Mapping Human Vulnerability to Extreme Heat: A Critical Assessment of Heat Vulnerability Indices Created Using Principal Components Analysis

Effects of extreme temperatures on public sentiment in 49 Chinese cities

The rising sentiment challenges of the metropolitan residents may be attributed to the extreme temperatures. However, nationwide real-time empirical studies that examine this claim are rare. In this research, we construct a daily extreme temperature index and sentiment metric using geotagged posts on one of China's largest social media sites, Weibo, to verify this hypothesis. We find that extreme temperatures causally decrease individuals' sentiment, and extremely low temperature may decrease more than extremely high temperature. Heterogeneity analyses reveal that individuals living in high levels of PM2.5, existing new COVID-19 diagnoses and low-disposable income cities on workdays are more vulnerable to the impact of extreme temperatures on sentiment. More importantly, the results also demonstrate that the adverse effects of extremely low temperatures on sentiment are more minor for people living in northern cities with breezes. Finally, we estimate that with a one-standard increase of extremely high (low) temperature, the sentiment decreases by approximately 0.161 (0.272) units. Employing social media to monitor public sentiment can assist policymakers in developing data-driven and evidence-based policies to alleviate the adverse impacts of extreme temperatures.

Exploring the relationship between land use/land cover and apparent temperature in China (1996-2020): implications for urban planning

In recent decades, rising air temperatures (AT) and apparent temperatures (AP) have posed growing health risks. In the context of China's rapid urbanization and global climate change, it is crucial to understand the impact of urban land use/land cover (LULC) changes on AP. This study investigates the spatial distribution and long-term variation patterns of AT and AP, using data from 834 meteorological stations across China from 1996 to 2020. It also explores the relationship between AT, AP, and LULC in the urban core areas of 30 major cities. Study reveals that AT and AP exhibit overall high spatial similarity, albeit with greater spatial variance in AP. Notably, regions with significant disparities between the two have been identified. Furthermore, it's observed that the spatial range of high AP change rates is wider than that of AT. Moreover, the study suggests a potential bivariate quadratic function relationship between ΔT (the difference between AT and AP) and Wa_ratio and Ar_ratio, indicating the presence of a Least Suitable Curve (LSC), [Formula: see text]. Urban LULC planning should carefully avoid intersecting with this curve. These findings can provide valuable insights for urban LULC planning, ultimately enhancing the thermal comfort of urban residents.

Mapping Heat Vulnerability of a Community Mental Health Center Population

Individuals with serious mental illness are vulnerable to extreme heat due to biological, social, and place-based factors. We examine the spatial correlation of prevalence of individuals treated at a community mental health center to heat vulnerability. We applied a heat vulnerability index (HVI) to the catchment of the Connecticut Mental Health Center in New Haven, Connecticut. Geocoded addresses were mapped to correlate patient prevalence with heat vulnerability of census tracts. Census tracts closer to the city center had elevated vulnerability scores. Patient prevalence was positively correlated with HVI score (Pearson's r(44) = 0.67, p < 0.01). Statistical significance persists after correction for spatial autocorrelation (modified t-test p < 0.01). The study indicates that individuals treated at this community mental health center are more likely to live in census tracts with high heat vulnerability. Heat mapping strategies can help communicate risk and target resources at the local scale.

Associations Among Organizational Capabilities, Organizational Performance and the Medical Alliance Implementation Effect in Community Health Centers in China: A Moderated Mediation Model

Purpose

Community health centers (CHCs) are an important part of the healthcare system worldwide. Based on the dual process model of organizational capabilities, this study explores the relationship between organizational capabilities and the organizational performance of CHCs, as well as the role played by the medical alliance implementation effect.

Methods

In this study, whole-group sampling was used to extract CHCs. All 135 CHCs in 8 of 16 districts of Beijing were selected as subjects. The organizational capabilities of the CHCs and the medical alliance implementation effect were evaluated using a questionnaire survey of 1957 managers and 3622 medical staff, respectively. A pathway analysis of the mediating role of the organizational capabilities of CHCs and the moderating role of the medical alliance implementation effect was conducted using Mplus 8.0.

