Tag: nevin manimala

Geospat Health. 2023 May 25;18(1). doi: 10.4081/gh.2023.1144.

ABSTRACT

Exploratory disease maps are designed to identify risk factors of disease and guide appropriate responses to disease and helpseeking behaviour. However, when produced using aggregatelevel administrative units, as is standard practice, disease maps may mislead users due to the Modifiable Areal Unit Problem (MAUP). Smoothed maps of fine-resolution data mitigate the MAUP but may still obscure spatial patterns and features. To investigate these issues, we mapped rates of Mental Health- Related Emergency Department (MHED) presentations in Perth, Western Australia, in 2018/19 using Australian Bureau of Statistics (ABS) Statistical Areas Level 2 (SA2) boundaries and a recent spatial smoothing technique: the Overlay Aggregation Method (OAM). Then, we investigated local variation in rates within high-rate regions delineated using both approaches. The SA2- and OAM-based maps identified two and five high-rate regions, respectively, with the latter not conforming to SA2 boundaries. Meanwhile, both sets of high-rate regions were found to comprise a select number of localised areas with exceptionally high rates. These results demonstrate how, due to the MAUP, disease maps that are produced using aggregate-level administrative units are unreliable as a basis for delineating geographic regions of interest for targeted interventions. Instead, reliance on such maps to guide responses may compromise the efficient and equitable delivery of healthcare. Detailed investigation of local variation in rates within high-rate regions identified using both administrative units and smoothing is required to improve hypothesis generation and the design of healthcare responses.

PMID:37246547 | DOI:10.4081/gh.2023.1144

Geospat Health. 2023 May 25;18(1). doi: 10.4081/gh.2023.1161.

ABSTRACT

COVID-19 is the most severe health crisis of the 21st century. COVID-19 presents a threat to almost all countries worldwide. The restriction of human mobility is one of the strategies used to control the transmission of COVID-19. However, it has yet to be determined how effective this restriction is in controlling the rise in COVID-19 cases, particularly in small areas. Using Facebook’s mobility data, our study explores the impact of restricting human mobility on COVID-19 cases in several small districts in Jakarta, Indonesia. Our main contribution is showing how the restriction of human mobility data can give important information about how COVID-19 spreads in different small areas. We proposed modifying a global regression model into a local regression model by accounting for the spatial and temporal interdependence of COVID-19 transmission across space and time. We applied Bayesian hierarchical Poisson spatiotemporal models with spatially varying regression coefficients to account for non-stationarity in human mobility. We estimated the regression parameters using an Integrated Nested Laplace Approximation. We found that the local regression model with spatially varying regression coefficients outperforms the global regression model based on DIC, WAIC, MPL, and R2 criteria for model selection. In Jakarta’s 44 districts, the impact of human mobility varies significantly. The impacts of human mobility on the log relative risk of COVID-19 range from -4.445 to 2.353. The prevention strategy involving the restriction of human mobility may be beneficial in some districts but ineffective in others. Therefore, a cost-effective strategy had to be adopted.

PMID:37246544 | DOI:10.4081/gh.2023.1161

Geospat Health. 2023 May 25;18(1). doi: 10.4081/gh.2023.1169.

ABSTRACT

Pulmonary tuberculosis (PTB) remains a serious public health problem, especially in areas of developing countries. This study aimed to explore the spatial-temporal clusters and associated risk factors of PTB in south-western China. Space-time scan statistics were used to explore the spatial and temporal distribution characteristics of PTB. We collected data on PTB, population, geographic information and possible influencing factors (average temperature, average rainfall, average altitude, planting area of crops and population density) from 11 towns in Mengzi, a prefecture-level city in China, between 1 January 2015 and 31 December 2019. A total of 901 reported PTB cases were collected in the study area and a spatial lag model was conducted to analyse the association between these variables and the PTB incidence. Kulldorff’s scan results identified two significant space-time clusters, with the most likely cluster (RR = 2.24, p < 0.001) mainly located in northeastern Mengzi involving five towns in the time frame June 2017 – November 2019. A secondary cluster (RR = 2.09, p < 0.05) was located in southern Mengzi, covering two towns and persisting from July 2017 to December 2019. The results of the spatial lag model showed that average rainfall was associated with PTB incidence. Precautions and protective measures should be strengthened in high-risk areas to avoid spread of the disease.

PMID:37246542 | DOI:10.4081/gh.2023.1169

Geospat Health. 2023 May 25;18(1). doi: 10.4081/gh.2023.1175.

