Spatial distribution pattern of immune cells is associated with patient prognosis in colorectal cancer.

BACKGROUND: The spatial context of tumor-infiltrating immune cells (TIICs) is important in predicting colorectal cancer (CRC) patients' clinical outcomes. However, the prognostic value of the TIIC spatial distribution is unknown. Thus, we aimed to investigate the association between TIICs in situ and patient prognosis in a large CRC sample. METHODS: We implemented multiplex immunohistochemistry staining technology in 190 CRC samples to quantify 14 TIIC subgroups in situ. To delineate the spatial relationship of TIICs to tumor cells, tissue slides were segmented into tumor cell and microenvironment compartments based on image recognition technology, and the distance between immune and tumor cells was calculated by implementing the computational pipeline phenoptr. RESULTS: MPO+ neutrophils and CD68+IDO1+ tumor-associated macrophages (TAMs) were enriched in the epithelial compartment, and myeloid lineage cells were located nearest to tumor cells. Except for CD68+CD163+ TAMs, other cells were all positively associated with favorable prognosis. The prognostic predictive power of TIICs was highly related to their distance to tumor cells. Unsupervised clustering analysis divided colorectal cancer into three subtypes with distinct prognostic outcomes, and correlation analysis revealed the synergy among B cells, CD68+IDO1+TAMs, and T lineage cells in producing an effective immune response. CONCLUSIONS: Our study suggests that the integration of spatial localization with TIIC abundance is important for comprehensive prognostic assessment.

Profile and Geospatial Analysis of Dog Bite Cases Attending the Antirabies Vaccine Outpatient Department at the Tertiary Level Hospital of Mumbai.

BACKGROUND: Dog bites pose a significant public health concern in India, necessitating an understanding of their epidemiological profile and spatial distribution. Adopting the One Health approach, which considers the interconnection of human, animal, and environmental health, is vital for developing effective interventions. OBJECTIVES: The study aimed to assess the epidemiological profile and geospatial trends of dog bite cases in an urban area, focusing on the age and gender distribution of victims, severity of bites, and spatial distribution of cases to inform prevention strategies. MATERIALS AND METHODS: A retrospective secondary data analysis was conducted on dog bite cases reported in 2022 at a tertiary care hospital in Mumbai. The epidemiological profile, including age, gender, and severity of bites, was examined. Quantum Geographic Information System (QGIS) was utilized for spatial distribution analysis, identifying hotspots within the urban area. RESULTS: Of the 3350 cases, 70.7% were below 40 years old, 81.6% were male, and 78.18% had Category III bites indicating severe injuries. Most cases (74%) were caused by stray dogs. QGIS analysis revealed five hotspots within the urban area. CONCLUSION: The study highlights the predominance of dog bites among younger males and the severity of injuries. Spatial analysis identified specific hotspots, underscoring the need for targeted interventions. Implementing a comprehensive surveillance system incorporating GIS technology and adopting a One Health approach can enhance the control and prevention of dog bite cases and reduce the risk of rabies outbreaks.

Spatial Variation and Determinants of Inadequate Minimum Meal Frequency among Children Aged 6-23 Months in Ethiopia: Spatial and multilevel analysis using Ethiopian Mini Demographic and Health Survey (EMDHS) 2019.

Introduction: Minimum meal frequency is the number of times children eat in a day. Without adequate meal frequency, infants and young children are prone to malnutrition. There is little information on the spatial distribution and determinants of inadequate meal frequency at the national level. Therefore, we aimed to investigate the spatial distribution and determinants of inadequate meal frequency among young children in Ethiopia. Methods: The most recent Ethiopian demographic and health survey data was used. The analysis was conducted using a weighted sample of 1,610 children aged 6-23 months old. The Global Moran's I was estimated to assess the regional variation in minimum meal frequency. Further, a multivariable multilevel logistic regression model was fitted to identify factors associated with inadequate meal frequency. The AOR (adjusted odds ratio) at 95% CI (confidence interval) was computed to assess the strength and significance of the relationship between explanatory variables and the outcome variable. Factors with a p-value of <0.05 are declared statistically significant. Results: This study revealed that the prevalence of inadequate meal frequency was found to be 30.56% (95% CI: 28.33-32.88). We identified statistically significant clusters of high inadequate meal frequency, notably observed in Somalia, northern Amhara, the eastern part of southern nations and nationalities, and the southwestern Oromia regions. Child age, antenatal care (ANC) visit, marital status, and community level illiteracy were significant factors that were associated with inadequate meal frequency. Conclusion: According to the study findings, the proportion of inadequate meal frequency among young children in Ethiopia was higher and also distributed non-randomly across Ethiopian regions. As a result, policymakers and other concerned bodies should prioritize risky areas in designing intervention. Thus, special attention should be given to the Somalia region, the northern part of Amhara, the eastern part of Southern nations and nationalities, and southwestern Oromia.

