DOI: 10.3886/e237163v1
title: Typhoon-Affected Countries Mental Health, Global Health Expenditure, World Development Indicators, and Typhoon Frequency Dataset

description: This dataset integrates multiple dimensions of information, including mental health burden (from the Global Burden of Disease - GBD), global health expenditure (from WHO), socioeconomic indicators (from the World Bank), and typhoon data (from EM-DAT), for countries frequently affected by typhoons. It aims to analyze the relationship between mental health outcomes, economic conditions, and the frequency and severity of typhoon events.
description: Data weighting is important when dealing with datasets that are not equally distributed across all categories. If certain regions or countries have a disproportionate number of observations (e.g., a large number of respondents from certain countries), weights may be applied to adjust for this imbalance. Weighting is particularly relevant when using data from EM-DAT or GBD, where the frequency of typhoon events or mental health conditions may vary greatly across countries or regions.
description: Response Rates: Public data source
description: Presence of Common Scales: In this study, different types of measurement scales were used to quantify the variables:


Nominal Scale: Used for categorical variables such as country names, typhoon events, and types of disasters.


Ordinal Scale: Used for variables such as typhoon intensity (e.g., low, moderate, high) and the severity of impact.


Interval/Ratio Scales: Used for continuous variables such as GDP per capita, unemployment rate, population density, and mental health burden (prevalence, incidence, DALYs).
description: This study employed cross-sectional and longitudinal sampling methods, selecting countries frequently affected by typhoons. The sample design included 85 countries, selected based on the frequency and severity of typhoons over the past 19 years. Only countries with available mental health data from the Global Burden of Disease (GBD) study, as well as health expenditure and socioeconomic data provided by the World Health Organisation (WHO) and the World Bank (WDI), were considered.<br>The sampling process ensured that regions severely affected by disasters were included in the analysis, which helps to explore the relationship between natural disasters and mental health outcomes.
description: Residents of Typhoon-Prone Countries；<br>Populations in the Global Burden of Disease (GBD) Study；<br>Socioeconomic and Health Indicators of Affected Countries；<br>Groups Affected by Natural Disasters. Smallest Geographic Unit: The geographic unit in this study is primarily the country or region level. Data on mental health, socioeconomic indicators, and typhoon events were analyzed at the national level for each country affected by frequent typhoons. In some cases, data were disaggregated by regional or local levels to examine localized effects of typhoons on mental health and socioeconomic conditions. The geographic unit allows for cross-country comparisons, focusing on regions that are most impacted by typhoons and assessing the differences in mental health burden, health expenditure, and socioeconomic status across countries and regions.
description: web-based survey; Data Sources:


Global Burden of Disease (GBD): The data on mental health indicators, including prevalence, incidence, and Disability-Adjusted Life Years (DALYs), comes from the Global Burden of Disease Study (GBD). It includes mental health burden data across different countries, regions, genders, and age groups.


Typhoon Data (EM-DAT): Data on typhoon occurrences (including landfall time, country affected, intensity, population affected, etc.) is sourced from the EM-DAT (International Disaster Database).


Global Health Expenditure (WHO): The Global Health Expenditure Database (GHED) provides data on government health expenditure (GGHE-D), out-of-pocket (OOP) health expenditure, and other health financing indicators.


World Development Indicators (WDI): The World Bank provides socioeconomic and demographic indicators such as GDP per capita, unemployment rate, population density, urbanization rate, Gini coefficient, income level, and education level.