Investigating the impacts of socio-economic vulnerability indicators on COVID-19: A case study of Yazd, Iran.

Socio-economic vulnerability plays a major role in affected countries by disasters and emergencies. This study aims to identify the most effective socio-economic vulnerability indicators on COVID-19 cases and severity in the Yazd city. This study was conducted in 2022. Regarding purpose of study, different methods were applied during this research. They were including reviewing scientific research, expert panel sessions, weighting the socio-economic vulnerability indicators by Analytic Hierarchy Process (AHP) and examining the spatially relationships between vulnerability indicators and COVID-19. Excel and GIS software were applied for data analysis using local correlation coefficient. AHP analysis showed that employment, population density, buildings quality and distance from hospitals were obtained the most weight in the related indicators of socio-economic vulnerability. GIS mapping overlying showed that four socio-economic vulnerability sub indicators including percentage of immigrants, age, population density and distance from health centers had spatially relationships with COVID-19 cases and severity. Western, northern and some central regions of Yazd were identified as the COVID-19 hot spots. Local officials and health authorities should pay immediate attention to the most influential socio-econimic vulnerability indicators that are dominant in the Yazd city. They incorporate measures to the regions identified as hot spots because people who located in these areas are more vulnerable to COVID-19 and the other future natual or man-made disasters.

Impact of social and cultural factors on incidence, transmission and control of Coronavirus disease in Iran: a qualitative study.

INTRODUCTION: COVID-19 pandemic has had mixed reactions from nations, people and governments about ways to cope with, prevent and control the disease. The current study identifies social, cultural and policy factors affecting the incidence and control of Coronavirus disease in Iran. METHODS: A qualitative study consists of content analysis as well as the views of 20 experienced and knowledgeable subjects specialized in social and cultural health management. The data were gathered using three semi-structured interviews and then continued by 17 semi-structured interviews. Data analysis was done using Graneheim approach. After each interview, the recorded audio files transcript and reviewed. Then codes extracted and divided to categories and sub-categories. RESULTS: There are distinct social and cultural factors in coping with Coronavirus disease. These consisted of three categories of governance, individual and community related factors. A total of 17 subcategories and 215 primary codes that were extracted from the text of interviews as variables of the study and in relation to the research question. Ten subdomains of governance including vaccination, political issues, knowledge, support services, administrative services, transportation, health and treatment, culturalization, legislation and, managerial and financial policies impacted the spread and mitigation of the pandemic at various levels. CONCLUSION: The management of pandemics requires a comprehensive capacity for identifying and determining social and cultural criteria. A healthy partnership between governments and the community may be required to remove unnecessary obstacles that hinder public health attempt to alleviate the risk. The obtained criteria and indicators from this study may be utilized by policy makers in an attempt to strengthen protocols for mitigating pandemics. Further studies may be warranted to confirm these findings.

Social vulnerability indicators in pandemics focusing on COVID-19: A systematic literature review.

INTRODUCTION: Social factors can affect the vulnerability of disaster-prone communities. This review aimed to identify and categorize social vulnerability indicators in the COVID-19 pandemic. METHODS: This systematic review was conducted in February 2021. Bibliographies, citation databases, and other available records were investigated based on the aim of the study. The Joanna Briggs Institute (JBI) critical appraisal tools were applied for assessing the included articles retrieved through the comprehensive and systematic literature search. Descriptive and thematic analyses were done to extract the indicators affecting social vulnerability in the COVID-19 pandemic. RESULTS: Thirty-one eligible articles were included and 85 indicators of social vulnerability were extracted. The indicators were categorized in seven main categories, including; Household, community composition; Race, minority status and language; Socioeconomic status; Community health status; Public health infra-structures; Education; Information, technology and communication. CONCLUSION: Regions with higher social vulnerability experienced greater mortality rates during the COVID-19 pandemic. Additional research is needed to measure the social vulnerability index in pandemics to prioritize distribution of scarce resources and ensure effectiveness and equity for all regions of countries.

Environmental extreme temperature and daily preterm birth in Sabzevar, Iran: a time-series analysis.

OBJECTIVES: Most of the studies on the effect of heat stress on preterm birth (PTB) are conducted in temperate climates. Evidence on this effect in hot and arid countries with low and middle income is limited. This paper describes the short-term effect of exposure to the hot and cold environment on a daily number of PTB in Iran. METHODS: The daily number of PTB was obtained from all hospitals of the city. Meteorological and air pollution data from 2011 to 2017 were obtained from a metrological station in the city. A semi-parametric generalized additive model following a quasi-Poisson distribution with the distributed lag non-linear model was selected as a modeling framework for time-series analysis to simultaneously model the short-term and lagged effect of heat stress on PTB in the Sabzevar city. RESULTS: The minimum and maximum daily temperature were - 11.2 and 45.4 °C respectively. The highest risk estimate at extreme cold temperature was found for apparent temperature (relative risk (RR) 1.83; 95% CI 1.61: 2.09). This pattern was seen for both models. For extreme hot temperatures, the model with mean temperature showed the highest risk increase for both the main model and air pollution adjusted model (RR 1.36; 95% CI 1.25: 1.49). The lowest risk estimate in extremely cold conditions was found in the model with mean temperature. However, for extremely hot temperature conditions, the lowest risk estimate was found for both maximum and apparent temperature. CONCLUSION: Obstetricians working in semi-arid areas should be aware of the influence of environmental extreme temperature on the incidence of PTB.