Introduction and Objectives

The Philippines is a low-middle income country with a wide socio-economic gap and poor primary healthcare. Climate change has shown that it is especially susceptible to its adverse effects. While there are systems, plans and policies for monitoring climate and health separately, there is still no system that links both specifically for long-term monitoring and policy making. This study aimed to assess and visualize climate and disaster health risk of 10 Philippine Provinces using a spatiotemporal approach. The specific objectives were as follows:

• To identify diseases among the top 48 (representing 80% of the burden) related to temperature, rainfall, and extreme weather events.
• To establish relationships between climate indicators and extreme weather events, and selected diseases
• To visualize average temperature and rainfall, average disease rates, and population status of each province

Methods

The study was divided into 2 phases. The first phase was a rapid review of health outcomes related to temperature, rainfall, and extreme weather events. Studies included here were not only limited to the Philippines, but in other countries with similar contexts. The second phase used a Generalized Linear Mixed Modeling to determine the climate and disease associations. Data used here were climate data from PAGASA and ERA5-Land gridded monthly data. It looked into the monthly municipal mean, minimum, and maximum data for temperature, humidity, rainfall, windspeed, and NDVI. The health outcomes used were the incidence rate and mortality rate based on the DOH-PIDSR counts and PSA deaths.

Results and Discussion

From the 48 most burdensome diseases, 11 were identified in literature as climate and disaster sensitive. These were further specified into 14 diseases, namely: dengue, unspecified diarrheal disease, acute bloody diarrhea, cholera, typhoid fever, pneumonia, bronchitis, tuberculosis, ischemic stroke, diabetes mellitus, asthma, ischemic heart disease, hypertensive heart disease, and dermatitis. Most diseases showed no clear upward or downward trend from 2009-2019. The spatial analysis showed mostly highly dense or urbanized cities were more likely to be spatial outliers or hotspots for high disease burden, even for non-infectious diseases. Infectious diseases also showed high incidence during the start of the rainy season, apart from cholera, which was highest during the hot season.

Recommendations and Conclusion

While some of the study results contrast with literature, this is likely due to differences in study design, methods, and granularity of exposure. Despite this, there are still significant associations found using the information available. Thus, the 14 diseases identified warrant further investigation for their associations with climate and disasters. With the impact of climate change increasing, the need for planetary health interventions is now more important than ever. The data from this study can serve as a baseline for research and eventually policies and interventions.