comparing the time periods when the policy was present versus absent (RR=0.506, 95% CI 0.454, 0.563). Moreover, age-stratified analyses identified different significant contributing factors and a varying magnitude of the "shelter-in-place" effect. CONCLUSIONS: In our study, SDOH including social environment and local emergency measures were identified in relation to COVID-19 incidence risk at the census tract level in a highly disadvantaged population with limited health care access and a high prevalence of chronic conditions. Results from our analysis provide key knowledge to design efficient testing strategies and assist local public health departments in COVID-19 control, mitigation, and implementation of vaccine strategies.


Subject(s)
COVID-19/epidemiology , Social Determinants of Health , Adolescent , Adult , Aged , Aged, 80 and over , Censuses , Female , Health Equity , Humans , Incidence , Male , Mexico/ethnology , Middle Aged , Minority Groups , Physical Distancing , SARS-CoV-2 , Socioeconomic Factors , Spatial Analysis , Texas/epidemiology , United States , Vulnerable Populations , Young Adult
3.
BMC Health Serv Res ; 22(1): 1364, 2022 Nov 17.
Article in English | MEDLINE | ID: covidwho-2115847

ABSTRACT

OBJECTIVE: Primary health care (PHC) is widely perceived to be the backbone of health care systems. Since the outbreak of COVID-19, PHC has not only provided primary medical services, but also served as a grassroots network for public health. Our research explored the accessibility, availability, and affordability of primary health care from a spatial perspective, to understand the social determinants affecting access to it in Hong Kong. METHOD: This constitutes a descriptive study from the perspective of spatial analysis. The nearest neighbor method was used to measure the geographic accessibility of PHC based on the road network. The 2SFCA method was used to measure spatial availability and affordability to primary health care, while the SARAR model, Spatial Error model, and Spatial Lag model were then constructed to explain potential factors influencing accessibility and availability of PHC. RESULTS: In terms of accessibility, 95% of residents in Hong Kong can reach a PHC institution within 15 minutes; in terms of availability, 83% of residents can receive PHC service within a month; while in terms of affordability, only 32% of residents can afford PHC services with the support of medical insurance and medical voucher. In Hong Kong, education status and household income show a significant impact on accessibility and availability of PHC. Regions with higher concentrations of residents with post-secondary education receive more PHC resources, while regions with higher concentrations of high-income households show poorer accessibility and poorer availability to PHC. CONCLUSION: The good accessibility and availability of primary health care reflects that the network layout of existing PHC systems in Hong Kong is reasonable and can meet the needs of most residents. No serious gap between social groups further shows equality in resource allocation of PHC in Hong Kong. However, affordability of PHC is not ideal. Indeed, narrowing the gap between availability and affordability is key to fully utilizing the capacity of the PHC system in Hong Kong. The private sector plays an important role in this, but the low coverage of medical insurance in outpatient services exacerbates the crowding of public PHC and underutilization of private PHC. We suggest diverting patients from public to private institutions through medical insurance, medical vouchers, or other ways, to relieve the pressure on the public health system and make full use of existing primary health care in Hong Kong.


Subject(s)
COVID-19 , Primary Health Care , Social Determinants of Health , Humans , Costs and Cost Analysis , COVID-19/epidemiology , Hong Kong/epidemiology , Spatial Analysis , Health Services Accessibility , Healthcare Disparities
4.
PLoS One ; 17(11): e0275532, 2022.
Article in English | MEDLINE | ID: covidwho-2098745

ABSTRACT

In this paper, we propose a portmanteau test for whether a graph-structured network dataset without replicates exhibits autocorrelation across units connected by edges. Specifically, the well known Ljung-Box test for serial autocorrelation of time series data is generalized to the network setting using a specially derived central limit theorem for a weakly stationary random field. The asymptotic distribution of the test statistic under the null hypothesis of no autocorrelation is shown to be chi-squared, yielding a simple and easy-to-implement procedure for testing graph-structured autocorrelation, including spatial and spatial-temporal autocorrelation as special cases. Numerical simulations are carried out to demonstrate and confirm the derived asymptotic results. Convergence is found to occur quickly depending on the number of lags included in the test statistic, and a significant increase in statistical power is also observed relative to some recently proposed permutation tests. An example application is presented by fitting spatial autoregressive models to the distribution of COVID-19 cases across counties in New York state.


Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Spatial Analysis , Time Factors , New York
5.
Int J Environ Res Public Health ; 19(16)2022 08 17.
Article in English | MEDLINE | ID: covidwho-2023660

ABSTRACT

Emergency response capability evaluation is an essential means to strengthen emergency response capacity-building and improve the level of government administration. Based on the whole life cycle of emergency management, the emergency capability evaluation index system is constructed from four aspects: prevention and emergency preparedness, monitoring and early warning, emergency response and rescue, and recovery and reconstruction. Firstly, the entropy method is applied to measure the emergency response capability level of 31 Chinese provinces from 2011 to 2020. Second, the Theil index and ESDA (Exploratory Spatial Data Analysis) are applied in exploring the regional differences and spatial-temporal distribution characteristics of China's emergency response capacity. Finally, the obstacle degree model is used to explore the obstacle factors and obstacle degrees that affect the emergency response capability. The results show that: (1) The average value of China's emergency response capacity is 0.277, with a steady growth trend and a gradient distribution of "high in the east, low in the west, and average in center and northeast" in the four major regions. (2) From the perspective of spatial distribution characteristics, the unbalanced regional development leads to the obvious aggregation effect of "high-efficiency aggregation and low-efficiency aggregation", and the interaction of the "centripetal effect" and "centrifugal effect" finally forms the spatial clustering result of emergency response capability level in China. (3) Examining the source of regional differences, inter-regional differences are the decisive factor affecting the overall differences in emergency response capability, and the inter-regional differences show a reciprocating fluctuation of narrowing-widening-narrowing from 2011 to 2020. (4) Main obstacles restricting the improvement of China's emergency response capabilities are "the business volume of postal and telecommunication services per capita", "the daily disposal capacity of city sewage" and "the general public budget revenue by region". The extent of the obstacles' impacts in 2020 are 12.19%, 7.48%, and 7.08%, respectively. Based on the evaluation results, the following countermeasures are proposed: to realize the balance of each stage of emergency management during the holistic process; to strengthen emergency coordination and balanced regional development; and to implement precise measures to make up for the shortcomings of emergency response capabilities.


Subject(s)
Economic Development , Efficiency , China , Entropy , Spatial Analysis
6.
Int J Environ Res Public Health ; 19(15)2022 07 30.
Article in English | MEDLINE | ID: covidwho-1994053

ABSTRACT

The development of rural tourism (RT) has great significance in reducing poverty and achieving rural vitalization. Qinghai-Tibetan Plateau (QTP) is a depressed area with rich RT resources due to its unspoiled nature and diverse culture. For future sustainable development of RT in QTP, this paper analyzes the spatial distribution characteristics and its influencing factors of RT villages using various spatial analysis methods, such as nearest neighbor index, kernel density estimation, vector buffer analysis, and geographic detectors. The results show the following. First, the RT villages present an agglomeration distribution tendency dense in the southeast and spare in the northwest. The inter-county imbalance distribution feature is obvious and four relatively high-density zones have been formed. Second, the RT villages have significant positive spatial autocorrelation, and the area of cold spots is larger and of hot spots is smaller. Third, the RT villages are mainly distributed with favorable topographic and climate conditions, near the road and water, around the city, and close to tourism resources. Fourth, the spatial distribution is the result of multifactor interactions. Socio-economic and tourism resource are the dominant factor in the mechanism network. Fifth, based on the above conclusions this study provides scientific suggestions for the sustainable development of the RT industry.