Spatial distribution of physicochemical parameters and drinking and irrigation water quality indices in the Jhelum River, Pakistan.

Sustainable management of river systems is a serious concern, requiring vigilant monitoring of water contamination levels that could potentially threaten the ecological community. This study focused on the evaluation of water quality in the Jhelum River (JR), Azad Jammu and Kashmir, and northern Punjab, Pakistan. To achieve this, 60 water samples were collected from various points within the JR Basin (JRB) and subjected to a comprehensive analysis of their physicochemical parameters. The study findings indicated that the concentrations of physicochemical parameters in the JRB water remained within safety thresholds for both drinking and irrigation water, as established by the World Health Organization and Pakistan Environmental Protection Agency. These physicochemical parameters refer to various chemical and physical characteristics of the water that can have implications for both human health (drinking water) and agricultural practices (irrigation water). The spatial variations throughout the river course distinguished between the upstream, midstream, and downstream sections. Specifically, the downstream section exhibited significantly higher values for physicochemical parameters and a broader range, highlighting a substantial decline in its quality. Significant disparities in mean values and ranges were evident, particularly in the case of nitrates and total dissolved solids, when the downstream section was compared with its upstream and midstream counterparts. These variations indicated a deteriorating downstream water quality profile, which is likely attributable to a combination of geological and anthropogenic influences. Despite the observed deterioration in the downstream water quality, this study underscores that the JRB within the upper Indus Basin remains safe and suitable for domestic and agricultural purposes. The JRB was evaluated for various irrigation water quality indices. The principal component analysis conducted in this study revealed distinct covariance patterns among water quality variables, with the first five components explaining approximately 79% of the total variance. Recommending the continued utilization of the JRB for irrigation, we advocate for the preservation and enhancement of water quality in the downstream regions.

A Marginalized Zero-Inflated Negative Binomial Model for Spatial Data: Modeling COVID-19 Deaths in Georgia.

Spatial count data with an abundance of zeros arise commonly in disease mapping studies. Typically, these data are analyzed using zero-inflated models, which comprise a mixture of a point mass at zero and an ordinary count distribution, such as the Poisson or negative binomial. However, due to their mixture representation, conventional zero-inflated models are challenging to explain in practice because the parameter estimates have conditional latent-class interpretations. As an alternative, several authors have proposed marginalized zero-inflated models that simultaneously model the excess zeros and the marginal mean, leading to a parameterization that more closely aligns with ordinary count models. Motivated by a study examining predictors of COVID-19 death rates, we develop a spatiotemporal marginalized zero-inflated negative binomial model that directly models the marginal mean, thus extending marginalized zero-inflated models to the spatial setting. To capture the spatiotemporal heterogeneity in the data, we introduce region-level covariates, smooth temporal effects, and spatially correlated random effects to model both the excess zeros and the marginal mean. For estimation, we adopt a Bayesian approach that combines full-conditional Gibbs sampling and Metropolis-Hastings steps. We investigate features of the model and use the model to identify key predictors of COVID-19 deaths in the US state of Georgia during the 2021 calendar year.

The Socio-Environmental Determinants of Childhood Malnutrition: A Spatial and Hierarchical Analysis.

