Table 2.
Partial correlations between cardiovascular disease incidence and independent variable when temperature pattern was included as the independent and confounder respectively.
| Variables | TMP partially correlated to CVDs | TMP kept constant statistically, other variables partially correlated to CVDs | ||||
| r | p | df | r | p | df | |
| TMP | −0.584 | <0.001 | 169 | – | – | – |
| Humidity | – | – | – | 0.728 | <0.001 | 189 |
| Ageing e [65] | – | – | – | 0.685 | <0.001 | 177 |
| GDP PPP | – | – | – | 0.004 | 0.952 | 189 |
| Obesity % | – | – | – | 0.410 | <0.001 | 177 |
| Urbanization | – | – | – | 0.518 | <0.001 | 189 |
All the data were log-transformed for correlation analysis.
- Included as the confounding factor.
Data source & definition: Climatically temperature pattern (TMP), °C, average yearly temperature for 30 years, 1988–2017, the World Bank Group CCKP; Cardiovascular disease (CVD) incidence rate (per 100,000) 2017, the Institute for Health Metrics and Evaluation; Ageing indexed with life expectancy at 65 year old in 2014, United Nations; Per capita GDP PPP, measured with the per capita purchasing power parity (PPP) value of all final goods and services produced within a territory in a given year, the World Bank 2014; Urbanization, measured with the percentage of population living in urban area, the World Bank 2014; Obesity prevalence, measured with the percentage of population aged 18+ with BMI equal to or over 30 kg/m2, the World Health Organization 2014.