Subject(s)
Climate , Tourism , China , Humans , Rural Population , Spatial Analysis , Tibet
7.
PLoS One ; 17(4): e0267001, 2022.
Article in English | MEDLINE | ID: covidwho-1968855

ABSTRACT

PURPOSE: The ongoing coronavirus disease 2019 (COVID-19) epidemic increasingly threatens the public health security worldwide. We aimed to identify high-risk areas of COVID-19 and understand how socioeconomic factors are associated with the spatial distribution of COVID-19 in China, which may help other countries control the epidemic. METHODS: We analyzed the data of COVID-19 cases from 30 provinces in mainland China (outside of Hubei) from 16 January 2020 to 31 March 2020, considering the data of demographic, economic, health, and transportation factors. Global autocorrelation analysis and Bayesian spatial models were used to present the spatial pattern of COVID-19 and explore the relationship between COVID-19 risk and various factors. RESULTS: Global Moran's I statistics of COVID-19 incidences was 0.31 (P<0.05). The areas with a high risk of COVID-19 were mainly located in the provinces around Hubei and the provinces with a high level of economic development. The relative risk of two socioeconomic factors, the per capita consumption expenditure of households and the proportion of the migrating population from Hubei, were 1.887 [95% confidence interval (CI): 1.469~2.399] and 1.099 (95% CI: 1.053~1.148), respectively. The two factors explained up to 78.2% out of 99.7% of structured spatial variations. CONCLUSION: Our results suggested that COVID-19 risk was positively associated with the level of economic development and population movements. Blocking population movement and reducing local exposures are effective in preventing the local transmission of COVID-19.


Subject(s)
COVID-19 , Bayes Theorem , COVID-19/epidemiology , China/epidemiology , Humans , SARS-CoV-2 , Spatial Analysis
8.
Sci Rep ; 12(1): 12781, 2022 07 27.
Article in English | MEDLINE | ID: covidwho-1960507

ABSTRACT

The main targets of this were to screen the factors that may influence the distribution of 25-hydroxyvitamin D[25(OH)D] reference value in healthy elderly people in China, and further explored the geographical distribution differences of 25(OH)D reference value in China. In this study, we collected the 25(OH)D of 25,470 healthy elderly from 58 cities in China to analyze the correlation between 25(OH)D and 22 geography secondary indexes through spearman regression analysis. Six indexes with significant correlation were extracted, and a ridge regression model was built, and the country's urban healthy elderly'25(OH)D reference value was predicted. By using the disjunctive Kriging method, we obtained the geographical distribution of 25(OH)D reference values for healthy elderly people in China. The reference value of 25(OH)D for healthy elderly in China was significantly correlated with the 6 secondary indexes, namely, latitude (°), annual temperature range (°C), annual sunshine hours (h), annual mean temperature (°C), annual mean relative humidity (%), and annual precipitation (mm). The geographical distribution of 25(OH)D values of healthy elderly in China showed a trend of being higher in South China and lower in North China, and higher in coastal areas and lower in inland areas. This study lays a foundation for further research on the mechanism of different influencing factors on the reference value of 25(OH)D index. A ridge regression model composed of significant influencing factors has been established to provide the basis for formulating reference criteria for the treatment factors of the vitamin D deficiency and prognostic factors of the COVID-19 using 25(OH)D reference value in different regions.


Subject(s)
COVID-19 , Vitamin D Deficiency , Aged , China/epidemiology , Geography , Humans , Spatial Analysis , Vitamin D/analogs & derivatives , Vitamin D Deficiency/epidemiology
9.
Int J Environ Res Public Health ; 19(13)2022 06 23.
Article in English | MEDLINE | ID: covidwho-1934031

