Heat Vulnerability Index Mapping for Milwaukee and Wisconsin

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.

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.

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.

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

BACKGROUND

Extreme heat poses current and future risks to human health. Heat vulnerability indices (HVIs), commonly developed using principal components analysis (PCA), are mapped to identify populations vulnerable to extreme heat. Few studies critically assess implications of analytic choices made when employing this methodology for fine-scale vulnerability mapping.

OBJECTIVE

We investigated sensitivity of HVIs created by applying PCA to input variables and whether training input variables on heat-health data produced HVIs with similar spatial vulnerability patterns for Detroit, Michigan, USA.

METHODS

We acquired 2010 Census tract and block group level data, land cover data, daily ambient apparent temperature, and all-cause mortality during May-September, 2000-2009. We used PCA to construct HVIs using: a) "unsupervised"-PCA applied to variables selected a priori as risk factors for heat-related health outcomes; b) "supervised"-PCA applied only to variables significantly correlated with proportion of all-cause mortality occurring on extreme heat days (i.e., days with 2-d mean apparent temperature above month-specific 95th percentiles).

RESULTS

Unsupervised and supervised HVIs yielded differing spatial vulnerability patterns, depending on selected land cover input variables. Supervised PCA explained 62% of variance in the input variables and was applied on half the variables used in the unsupervised method. Census tract-level supervised HVI values were positively associated with increased proportion of mortality occurring on extreme heat days; supervised PCA could not be applied to block group data. Unsupervised HVI values were not associated with extreme heat mortality for either tracts or block groups.

DISCUSSION

HVIs calculated using PCA are sensitive to input data and scale. Supervised HVIs may provide marginally more specific indicators of heat vulnerability than unsupervised HVIs. PCA-derived HVIs address correlation among vulnerability indicators, although the resulting output requires careful contextual interpretation beyond generating epidemiological research questions. Methods with reliably stable outputs should be leveraged for prioritizing heat interventions. https://doi.org/10.1289/EHP4030.

Local Extreme Heat Planning: an Interactive Tool to Examine a Heat Vulnerability Index for Philadelphia, Pennsylvania

Exposure to extreme heat contributes to high morbidity and mortality relative to other climate hazards. The city of Philadelphia, PA is particularly vulnerable to the impacts of extreme heat, due to the urban heat island effect and high prevalence of sensitive populations. We developed a heat vulnerability index, which identified priority areas that are most at-risk of experiencing adverse heat-related health outcomes and in need of preparedness and mitigation interventions. An interactive website was created to display the maps and allow the public to navigate the data with links to potential resources for relief from extreme heat days. Such methods can be adapted for other cities that wish to identify and target long-term priority areas.

Housing as a critical determinant of heat vulnerability and health

Municipalities use Heat Vulnerability Indices (HVIs) to quantify and map relative distribution of risks to human health in the event of a heatwave. These maps ostensibly allow public agencies to identify the highest-risk neighborhoods, and to concentrate emergency planning efforts and resources accordingly (e.g., to establish the locations of cooling centers). The method of constructing an HVI varies by municipality, but common inputs include demographic variables such as age and income - and to some extent, metrics such as land cover. However, taking demographic data as a proxy for heat vulnerability may provide an incomplete or inaccurate assessment of risk. A critical limitation in HVIs may be a lack of focus on housing characteristics and how they mediate indoor heat exposure. To provide an objective assessment of this limitation, we first reviewed HVIs in the literature and those published or commissioned by municipalities. We subsequently verified that most of these HVIs excluded housing factors. Next, to scope the potential consequences, we used physics-based simulations of housing prototypes (46,000 housing permutations per city) to estimate the variation in indoor heat exposure within high-vulnerability neighborhoods in Boston and Phoenix. The results show that by excluding building-level determinants of exposure, HVIs fail to capture important components of heat vulnerability. Moreover, we demonstrate how these maps currently overlook important nuances regarding the impact of building age and air conditioning functionality. Finally, we discuss the challenges of implementing housing stock characteristics in HVIs and propose methods for overcoming these challenges.

