“The World Bank provides maternal and child health data from the UN Maternal Mortality Estimation Inter-agency Group and the UN Inter-agency Group for Child Mortality Estimation, whose task has been to improve data quality and produce yearly estimates for each country. Data are primarily taken from vital registration systems (adjusting for underestimation); however, absent or incomplete data are modelled using multilevel regressions with social indicators (e.g., fertility rates, educational attainment, and health service coverage). The resulting mortality estimates are comparable across countries and years. The latest maternal mortality data were available from 2000 to 2017, and child mortality data from 2000 to 2019.”
Institute for Health Metrics and Evaluation (IHME)
Takes a while to figure out the website, but once you understand the codebook and covariate descriptions, it can get easier to find what you are looking for…
Population density (percentage of the population living in a density of >1,000 people/km\(^2\))
Raw data is in proportion – need to convert to percentage for analysis?
Urban residence (percentage of the population living in urban areas)
Raw data is in proportion – need to convert to percentage for analysis?
Male education (mean number of years of educational attainment per capita - age standardized)
Raw data consists of data stratified by sex. Due to collinearity, authors removed female education and only used male education in the model.
Population-weighted mean temperature
Population-weighted mean rainfall
International Disaster Database
Earthquake and drought data are available by selecting “Geophysical” and “Climatological” data under “Natural” disasters from the International Disaster Database (Registration required)
From the Data section: “One data source in this study was the Uppsala Conflict Data Program (UCDP) Georeferenced Event Dataset (UCDP GED) [20]. The UCDP has dedicated research teams that systematically determine the presence of conflict using definitions that are consistent and comparable across countries and years [20]. The UCDP GED uses ‘events’ as the unit of analysis, defined as ‘the use of armed force by an organised actor against another organised actor or civilians, resulting in at least one direct death for that location and time’ [20]. An armed conflict is present when events cumulatively result in 25 or more battle-related deaths within in calendar-conflict year. The UCDP GED has been used in peer-reviewed publications previously [8,10–12], and more details on its methodology can be found elsewhere [20]. The UCDP GED was aggregated into country–year observations for the analysis. Data for Palestine and Israel were omitted as the UCDP does not distinguish between them, which is problematic for attributing associated health outcomes for these states separately.”
From the Measures section: “The exposure variable of interest was armed conflict, as defined by the UCDP. Due to the complexity of measuring conflict, 4 specifications were explored in the analyses. First, the most commonly employed specification of conflict in the literature is a binary variable indicating the presence of conflict for each country–year observation (0 = no, <25 battle-related deaths; 1 = yes, \(\geq\) 25 battle-related deaths) [29]. Second, to better account for conflict intensity, this specification was expanded into a categorical variable with recommended cutoffs from the UCDP (0 = no, <25 battle-related deaths; 1 = minor conflict, 25–999 battle-related deaths; 2 = war, ≥1,000 battle-related deaths). This categorization was made for each conflict per country-year observation, meaning that countries with many minor conflicts summing to 1,000 or more battle-related deaths per year were not categorised as war-affected. The rationale behind this decision was to prevent bias where large countries with many minor conflicts, such as India, were incorrectly classified as war-affected. This decision meant that 68 country–year observations were categorised as experiencing minor conflicts despite having 1,000 or more battle-related deaths; we found no difference in models that categorised these 68 country–year observations as minor conflict or war, so we opted for the former. Third, to account for the scale of battle-related deaths relative to the population, battle-related deaths per 100,000 population was used as a continuous variable. Fourth, this continuous measure was expressed as quintiles to model a non-linear relationship between conflict and mortality [10]. It should be noted that the ‘no conflict’ category for the binary and categorical specifications referred to fewer than 25 battle-related deaths, whereas for the quintile specification it referred to no battle-related deaths.”
OECD membership (oecd_countries_2023.csv)
Although not listed in the Data Availability Statement, OECD (Organisation for Economic Co-operation and Development) membership was included as a covariate in the primary analysis (see Table 2). It is unclear which year’s membership status was used in the analysis, because over time countries were added to OECD.