ABSTRACT

This study considers the Point of Interest data of tourism resources in Xinjiang and studies their spatial distribution by combining geospatial analysis methods, such as the average nearest neighbor index, standard deviation ellipse, kernel density analysis, and hotspot analysis, to explore their spatial distribution characteristics. Based on the analysis results, the following conclusions are made. Different categories of tourism resource sites have different spatial distributions, and all categories of tourism resources in Xinjiang are clustered in Urumqi city. The geological landscape resource sites are widely distributed and have a ring-shaped distribution in the desert area of southern Xinjiang. The biological landscape resources are distributed in a strip along the Tianshan Mountains. The water landscape resources are concentrated in the northern Xinjiang area. The site ruins are mostly distributed in the western region of Xinjiang. The distributions of the architectural landscape and entertainment and shopping resources are highly coupled with the distribution of cities. The distributions of the six categories of tourism resource points are in the northeast-southwest direction. The centripetal force and directional nature of the resource points of the water landscape are not obvious. The remaining five categories of resource points have their own characteristics. The distribution of resources in the site ruins is relatively even, and there are many hotspot areas in the geomantic and architectural landscapes, which are mainly concentrated in Bazhou and other places. The biological landscape has many cold-spot areas, distributed in areas such as Altai in northern Xinjiang and Hotan in southern Xinjiang. The remaining four categories have cold-spot and hotspot areas with different distributions. Tourism is an important thrust for economic development. The study of the distribution of tourism resources on the spatial distribution of tourism resources has clear guidance for later tourism development, can help the tourism industry optimize the layout of resources, and can promote tourism resources to achieve maximum benefits. The government can implement effective control and governance.


Subject(s)
Tourism , Water Resources , China , Electronics , Spatial Analysis , Water
10.
PLoS One ; 17(5): e0268130, 2022.
Article in English | MEDLINE | ID: covidwho-1923682

ABSTRACT

Bayesian empirical likelihood (BEL) models are becoming increasingly popular as an attractive alternative to fully parametric models. However, they have only recently been applied to spatial data analysis for small area estimation. This study considers the development of spatial BEL models using two popular conditional autoregressive (CAR) priors, namely BYM and Leroux priors. The performance of the proposed models is compared with their parametric counterparts and with existing spatial BEL models using independent Gaussian priors and generalised Moran basis priors. The models are applied to two benchmark spatial datasets, simulation study and COVID-19 data. The results indicate promising opportunities for these models to capture new insights into spatial data. Specifically, the spatial BEL models outperform the parametric spatial models when the underlying distributional assumptions of data appear to be violated.


Subject(s)
COVID-19 , Bayes Theorem , COVID-19/epidemiology , Humans , Likelihood Functions , Normal Distribution , Spatial Analysis
11.
Environ Pollut ; 309: 119719, 2022 Sep 15.
Article in English | MEDLINE | ID: covidwho-1914336

ABSTRACT

This study aims to investigate the effect of transportation infrastructure on the decrease of NO2 air pollution during three COVID-19-induced lockdowns in a vast region of France. For this purpose, using Sentinel-5P satellite data, the relative change in tropospheric NO2 air pollution during the three lockdowns was calculated. The estimation of regional infrastructure intensity was performed using Kernel Density Estimation, being the predictor variable. By performing hotspot-coldspot analysis on the relative change in NO2 air pollution, significant spatial clusters of decreased air pollution during the three lockdowns were identified. Based on the clusters, a novel spatial index, the Clustering Index (CI) was developed using its Coldspot Clustering Index (CCI) variant as a predicted variable in the regression model between infrastructure intensity and NO2 air pollution decline. The analysis revealed that during the three lockdowns there was a strong and statistically significant relationship between the transportation infrastructure and the decline index, CCI (r = 0.899, R2 = 0.808). The results showed that the largest decrease in NO2 air pollution was recorded during the first lockdown, and in this case, there was the strongest inverse correlation with transportation infrastructure (r = -0.904, R2 = 0.818). Economic and population predictors also explained with good fit the decrease in NO2 air pollution during the first lockdown: GDP (R2 = 0.511), employees (R2 = 0.513), population density (R2 = 0.837). It is concluded that not only economic-population variables determined the reduction of near-surface air pollution but also the transportation infrastructure. Further studies are recommended to investigate other pollutant gases as predicted variables.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , Communicable Disease Control , Environmental Monitoring/methods , Humans , Nitrogen Dioxide/analysis , Particulate Matter/analysis , Spatial Analysis
12.
Geospat Health ; 17(s1)2022 06 23.
Article in English | MEDLINE | ID: covidwho-1903632