Results

The development capabilities had a positive impact on basic capabilities (β = 0.878, P < 0.001), and core capabilities (β = 0.952, P < 0.001), but had no direct impact on organizational performance. Basic capabilities positively affected organizational performance (β = 1.163, P < 0.001), and core capabilities negatively affected organizational performance (β =- 0.886, P = 0.004). Both basic capabilities (β =1.022, P < 0.001) and core capabilities (β =- 0.843, P = 0.005) played a mediating role in the relationship between development capabilities and organizational performance. The moderating role of the medical alliance implementation effect was not significant.

Conclusion

This study found that strengthening the organizational capabilities of CHCs can effectively improve their performance, with the development of basic capabilities being a primary concern. The medical alliance implementation effect has not had a significant impact on organizational performance, and the cooperation between CHCs and high-level hospitals should be further promoted to give full play to the medical alliance's role and improve the organizational performance of CHCs.

Socio-geographical factors and vulnerability to leptospirosis in South Brazil

BACKGROUND

Leptospirosis, caused by the Leptospira bacteria, is an acute infectious disease that is mainly transmitted by exposure to contaminated soil or water, thereby presenting a wide range of subsequent clinical conditions. This study aimed to assess the distribution of cases and deaths from leptospirosis and its association with social vulnerability in the state of Rio Grande do Sul, Brazil, between 2010 and 2019.

METHODS

The lethality rates and incidence of leptospirosis and their association with gender, age, education, and skin color were analyzed using chi-square tests. The spatial relationship between the environmental determinants, social vulnerability, and the incidence rate of leptospirosis in the different municipalities of Rio Grande do Sul was analyzed through spatial regression analysis.

RESULTS

During the study period, a total of 4,760 cases of leptospirosis, along with 238 deaths, were confirmed. The mean incidence rate was 4.06 cases/100,000 inhabitants, while the mean fatality rate was 5%. Although the entire population was susceptible, white-colored individuals, males, people of the working-age group, along with less-educated individuals, were more affected by the disease. Lethality was higher in people with dark skin, and the prime risk factor associated with death was the direct contact of the patients with rodents, sewage, and garbage. The social vulnerability was positively associated with the incidence of leptospirosis in the Rio Grande do Sul, especially in municipalities located in the center of the state.

CONCLUSIONS

It is evident that the incidence of the disease is significantly related to the vulnerability of the population. The use of the health vulnerability index showed great relevance in the evaluation of leptospirosis cases and can be used further as a tool to help municipalities identify disease-prone areas for intervention and resource allocation.

Human Health and Well-being in a Warming World

Policy Points After decades of scientific progress and growth in academic literature, there is a recognition that climate change poses a substantial threat to the health and well-being of individuals and communities both in the United States and globally. Solutions to mitigate and adapt to climate change can have important health cobenefits. A vital component of these policy solutions is that they must also take into consideration historic issues of environmental justice and racism, and implementation of these policies must have a strong equity lens.

Geographic and demographic variation in worry about extreme heat and COVID-19 risk in summer 2020

Extreme heat is a major health hazard that is exacerbated by ongoing human-caused climate change. However, how populations perceive the risks of heat in the context of other hazards like COVID-19, and how perceptions vary geographically, are not well understood. Here we present spatially explicit estimates of worry among the U.S. public about the risks of heat and COVID-19 during the summer of 2020, using nationally representative survey data and a multilevel regression and poststratification (MRP) model. Worry about extreme heat and COVID-19 varies both across states and across demographic groups, in ways that reflect disparities in the impact of each risk. Black or African American and Hispanic or Latino populations, who face greater health impacts from both COVID-19 and extreme heat due to institutional and societal inequalities, also tend to be much more worried about both risks than white, non-Hispanic populations. Worry about heat and COVID-19 were correlated at the individual and population level, and patterns tended to be related to underlying external factors associated with the risk environment. In the face of a changing climate there is an urgent need to address disparities in heat risk and develop responses that ensure the most at-risk populations are protected.

