CARDIOVASCULAR JOURNAL OF AFRICA • Volume 33, No 5, September/October 2022 AFRICA 261 defibrillation of shockable dysrhythmias.16-18 CPR training, either through mass public training events or training targeted at family and friends of patients with high risk of sudden cardiac arrest, has been shown to increase the likelihood of bystander CPR and the quality of CPR performed.19 Similarly, public access placement of automated external defibrillators have been shown to decrease the time delays from collapse to defibrillation in OHCA.19 These interventions may be cost effective when they are targeted to areas of both a high concentration of potential victims and potential resuscitators. 18 Low- to middle-income countries (LMICs) have limited healthcare resources. For this reason, interventions should be targeted geographically and otherwise, to areas where a small investment may yield a high return on investment. Furthermore, should interventions be implemented in areas with a high incidence of disease, more lives could be saved. Unfortunately, there is currently a paucity of epidemiological data on OHCA within the South African and Western Cape settings. The aim of this study was therefore to describe the temporal and geographical distribution of OHCA in the City of Cape Town metropole in the Western Cape province of South Africa. A secondary aim was to describe OHCA locations as they relate to percutaneous coronary intervention (PCI) resources available in the metropole. Methods This study followed a retrospective, descriptive design. OHCA data over a one-year period (1 January – 31 December 2018) were extracted from the public emergency medical service (EMS) and the largest private EMS in the Western Cape province. Ethical approval for this study was obtained from the Human Research Ethics Committee of the University of Cape Town (HREC ref: 791/2018) and from the public and private EMS organisations. The study was approved with waiver of informed consent and all data were anonymised. South Africa has a dual healthcare system where insured patients are treated and transported by private EMS, and uninsured patients are transported by the provincial services as supplied by the government. However, constitutionally, no patient may be refused emergency care. TheWestern Cape province has a population of approximately 6.3 million and accounts for 11% of the total population in South Africa. Approximately 64% of the population resides in the city of Cape Town metropole, which has a population of just over four million. The Western Cape province ranks ischaemic heart disease, the most important aetiology of OHCA, 20 as one of the top three causes of death.21 All patients with OHCA over the period of 1 January to 31 December 2018 were eligible for analysis. Cases of OHCA were identified in two ways, commensurate with the systems of the EMS. For the public EMS, OHCA data were first identified from the computer-aided dispatch (CAD) system, where a patient was denoted as being ‘unresponsive’. This was necessary as there was no specific ‘cardiac arrest’ call category. Hereafter, the unique case number was linked to the corresponding electronic patient care records (ePCR), which was extracted and interrogated for the presence of OHCA. For the private EMS, cases were identified using two methods. Any cases that were denoted as cardiac arrest or ‘CPR in progress’ on the CAD system were identified and the patient report form (PRF) for the individual cases was extracted. Secondly, PRFs that were classified as resuscitation, no service declaration or were treated at an advanced life-support level were reviewed for OHCA. In both cases, OHCA was defined as a paramedic diagnosis of ‘death’ or ‘cardiac arrest’ or any instance where CPR or defibrillation was performed. We further included any patients who were unresponsive and pulseless or had a corresponding declaration-of-death form attached. Arrest location was determined by either the address given or notes related to location on the dispatch system. Where the arrest location was ambiguous or unclear, it was reported as missing. Response time was defined as the time from first call in the dispatch centre to arrival on scene. We excluded all traumatic cardiac arrests, any instances where a patient arrested in the presence of EMS or where an ambulance unit was cancelled prior to reaching the patient. Patients who were unresponsive but not pulseless (such as hypoglycaemia) were also excluded. OHCA location was obtained directly from the CAD system or as described on paper records of ambulance PRFs. In order to comply with anonymisation requirements of ethical review, data were aggregated to the closest suburb, instead of physical address. The location of PCI-capable facilities was obtained from previous studies,22,23 and manually verified to confirm that no additional facilities had been opened since. Statistical analysis Clinical data were analysed descriptively and expressed as incidence. We report in-depth clinical data elsewhere.24 This article extends these clinical analyses and expresses OHCA as a factor of time and geography to assist with public health planning. For the temporal analysis, distribution of OHCA according to time of day, day of the week and month of the year were subjected to chi-squared testing. A significance level of 95% (p < 0.05) was set. Statistical analyses were performed using SPSS version 25 (IBM Corp, Armonk, NY). For geospatial analysis, data were geocoded using the ESRI World Geocoder in ArcGIS Online (Esri, California, United States). Data were geocoded to the suburb level to protect exact patient identifiers. Data points were aggregated by areas for analysis in two manners: (1) census tract sub-place was used as a proxy for the city of Cape Town’s suburbs, and (2) in order to correct for particular biases that may occur due to the widely varying sizes of the city of Cape Town’s census tract sub-places, which could result in over-representation of some areas, data were aggregated through hexagonal tessellation (4 km2/hexagon). This can be visualised in the high occurrence of outliers. Data were subjected to cluster and outlier analysis (Anslin Moran’s I) to identify clusters with statistically significantly higher and lower incidence. Outliers are identified as areas that are part of a cluster but have an opposite trend. Therefore, a high–low outlier is an outlier cluster with a high incidence that is immediately adjacent to one with a low incidence, and vice versa. Hotspot analysis (Getis-Ord Gi) was performed on the data aggregated by tessellation. Hotspot analysis identifies spatial clusters with a statistically significant higher (hotspots)
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