ABSTRACT

The first case of COVID-19 in continental Portugal was documented on the 2nd of March 2020 and about seven months later more than 75 thousand infections had been reported. Although several factors correlate significantly with the spatial incidence of COVID-19 worldwide, the drivers of spatial incidence of this virus remain poorly known and need further exploration. In this study, we analyse the spatiotemporal patterns of COVID-19 incidence in the at the municipality level and test for significant relationships between these patterns and environmental, socioeconomic, demographic and human mobility factors to identify the mains drivers of COVID-19 incidence across time and space. We used a generalized liner mixed model, which accounts for zero inflated cases and spatial autocorrelation to identify significant relationships between the spatiotemporal incidence and the considered set of driving factors. Some of these relationships were particularly consistent across time, including the 'percentage of employment in services'; 'average time of commuting using individual transportation'; 'percentage of employment in the agricultural sector'; and 'average family size'. Comparing the preventive measures in Portugal (e.g., restrictions on mobility and crowd around) with the model results clearly show that COVID-19 incidence fluctuates as those measures are imposed or relieved. This shows that our model can be a useful tool to help decision-makers in defining prevention and/or mitigation policies.


Subject(s)
COVID-19 , COVID-19/epidemiology , Humans , Incidence , Portugal/epidemiology , Spatial Analysis , Transportation
13.
BMC Public Health ; 22(1): 1212, 2022 06 18.
Article in English | MEDLINE | ID: covidwho-1896340

ABSTRACT

BACKGROUND: Spatial variability of COVID-19 cases may suggest geographic disparities of social determinants of health. Spatial analyses of population-level data may provide insight on factors that may contribute to COVID-19 transmission, hospitalization, and death. METHODS: Generalized additive models were used to map COVID-19 risk from March 2020 to February 2021 in Orange County (OC), California. We geocoded and analyzed 221,843 cases to OC census tracts within a Poisson framework while smoothing over census tract centroids. Location was randomly permuted 1000 times to test for randomness. We also separated the analyses temporally to observe if risk changed over time. COVID-19 cases, hospitalizations, and deaths were mapped across OC while adjusting for population-level demographic data in crude and adjusted models. RESULTS: Risk for COVID-19 cases, hospitalizations, and deaths were statistically significant in northern OC. Adjustment for demographic data substantially decreased spatial risk, but areas remained statistically significant. Inclusion of location within our models considerably decreased the magnitude of risk compared to univariate models. However, percent minority (adjusted RR: 1.06, 95%CI: 1.06, 1.07), average household size (aRR: 1.06, 95%CI: 1.05, 1.07), and percent service industry (aRR: 1.05, 95%CI: 1.04, 1.06) remained significantly associated with COVID-19 risk in adjusted spatial models. In addition, areas of risk did not change between surges and risk ratios were similar for hospitalizations and deaths. CONCLUSION: Significant risk factors and areas of increased risk were identified in OC in our adjusted models and suggests that social and environmental factors contribute to the spread of COVID-19 within communities. Areas in north OC remained significant despite adjustment, but risk substantially decreased. Additional investigation of risk factors may provide insight on how to protect vulnerable populations in future infectious disease outbreaks.


Subject(s)
COVID-19 , COVID-19/epidemiology , Humans , Pandemics , Risk Factors , Socioeconomic Factors , Spatial Analysis
14.
Sci Rep ; 12(1): 9364, 2022 Jun 07.
Article in English | MEDLINE | ID: covidwho-1878545

