BackgroundTransfer delay provokes prolongation of prehospital time, which contributes to treatment delay that endangers patients with ST-segment elevation myocardial infarction (STEMI). A key constraint in reducing transfer delay is the shortage of emergency healthcare workers. This study was to explore the influence of the quality and quantity of healthcare professionals at emergency medical stations on transfer delay and in-hospital mortality among STEMI patients.MethodsA cross-sectional study using mixed methods was conducted at 89 emergency stations in 9 districts in China's Shenzhen province. Based on a sample of 31 hospitals, 1,255 healthcare professionals, and 3,131 patients with STEMI, a generalized linear model was used to explore the associations between the quality and quantity of healthcare professionals and transfer delay and in-hospital mortality among STEMI patients. Qualitative data were collected and analyzed to explore the reasons for the lack of qualified healthcare professionals at emergency medical stations.ResultsThe analysis of the quantity of healthcare professionals showed that an increase of one physician per 100,000 individuals was associated with decreased transfer delay for patients with STEMI by 5.087 min (95% CI −6.722, −3.452; P < 0.001). An increase of one nurse per 100,000 individuals was associated with decreased transfer delay by 1.471 min (95% CI −2.943, 0.002; P=0.050). Analysis of the quality of healthcare professionals showed that an increase of one physician with an undergraduate degree per 100,000 individuals was associated with decreased transfer delay for patients with STEMI by 8.508 min (95% CI −10.457, −6.558; P < 0.001). An increase of one nurse with an undergraduate degree per 100,000 individuals was associated with decreased transfer delay by 6.645 min (95% CI −8.218, −5.072; P < 0.001). Qualitative analysis illustrated that the main reasons for low satisfaction of healthcare professionals at emergency medical stations included low income, limited promotion opportunities, and poor working environment.ConclusionsThe quantity and quality of emergency healthcare professionals are key factors influencing transfer delay in STEMI patients. The government should increase the quantity of healthcare professionals at emergency medical stations, strengthen the training, and improve their performance by linking with clinical pathways to enhance job enthusiasm among emergency healthcare professionals.

Background Allocation of healthcare resources has a great influence on treatment and outcome of patients. This study aimed to access the inequality of ambulance allocation across regions, and estimate the associations between ambulance density and pre-hospital transfer time and mortality of acute coronary syndromes (ACS) patients. Methods This cross-sectional study was based on an integrated database of electronic medical system for 3588 ACS patients from 31 hospitals, ambulance information of 89 emergency medical stations, and public geographical information of 8 districts in Shenzhen, China. The primary outcomes were the associations between ambulance allocation and transfer delay and in-hospital mortality of ACS patients. The Theil index and Gini coefficient were used to assess the fairness and inequality degree of ambulance allocation. Logistic regression was used to model the associations. Results There was a significant inequality in ambulance allocation in Shenzhen (Theil index: 0.59), and the inequality of inter-districts (Theil index: 0.38) was greater than that of intra-districts (Theil index: 0.21). The gap degree of transfer delay, ambulance allocation, and mortality across districts resulted in a Gini coefficient of 0.35, 0.53, 0.65, respectively. Ambulance density was negatively associated with pre-hospital transfer time (OR = 0.79, 95%CI: 0.64,0.97, P = 0.026), with in-hospital mortality (OR = 0.31, 95%CI:0.14,0.70, P = 0.005). The ORs of Theil index in transfer time and in-hospital mortality were 1.09 (95%CI:1.01,1.10, P < 0.001) and 1.80 (95%CI:1.15,3.15, P = 0.009), respectively. Conclusions Regional inequities existed in ambulance allocation and has a significant impact on pre-hospital transfer delay and in-hospital mortality of ACS patients. It was suggested to increase the ambulance accessibility and conduct health education for public.

BACKGROUND Associations between short-term exposure to ambient particulate matter (PM) air pollutants and mortality or hospital admissions have been well-documented in previous studies. Less is known about the associations of hourly exposure to PM air pollutants with ambulance emergency calls (AECs) for all causes and specific causes by conducting a case-crossover study. In addition, different patterns of AECs may be attributed to different seasons and daytime or nighttime periods. OBJECTIVE In this study, we quantified the risk of all-cause and cause-specific AECs associated with hourly PM air pollutants between January 1, 2013, and December 31, 2019, in Shenzhen, China. We also examined whether the observed associations of PM air pollutants with AECs for all causes differed across strata defined by sex, age, season, and the time of day. METHODS We used ambulance emergency dispatch data and environmental data between January 1, 2013, and December 31, 2019, from the Shenzhen Ambulance Emergency Centre and the National Environmental Monitor Station to conduct a time-stratified case-crossover study to estimate the associations of air pollutants (ie, PM with an aerodynamic diameter less than 2.5 µm [PM_2.5] or 10 µm [PM₁₀]) with all-cause and cause-specific AECs. We generated a well-established, distributed lag nonlinear model for nonlinear concentration response and nonlinear lag-response functions. We used conditional logistic regression to estimate odds ratios with 95% CIs, adjusted for public holidays, season, the time of day, the day of the week, hourly temperature, and hourly humidity, to examine the association of all-cause and cause-specific AECs with hourly air pollutant concentrations. RESULTS A total of 3,022,164 patients were identified during the study period in Shenzhen. Each IQR increase in PM_2.5 (24.0 µg/m³) and PM₁₀ (34.0 µg/m³) concentrations over 24 hours was associated with an increased risk of AECs (PM_2.5: all-cause, 1.8%, 95% CI 0.8%-2.4%; PM₁₀: all-cause, 2.0%, 95% CI 1.1%-2.9%). We observed a stronger association of all-cause AECs with PM_2.5 and PM₁₀ in the daytime than in the nighttime (PM_2.5: daytime, 1.7%, 95% CI 0.5%-3.0%; nighttime, 1.4%, 95% CI 0.3%-2.6%; PM₁₀: daytime, 2.1%, 95% CI 0.9%-3.4%; nighttime, 1.7%, 95% CI 0.6%-2.8%) and in the older group than in the younger group (PM_2.5: 18-64 years, 1.4%, 95% CI 0.6%-2.1%; ≥65 years, 1.6%, 95% CI 0.6%-2.6%; PM₁₀: 18-64 years, 1.8%, 95% CI 0.9%-2.6%; ≥65 years, 2.0%, 95% CI 1.1%-3.0%). CONCLUSIONS The risk of all-cause AECs increased consistently with increasing concentrations of PM air pollutants, showing a nearly linear relationship with no apparent thresholds. PM air pollution increase was associated with a higher risk of all-cause AECs and cardiovascular diseases–, respiratory diseases–, and reproductive illnesses–related AECs. The results of this study may be valuable to air pollution attributable to the distribution of emergency resources and consistent air pollution control.