2.0 Methods

2.1 Study context

The province of BC is located on the west coast of Canada. It spans nearly one million km2 and had a 2021 population of approximately 5.2 million people (Government of British Columbia, 2021). The climate in BC is generally temperate, and most homes do not have air conditioning, especially in more densely populated southern coastal areas (BC Hydro, 2020).
The 2021 EHE was caused by a high-pressure system known as a ‘heat dome’ (Philip et al., 2022) and was characterized primarily by unprecedented high temperatures, and secondarily by increased concentrations of ground-level ozone (O3) and fine particulate matter (PM2.5) in many regions (Table S1). Specifically, this event resulted in daily high temperatures 10-20°C above seasonal norms across the province (Figure 1; Table S1) and it happened following the summer solstice, when daylight ranges from ~16 to ~19 hours, south to north (Henderson, McLean, et al., 2022; Slattery, 2016; Philip et al. 2022). More than half of the provincial population (~3.05 million) lives in the greater Vancouver region, where more than half of the deaths occurred (Figure 2). This area was under an air quality advisory during the EHE because of high ground-level O3 concentrations (Henderson, McLean, et al., 2022). We consider the overall impact of the EHE period in this study, which includes the effects of both extreme heat and increased air pollution.

2.2 Study design

This study compares adults who died during the EHE with adults who died during more typical summer weather to identify differences between the two groups with respect to 26 chronic diseases. The EHE period was defined as 25 June – 02 July 2021 because this is when there was statistically significant excess daily mortality (Figure 1). Statistical significance was determined using the Public Health Intelligence for Disease Outbreak (PHIDO) algorithm, which compares observed daily mortality counts with the expected range based on a model using data from the past five years (Henderson et al., 2021). The PHIDO method was developed by BCCDC, and is similar to other anomaly detection techniques such as the Farrington method (Salmon et al., 2016). The typical weather period was defined as 25 June – 02 July from 2012-2020, so that comparator deaths were drawn from the same dates as the EHE deaths. We used data from 2012 onward to ensure that we included at least four typical weather deaths for each EHE death in the analyses. Using an approximate 4:1 ratio maximized statistical power while minimizing the time between the EHE and typical weather groups (Foppa & Spiegelman, 1997; Gordis, 2014).

2.3 The COVID-19 Data Library

During the COVID-19 pandemic, BC established the COVID-19 Data Library (Wilton et al., 2022) to support rapid public health informatics. This platform facilitates individual-level linkage between multiple administrative databases. BC has a single-payer healthcare system, and health records are captured in different datasets for every individual covered by the provincial Medical Services Plan. Datasets are linkable by a unique and anonymous patient master key. The BCCDC obtained authorization from the BC Ministry of Health to use the COVID-19 Data Library to generate evidence about the public health impacts of the EHE.

2.4 Mortality

Deaths were extracted from BC vital statistics records in the COVID-19 Data Library. Each record includes one underlying cause of death, age, sex, and geographic health unit of residence. The underlying cause of death is coded by the BC Vital Statistics Agency (BCVSA) according to the 10th revision of the International Classification of Diseases (ICD-10), based on information from the certificate of death. Deaths among children (<18 years) were excluded from the study because the BC Coroners Service (BCCS) reported that all heat-related deaths during the 2021 EHE occurred among adults (BC Coroners Service, 2022a). Deaths missing information on age, sex, or location were also excluded.

2.5 Chronic disease registries

The BC Ministry of Health maintains 26 administrative chronic disease registries based on individual patterns of healthcare usage. They reflect the prevalence of conditions such as asthma, heart disease, and diabetes. Each registry has different publicly available inclusion criteria (BCCDC, 2022). All registries are updated annually at the end of the BC fiscal year (31 March), with a 1-year lag period. As such, data for this study were available to 31 March 2020. To ensure comparability between the EHE and typical weather deaths, we matched all deaths to the chronic disease registries that ended on 31 March of the year prior to the death.
Individuals with multiple chronic diseases are included in multiple registries. We linked each death in the EHE and typical weather groups with all 26 registries to capture chronic conditions for each decedent at the time of death. The underlying cause of death in the vital statistics data is independent of the information derived from the chronic disease registries. As such, the cause of death may not reflect any of the chronic conditions of the decedent at the time of death. Registries were excluded if they were associated with <1% of EHE deaths or if they were collinear with another registry (≥ 85% overlap).

2.6 Statistical analyses

We compared EHE deaths with typical weather deaths using conditional logistic regression. Models were conditioned on geographic health unit (N=16) of residence (Government of British Columbia, 2022) so EHE deaths were compared with others in the same area (Figure 2). We report odds ratios (ORs) to quantify the association between EHE mortality and each chronic disease adjusted for age, sex, and all other chronic diseases (Eq.1). The supplementary appendix includes results for each chronic disease adjusted for age and sex, but not the other chronic diseases (Table S2).
\begin{equation} \mathbf{Eq.1.}\ \ EHE\ Death\ |\ Geographic\ Health\ Unit=\text{Chronic\ Disease}_{1}+\text{Chronic\ Disease}_{2}+\ldots+\text{Chronic\ Disease}_{21}+Age+Sex\nonumber \\ \end{equation}
Decedents were also classified by their total number of chronic diseases (maximum 10+) to assess the overall burden of disease effects. Conditional logistic regression was used to estimate the OR for EHE mortality associated with the number of chronic diseases, with 0 as the reference category (Eq.2).
\begin{equation} \mathbf{Eq.2.\ }\ EHE\ Death\ |\ Geographic\ Health\ Unit=Number\ of\ Comorbidities+Age+Sex\nonumber \\ \end{equation}

2.7 Subgroup analysis

The BC Coroners Service (BCCS) investigates all unattended deaths, public deaths, deaths among children, and deaths otherwise reported by healthcare providers or the public. For all such deaths, the BC Vital Statistics Agency (BCVSA) must receive the coroner’s certificate of death before assigning the underlying cause of death in the vital statistics data. There are often long reporting delays (months or years) between the two agencies, and BCVSA assigns the ICD-10 code R99 as the underlying cause of death while it is waiting to receive a coroner’s certificate of death. BCCS investigated hundreds of deaths that occurred during the EHE and reported that 562 were due to extreme heat from 25 June – 02 July 2021. The role of extreme heat was assessed using a protocol to review the physical and circumstantial evidence available for each case (BC Coroners Service, 2022a).
When BCVSA receives a coroner’s certificate indicating death due to extreme heat, it codes X30 as the underlying cause of death. BCVSA cannot code X30 for certificates of death completed by anyone other than a coroner. When it receives a certificate of death indicating extreme heat from another source, it forwards the case to the BCCS for investigation. We extracted the vital statistics data for the study on 30 November 2022. At this time, BCVSA had not yet received all the EHE death certificates from BCCS, so some of the heat-related deaths were coded as X30 and others were still coded as R99. To assess how chronic diseases varied among heat-related deaths (X30), deaths with a pending or unknown underlying cause (R99), and non-heat-related deaths (not X30 or R99), we reran regression models separately for each of the three subgroups.