Residential and Race/Ethnicity Disparities in Heat Vulnerability in the United States

Adverse health outcomes caused by extreme heat represent the most direct human health threat associated with the warming of the Earth's climate. Socioeconomic, demographic, health, land cover, and temperature determinants contribute to heat vulnerability; however, nationwide patterns of residential and race/ethnicity disparities in heat vulnerability in the United States are poorly understood. This study aimed to develop a Heat Vulnerability Index (HVI) for the United States; to assess differences in heat vulnerability across geographies that have experienced historical and/or contemporary forms of marginalization; and to quantify HVI by race/ethnicity. Principal component analysis was used to calculate census tract level HVI scores based on the 2019 population characteristics of the United States. Differences in HVI scores were analyzed across the Home Owners' Loan Corporation (HOLC) "redlining" grades, the Climate and Economic Justice Screening Tool (CEJST) disadvantaged versus non-disadvantaged communities, and race/ethnicity groups. HVI scores were calculated for 55,267 U.S. census tracts. Mean HVI scores were 17.56, 18.61, 19.45, and 19.93 for HOLC grades "A"-"D," respectively. CEJST-defined disadvantaged census tracts had a significantly higher mean HVI score (19.13) than non-disadvantaged tracts (16.68). The non-Hispanic African American or Black race/ethnicity group had the highest HVI score (18.51), followed by Hispanic or Latino (18.19). Historically redlined and contemporary CEJST disadvantaged census tracts and communities of color were found to be associated with increased vulnerability to heat. These findings can help promote equitable climate change adaptation policies by informing policymakers about the national distribution of place- and race/ethnicity-based disparities in heat vulnerability.

A Multistate Study on Housing Factors Influential to Heat-Related Illness in the United States

As climate change increases the frequency and intensity of devastating and unpredictable extreme heat events, developments to the built environment should consider instigating practices that minimize the likelihood of indoor overheating during hot weather. Heatwaves are the leading cause of death among weather-related causes worldwide, including in developed and developing countries. In this empirical study, a four-step approach was used to collect, extract and analyze data from twenty-seven states in the United States. Three housing characteristic categories (i.e., general housing conditions, living conditions, and housing thermal inertia) and eight variables were extracted from the American Housing Survey database, ResStock database and CDC's National Environmental Public Health Tracking Network. Multivariable regression models were used to understand the influential variables, a multicollinearity test was used to determine the dependence of those variables, and then a logistic model was used to verify the results. Three variables-housing age (HA), housing crowding ratio (HCR), and roof condition (RC)-were found to be correlated with the risk of heat-related illness (HRI) indexes. Then, a logistic regression model was generated using the three variables to predict the risk of heat-related emergency department visits (EDV) and heat-related mortality (MORD) on a state level. The results indicate that the proposed logistic regression model correctly predicted 100% of the high-risk states for MORD for the eight states tested. Overall, this analysis provides additional evidence about the housing character variables that influence HRI. The outcomes also reinforce the concept of the built environment determined health and demonstrate that the built environment, especially housing, should be considered in techniques for mitigating climate change-exacerbated health conditions.

Characteristics of Households' Vulnerability to Extreme Heat: An Analytical Cross-Sectional Study from India

High ambient temperature is a key public health problem, as it is linked to high heat-related morbidity and mortality. We intended to recognize the characteristics connected to heat vulnerability and the coping practices among Indian urbanites of Angul and Kolkata. In 2020, a cross-sectional design was applied to 500 households (HHs) each in Angul and Kolkata. Information was gathered on various characteristics including sociodemographics, household, exposure, sensitivity, and coping practices regarding heat and summer heat illness history, and these characteristics led to the computation of a heat vulnerability index (HVI). Bivariate and multivariable logistic regression analyses were used with HVI as the outcome variable to identify the determinants of high vulnerability to heat. The results show that some common and some different factors are responsible for determining the heat vulnerability of a household across different cities. For Angul, the factors that influence vulnerability are a greater number of rooms in houses, the use of cooling methods such as air conditioning, having comorbid conditions, the gender of the household head, and distance from nearby a primary health centre (PHC). For Kolkata, the factors are unemployment, income, the number of rooms, sleeping patterns, avoidance of nonvegetarian food, sources of water, comorbidities, and distance from a PHC. The study shows that every city has a different set of variables that influences vulnerability, and each factor should be considered in design plans to mitigate vulnerability to extreme heat.