ABSTRACT

The first case of coronavirus disease 2019 (COVID-19) in South Korea was confirmed on January 20, 2020, approximately three weeks after the report of the first COVID-19 case in Wuhan, China. By September 15, 2021, the number of cases in South Korea had increased to 277,989. Thus, it is important to better understand geographical transmission and design effective local-level pandemic plans across the country over the long term. We conducted a spatiotemporal analysis of weekly COVID-19 cases in South Korea from February 1, 2020, to May 30, 2021, in each administrative region. For the spatial domain, we first covered the entire country and then focused on metropolitan areas, including Seoul, Gyeonggi-do, and Incheon. Moran's I and spatial scan statistics were used for spatial analysis. The temporal variation and dynamics of COVID-19 cases were investigated with various statistical visualization methods. We found time-varying clusters of COVID-19 in South Korea using a range of statistical methods. In the early stage, the spatial hotspots were focused in Daegu and Gyeongsangbuk-do. Then, metropolitan areas were detected as hotspots in December 2020. In our study, we conducted a time-varying spatial analysis of COVID-19 across the entirety of South Korea over a long-term period and found a powerful approach to demonstrating the current dynamics of spatial clustering and understanding the dynamic effects of policies on COVID-19 across South Korea. Additionally, the proposed spatiotemporal methods are very useful for understanding the spatial dynamics of COVID-19 in South Korea.


Subject(s)
COVID-19 , COVID-19/epidemiology , Humans , Pandemics , Republic of Korea/epidemiology , Spatial Analysis , Spatio-Temporal Analysis
15.
Int J Environ Res Public Health ; 19(11)2022 05 31.
Article in English | MEDLINE | ID: covidwho-1869623

ABSTRACT

This study aimed to assess the gap between the supply and demand of adult surgical masks under limited resources. Owing to the implementation of the real-name mask rationing system, the historical inventory data of aggregated mask consumption in a pharmacy during the early period of the COVID-19 outbreak (April and May 2020) in Taiwan were analyzed for supply-side analysis. We applied the Voronoi diagram and areal interpolation methods to delineate the average supply of customer counts from a pharmacy to a village (administrative level). On the other hand, the expected number of demand counts was estimated from the population data. The relative risk (RR) of supply, which is the average number of adults served per day divided by the expected number in a village, was modeled under a Bayesian hierarchical framework, including Poisson, negative binomial, Poisson spatial, and negative binomial spatial models. We observed that the number of pharmacies in a village is associated with an increasing supply, whereas the median annual per capita income of the village has an inverse relationship. Regarding land use percentages, percentages of the residential and the mixed areas in a village are negatively associated, while the school area percentage is positively associated with the supply in the Poisson spatial model. The corresponding uncertainty measurement: villages where the probability exceeds the risk of undersupply, that is, Pr (RR < 1), were also identified. The findings of the study may help health authorities to evaluate the spatial allocation of anti-epidemic resources, such as masks and rapid test kits, in small areas while identifying priority areas with the suspicion of undersupply in the beginning stages of outbreaks.


Subject(s)
COVID-19 , Adult , Bayes Theorem , COVID-19/epidemiology , Humans , Masks , Pandemics , Spatial Analysis
16.
Int J Environ Res Public Health ; 19(9)2022 04 27.
Article in English | MEDLINE | ID: covidwho-1809923

ABSTRACT

The aim of this study was to identify how the literature analyzes (identifies, evaluates, forecasts, etc.) the relationship between health issues and urban policy in relation to the COVID-19 pandemic. Four main levels were identified in these cases: (1) direct demands for changes in health care, (2) social issues, (3) spatial organization and (4) redefining the tasks of public authority in the face of identified challenges. The basic working method used in the study assumed a critical analysis of the literature on the subject. The time scope of the search covered articles from January 2020 to the end of August 2021 (thus covering the period of three pandemic waves). Combinations of keywords in the titles were used to search for articles. The health perspective pointed to the need for urban policies to develop a balance between health and economic costs and for coordination between different professionals/areas. A prerequisite for such a balance in cities is the carrying out of social and spatial analyses. These should illustrate the diversity of the social situations in individual cities (and more broadly in urban areas, including, sometimes, large suburbs) and the diversity's relationship (both in terms of causes and consequences) to the severity of pandemics and other health threats.


