Pertussis (whooping cough) remains an important public health problem, especially in infants under 1 year of age, who are at the highest risk of severe illness, hospitalization, and death. Although vaccination has greatly reduced the global burden, pertussis continues to persist in many countries and remains unequally distributed across development levels.
In this study, I used data from the Global Burden of Disease (GBD), a large international research program coordinated by the Institute for Health Metrics and Evaluation (IHME) that provides standardized estimates of disease, injury, and health loss across countries, age groups, and time periods. I chose GBD because it offers comparable measures such as incidence, mortality, and disability-adjusted life years (DALYs), which helped me examine how serious pertussis burden is, compare patterns between populations, and assess changes over time even when local health data may be limited or incomplete.
GBD data reference:
https://vizhub.healthdata.org/gbd-results/
This analysis examines three indicators of pertussis burden in infants under 1 year:
- Incidence rate - the number of new pertussis cases per 100,000 population
- Mortality rate - the number of deaths caused by pertussis per 100,000 population
- DALY rate - the total health loss per 100,000 population, combining fatal and non-fatal burden
DALYs are included because they capture both premature death and health loss due to illness in a single measure. They combine years of life lost (YLLs) and years lived with disability (YLDs), providing a broader picture of disease burden than incidence or mortality alone.
The Socio-Demographic Index (SDI) is a composite indicator used in the GBD study to measure a country’s level of development. SDI is based on three components: income per capita, educational attainment, and fertility rate in individuals younger than 25 years.
Countries are classified into five SDI groups, which allows comparison of pertussis burden across different development settings:
- Low SDI - ex. Niger and Chad
- Low-middle SDI - ex. Laos and India
- Middle SDI - ex. Brazil and Egypt
- High-middle SDI - ex. China and Hungary
- High SDI - ex. United States of America and Germany
Socio-demographic index reference:
https://ghdx.healthdata.org/record/gbd-2023-socio-demographic-index-sdi
This analysis focuses on the following questions:
- How do incidence, mortality, and DALY rates differ between countries?
- How does pertussis burden vary across SDI groups?
Statistical analysis:
- Differences in pertussis burden rates were analyzed using the non-parametric test Kruskal–Wallis test because the data is not normally distributed.
- When significant differences were identified, Dunn’s post hoc test was applied for pairwise comparisons, with FDR adjustment used to account for multiple testing.
- Is there a positive or negative relationship between SDI and pertussis incidence, mortality, or DALY rates?
Statistical analysis:
- The relationship between SDI and pertussis burden indicators was examined using linear regression analysis.
- Have incidence, mortality, and DALY rates increased or decreased from 1990 to 2023?
- Are there significant turning points in the trends?
Statistical analysis:
- Temporal patterns in incidence, mortality, and DALY rates were first explored through descriptive trend analysis.
- Joinpoint regression was then used to identify statistically significant changes in trend over time.
- Were there noticeable disruptions or shifts during the pandemic and post-pandemic years?
Statistical analysis:
- Potential changes during the COVID-19 and post-pandemic period were interpreted from the descriptive trend results and joinpoint regression outputs.
This project was developed using:
- R programming language, using packages from the tidyverse such as dplyr for data manipulation and ggplot2 for data visualization
- ggstatplot for enhanced boxplot visualization
- Joinpoint Regression Program developed by the National Cancer Institute
Full report and interpretation can be accessed:
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