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During the first year of the COVID-19 pandemic in Jakarta, Indonesia, the government designated some hospitals as specific COVID-19 healthcare centers to meet demand and ensure accessibility. However, the policy demand evaluation was based on a purely spatial approach. Studies on accessibility to healthcare are widely available, but those that consider temporal as well as spatial dynamics are lacking. This study aims to analyze the spatiotemporal dynamics of healthcare accessibility against COVID-19 cases within the first year of the COVID-19 pandemic, and the overall pattern of spatiotemporal accessibility. A two-step floating catchment area (2SFCA) was used to analyze the accessibility of COVID-19 healthcare against the monthly data of the COVID-19 infected population, as the demand. Such a spatiotemporal approach to 2SFCA has never been used in previous studies. Furthermore, rather than the traditional buffer commonly used to define catchments, the 2SFCA in this study was improved with automated delineation based on the road network using ArcGIS Service Areas Analysis tools. The accessibility tends to follow the distance decay principle, which is relatively high in the city's center and low in the outskirts. This contrasts with the city's population distribution, which is higher on the outskirts and lower in the center. This research is a step toward optimizing the spatial distribution of hospital locations to correspond with the severity of the pandemic condition. One method to stop the transmission of disease during a pandemic that requires localizing the infected patient is to designate specific healthcare facilities to manage the sick individuals. 'What-if' scenarios may be used to experiment with the locations of these healthcare facilities, which are then assessed using the methodology described in this work to obtain the distribution that is most optimal. [ABSTRACT FROM AUTHOR]

Abstract Background Assessing asthma control using standardised questionnaires is recommended as good clinical practice but there is little evidence validating their use within primary care. There is however, strong empirical evidence to indicate that age, weight, gender, smoking, symptom pattern, medication use, health service resource use, geographical location, deprivation, and organisational issues, are factors strongly associated with asthma control. A good control measure is therefore one whose variation is most explained by these factors. Method Eight binary (Yes = poor control, No = good control) models of asthma control were constructed from a large UK primary care dataset: the Royal College of Physicians 3-Questions (RCP-3Qs); the Jones Morbidity Index; three composite measures; three single component models. Accounting for practice clustering of patients, we investigated the effects of each model for assessing control. The binary models were assessed for goodness-of-fit statistics using Pseudo R-squared and Akaikes Information Criteria (AIC), and for performance using Area Under the Receiver Operator Characteristic (AUROC). In addition, an expanded RCP-3Q control scale (0-9) was derived and assessed with linear modelling. The analysis identified which model was best explained by the independent variables and thus could be considered a good model of control assessment. Results 1,205 practices provided information on 64,929 patients aged 13+ years. The RCP-3Q model provided the best fit statistically, with a Pseudo R-squared of 18%, and an AUROC of 0.79. By contrast, the composite model based on the GINA definition of controlled asthma had a higher AIC, an AUROC of 0.72, and only 10% variability explained. In addition, although the Peak Expiratory Flow Rate (PEFR) model had the lowest AIC, it had an AUROC of 71% and only 6% of variability explained. However, compared with the RCP-3Qs binary model, the linear RCP-3Q Total Score Model (Scale 0-9), was found to be a more robust 'tool' for assessing asthma control with a lower AIC (28,6163) and an R-squared of 33%. Conclusion In the absence of a gold standard for assessing asthma control in primary care, the results indicate that the RCP-3Qs is an effective control assessment tool but, for maximum effect, the expanded scoring model should be used.

Background: Studies investigating the population-mixing hypothesis in childhood leukemia principally use two analytical approaches: (1) nonrandom selection of areas according to specific characteristics, followed by comparisons of their incidence of childhood leukemia with that expected based on the national average; and (2) regression analyses of region-wide data to identify characteristics associated with the incidence of childhood leukemia. These approaches have generated contradictory results. We compare these approaches using observed and simulated data.Methods: We generated 10,000 simulated regions using the correlation structure and distributions from a United Kingdom dataset. We simulated cases using a Poisson distribution with the incidence rate set to the national average assuming the null hypothesis that only population size drives the number of cases. Selection of areas within each simulated region was based on characteristics considered responsible for elevated infection rates (population density and inward migration) and/or elevated leukemia rates. We calculated effect estimates for 10,000 simulations and compared results to corresponding observed data analyses.Results: When the selection of areas for analysis is based on apparent clusters of childhood leukemia, biased assessments occur; the estimated 5-year incidence of childhood leukemia ranged between zero and eight per 10,000 children in contrast to the simulated two cases per 10,000 children, similar to the observed data. Performing analyses on region-wide data avoids these biases.Conclusions: Studies using nonrandom selection to investigate the association between childhood leukemia and population mixing are likely to have generated biased findings. Future studies can avoid such bias using a region-wide analytical strategy. See video abstract at, http://links.lww.com/EDE/B431. [ABSTRACT FROM AUTHOR]