Epidemiologic Survey of Legionella Urine Antigen Testing Within a Large Wisconsin-Based Health Care System

Purpose

Legionella pneumophila pneumonia is a life-threatening, environmentally acquired infection identifiable via Legionella urine antigen tests (LUAT). We aimed to identify cumulative incidence, demographic distribution, and undetected disease outbreaks of Legionella pneumonia via positive LUAT in a single eastern Wisconsin health system, with a focus on urban Milwaukee County.

Methods

A multilevel descriptive ecologic study was conducted utilizing electronic medical record data from a large integrated health care system of patients who underwent LUAT from 2013 to 2017. A random sample inclusive of all positive tests was reviewed to investigate geodemographic differences among patients testing positive versus negative. Statistical comparisons used chi-squared or 2-sample t-tests; stepwise regression followed by binary logistic regression was used for multivariable analysis. Positive cases identified by LUAT were mapped to locate hotspots; positive cases versus total tests performed also were mapped by zip code.

Results

Of all LUAT performed (n=21,599), 0.68% were positive. Among those in the random sample (n=11,652), positive cases by LUAT were more prevalent in the June-November time period (86.2%) and younger patients (59.4 vs 67.7 years) and were disproportionately male (70.3% vs 29.7%) (P<0.0001 for each). Cumulative incidence was higher among nonwhite race/ethnicity (1.91% vs 1.01%, P<0.0001) but did not remain significant on multivariable analysis. Overall, 5507 tests were performed in Milwaukee County zip codes, yielding 82 positive cases by LUAT (60.7% of all positive cases in the random sample). A potential small 2016 outbreak was identified.

Conclusions

Cumulative incidence of a positive LUAT was less than 1%. LUAT testing, if done in real time by cooperative health systems, may complement public health detection of Legionella pneumonia outbreaks.

Is Sensible Heat Flux Useful for the Assessment of Thermal Vulnerability in Seoul (Korea)?

Climate change has led to increases in global temperatures, raising concerns regarding the threat of lethal heat waves and deterioration of the thermal environment. In the present study, we adopted two methods for spatial modelling of the thermal environment based on sensible heat and temperature. A vulnerability map reflecting daytime temperature was derived to plot thermal vulnerability based on sensible heat and climate change exposure factors. The correlation (0.73) between spatial distribution of sensible heat vulnerability and mortality rate was significantly greater than that (0.30) between the spatial distribution of temperature vulnerability and mortality rate. These findings indicate that deriving thermally vulnerable areas based on sensible heat are more objective than thermally vulnerable areas based on existing temperatures. Our findings support the notion that the distribution of sensible heat vulnerability at the community level is useful for evaluating the thermal environment in specific neighbourhoods. Thus, our results may aid in establishing spatial planning standards to improve environmental sustainability in a metropolitan community.

A Raster-Based Subdividing Indicator to Map Urban Heat Vulnerability: A Case Study in Sydney, Australia

Assessing and mapping urban heat vulnerability has developed significantly over the past decade. Many studies have mapped urban heat vulnerability with a census unit-based general indicator (CGI). However, this kind of indicator has many problems, such as inaccurate assessment results and lacking comparability among different studies. This paper seeks to address this research gap and proposes a raster-based subdividing indicator to map urban heat vulnerability. We created a raster-based subdividing indicator (RSI) to map urban heat vulnerability from 3 aspects: exposure, sensitivity and adaptive capacity. We applied and compared it with a raster-based general indicator (RGI) and a census unit-based general indicator (CGI) in Sydney, Australia. Spatial statistics and analysis were used to investigate the performance among those three indicators. The results indicate that: (1) compared with the RSI framework, 67.54% of very high heat vulnerability pixels were ignored in the RGI framework; and up to 83.63% of very high heat vulnerability pixels were ignored in the CGI framework; (2) Compared with the previous CGI framework, a RSI framework has many advantages. These include more accurate results, more flexible model structure, and higher comparability among different studies. This study recommends using a RSI framework to map urban heat vulnerability in the future.