Assessing the Spatial Mapping of Heat Vulnerability under Urban Heat Island (UHI) Effect in the Dhaka Metropolitan Area

The urban heat island (UHI) phenomenon gets intensified in the process of urbanization, which increases the vulnerability of urban dwellers to heatwaves. The UHI-induced vulnerability to heatwaves has increased in Bangladesh during past decades. Thus, this study aims to examine the UHI and vulnerability to heatwaves in the city of Dhaka using a heat vulnerability index (HVI). The HVI is constructed using various demographic, socioeconomic, and environmental risk variables at thana level. Principal component analysis (PCA) was applied to the 26 normalized variables for each of the 41 thanas of Dhaka to prepare the HVI. Result shows that more than 60% of the city is under built-up areas, while vegetation cover and water bodies are in low proportion. Analysis of HVI shows that the very high- and high-risk zones comprise 6 and 11 thanas, while low- and very low-risk zones comprise only 5 and 8 thanas. The correlation of HVI with variables such as exposure (0.62) and sensitivity (0.80) was found to be highly positive, while adaptive capacity had a negative correlation (−0.26) with the HVI. Findings of this study can be utilized in the mitigation of UHI phenomenon and maintaining the thermal comfort of Dhaka.

Mapping Heat-Health Vulnerability Based on Remote Sensing: A Case Study in Karachi

As a result of global climate change, the frequency and intensity of heat waves have increased significantly. According to the World Meteorological Organization (WMO), extreme temperatures in southwestern Pakistan have exceeded 54 °C in successive years. The identification and assessment of heat-health vulnerability (HHV) are important for controlling heat-related diseases and mortality. At present, heat waves have many definitions. To better describe the heat wave mortality risk, we redefine the heat wave by regarding the most frequent temperature (MFT) as the minimum temperature threshold for HHV for the first time. In addition, different indicators that serve as relevant evaluation factors of exposure, sensitivity and adaptability are selected to conduct a kilometre-level HHV assessment. The hesitant analytic hierarchy process (H-AHP) method is used to evaluate each index weight. Finally, we incorporate the weights into the data layers to establish the final HHV assessment model. The vulnerability in the study area is divided into five levels, high, middle-high, medium, middle-low and low, with proportions of 3.06%, 46.55%, 41.85%, 8.53% and 0%, respectively. Health facilities and urbanization were found to provide advantages for vulnerability reduction. Our study improved the resolution to describe the spatial heterogeneity of HHV, which provided a reference for more detailed model construction. It can help local government formulate more targeted control measures to reduce morbidity and mortality during heat waves.

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement

BACKGROUND

As the world's largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety.

METHODS

Qualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee.

RESULTS

The literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020.

CONCLUSIONS

The findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments.

A Heat Vulnerability Index: Spatial Patterns of Exposure, Sensitivity and Adaptive Capacity for Urbanites of Four Cities of India

Extreme heat and heat waves have been established as disasters which can lead to a great loss of life. Several studies over the years, both within and outside of India, have shown how extreme heat events lead to an overall increase in mortality. However, the impact of extreme heat, similar to other disasters, depends upon the vulnerability of the population. This study aims to assess the extreme heat vulnerability of the population of four cities with different characteristics across India. This cross-sectional study included 500 households from each city across the urban localities (both slum and non-slum) of Ongole in Andhra Pradesh, Karimnagar in Telangana, Kolkata in West Bengal and Angul in Odisha. Twenty-one indicators were used to construct a household vulnerability index to understand the vulnerability of the cities. The results have shown that the majority of the households fell under moderate to high vulnerability level across all the cities. Angul and Kolkata were found to be more highly vulnerable as compared to Ongole and Karimnagar. Further analysis also revealed that household vulnerability is more significantly related to adaptive capacity than sensitivity and exposure. Heat Vulnerability Index can help in identifying the vulnerable population and scaling up adaptive practices.