Previous studies have found marked increases in melanoma incidence. The increase among young people in northern England was especially apparent among females. However, overall 5-year survival has greatly improved. The present study aimed to determine whether socioeconomic factors may be involved in both etiology and survival. All 224 cases of malignant melanoma diagnosed in patients aged 10-24 years during 1968-2003 were extracted from a specialist population-based regional registry. Negative binomial regression was used to examine the relationship between incidence and area-based measures of socioeconomic deprivation and small-area population density. Cox regression was used to analyze the relationship between survival and deprivation and population density. There was significantly decreased risk associated with living in areas of higher unemployment (relative risk per 1% increase in unemployment=0.93; 95% confidence interval (CI) 0.90-0.96, P<0.001). Survival was better in less deprived areas (hazard ratio (HR) per tertile of household overcrowding=1.52; 95% CI 1.05-2.20; P=0.026), but this effect was reduced in the period 1986-2003 (HR=0.61; 95% CI 0.40-0.92; P=0.018). This study found that increased risk of melanoma was linked with some aspects of greater affluence. In contrast, worse survival was associated with living in a more deprived area. [ABSTRACT FROM AUTHOR]

Purpose: Previous research from developed countries has shown a marked increase in the incidence of testicular cancer in the past 50 years. This has also been demonstrated in northern England, along with improving 5-year survival. The present study aims to determine if socioeconomic factors may play a role in both etiology and survival from non-seminoma testicular cancer.Materials and Methods: We extracted all 214 cases of non-seminoma testicular cancer diagnosed in teenage and young adult men aged between 15 and 24 years during 1968 to 2006 from the Northern Region Young Persons' Malignant Disease Registry, which is a population-based specialist regional registry. Negative binomial regression was used to examine the relationship between incidence and both the Townsend deprivation score (and component variables) and small-area population density. Cox regression was used to analyze the relationship between survival and both deprivation and population density.Results: Decreased incidence was associated with living in areas of higher household overcrowding for young adults aged between 20 and 24 years (relative risk per 1% increase in household overcrowding = 0.79; 95% CI: 0.66-0.94) but no association was detected for young people aged between 15 and 19 years. Community-level household unemployment was associated with worse survival (hazard ratio per 1% increase in household unemployment = 1.04; 95% CI: 1.00-1.08).Conclusions: This study has shown that increased risk of non-seminoma testicular cancer in teenage and young adult men may be associated with some aspect of more advantaged living. In contrast, greater deprivation is linked with worse survival prospects. The study was ecological by design and so these area-based results may not necessarily apply to individuals. [ABSTRACT FROM AUTHOR]

Background: Assessing asthma control using standardised questionnaires is recommended as good clinical practice but there is little evidence validating their use within primary care. There is however, strong empirical evidence to indicate that age, weight, gender, smoking, symptom pattern, medication use, health service resource use, geographical location, deprivation, and organisational issues, are factors strongly associated with asthma control. A good control measure is therefore one whose variation is most explained by these factors., Method: Eight binary (Yes = poor control, No = good control) models of asthma control were constructed from a large UK primary care dataset: the Royal College of Physicians 3-Questions (RCP-3Qs); the Jones Morbidity Index; three composite measures; three single component models. Accounting for practice clustering of patients, we investigated the effects of each model for assessing control. The binary models were assessed for goodness-of-fit statistics using Pseudo R-squared and Akaikes Information Criteria (AIC), and for performance using Area Under the Receiver Operator Characteristic (AUROC). In addition, an expanded RCP-3Q control scale (0-9) was derived and assessed with linear modelling. The analysis identified which model was best explained by the independent variables and thus could be considered a good model of control assessment., Results: 1,205 practices provided information on 64,929 patients aged 13+ years. The RCP-3Q model provided the best fit statistically, with a Pseudo R-squared of 18%, and an AUROC of 0.79. By contrast, the composite model based on the GINA definition of controlled asthma had a higher AIC, an AUROC of 0.72, and only 10% variability explained. In addition, although the Peak Expiratory Flow Rate (PEFR) model had the lowest AIC, it had an AUROC of 71% and only 6% of variability explained. However, compared with the RCP-3Qs binary model, the linear RCP-3Q Total Score Model (Scale 0-9), was found to be a more robust 'tool' for assessing asthma control with a lower AIC (28,6163) and an R-squared of 33%., Conclusion: In the absence of a gold standard for assessing asthma control in primary care, the results indicate that the RCP-3Qs is an effective control assessment tool but, for maximum effect, the expanded scoring model should be used.