Application of Moran's test with an empirical Bayesian rate to leading health care problems in Taiwan in a 7-year period (2002-2008)

Evaluating Changes in Health Risk from Drought over the Contiguous United States

The outcomes of drought can be difficult to assess due to the complexity of its effects. While most risk assessments of drought are developed for agriculture or water resources, the associations with human health are not well studied due to unclear and complex pathways. This study is the first to assess potential changes in health risk from droughts during the last decade in the contiguous United States. To assess the risk, we spatially superimposed vulnerability variables associated with drought on historical drought exposure over the last decade. Different variations in Local Moran's I statistics were used to assess the spatial distribution of health vulnerability, risk of drought, and changes in the two five-year study periods (2010-2014 and 2015-2019). Our results show large clusters of the western United States had a significant increase in risk during the latter part of the study period due to increases in vulnerability and hazard. In addition, southern areas of the United States were consistently above the national average in drought risk. Since our vulnerability variables include agriculture, drinking water, and sociodemographic indicators, the results of this study can help various experts interested in drought preparedness efforts associated with human health.

Coupling Coordination of the Regional Economy, Tourism Industry, and the Ecological Environment: Evidence from Western China

The economic–tourism–environment (ETE) is a complex system with extensive connotations and coupling characteristics. The exact understanding of these systems forms the prerequisite and basis for regional development. This study explores the impact of the coupling coordination of the ETE system on the level and influencing factors of sustainable development in western China. An evaluation index system has been developed for assessing the coupling coordination (CC) of the ETE system based on the context of 12 provinces and cities in western China. Entropy and spatial autocorrelation methods have been used to evaluate the comprehensive benefit value and influencing factors of the ETE systems in the region. The spatial Durbin model has been used to assess empirical data from 2010–2019. The coupling coordination degree (CCD) of each subsystem was used as the main explanatory variable to quantify the spatial spillover effect. The research results show that the ETE system of the provinces and cities in western China have a strong correlation. The three subsystems interact, restrict, and promote each other, operating together in contradictory unity. The degree of coupling and coordination of the ETE systems are mainly stable but fluctuate from the time perspective. From the spatial perspective, the level of coupling and coordination have clear spatial correlation and spatial dependence. However, since 2013, the spatial dependence has significantly weakened, indicating that the level of coupling coordination is moving towards a balanced direction. The ETE subsystems not only promote the improvement of the coupling coordination of the region itself, but also optimize the coupling coordination of neighboring areas. The promotion effect of each subsystem on the degree of self-coupling and coordination is greater than the promotion effect on neighboring areas.

Environmental and socioeconomic risk factors for visceral and cutaneous leishmaniasis in São Paulo, Brazil

Leishmaniasis is a vector-borne disease caused by the protozoan Leishmania spp. mainly affecting individuals of low socioeconomic status. In tropical regions the transmission risk to humans depends not only on environmental factors, such as vegetation cover and climate, but also on the socioeconomic characteristics of human populations. However, the relative contribution of these factors to disease risk and incidence is not well understood. Yet this information is critical for the development of epidemiological surveillance schemes and control practices. Leishmaniasis cases have increased in São Paulo state, Brazil over recent years but the underlying risk factors for transmission remain understudied. Here, we use generalized linear mixed models to quantify the association between occurrence and incidence (number of cases) of cutaneous (CL) and visceral (VL) leishmaniasis from 1998 to 2015, and landscape (native vegetation cover), climate (seasonal and interannual variation in precipitation and temperature) and socioeconomic factors (population, number of cattle heads, Human Development Index - HDI, Gini inequality index and income per capita) across the 645 municipalities of São Paulo state, Brazil. For CL, probability of occurrence was greater in municipalities with high native vegetation cover and economic inequality and in years with greater average winter precipitation. For VL, probability of occurrence was greater in years with high minimum spring precipitation and maximum annual temperatures, and in municipalities with larger HDI values and a greater number of cattle heads. The number of VL cases increased during years with high mean fall precipitation and, for both CL and VL the number of cases was greater in years of high annual mean temperature. Understanding how these risk factors influence spatial and temporal variation in the risk and incidence of leishmaniasis can contribute to the development of effective public health policies and interventions.