Despite a remarkable reduction in global poverty and famines, substantial childhood malnutrition continues to persist. In 2017, over 50 million and 150 million young children suffered from acute malnutrition (wasting) and chronic malnutrition (stunting), respectively. Yet, the measurable impact of determinants is obscure. We evaluate proposed socio-environmental related determinants of stunting and wasting across Kenya and Nigeria and quantify their effectiveness. We combine health and demographic data from Kenya and Nigeria Demographic Health Surveys (2003, 2008-2009, 2013, 2014) with spatially explicit precipitation, temperature, and vegetation data. Geospatial and disaggregated data help to understand better who is at risk and where to target mitigation efforts. We evaluate the responsiveness of malnutrition indicators using a four-level random intercept hierarchical generalized logit model. We find that spatial and hierarchical relationships explain 28% to 36% of malnutrition outcome variation. Temporal variation in precipitation, temperature, and vegetation corresponds with more than a 50% change in malnutrition rates. Wasting is most impacted by mother's education, family wealth, clinical delivery, and vaccinations. Stunting is most impacted by family wealth, mother's education, clinical delivery, vaccinations, and children asymptomatic of fever, cough, or diarrhea. Remotely monitored climatic variables are powerful determinants, however, their effects are inconsistent across different indicators and locations.

Adverse Events of COVID-19 Vaccines in the United States: Temporal and Spatial Analysis.

BACKGROUND: The COVID-19 pandemic, caused by SARS-CoV-2, has had a profound impact worldwide, leading to widespread morbidity and mortality. Vaccination against COVID-19 is a critical tool in controlling the spread of the virus and reducing the severity of the disease. However, the rapid development and deployment of COVID-19 vaccines have raised concerns about potential adverse events following immunization (AEFIs). Understanding the temporal and spatial patterns of these AEFIs is crucial for an effective public health response and vaccine safety monitoring. OBJECTIVE: This study aimed to analyze the temporal and spatial characteristics of AEFIs associated with COVID-19 vaccines in the United States reported to the Vaccine Adverse Event Reporting System (VAERS), thereby providing insights into the patterns and distributions of the AEFIs, the safety profile of COVID-19 vaccines, and potential risk factors associated with the AEFIs. METHODS: We conducted a retrospective analysis of administration data from the Centers for Disease Control and Prevention (n=663,822,575) and reports from the surveillance system VAERS (n=900,522) between 2020 and 2022. To gain a broader understanding of postvaccination AEFIs reported, we categorized them into system organ classes (SOCs) according to the Medical Dictionary for Regulatory Activities. Additionally, we performed temporal analysis to examine the trends of AEFIs in all VAERS reports, those related to Pfizer-BioNTech and Moderna, and the top 10 AEFI trends in serious reports. We also compared the similarity of symptoms across various regions within the United States. RESULTS: Our findings revealed that the most frequently reported symptoms following COVID-19 vaccination were headache (n=141,186, 15.68%), pyrexia (n=122,120, 13.56%), and fatigue (n=121,910, 13.54%). The most common symptom combination was chills and pyrexia (n=56,954, 6.32%). Initially, general disorders and administration site conditions (SOC 22) were the most prevalent class reported. Moderna exhibited a higher reporting rate of AEFIs compared to Pfizer-BioNTech. Over time, we observed a decreasing reporting rate of AEFIs associated with COVID-19 vaccines. In addition, the overall rates of AEFIs between the Pfizer-BioNTech and Moderna vaccines were comparable. In terms of spatial analysis, the middle and north regions of the United States displayed a higher reporting rate of AEFIs associated with COVID-19 vaccines, while the southeast and south-central regions showed notable similarity in symptoms reported. CONCLUSIONS: This study provides valuable insights into the temporal and spatial patterns of AEFIs associated with COVID-19 vaccines in the United States. The findings underscore the critical need for increasing vaccination coverage, as well as ongoing surveillance and monitoring of AEFIs. Implementing targeted monitoring programs can facilitate the effective and efficient management of AEFIs, enhancing public confidence in future COVID-19 vaccine campaigns.

Investigating the impact of urban growth on land use using spatial autocorrelation methods in Konya/Türkiye.

Land use land cover (LULC) change, global environmental change, and sustainable change are frequently discussed topics in research at the moment. It is important to determine the historical LULC change process for effective environmental planning and the most appropriate use of land resources. This study analysed the spatial autocorrelation of the land use structure in Konya between 1990 and 2018. For this, Global and Local Moran's I indices based on land use data from 122 neighbourhoods and hot spot analysis (Getis-Ord Gi*) methods were applied to measure the spatial correlation of changes and to determine statistically significant hot and cold spatial clusters. According to the research results, the growth of urban areas has largely destroyed the most productive agricultural lands in the region. This change showed high spatial clustering both on an area and a proportional basis in the northern and southern parts of the city. On the other hand, the growth in the industrial area suppressed the pasture areas the most in the north-eastern region of the city, and this region showed high spatial clustering on both spatial and proportional scales.

