Author: Maraqa, Beesan; Al-Shakhra, Kamal; Alawneh, Moath; Jallad, Rania; Alkaila, Mai
Title: Demographic factors associated with COVID-19-related death in Palestine Cord-id: pdf1n3ty Document date: 2021_5_18
ID: pdf1n3ty
Snippet: OBJECTIVES: Understanding the case and death rates of COVID-19 in different countries should include socio-demographic variables to better guide health policies. We analysed COVID-19 cases in the Occupied Palestinian Territories (OPT) with attention to socio-demographic factors. STUDY DESIGN: A retrospective chart review of laboratory confirmed COVID-19 cases was conducted between March and September 2020. METHODS: Demographic data such as age, gender, place of residence, pregnancy, and symptoms
Document: OBJECTIVES: Understanding the case and death rates of COVID-19 in different countries should include socio-demographic variables to better guide health policies. We analysed COVID-19 cases in the Occupied Palestinian Territories (OPT) with attention to socio-demographic factors. STUDY DESIGN: A retrospective chart review of laboratory confirmed COVID-19 cases was conducted between March and September 2020. METHODS: Demographic data such as age, gender, place of residence, pregnancy, and symptoms were analysed. Patients were divided into two outcome groups: discharged from quarantine restrictions and dead. RESULTS: A total of 15,338 confirmed cases was examined. COVID-19 cases tended to be young (48.2% were less than 30 years of age) with an average age of 34.3 ± 27.3, most were female (55.5%),and 20% smoked. Overall, 5183 (38%) were symptomatic and if pregnant, symptoms were more commonly reported (65.3%). The overall case-fatality was 0.93 [95% CI 0.83–1.04]. Males had a greater risk of death (OR = 2.7 [95%CI = 1.7–2.8], P < 0.001), as did those 60 years of age and older (OR = 52.0 [30.5–89.7], P < 0.001). CONCLUSION: Early detection of socio-demographic risk factors helps understand the case distribution and guide better planning, especially in countries with limited resources. Better targeting of interventions may help to limit more expensive interventions such as intensive care admissions and avoid deaths. Such data are also important for planning vaccination campaigns.
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