Neglected tropical zoonotic diseases in Tigray region, northern Ethiopia: Spatial distribution and trend analysis of rabies, tuberculosis, schistosomiasis, and visceral leishmaniasis in humans

Neglected tropical zoonotic diseases (NTZDs) continue to affect the health and livelihoods of humans particularly the poor and marginalized populations in developing countries. Mapping the distribution and burden of these diseases will support making an informed decision. A retrospective study was conducted to map the spatial distribution and analyse trend of NTZDs in Tigray region, Northern Ethiopia. A health management information system (HMIS) data between 2012/13 and 2016/17 was obtained from Tigray National Regional State Health Bureau. The Quantum-GIS software was used to map the spatial distribution and burden of selected NTZDs at zonal level. Only four of the NTZDs namely rabies, tuberculosis (TB), schistosomiasis and visceral leishmaniasis (VL) from the records of HMIS data were considered. A high incidence rate (IR) at least for one of the NTZDs was reported in males above 15 years old. In the 5-year period, of the 60,099 reported NTZD cases, 30.3% (18,220), 26.6% (16,005), 23.3% (14,001), 14.4% (8,661), and 5.3% (3,212) were, respectively, schistosomiasis, extra-pulmonary TB, rabies, pulmonary TB, and visceral leishmaniasis. The year-wise analysis showed an irregular trend for the NTZDs where both the decreasing and increasing trends didn't show statistically significant variation. However, the overall regional number of TB cases showed a decreasing trend, where the decrease for extra-pulmonary TB (1.8 cases per 100,000 population) was higher than pulmonary TB (0.5 cases per 100,000 population). Similarly, the annual number of rabies and VL cases showed a decreasing trend. On the other hand, the number of schistosomiasis cases showed an increasing trend (8.2 cases per 100,000 population). The annual average number of TB (171 cases per 100,000 population), schistosomiasis (354 cases per 100,000 population), and VL (63 cases per 100,000 population) cases were much higher in Western zone compared to the rest of the zones. The incidence rate of rabies was higher in Mekelle and Southeastern (100 cases per 100,000 population) and Northwestern (97 cases per 100,000 population) zones. Intervention strategies applied in the region should take into account the zonal distribution and burden of NTZDs.

Spatial Distribution of Cancer Cases Seen in Three Major Public Hospitals in KwaZulu-Natal, South Africa

Background:

Noncommunicable diseases (NCDs) like cancer are posing a challenge in the health system especially in low- and middle-income countries (LMICs). In South Africa, cancer is under-reported due to the lack of a comprehensive cancer surveillance system. The limited knowledge on the extent of cancer burden has led to inaccurate allocation of public health resources. The aim of this study was to describe cancer incidence and spatial distribution of cancer cases seen at 3 main public oncology facilities in KwaZulu-Natal.

Methods:

In this retrospective study, cases of cancer observed from year 2015 to 2017 were extracted from medical records. The crude incidence rate was estimated for the total cancer cases and for different type of cancer reported over that period. Age-standardised incidence rates (ASR) per 100 000 was calculated per year using age groups and sex according to the district population data of KwaZulu-Natal. The comparisons of cancer diagnosed incidences were made between 11 districts using the ASR. Choropleth spatial maps and Moran’s Index were used to assess the ASR cancer spatial distribution along with geographical patterns among the districts. One sample chi-square test was used to assess the significant increase/decrease over time.

Results:

The study lost numerous cases due to incompleteness. A total of 4909 new cases were diagnosed with cancer during 2015 to 2017, 62% of which were female. Both uMgungundlovu and eThekwini districts had the highest ASR among district municipalities of KwaZulu-Natal for both male and female (83.6 per 100 000 per men year for men, 158.2 per 100 000 women per year, and 60.1 per 100 000 men per year and 96.9 per 100 000 women per year, respectively). Random distribution of reported cancer cases in KwaZulu-Natal was observed with a high concentration being in and around 2 metropolitan districts. Spatial variation showed a significant difference from year to year between the districts with the random spatial distribution. Overall, there was a significant decline of cancer incidences observed from 2015 to 2017 (P < .05) in the province.

Conclusion:

The overall cancer incidence in the study shows that female cancers (breast and cervical) are still on the rise and still need to be given priority as they were most prevalent in KwaZulu-Natal. Spatial analysis (choropleth maps) was used to show a pattern of higher concentration of cancer incidence in the north-western parts of the province.

Geo-visual integration of health outcomes and risk factors using excess risk and conditioned choropleth maps: a case study of malaria incidence and sociodemographic determinants in Ghana

Background

Recently, exploratory spatial data analysis is for problem solving, hypothesis generation and knowledge construction. Unless geographically weighted regression, sophisticated spatial regression models best control spatial heterogeneity in outcomes and the associated risk factors but cannot visually display and identify areas of the significant associations. The under-utilised excess risk maps (ERMs) and conditioned choropleth maps (CCMs) are useful to address this issue and simplify epidemiological information to public health stakeholders without much statistical backgrounds. Using malaria and sociodemographic determinants in Ghana as case study, this paper applied ERM and CCM techniques for identification of areas at elevated risk of disease-risk factor co-location.