Pooled prevalence, spatial variation and associated factors of HIV testing uptake among multiple sexual partners in Sub Saharan Africa: Spatial and multilevel analysis.

BACKGROUND: Uptake of HIV testing is vital for the early diagnosis of HIV infection and initiation of treatment, which are used to eliminate the disease's progression and reduce HIV-related mortality. Even if determining HIV testing is imperative to prevent HIV/AIDS among multiple sexual partners who are at higher risk of sexually transmitted infections, most of the countries in Sub Saharan Africa did not fulfil the global targets of UNAIDS. Moreover there is a paucity of literature on spatial variation and factors associated with HIV testing among high-risk groups in SSA. This study aimed to assess the pooled prevalence, spatial variation and determinants of HIV testing uptake among multiple sexual partners in Sub Saharan Africa. METHODS: The Demographic and Health Surveys data conducted between 2011 and 2021 in 30 Sub-Saharan Africa countries was used to analyze total weighted sample of 56,210 multiple sexual partners. Exploratory spatial data analysis, with countries as the unit of analysis was conducted using ArcGIS V10.7.1 and Sat Scan V 10.1 soft wares. A multilevel binary logistic regression model was used to identify the factors associated with the HIV testing uptake. The Adjusted odds Ratio with a 95% confidence interval was reported to declare the strength of association and their statistical significance. RESULTS: The spatial patterns of HIV testing uptake were found to be non-random. Primary clusters were identified around western and central sub- regions. Multiple sexual partners who were ever married, those attended primary level and above education, those from rich wealth status, aged above 24 years, having good HIV related knowledge, and exposed to media were positive association with HIV testing uptake. However, being male, having working status and living in rural area were negatively associated with HIV testing uptake. At the community-level, multiple sexual partners from communities in Eastern and southern sub regions, countries with upper middle income and countries with the survey year after 2014 were more likely to utilize HIV testing services compared with their counterparts. CONCLUSION: In this study, the pooled prevalence of the HIV testing uptake among multiple sexual partners was found to be lower than the universal target and showed differences in HIV testing uptake across Sub-Saharan Africa region. Both individual and community-level factors affected HIV testing uptake among multiple sexual partners. Stakeholders should implement interventions to help increase the uptake of HIV testing among those risky groups in this region.

Geospatial pattern of HIV seropositivity and its predictors among women in Ethiopia. A spatial and multiscale geographically weighted regression analysis.

BACKGROUND: Although promising efforts have been made so far, HIV remains a public health concern. Women in Ethiopia are disproportionately affected by HIV, accounting for a majority of new infections and AIDS-related deaths. However, the geospatial distribution of HIV among women in Ethiopia is not well understood, making it challenging to develop geographically targeted measures. Besides, to accelerate the pathway of decreasing HIV prevalence and plan geographically specific interventions, understanding the geospatial distribution of HIV seropositivity and its predictors among women plays a significant role. METHODS: A spatial and multiscale geographically weighted regression analysis was conducted using the 2016 EDHS dataset, comprising 14,778 weighted samples of women in the reproductive age group. The EDHS sample underwent two-stage stratification and selection. The data were extracted between October 18 and 30, 2023. Non-spatial analysis was carried out using STATA version 17. Additionally, ArcGIS Pro and Sat Scan version 9.6 were used to visually map HIV seropositivity. Global Moran's I was computed to evaluate the distribution of HIV seropositivity. The Getis-Ord Gi* spatial statistic was utilized to identify significant spatial clusters of cold and hot spot areas. Geographically weighted regression analysis was subsequently performed to identify significant predictors of HIV seropositivity. Significance was established at a P-value <0.05 throughout all statistical analyses. RESULTS: HIV seropositivity among women in Ethiopia is distributed non-randomly (Global Moran's I = 0.16, p-value <0.001 and Z-score = 7.12). Significant hotspot clustering of HIV seropositivity was found in the Addis Ababa, Harari, Dire Dawa, and Gambela region. Poor wealth index, being divorced and widowed, having more than one sexual partner, and early first sexual experience (<15 years) were found to be predictors of geographical variation of HIV seropositivity among women. CONCLUSION: HIV seropositivity among women in Ethiopia varies geographically. Thus, deploying additional resources in high hotspot regions is recommended. Programs should focus on improving the economic empowerment of women to prevent the from engaging in risky sexual behaviors. Furthermore, comprehensive sex education programs in schools and community settings regarding the consequences of early first sexual debut might play a role in reducing HIV seropositivity among women in Ethiopia.

