Nonparametric correlogram to identify the geographic distance of spatial dependence on land prices

The spatial autocorrelation measurement of land prices uses a covariance function to describe the spatial dependence and it can be identified as a geographic distance on the correlogram. The geographic distance of spatial dependence can state that land prices are interdependent to each other and scattered in the research area. Therefore, the purpose of this research is to define the geographic distance of spatial dependence on land prices using a nonparametric correlogram. A nonparametric approach to covariance functions using the composition of Bessel and Gaussian-type functions are adopted because they correspond to the positive definite characteristics. The cubic spline interpolation is used to refine the curve fitting, while the intersection between the nonparametric correlogram value C(h) against the horizontal axis is determined using the Jenkins Traub algorithm. The results showed that the nonparametric correlogram identified a geographic distance of land prices smaller than the correlogram used so far. A small distance means that the land price in a location is greatly affected by the neighbors compared to a larger distance

Intra-urban variation in tuberculosis and community socioeconomic deprivation in Lisbon metropolitan area: a Bayesian approach

Background: Multidrug resistant tuberculosis (MDR-TB) is a recognized threat to global efforts to TB control and remains a priority of the National Tuberculosis Programs. Additionally, social determinants and socioeconomic deprivation have since long been associated with worse health and perceived as important risk factors for TB. This study aimed to analyze the spatial distribution of non-MDR-TB and MDR-TB across parishes of the Lisbon metropolitan area of Portugal and to estimate the association between non-MDR-TB and MDR-TB and socioeconomic deprivation. Methods: In this study, we used hierarchical Bayesian spatial models to analyze the spatial distribution of notification of non-MDR-TB and MDR-TB cases for the period from 2000 to 2016 across 127 parishes of the seven municipalities of the Lisbon metropolitan area (Almada, Amadora, Lisboa, Loures, Odivelas, Oeiras, Sintra), using the Portuguese TB Surveillance System (SVIG-TB). In order to characterise the populations, we used the European Deprivation Index for Portugal (EDI-PT) as an indicator of poverty and estimated the association between non-MDR-TB and MDR-TB and socioeconomic deprivation. Results: The notification rates per 10,000 population of non-MDR TB ranged from 18.95 to 217.49 notifications and that of MDR TB ranged from 0.83 to 3.70. We identified 54 high-risk areas for non-MDR-TB and 13 high-risk areas for MDR-TB. Parishes in the third [relative risk (RR) = 1.281, 95% credible interval (CrI): 1.021–1.606], fourth (RR = 1.786, 95% CrI: 1.420–2.241) and fifth (RR = 1.935, 95% CrI: 1.536–2.438) quintile of socioeconomic deprivation presented higher non-MDR-TB notifications rates. Parishes in the fourth (RR = 2.246, 95% CrI: 1.374–3.684) and fifth (RR = 1.828, 95% CrI: 1.049–3.155) quintile of socioeconomic deprivation also presented higher MDR-TB notifications rates. Conclusions: We demonstrated significant heterogeneity in the spatial distribution of both non-MDR-TB and MDR-TB at the parish level and we found that socioeconomically disadvantaged parishes are disproportionally affected by both non-MDR-TB and MDR-TB. Our findings suggest that the emergence of MDR-TB and transmission are specific from each location and often different from the non-MDR-TB settings. We identified priority areas for intervention for a more efficient plan of control and prevention of non-MDR-TB and MDR-TB. Graphical Abstract: [Figure not available: see fulltext.] © 2022, The Author(s).This work has been funded by National funds, through the Foundation for Science and Technology (FCT)—project UIDB/50026/2020, UIDP/50026/2020 and PTDC/SAU-PUB/29521/2017. This study was also supported by FEDER through the Operational Programme Competitiveness and Internationalization and national funding through the Foundation for Science and Technology—FCT (Portuguese Ministry of Science, Technology and Higher Education) under the Unidade de Investigação em Epidemiologia—Instituto de Saúde Pública da Universidade do Porto (EPIUnit) (UIDB/04750/2020). Ana Isabel Ribeiro was supported by National Funds through FCT, under the programme of ‘Stimulus of Scientific Employment—Individual Support’ within the contract CEECIND/02386/2018

A Spatial Analysis Framework to Monitor and Accelerate Progress towards SDG 3 to End TB in Bangladesh

Global efforts to end the tuberculosis (TB) epidemic by 2030 (SDG3.3) through improved TB case detection and treatment have not been effective to significantly reduce the global burden of the TB epidemic. This study presents an analytical framework to evaluate the use of TB case notification rates (CNR) to monitor and to evaluate TB under-detection and under-diagnoses in Bangladesh. Local indicators of spatial autocorrelation (LISA) were calculated to assess the presence and scale of spatial clusters of TB CNR across 489 upazilas in Bangladesh. Simultaneous autoregressive models were fit to the data to identify associations between TB CNR and poverty, TB testing rates and retreatment rates. CNRs were found to be significantly spatially clustered, negatively correlated to poverty rates and positively associated to TB testing and retreatment rates. Comparing the observed pattern of CNR with model-standardized rates made it possible to identify areas where TB under-detection is likely to occur. These results suggest that TB CNR is an unreliable proxy for TB incidence. Spatial variations in TB case notifications and subnational variations in TB case detection should be considered when monitoring national TB trends. These results provide useful information to target and prioritize context specific interventions

Bridging the financial and academic gap in key Sustainable Development Goal(s): comprehensive bibliometric analysis

Три найбільш часто згадувані Цілі сталого розвитку в наукових дослідженнях: Ціль сталого розвитку 3 Добре здоров’я та добробут (2 173 321 публікацій), Ціль сталого розвитку 7 Доступна та чиста енергія (711 226) та Ціль сталого розвитку 9: Промисловість, інновації та інфраструктура (378,55 публікацій). У цій монографії представлено вичерпний бібліометричний метааналіз Цілей сталого розвитку 3, 7 та 9 у вирішенні проблеми фінансування цих Цілей.Три наиболее часто упоминаемые Цели устойчивого развития в научных исследованиях: Цель устойчивого развития 3 Хорошее здоровье и благополучие (2 173 321 публикаций), Цель устойчивого развития 7 Доступная и чистая энергия (711 226) и Цель устойчивого развития 9: Промышленность, инновации и инфраструктура (378,55 публикаций). В этой монографии представлен исчерпывающий библиометрический метаанализ Целей устойчивого развития 3, 7 и 9 в решении проблемы финансирования этих Целей.The three most frequently mentioned goals in the academic research are Sustainable Development Goal 3 Good Health and Well Being (2,173,321 publications), Sustainable Development Goal 7 Affordable and Clean Energy (711,226) and Sustainable Development Goal 9: Industry, innovation and infrastructure (378,553 publications). This monograph provides a comprehensive bibliometric meta-analysis of the most popular among academicians Sustainable Development Goals 3, 7 and 9 in solving the problem of these Goals financing