Subject(s)
COVID-19 , COVID-19/epidemiology , Cities/epidemiology , Humans , Pandemics , Policy , Spatial Analysis
17.
Infect Dis Poverty ; 11(1): 44, 2022 Apr 15.
Article in English | MEDLINE | ID: covidwho-1793809

ABSTRACT

BACKGROUND: A remarkable drop in tuberculosis (TB) incidence has been achieved in China, although in 2019 it was still considered the second most communicable disease. However, TB's spatial features and risk factors in urban areas remain poorly understood. This study aims to identify the spatial differentiations and potential influencing factors of TB in highly urbanized regions on a fine scale. METHODS: This study included 18 socioeconomic and environmental variables in the four central districts of Guangzhou, China. TB case data obtained from the Guangzhou Institute of Tuberculosis Control and Prevention. Before using Pearson correlation and a geographical detector (GD) to identify potential influencing factors, we conducted a global spatial autocorrelation analysis to select an appropriate spatial scales. RESULTS: Owing to its strong spatial autocorrelation (Moran's I = 0.33, Z = 4.71), the 2 km × 2 km grid was selected as the spatial scale. At this level, TB incidence was closely associated with most socioeconomic variables (0.31 < r < 0.76, P < 0.01). Of five environmental factors, only the concentration of fine particulate matter displayed significant correlation (r = 0.21, P < 0.05). Similarly, in terms of q values derived from the GD, socioeconomic variables had stronger explanatory abilities (0.08 < q < 0.57) for the spatial differentiation of the 2017 incidence of TB than environmental variables (0.06 < q < 0.27). Moreover, a much larger proportion (0.16 < q < 0.89) of the spatial differentiation was interpreted by pairwise interactions, especially those (0.60 < q < 0.89) related to the 2016 incidence of TB, officially appointed medical institutions, bus stops, and road density. CONCLUSIONS: The spatial heterogeneity of the 2017 incidence of TB in the study area was considerably influenced by several socioeconomic and environmental factors and their pairwise interactions on a fine scale. We suggest that more attention should be paid to the units with pairwise interacting factors in Guangzhou. Our study provides helpful clues for local authorities implementing more effective intervention measures to reduce TB incidence in China's municipal areas, which are featured by both a high degree of urbanization and a high incidence of TB.


Subject(s)
Epidemics , Tuberculosis , China/epidemiology , Geography , Humans , Incidence , Spatial Analysis , Tuberculosis/epidemiology
18.
Rev Saude Publica ; 56: 14, 2022.
Article in English, Portuguese | MEDLINE | ID: covidwho-1780268

ABSTRACT

OBJECTIVE: To analyze the spatial correlation between confirmed cases of covid-19 and the intensive care unit beds exclusive to the disease in municipalities of Paraná. METHODS: This is an epidemiological study of ecological type which used data from the Epidemiological Report provided by the Department of Health of Paraná on the confirmed cases of covid-19 from March 12, 2020, to January 18, 2021. The number of intensive care beds exclusive to covid-19 in each municipality of Paraná was obtained by the Cadastro Nacional de Estabelecimentos de Saúde (CNES - National Registry of Health Establishments), provided online by the Departamento de Informática do Sistema Único de Saúde (Datasus - Informatics Department of the Brazilian Unified Health System). The Bivariate Moran's Index (local and global) was used to analyze the intensive care bed variable and spatial correlation, with a 5% significance level. LISA Map was used to identify critical and transition areas. RESULTS: In the analyzed period, we found 499,777 confirmed cases of covid-19 and 1,029 intensive care beds exclusive to the disease in Paraná. We identified a positive spatial autocorrelation between the confirmed cases of covid-19 (0.404-p ≤ 0.001) and intensive care beds exclusive to the disease (0.085-p ≤ 0.001) and disparities between the regions of Paraná. CONCLUSION: Spatial analysis indicated that confirmed cases of covid-19 are related to the distribution of intensive care beds exclusive to the disease in Paraná, allowing us to find priority areas of care in the state regarding the dissemination and control of the disease.