Spatial and trend analysis of gestational syphilis cases in Brazil from 2011 to 2020: an ecological study.

OBJECTIVES: To analyze the rate of gestational syphilis (GS) based on temporal trends over 11 years, as well as the spatial distribution of GS in Brazil, based on the identification of spatial clusters. METHODOLOGY: An ecological, using Brazil and its regions as an analysis unit, based on gestational syphilis data reported in the Notifiable Diseases Information System (SINAN), from 2011 to 2020. Thematic maps were built for spatial data analysis, and the Prais-Winsten autoregressive model was used to verify the trend. Spatial analysis identified the distribution of clusters (high-high; low-low; high-low and low-high) of distribution of GS across Brazilian municipalities, using a 5% significance level. RESULTS: Gestational syphilis experienced a considerable increase in cases during the studied period, with a peak of 37,436 cases in 2018. The spatial distribution of the disease is heterogeneous in the country. A growing trend was observed in all states of Brazil, except for Espírito Santo, where it remained stationary, with a monthly variation of 10.32%. CONCLUSION: The spatial and temporal trend analysis point to syphilis as an important public health problem. The numbers are alarming and show the urgent need for measures to prevent and control syphilis during pregnancy.

Decline in Vaccination Coverage against Poliomyelitis in the municipalities of Vale do Paraíba (SP) under a spatial approach.

OBJECTIVE: To analyze vaccination coverage (VC) for polio in the municipalities of Vale do Paraíba in the State of São Paulo. METHODS: This is an ecological and exploratory study of VC in 35 municipalities using a spatial approach; VC data were obtained from the IT Department of the Unified Health System (DATASUS), for the years 2015 and 2019, and categorized into Low (VC<95%) and ideal (≥95%). Information was obtained on gross domestic product (GDP), professional rates and number of basic health units (UBS) and maternal data such as age, marital status (MS) and education. Univariate and bivariate Moran indices were estimated for the years 2015 and 2019, and thematic maps were created for CV values. RESULTS: The average VC values were 107.7%±27.2 in 2015, and 94.2%±27.8 in 2019 (p<0.05). In 2015 vs. 2019, there were 10 vs. 25 municipalities in the Low category. In 2015, the variables VC, number of UBS, age, education, and MS were spatially correlated, but in 2019 only maternal age and education were spatially correlated. The bivariate Moran was significant and negative for VC in 2019 with maternal education. There was an increase in municipalities with worsening VC values. CONCLUSIONS: The spatial approach identified a decrease in polio vaccination coverage in the studied region.

Risk factors for persistent fatal opioid-involved overdose clusters in Massachusetts 2011-2021: a spatial statistical analysis with socio-economic, accessibility, and prescription factors.