Method

We computed and smoothed mean district-specific malaria incidences for the period 2010 to 2014 as a function of sociodemographic determinants. The spatial distribution of malaria was investigated through global and local spatial autocorrelations, and the association with sociodemographic risk factors evaluated with bivariate correlations. ERMs and CCMs were produced for the statistically significant risk factors.

Results

The incidence of malaria increased over time with cluster locations detected, predominantly at the northern parts but later few spread to the middle parts of the country. Our results suggested that with respect to sociodemographic determinants, district variations in malaria rates might be explained by inequalities in seven sociodemographics, including an unexpected significant negative association with non-religious affiliation. The sociodemographics had positive spatial autocorrelations, exhibited statistically significant interactions and the strongest was observed in urbanisation-basic education correlation (p< 0.01, r = +0.969). The ERMs and CCMs specifically identified locations with lower or higher than expected rates with respect to particular risk factor(s) where improving risk factor(s) such as employment-to-population ratio in rural areas, basic education could have cascade effects to reduce the expected malaria incidence in endemic areas.

Conclusion

Ghana remains malaria hyperendemic region with district-level spatial heterogeneity. Significant association between malaria and sociodemographics was detected and the ERMs and CCMs geo-visually pinpointed locations of these significant associations. To complement sophisticated spatial regression models, the easily interpretable ERMs and CCMs could be used to specify where disease-risk factor associations are significant, simplifying complex spatial epidemiological information for efficient public health administration.

Spatiotemporal patterns of hemorrhagic fever with renal syndrome in Hebei Province, China, 2001-2016

Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in China, where approximately 90% of the total human cases in the world are reported. The Hebei province, one of areas with the highest prevalence, has reported HFRS cases every year in the last two decades. This study describes the spatiotemporal patterns of HFRS in the Hebei province from 2001 to 2016, detects the high-risk spatiotemporal clusters of HFRS, and provides valuable information for planning and implementation of local preventive measures. For the purpose of the analysis, HFRS cases recorded during the sixteen years in the Hebei province were aggregated into three temporal periods (2001-2006, 2007-2012, and 2013-2016). Spatiotemporal analyses, including Global spatial autocorrelation analysis and Kulldorff's scan statistical analysis, were applied to analyze te spatiotemporal clusters of HFRS at the county level. The results revealed that the spatial extent of the HFRS epidemic in the Hebei province changed dynamically from 2001 to 2016, which indicated that a comprehensive preventative strategy should be implemented in the northeastern regions of the Hebei province in spring.

The Spatial Distribution of Adult Obesity Prevalence in Denver County, Colorado: An Empirical Bayes Approach to Adjust EHR-Derived Small Area Estimates

Objectives

Measuring obesity prevalence across geographic areas should account for environmental and socioeconomic factors that contribute to spatial autocorrelation, the dependency of values in estimates across neighboring areas, to mitigate the bias in measures and risk of type I errors in hypothesis testing. Dependency among observations across geographic areas violates statistical independence assumptions and may result in biased estimates. Empirical Bayes (EB) estimators reduce the variability of estimates with spatial autocorrelation, which limits the overall mean square-error and controls for sample bias.

Methods

Using the Colorado Body Mass Index (BMI) Monitoring System, we modeled the spatial autocorrelation of adult (≥ 18 years old) obesity (BMI ≥ 30 kg m²) measurements using patient-level electronic health record data from encounters between January 1, 2009, and December 31, 2011. Obesity prevalence was estimated among census tracts with >=10 observations in Denver County census tracts during the study period. We calculated the Moran's I statistic to test for spatial autocorrelation across census tracts, and mapped crude and EB obesity prevalence across geographic areas.

Results

In Denver County, there were 143 census tracts with 10 or more observations, representing a total of 97,710 adults with a valid BMI. The crude obesity prevalence for adults in Denver County was 29.8 percent (95% CI 28.4-31.1%) and ranged from 12.8 to 45.2 percent across individual census tracts. EB obesity prevalence was 30.2 percent (95% CI 28.9-31.5%) and ranged from 15.3 to 44.3 percent across census tracts. Statistical tests using the Moran's I statistic suggest adult obesity prevalence in Denver County was distributed in a non-random pattern. Clusters of EB obesity estimates were highly significant (alpha=0.05) in neighboring census tracts. Concentrations of obesity estimates were primarily in the west and north in Denver County.

Conclusions

Statistical tests reveal adult obesity prevalence exhibit spatial autocorrelation in Denver County at the census tract level. EB estimates for obesity prevalence can be used to control for spatial autocorrelation between neighboring census tracts and may produce less biased estimates of obesity prevalence.