Subject(s)
COVID-19 , Brazil/epidemiology , COVID-19/epidemiology , Government Programs , Humans , Intensive Care Units , Spatial Analysis
19.
Front Public Health ; 10: 836358, 2022.
Article in English | MEDLINE | ID: covidwho-1753421

ABSTRACT

Introduction: The unprecedented COVID-19 pandemic has greatly affected human health and socioeconomic backgrounds. This study examined the spatiotemporal spread pattern of the COVID-19 pandemic in Malaysia from the index case to 291,774 cases in 13 months, emphasizing on the spatial autocorrelation of the high-risk cluster events and the spatial scan clustering pattern of transmission. Methodology: We obtained the confirmed cases and deaths of COVID-19 in Malaysia from the official GitHub repository of Malaysia's Ministry of Health from January 25, 2020 to February 24, 2021, 1 day before the national vaccination program was initiated. All analyses were based on the daily cumulated cases, which are derived from the sum of retrospective 7 days and the current day for smoothing purposes. We examined the daily global, local spatial autocorrelation and scan statistics of COVID-19 cases at district level using Moran's I and SaTScan™. Results: At the initial stage of the outbreak, Moran's I index > 0.5 (p < 0.05) was observed. Local Moran's I depicted the high-high cluster risk expanded from west to east of Malaysia. The cases surged exponentially after September 2020, with the high-high cluster in Sabah, from Kinabatangan on September 1 (cumulative cases = 9,354; Moran's I = 0.34; p < 0.05), to 11 districts on October 19 (cumulative cases = 21,363, Moran's I = 0.52, p < 0.05). The most likely cluster identified from space-time scanning was centered in Jasin, Melaka (RR = 11.93; p < 0.001) which encompassed 36 districts with a radius of 178.8 km, from November 24, 2020 to February 24, 2021, followed by the Sabah cluster. Discussion and Conclusion: Both analyses complemented each other in depicting underlying spatiotemporal clustering risk, giving detailed space-time spread information at district level. This daily analysis could be valuable insight into real-time reporting of transmission intensity, and alert for the public to avoid visiting the high-risk areas during the pandemic. The spatiotemporal transmission risk pattern could be used to monitor the spread of the pandemic.


Subject(s)
COVID-19 , COVID-19/epidemiology , Humans , Malaysia/epidemiology , Pandemics , Retrospective Studies , Spatial Analysis
20.
Int J Environ Res Public Health ; 19(6)2022 03 16.
Article in English | MEDLINE | ID: covidwho-1742475

ABSTRACT

The COVID-19 pandemic is one of the most devastating public health emergencies in history. In late 2020 and after almost a year from the initial outbreak of the novel coronavirus (SARS-CoV-2), several vaccines were approved and administered in most countries. Saudi Arabia has established COVID-19 vaccination centers in all regions. Various facilities were selected to set up these vaccination centers, including conference and exhibition centers, old airport terminals, pre-existing medical facilities, and primary healthcare centers. Deciding the number and locations of these facilities is a fundamental objective for successful epidemic responses to ensure the delivery of vaccines and other health services to the entire population. This study analyzed the spatial distribution of COVID-19 vaccination centers in Jeddah, a major city in Saudi Arabia, by using GIS tools and methods to provide insight on the effectiveness of the selection and distribution of the COVID-19 vaccination centers in terms of accessibility and coverage. Based on a spatial analysis of vaccine centers' coverage in 2020 and 2021 in Jeddah presented in this study, coverage deficiency would have been addressed earlier if the applied GIS analysis methods had been used by authorities while gradually increasing the number of vaccination centers. This study recommends that the Ministry of Health in Saudi Arabia evaluated the assigned vaccination centers to include the less-populated regions and to ensure equity and fairness in vaccine distribution. Adding more vaccine centers or reallocating some existing centers in the denser districts to increase the coverage in the uncovered sparse regions in Jeddah is also recommended. The methods applied in this study could be part of a strategic vaccination administration program for future public health emergencies and other vaccination campaigns.


Subject(s)
COVID-19 Vaccines , COVID-19 , COVID-19/epidemiology , COVID-19/prevention & control , Humans , Pandemics , SARS-CoV-2 , Saudi Arabia/epidemiology , Spatial Analysis
SELECTION OF CITATIONS
SEARCH DETAIL