BACKGROUND: Fatal opioid-involved overdose rates increased precipitously from 5.0 per 100,000 population to 33.5 in Massachusetts between 1999 and 2022. METHODS: We used spatial rate smoothing techniques to identify persistent opioid overdose-involved fatality clusters at the ZIP Code Tabulation Area (ZCTA) level. Rate smoothing techniques were employed to identify locations of high fatal opioid overdose rates where population counts were low. In Massachusetts, this included areas with both sparse data and low population density. We used Local Indicators of Spatial Association (LISA) cluster analyses with the raw incidence rates, and the Empirical Bayes smoothed rates to identify clusters from 2011 to 2021. We also estimated Empirical Bayes LISA cluster estimates to identify clusters during the same period. We constructed measures of the socio-built environment and potentially inappropriate prescribing using principal components analysis. The resulting measures were used as covariates in Conditional Autoregressive Bayesian models that acknowledge spatial autocorrelation to predict both, if a ZCTA was part of an opioid-involved cluster for fatal overdose rates, as well as the number of times that it was part of a cluster of high incidence rates. RESULTS: LISA clusters for smoothed data were able to identify whether a ZCTA was part of a opioid involved fatality incidence cluster earlier in the study period, when compared to LISA clusters based on raw rates. PCA helped in identifying unique socio-environmental factors, such as minoritized populations and poverty, potentially inappropriate prescribing, access to amenities, and rurality by combining socioeconomic, built environment and prescription variables that were highly correlated with each other. In all models except for those that used raw rates to estimate whether a ZCTA was part of a high fatality cluster, opioid overdose fatality clusters in Massachusetts had high percentages of Black and Hispanic residents, and households experiencing poverty. The models that were fitted on Empirical Bayes LISA identified this phenomenon earlier in the study period than the raw rate LISA. However, all the models identified minoritized populations and poverty as significant factors in predicting the persistence of a ZCTA being part of a high opioid overdose cluster during this time period. CONCLUSION: Conducting spatially robust analyses may help inform policies to identify community-level risks for opioid-involved overdose deaths sooner than depending on raw incidence rates alone. The results can help inform policy makers and planners about locations of persistent risk.

Evaluating China's primary healthcare services' efficiency and spatial correlation: a three-stage DEA-Malmquist model.

Introduction: Enhancing the efficiency of primary healthcare services is essential for a populous and developing nation like China. This study offers a systematic analysis of the efficiency and spatial distribution of primary healthcare services in China. It elucidates the fundamental landscape and regional variances in efficiency, thereby furnishing a scientific foundation for enhancing service efficiency and fostering coordinated regional development. Methods: Employs a three-stage DEA-Malmquist model to assess the efficiency of primary healthcare services across 31 provincial units in mainland China from 2012 to 2020. Additionally, it examines the spatial correlation of efficiency distribution using the Moran Index. Results: The efficiency of primary healthcare services in China is generally suboptimal with a noticeable declining trend, highlighting significant potential for improvement in both pure technical efficiency and scale efficiency. There is a pronounced efficiency gap among provinces, yet a positive spatial correlation is evident. Regionally, efficiency ranks in the order of East > Central > West. Factors such as GDP per capita and population density positively influence efficiency enhancements, while urbanization levels and government health expenditures appear to have a detrimental impact. Discussion: The application of the three-stage DEA-Malmquist model and the Moran Index not only expands the methodological framework for researching primary healthcare service efficiency but also provides scientifically valuable insights for enhancing the efficiency of primary healthcare services in China and other developing nations.

Geospatial pattern of level of minimum acceptable diet and its determinants among children aged 6-23 months in Ethiopia. Spatial and multiscale geographically weighted regression analysis.

Background: Despite prior progress and the proven benefits of optimal feeding practices, improving child dietary intake in developing countries like Ethiopia remains challenging. In Ethiopia, over 89% of children fail to meet the minimum acceptable diet. Understanding the geographical disparity and determinants of minimum acceptable diet can enhance child feeding practices, promoting optimal child growth. Methods: Spatial and multiscale geographically weighted regression analysis was conducted among 1,427 weighted sample children aged 6-23 months. ArcGIS Pro and SatScan version 9.6 were used to map the visual presentation of geographical distribution failed to achieve the minimum acceptable diet. A multiscale geographically weighted regression analysis was done to identify significant determinants of level of minimum acceptable diet. The statistical significance was declared at P-value <0.05. Results: Overall, 89.56% (95CI: 87.85-91.10%) of children aged 6-23 months failed to achieve the recommended minimum acceptable diet. Significant spatial clustering was detected in the Somali, Afar regions, and northwestern Ethiopia. Children living in primary clusters were 3.6 times more likely to be unable to achieve the minimum acceptable diet (RR = 3.61, LLR =13.49, p < 0.001). Mother's with no formal education (Mean = 0.043, p-value = 0.000), family size above five (Mean = 0.076, p-value = 0.005), No media access (Mean = 0.059, p-value = 0.030), home delivery (Mean = 0.078, p-value = 0.002), and no postnatal checkup (Mean = 0.131, p-value = 0.000) were found to be spatially significant determinants of Inadequate minimum acceptable diet. Conclusion: Level of minimum acceptable diet among children in Ethiopia varies geographically. Therefore, to improve child feeding practices in Ethiopia, it is highly recommended to deploy additional resources to high-need areas and implement programs that enhance women's education, maternal healthcare access, family planning, and media engagement.