Approaches for identifying heat-vulnerable populations and locations: A systematic review

Heat related morbidity and mortality, especially during extreme heat events, are increasing due to climate change. More Americans die from heat than from all other natural disasters combined. Identifying the populations and locations that are under high risk of heat vulnerability is important for urban planning and design policy making as well as health interventions. An increasing number of heat vulnerability/risk models and indices (HV/R) have been developed based on indicators related to population heat susceptibility such as sociodemographic and environmental factors. The objectives of this study are to summarize and analyze current HV/R's construction, calculation, and validation, evaluate the limitation of these methods, and provide directions for future HV/R and related studies. This systematic review used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and used 5 datasets for the literature search. Journal articles that developed indices or models to assess population level heat-related vulnerability or risks in the past 50 years were included. A total of 52 papers were included for analysis on model construction, data sources, weighting schemes and model validation. By synthesizing the findings, we suggested: (1) include relevant and accurately measured indicators; (2) select rational weighting methods and; (3) conduct model validation. We also concluded that it is important for future heat vulnerability models and indices studies to: (1) be conducted in more tropical areas; (2) include a comprehensive understanding of energy exchanges between landscape elements and humans; and (3) be applied in urban planning and policy making practice.

Heat illness data strengthens vulnerability maps

Background

Previous extreme heat and human health studies have investigated associations either over time (e.g. case-crossover or time series analysis) or across geographic areas (e.g. spatial models), which may limit the study scope and regional variation. Our study combines a case-crossover design and spatial analysis to identify: 1) the most vulnerable counties to extreme heat; and 2) demographic and socioeconomic variables that are most strongly and consistently related to heat-sensitive health outcomes (cardiovascular disease, dehydration, heat-related illness, acute renal disease, and respiratory disease) across 67 counties in the state of Florida, U. S over 2008–2012.

Methods

We first used a case-crossover design to examine the effects of air temperature on daily counts of health outcomes. We employed a time-stratified design with a 28-day comparison window. Referent periods were extracted from ±7, ±14, or ± 21 days to address seasonality. The results are expressed as odds ratios, or the change in the likelihood of each health outcome for a unit change in heat exposure. We then spatially examined the case-crossover extreme heat and health odds ratios and county level demographic and socioeconomic variables with multiple linear regression or spatial lag models.

Results

Results indicated that southwest Florida has the highest risks of cardiovascular disease, dehydration, acute renal disease, and respiratory disease. Results also suggested demographic and socioeconomic variables were significantly associated with the magnitude of heat-related health risk. The counties with larger populations working in farming, fishing, mining, forestry, construction, and extraction tended to have higher risks of dehydration and acute renal disease, whereas counties with larger populations working in installation, maintenance, and repair workers tended to have lower risks of cardiovascular, dehydration, acute renal disease, and respiratory disease. Finally, our results showed that high income counties consistently have lower health risks of dehydration, heat-related illness, acute renal disease, and respiratory disease.

Conclusions

Our study identified different relationships with demographic/socioeconomic variables for each heat-sensitive health outcome. Results should be incorporated into vulnerability or risk indices for each health outcome.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-12097-6.