Countries included in the analysis (country_included.csv)
I contacted the corresponding author of the paper, Mohammed Jawad and received the list of countries that were included in the analysis. The paper does not provide the list. However, the list I received included 186 countries (5 more than stated in the paper).
Ethnic Fractionalization Dataset (HIEF)
Ethnic Fractionalisation Index can be obtained here
Data missing in 46.0% – omitted from main model but included in sensitivity analysis
Note: Some observations are duplicated, for example, “Republic of Korea” has three observations in 2000.
library(here)
here() starts at C:/Users/ayami/OneDrive - University of Toronto/Teaching/Programming and Computation in Health
Country Year EFindex
7532 Republic of Korea 1948 0.000
7533 Republic of Korea 1949 0.000
7534 Republic of Korea 1950 0.000
7535 Republic of Korea 1951 0.000
7536 Republic of Korea 1952 0.000
7537 Republic of Korea 1953 0.000
7538 Republic of Korea 1954 0.000
7539 Republic of Korea 1955 0.000
7540 Republic of Korea 1956 0.000
7541 Republic of Korea 1957 0.000
7542 Republic of Korea 1958 0.000
7543 Republic of Korea 1959 0.000
7544 Republic of Korea 1960 0.000
7545 Republic of Korea 1961 0.000
7546 Republic of Korea 1962 0.000
7547 Republic of Korea 1963 0.000
7548 Republic of Korea 1964 0.000
7549 Republic of Korea 1965 0.000
7550 Republic of Korea 1966 0.000
7551 Republic of Korea 1967 0.000
7552 Republic of Korea 1968 0.000
7553 Republic of Korea 1969 0.000
7554 Republic of Korea 1970 0.000
7555 Republic of Korea 1970 0.000
7556 Republic of Korea 1971 0.000
7557 Republic of Korea 1972 0.000
7558 Republic of Korea 1973 0.000
7559 Republic of Korea 1974 0.000
7560 Republic of Korea 1975 0.001
7561 Republic of Korea 1976 0.001
7562 Republic of Korea 1977 0.001
7563 Republic of Korea 1978 0.001
7564 Republic of Korea 1979 0.001
7565 Republic of Korea 1980 0.001
7566 Republic of Korea 1981 0.001
7567 Republic of Korea 1982 0.001
7568 Republic of Korea 1983 0.002
7569 Republic of Korea 1984 0.002
7570 Republic of Korea 1985 0.002
7571 Republic of Korea 1986 0.003
7572 Republic of Korea 1987 0.003
7573 Republic of Korea 1988 0.003
7574 Republic of Korea 1989 0.004
7575 Republic of Korea 1990 0.004
7576 Republic of Korea 1991 0.005
7577 Republic of Korea 1992 0.006
7578 Republic of Korea 1993 0.007
7579 Republic of Korea 1994 0.008
7580 Republic of Korea 1995 0.009
7581 Republic of Korea 1996 0.010
7582 Republic of Korea 1997 0.011
7583 Republic of Korea 1998 0.013
7584 Republic of Korea 1999 0.015
7585 Republic of Korea 2000 0.017
7586 Republic of Korea 2000 0.017
7587 Republic of Korea 2000 0.017
7588 Republic of Korea 2001 0.020
7589 Republic of Korea 2002 0.023
7590 Republic of Korea 2003 0.026
7591 Republic of Korea 2004 0.030
7592 Republic of Korea 2005 0.034
7593 Republic of Korea 2006 0.039
7594 Republic of Korea 2007 0.044
7595 Republic of Korea 2008 0.050
7596 Republic of Korea 2009 0.057
7597 Republic of Korea 2010 0.065
7598 Republic of Korea 2011 0.074
7599 Republic of Korea 2012 0.084
7600 Republic of Korea 2013 0.095
Electoral Democracy Index
Available from vdemdata package
According to the codebook that can be downloaded from here, the variable name for EDI is v2x_polyarchy
Data missing in 11.8% – omitted from main model but included in sensitivity analysis