A Shared-Frailty Spatial Scan Statistic Model for Time-to-Event Data.

Spatial scan statistics are well-known methods widely used to detect spatial clusters of events. Furthermore, several spatial scan statistics models have been applied to the spatial analysis of time-to-event data. However, these models do not take account of potential correlations between the observations of individuals within the same spatial unit or potential spatial dependence between spatial units. To overcome this problem, we have developed a scan statistic based on a Cox model with shared frailty and that takes account of the spatial dependence between spatial units. In simulation studies, we found that (i) conventional models of spatial scan statistics for time-to-event data fail to maintain the type I error in the presence of a correlation between the observations of individuals within the same spatial unit and (ii) our model performed well in the presence of such correlation and spatial dependence. We have applied our method to epidemiological data and the detection of spatial clusters of mortality in patients with end-stage renal disease in northern France.

Spatial distribution of inadequate meal frequency and its associated factors among children aged 6-23 months in Ethiopia: Multilevel and spatial analysis.

INTRODUCTION: More than two-third of global child death is occurred due to inappropriate feeding practice that happened during early childhood period. Evidence on meal frequency status among infant and young children at national level can be used to design appropriate interventions to improve the recommended feeding frequency. Therefore, this study was aimed to explore the spatial distribution and identify associated factors of inadequate meal frequency among children aged 6-23 months in Ethiopia. METHODS: Secondary data analysis was conducted using the 2019 mini Ethiopian Demographic and Health Survey data. A total weighted sample of 1,532 children aged 6-23 months were included. To identify significant factors associated with of inadequate meal frequency, multilevel binary logistic regression model was fitted. Variables with p-value < 0.25 from the bi-variable model were exported to multivariable analysis. In the multivariable model, variables with p-value < 0.05 were declared as significantly associated factors and adjusted odds ratio (AOR) with its 95% confidence interval were reported. Multilevel models were compared using deviance and log-likelihood. Spatial analysis tools were utilized to visualize the distribution of inadequate meal frequency. Bernoulli model was fitted using SaTScan V.9.6 to identify most likely clusters and ArcGIS V.10.8 was used to map the hotspot areas. Ordinary least square and geographic weighted regression models were used and compared using information criteria and adjusted-R2. Local coefficients of factors associated with hotspots of inadequate meal frequency were mapped. RESULTS: The prevalence of inadequate meal frequency was 47.03% (95% CI: 44.54%, 49.53%) in Ethiopia. Age of the child, sex of the household head, timely initiation of breastfeeding, current breastfeeding status, number of antenatal care visit, maternal education, and region were significantly associated with inadequate meal frequency. The spatial distribution of inadequate meal frequency was showed significant variation across Ethiopia (Global Moran's I = 0.164, p-value <0.001). A total of 38 significant clusters were detected through SaTScan analysis, from these the 22 primary clusters were located in Somali and Harari. CONCLUSION AND RECOMMENDATION: The prevalence of inadequate meal frequency was high in Ethiopia and had significant clustering patter. Significant hotspot clusters were located in Somali, northern Afar, Harari, Amhara, Gambela, and eastern South nation nationalities and peoples' region. Therefore, public health interventions which enhance breastfeeding practice, optimal number of antenatal care visits, educational empowerments should target hotspot areas to decrease inadequate meal frequency practice.

Spatial distribution and determinants of measles vaccination dropout among under-five children in Ethiopia: A spatial and multilevel analysis of 2019 Ethiopian demographic and health survey.