Mapping Heat Vulnerability Index Based on Different Urbanization Levels in Nebraska, USA

Heatwaves cause excess mortality and physiological impacts on humans throughout the world, and climate change will intensify and increase the frequency of heat events. Many adaptation and mitigation studies use spatial distribution of highly vulnerable local populations to inform heat reduction and response plans. However, most available heat vulnerability studies focus on urban areas with high heat intensification by Urban Heat Islands (UHIs). Rural areas encompass different environmental and socioeconomic issues that require alternate analyses of vulnerability. We categorized Nebraska census tracts into four urbanization levels, then conducted factor analyses on each group and captured different patterns of socioeconomic vulnerabilities among resultant Heat Vulnerability Indices (HVIs). While disability is the major component of HVI in two urbanized classes, lower education, and races other than white have higher contributions in HVI for the two rural classes. To account for environmental vulnerability of HVI, we considered different land type combinations for each urban class based on their percentage areas and their differences in heat intensifications. Our results demonstrate different combinations of initial variables in heat vulnerability among urban classes of Nebraska and clustering of high and low heat vulnerable areas within the highest urbanized sections. Less urbanized areas show no spatial clustering of HVI. More studies with separation on urbanization level of residence can give insights into different socioeconomic vulnerability patterns in rural and urban areas, while also identifying changes in environmental variables that better capture heat intensification in rural settings.

The association of compound hot extreme with mortality risk and vulnerability assessment at fine-spatial scale

The frequency and intensity of compound hot extremes will be likely to increase in the context of global warming. Epidemiological studies have demonstrated the adverse effect of simple hot extreme events on mortality, but little is known about the effects of compound hot extremes on mortality. Daily meteorological, demographic, and mortality data during 2011-2017 were collected from 160 streets in Guangzhou City, China. We used distributed lag non-linear model (DLNM) to analyze the associations of different hot extremes with mortality risk in each street. Street-specific associations were then combined using a meta-analysis approach. To assess the spatial distribution of vulnerability to compound hot extremes, vulnerable characteristics at street level were selected using random forest model, and then we calculated and mapped spatial vulnerability index (SVI) at each street in Guangzhou. At street level, compared with normal day, compound hot extreme significantly increased mortality risk (relative risk(RR)=1.43, 95%CI:1.28-1.59) with higher risk for female (RR=1.54 [1.35-1.76]) and the elderly(RR for aged 65-74=1.41 [1.14-1.74]; RR for ≥75years=1.63 [1.45-1.84]) than male (RR=1.32 [1.15-1.52]) and population <65 years (RR=1.01 [0.83-1.22]). Areas with high vulnerability were in the urban center and the edge of suburban. High proportion of population over 64 years old in urban center, and high proportions of outdoor workers and population with illiteracy in suburban areas were the determinants of spatial vulnerability. We found that compound hot extreme significantly increased mortality risk at street level, which is modified by socio-economic and demographic factors. Our findings help allocate resources targeting vulnerable areas at fine-spatial scale.°.

A Systematic Review of the Development and Validation of the Heat Vulnerability Index: Major Factors, Methods, and Spatial Units

PURPOSE OF REVIEW

This review aims to identify the key factors, methods, and spatial units used in the development and validation of the heat vulnerability index (HVI) and discuss the underlying limitations of the data and methods by evaluating the performance of the HVI.

RECENT FINDINGS

Thirteen studies characterizing the factors of the HVI development and relating the index with validation data were identified. Five types of factors (i.e., hazard exposure, demographic characteristics, socioeconomic conditions, built environment, and underlying health) of the HVI development were identified, and the top five were social cohesion, race, and/or ethnicity, landscape, age, and economic status. The principal component analysis/factor analysis (PCA/FA) was often used in index development, and four types of spatial units (i.e., census tracts, administrative area, postal code, grid) were used for establishing the relationship between factors and the HVI. Moreover, although most studies showed that a higher HVI was often associated with the increase in health risk, the strength of the relationship was weak.

SUMMARY

This review provides a retrospect of the major factors, methods, and spatial units used in development and validation of the HVI and helps to define the framework for future studies. In the future, more information on the hazard exposure, underlying health, governance, and protection awareness should be considered in the HVI development, and the duration and location of validation data should be strengthened to verify the reliability of HVI.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40641-021-00173-3.