Spatial Clustering of Severe Hand-Foot-Mouth Disease Cases on Hainan Island, China

The incidence of severe hand-foot-mouth disease (HFMD) in Southeast and East Asia has increased in recent years. This study explored spatial clusters of the incidence and proportion of severe HFMD cases on Hainan Island, where the prevalence and mortality of HFMD were the highest in China during 2011. A spatial autocorrelation statistic (Anselin's Local Moran I) was calculated for the Empirical Bayesian (EB)-smoothed dataset of severe HFMD cases. Significant spatial clusters were detected for both the incidence and proportion of severe HFMD cases. Population density was higher in spatial clusters with a high proportion of severe HFMD cases among total HFMD cases. We speculate that a higher proportion of severe HFMD cases were diagnosed in densely populated townships. This should be considered when analyzing the HFMD database of Hainan Island.

Climate change and sugarcane expansion increase Hantavirus infection risk

Hantavirus Cardiopulmonary Syndrome (HCPS) is a disease caused by Hantavirus, which is highly virulent for humans. High temperatures and conversion of native vegetation to agriculture, particularly sugarcane cultivation can alter abundance of rodent generalist species that serve as the principal reservoir host for HCPS, but our understanding of the compound effects of land use and climate on HCPS incidence remains limited, particularly in tropical regions. Here we rely on a Bayesian model to fill this research gap and to predict the effects of sugarcane expansion and expected changes in temperature on Hantavirus infection risk in the state of São Paulo, Brazil. The sugarcane expansion scenario was based on historical data between 2000 and 2010 combined with an agro-environment zoning guideline for the sugar and ethanol industry. Future evolution of temperature anomalies was derived using 32 general circulation models from scenarios RCP4.5 and RCP8.5 (Representative greenhouse gases Concentration Pathways adopted by IPCC). Currently, the state of São Paulo has an average Hantavirus risk of 1.3%, with 6% of the 645 municipalities of the state being classified as high risk (HCPS risk ≥ 5%). Our results indicate that sugarcane expansion alone will increase average HCPS risk to 1.5%, placing 20% more people at HCPS risk. Temperature anomalies alone increase HCPS risk even more (1.6% for RCP4.5 and 1.7%, for RCP8.5), and place 31% and 34% more people at risk. Combined sugarcane and temperature increases led to the same predictions as scenarios that only included temperature. Our results demonstrate that climate change effects are likely to be more severe than those from sugarcane expansion. Forecasting disease is critical for the timely and efficient planning of operational control programs that can address the expected effects of sugarcane expansion and climate change on HCPS infection risk. The predicted spatial location of HCPS infection risks obtained here can be used to prioritize management actions and develop educational campaigns.

Landscape, Environmental and Social Predictors of Hantavirus Risk in São Paulo, Brazil

Hantavirus Pulmonary Syndrome (HPS) is a disease caused by Hantavirus, which are negative-sense RNA viruses in the family Bunyaviridae that are highly virulent to humans. Numerous factors modify risk of Hantavirus transmission and consequent HPS risk. Human-driven landscape change can foster transmission risk by increasing numbers of habitat generalist rodent species that serve as the principal reservoir host. Climate can also affect rodent population dynamics and Hantavirus survival, and a number of social factors can influence probability of HPS transmission to humans. Evaluating contributions of these factors to HPS risk may enable predictions of future outbreaks, and is critical to development of effective public health strategies. Here we rely on a Bayesian model to quantify associations between annual HPS incidence across the state of São Paulo, Brazil (1993–2012) and climate variables (annual precipitation, annual mean temperature), landscape structure metrics (proportion of native habitat cover, number of forest fragments, proportion of area planted with sugarcane), and social factors (number of men older than 14 years and Human Development Index). We built separate models for the main two biomes of the state (cerrado and Atlantic forest). In both biomes Hantavirus risk increased with proportion of land cultivated for sugarcane and HDI, but proportion of forest cover, annual mean temperature, and population at risk also showed positive relationships in the Atlantic forest. Our analysis provides the first evidence that social, landscape, and climate factors are associated with HPS incidence in the Neotropics. Our risk map can be used to support the adoption of preventive measures and optimize the allocation of resources to avoid disease propagation, especially in municipalities that show medium to high HPS risk (> 5% of risk), and aimed at sugarcane workers, minimizing the risk of future HPS outbreaks.

Area variations in multiple morbidity using a life table methodology

Analysis of healthy life expectancy is typically based on a binary distinction between health and ill-health. By contrast, this paper considers spatial modelling of disease free life expectancy taking account of the number of chronic conditions. Thus the analysis is based on population sub-groups with no disease, those with one disease only, and those with two or more diseases (multiple morbidity). Data on health status is accordingly modelled using a multinomial likelihood. The analysis uses data for 258 small areas in north London, and shows wide differences in the disease burden related to multiple morbidity. Strong associations between area socioeconomic deprivation and multiple morbidity are demonstrated, as well as strong spatial clustering.