Do chronic illnesses and poverty go hand in hand?
Ruwan JayathilakaSheron JoachimVenuri MallikarachchiNishali PereraDhanushika RanawakaPublished in: PloS one (2020)
In the global context, the health and quality of life of people are adversely affected by either one or more types of chronic diseases. The chronic pain associated with diagnosed patients may include heavy medical expenditure along with the physical and mental suffering they undergo. Usually, unbearable amounts of medical expenses are incurred, to improve or sustain the health condition of the patient. Consequently, the heavy financial burden tends to push households from a comfortable or secure life, or even from bad to worse, towards the probability of becoming poor. Hence, this study is conducted to identify the impact chronic illnesses have on poverty using data from a national survey referred as the Household Income and Expenditure Survey (HIES), with data gathered by the Department of Census and Statistics (DCS) of Sri Lanka in 2016. As such, this study is the first of its kind in Sri Lanka, declaring the originality of the study based on data collected from the local arena. Accordingly, the study discovered that married females who do not engage in any type of economic activity, in the age category of 40-65, having an educational level of tertiary level or below and living in the urban sector have a higher likelihood of suffering from chronic diseases. Moreover, it was inferred that, if a person is deprived from access to basic education in the level of education, lives in the rural or estate sector, or suffers from a brain disease, cancer, heart disease or kidney disease, he is highly likely to be poor. Some insights concluded from this Sri Lankan case study can also be applied in the context of other developing countries, to minimise chronic illnesses and thereby the probability of falling into poverty.
Keyphrases
- healthcare
- chronic pain
- mental health
- public health
- physical activity
- electronic health record
- squamous cell carcinoma
- multiple sclerosis
- machine learning
- end stage renal disease
- pulmonary hypertension
- risk assessment
- artificial intelligence
- drug induced
- south africa
- subarachnoid hemorrhage
- newly diagnosed
- ejection fraction
- health promotion
- data analysis
- lymph node metastasis
- papillary thyroid