BACKGROUND: Each year, vaccine-preventable diseases cost the lives of 8.8 million under-five children. Although vaccination prevents 1-2 million childhood deaths worldwide, measles vaccination dropouts are not well studied in developing countries, particularly in Ethiopia. Therefore, this study aims to assess the spatial distribution of the measles vaccination dropout and its determinants among under-five children in Ethiopia. METHODS: Data from Ethiopian Demographic and Health Survey 2019 was used for data analysis. The study used a total of 5,753 children. Spatial autocorrelations was used to determine the spatial dependency of measles vaccination dropout. Ordinary interpolation was employed to forecast measles vaccination dropout. Factors associated with measles vaccination dropout were declared significant at p-values <0.05. The data were interpreted using the confidence interval and adjusted odds ratio. A model with the lowest deviance and highest logliklihood ratio was selected as the best-fit model. RESULTS: In Ethiopia, one in three under-five children had measles vaccination dropouts. Factors such as birth interval (AOR = 1.87, 95% CI: 1.30, 2.70), unmarried marital status women (AOR = 3.98, 95% CI: 1.08, 8.45), ≤1 number of under-five children (AOR = 3.86, 95% CI: 2.56, 5.81), rural place of residence (AOR = 2.43, 95% CI: 2.29, 3.11), low community-level ANC utilization (AOR = 3.20, 95% CI: 2.53, 3.56), and residing in Benishangul Gumuz (AOR = 1.80, 95% CI: 1.061, 3.06) had higher odds of measles vaccination dropout. CONCLUSIONS: Measles vaccination dropout rates in Ethiopia among under-five children were high compared to the maximum tolerable vaccination dropout level of 10% by the WHO. Both individual and community-level variables were determinants of measles vaccination dropout. The ministry of health in Ethiopia should give attention to those mothers of under-five children who reported underutilization of ANC services and rural residences while designing policies and strategies in areas of high spatial clustering of vaccine dropout in Ethiopia.

Spatial variation and geographical weighted regression analysis to explore open defecation practice and its determinants among households in Ethiopia.

BACKGROUND: In Ethiopia, recent evidence revealed that over a quarter (27%) of households (HHs) defecated openly in bush or fields, which play a central role as the source of many water-borne infectious diseases, including cholera. Ethiopia is not on the best track to achieve the SDG of being open-defecation-free by 2030. Therefore, this study aimed to explore the spatial variation and geographical inequalities of open defecation (OD) among HHs in Ethiopia. METHODS: This was a country-wide community-based cross-sectional study among a weighted sample of 8663 HHs in Ethiopia. The global spatial autocorrelation was explored using the global Moran's-I, and the local spatial autocorrelation was presented by Anselin Local Moran's-I to evaluate the spatial patterns of OD practice in Ethiopia. Hot spot and cold spot areas of OD were detected using ArcGIS 10.8. The most likely high and low rates of clusters with OD were explored using SaTScan 10.1. Geographical weighted regression analysis (GWR) was fitted to explore the geographically varying coefficients of factors associated with OD. RESULTS: The prevalence of OD in Ethiopia was 27.10% (95% CI: 22.85-31.79). It was clustered across enumeration areas (Global Moran's I = 0.45, Z-score = 9.88, P-value ≤ 0.001). Anselin Local Moran's I analysis showed that there was high-high clustering of OD at Tigray, Afar, Northern Amhara, Somali, and Gambela regions, while low-low clustering of OD was observed at Addis Ababa, Dire-Dawa, Harari, SNNPR, and Southwest Oromia. Hotspot areas of OD were detected in the Tigray, Afar, eastern Amhara, Gambela, and Somali regions. Tigray, Afar, northern Amhara, eastern Oromia, and Somali regions were explored as having high rates of OD. The GWR model explained 75.20% of the geographical variation of OD among HHs in Ethiopia. It revealed that as the coefficients of being rural residents, female HH heads, having no educational attainment, having no radio, and being the poorest HHs increased, the prevalence of OD also increased. CONCLUSION: The prevalence of OD in Ethiopia was higher than the pooled prevalence in sub-Saharan Africa. Tigray, Afar, northern Amhara, eastern Oromia, and Somali regions had high rates of OD. Rural residents, being female HH heads, HHs with no educational attainment, HHs with no radio, and the poorest HHs were spatially varying determinants that affected OD. Therefore, the government of Ethiopia and stakeholders need to design interventions in hot spots and high-risk clusters. The program managers should plan interventions and strategies like encouraging health extension programs, which aid in facilitating basic sanitation facilities in rural areas and the poorest HHs, including female HHs, as well as community mobilization with awareness creation, especially for those who are uneducated and who do not have radios.