ABSTRACT

The rapid increase in out-of-pocket expenditures regressively raises the issue of equity in medical access opportunities according to income class and negatively affects public health. Factors related to out-of-pocket expenses have been analyzed in previous studies using an ordinary regression model (Ordinary Least Squares [OLS]). However, as OLS assumes equal error variance, it does not consider spatial variation due to spatial heterogeneity and dependence. Accordingly, this study presents a spatial analysis of outpatient out-of-pocket expenses from 2015 to 2020, targeting 237 local governments nationwide, excluding islands and island regions. R (version 4.1.1) was used for statistical analysis, and QGIS (version 3.10.9), GWR4 (version 4.0.9), and Geoda (version 1.20.0.10) were used for the spatial analysis. As a result, in OLS, it was found that the aging rate and number of general hospitals, clinics, public health centers, and beds had a positive (+) significant effect on outpatient out-of-pocket expenses. The Geographically Weighted Regression (GWR) suggests regional differences exist concerning out-of-pocket payments. As a result of comparing the OLS and GWR models through the Adj. R² and Akaike’s Information Criterion indices, the GWR model showed a higher fit. This study provides public health professionals and policymakers with insights that could inform effective regional strategies for appropriate out-of-pocket cost management.

PMID:37246540 | DOI:10.4081/gh.2023.1175

Geospat Health. 2023 May 25;18(1). doi: 10.4081/gh.2023.1186.

ABSTRACT

We aimed to explore the district-level temporal dynamics and sub-district level geographical variations of colorectal cancer (CRC) incidence in the Special Region of Yogyakarta Province. We performed a cross-sectional study using data from the Yogyakarta population-based cancer registry (PBCR) comprised of 1,593 CRC cases diagnosed in 2008-2019. The age-standardized rates (ASRs) were determined using 2014 population data. The temporal trend and geographical distribution of cases were analysed using joinpoint regression and Moran’s I statistics. During 2008-2019, CRC incidence increased by 13.44% annually. Joinpoints were identified in 2014 and 2017, which were also the periods when annual percentage change (APC) was the highest throughout the observation periods (18.84). Significant APC changes were observed in all districts, with the highest in Kota Yogyakarta (15.57). The ASR of CRC incidence per 100,000 person- years was 7.03 in Sleman, 9.20 in Kota Yogyakarta, and 7.07 in Bantul district. We found a regional variation of CRC ASR with a concentrated pattern of hotspots in the central sub-districts of the catchment areas and a significant positive spatial autocorrelation of CRC incidence rates in the province (I=0.581, p<0.001). The analysis identified four high-high clusters sub-districts in the central catchment areas. This is the first Indonesian study reported from PBCR data, showing an increased annual CRC incidence during an extensive observation period in the Yogyakarta region. A heterogeneous distribution map of CRC incidence is included. These findings may serve as basis for CRC screening implementation and healthcare services improvement.

PMID:37246534 | DOI:10.4081/gh.2023.1186

Geospat Health. 2023 May 25;18(1). doi: 10.4081/gh.2023.1200.

ABSTRACT

This article examines three spatiotemporal methods used for analyzing of infectious diseases, with a focus on COVID-19 in the United States. The methods considered include inverse distance weighting (IDW) interpolation, retrospective spatiotemporal scan statistics and Bayesian spatiotemporal models. The study covers a 12-month period from May 2020 to April 2021, including monthly data from 49 states or regions in the United States. The results show that the spread of COVID-19 pandemic increased rapidly to a high value in winter of 2020, followed by a brief decline that later reverted into another increase. Spatially, the COVID-19 epidemic in the United States exhibited a multi-centre, rapid spread character, with clustering areas represented by states such as New York, North Dakota, Texas and California. By demonstrating the applicability and limitations of different analytical tools in investigating the spatiotemporal dynamics of disease outbreaks, this study contributes to the broader field of epidemiology and helps improve strategies for responding to future major public health events.

PMID:37246533 | DOI:10.4081/gh.2023.1200

Geospat Health. 2023 May 25;18(1). doi: 10.4081/gh.2023.1203.

ABSTRACT

Chronic respiratory diseases (CRDs) constitute 4% of the global disease burden and cause 4 million deaths annually. This cross-sectional study used QGIS and GeoDa to explore the spatial pattern and heterogeneity of CRDs morbidity and spatial autocorrelation between socio-demographic factors and CRDs in Thailand from 2016 to 2019. We found an annual, positive, spatial autocorrelation (Moran’s I >0.66, p<0.001) showing a strong clustered distribution. The local indicators of spatial association (LISA) identified hotspots mostly in the northern region, while coldspots were mostly seen in the central and north-eastern regions throughout the study period. Of the socio-demographic factors, the density of population, households, vehicles, factories and agricultural areas, correlated with the CRD morbidity rate, with statistically significant negative spatial autocorrelations and coldspots in the north-eastern and central areas (except for agricultural land) and two hotspots between farm household density and CRD in the southern region in 2019. This study identified vulnerable provinces with high risk of CRDs and can guide prioritization of resource allocation and provide target interventions for policy makers.

PMID:37246531 | DOI:10.4081/gh.2023.1203

Geospat Health. 2023 May 25;18(1). doi: 10.4081/gh.2023.1210.

ABSTRACT

Researchers in many fields have discovered the advantage of using geographical information systems (GIS), spatial statistics and computer modelling, but these techniques are only sparingly applied in archaeological research. Writing 30 years ago, Castleford (1992) noted the considerable potential of GIS, but he also felt that its then atemporal structure was a serious flaw. It is clear that the study of dynamic processes suffers if past events cannot be linked to each other, or to the present, but today’s powerful tools have overcome this drawback. Importantly, with location and time as key indices, hypotheses about early human population dynamics can be tested and visualized in ways that can potentially reveal hidden relationships and patterns. […].

PMID:37246530 | DOI:10.4081/gh.2023.1210

Lupus. 2023 May 29:9612033231180358. doi: 10.1177/09612033231180358. Online ahead of print.

ABSTRACT

OBJECTIVES: To determine whether age at menarche (AAM), age at first live birth (AFB), and estradiol levels are causally correlated with the development of systemic lupus erythematosus (SLE).

METHODS: A two-sample Mendelian randomization (MR) analysis was performed after data was collected from a dataset of genome-wide association studies (GWASs) related to SLE (as outcome), and from open access databases to find statistics related to AAM, AFB, and estradiol levels (as exposure).

RESULT: In our study, a negative causal correlation between AAM and SLE was confirmed by MR analysis (MR egger: beta = 0.116, SE = 0.948, p = 0.909; weighted median: beta = -0.416, SE = 0.192, p = 0.030; and IVW: beta = -0.395, SE = 0.165, p = 0.016). However, there were no genetic causal effects of AFB and the estradiol levels on SLE, based on the results of MR analysis as follows: AFB (MR egger: beta = – 2.815, SE = 1.469, p = 0.065; Weighted median: beta = 0.334, SE = 0.378, p = 0.377; and IVW: beta = 0.188, SE = 0.282, p = 0.505) and the estradiol levels (MR egger: beta = 0.139, SE = 0.294, p = 0.651; weighted median: beta = 0.063, SE = 0.108, p = 0.559; IVW: beta = 0.126, SE = 0.097, p = 0.192).

CONCLUSIONS: Our findings revealed that AAM may be associated with increased risk of the development of SLE, while there were no such causal effects from AFB and estradiol levels.

PMID:37246529 | DOI:10.1177/09612033231180358

Technol Cancer Res Treat. 2023 Jan-Dec;22:15330338231175733. doi: 10.1177/15330338231175733.

ABSTRACT

Human cancer statistics show that an increased incidence of urologic cancers such as bladder cancer, prostate cancer, and renal cell carcinoma. Due to the lack of early markers and effective therapeutic targets, their prognosis is poor. Fascin-1 is an actin-binding protein, which functions in the formation of cell protrusions by cross-linking with actin filaments. Studies have found that fascin-1 expression is elevated in most human cancers and is related to outcomes such as neoplasm metastasis, reduced survival, and increased aggressiveness. Fascin-1 has been considered as a potential therapeutic target for urologic cancers, but there is no comprehensive review to evaluate these studies. This review aimed to provide an enhanced literature review, outline, and summarize the mechanism of fascin-1 in urologic cancers and discuss the therapeutic potential of fascin-1 and the possibility of its use as a potential marker. We also focused on the correlation between the overexpression of fascin-1 and clinicopathological parameters. Mechanistically, fascin-1 is regulated by several regulators and signaling pathways (such as long noncoding RNA, microRNA, c-Jun N-terminal kinase, and extracellular regulated protein kinases). The overexpression of fascin-1 is related to clinicopathologic parameters such as pathological stage, bone or lymph node metastasis, and reduced disease-free survival. Several fascin-1 inhibitors (G2, NP-G2-044) have been evaluated in vitro and in preclinical models. The study proved the promising potential of fascin-1 as a newly developing biomarker and a potential therapeutic target that needs further investigation. The data also highlight the inadequacy of fascin-1 to serve as a novel biomarker for prostate cancer.

PMID:37246525 | DOI:10.1177/15330338231175733