In 2011, Song and other researchers studied how social determinants influence the statistics on diseases. They studied data from American Community Center to learn more about AIDS. Trying to understand how socioeconomic factors correlate with the diagnosis rates of AIDS, they found out that these rates often correlate with a population density and marital status. Other factors that affect AIDS statistics are unemployment and the level of education.
Socioeconomic status is a common cause of heart diseases and cardiovascular disease in particular. Such a correlation is observed in both developing and developed countries. Considering the risk of cardiovascular disease, studies showed that one of the most important determinants is education. Another common reason for such a disease is smoking. According to Janati et al (2011), Reddy et al (2002), Thurston et al (2005), and other researchers, high levels of cardiovascular risk factors are often related to low socioeconomic status.
Another research was carried out by Lantz et al (1998). According to such data, levels of income and education were correlated with the mortality rate. This research was focused on people who are in the risk category and considered their health, education, behavior, and income.
In 2012, Habib et al decided to study impacts of demographic, social, geographic, and economic factors on bronchial asthma. According to the analysis of Kashmir valley in SPSS, women who work with animals, men who work in farms and non-smokers demonstrated higher rates of bronchial asthma. There was also a significant correlation between asthma and age.
In 2004-2005, the Federal Bureau of Statistics published its “The Pakistan Social and living standard”. This publication was used by Arif and Naheed in 2012, in order to understand which geographical, socioeconomic, environmental, and demographic factors have an impact on diarrhea morbidity among children. According to results of the study, diarrhea morbidity correlates with several economic factors, such as housing conditions and ownership of livestock and land. Other factors that cause a significant impact on statistics are the age of a mother, age and gender of children, sources of drinking water, education, and the number of children in a family.
In São Paulo, Brazil, Aranha et al (2011) conducted a survey to understand how socioeconomic status of families, parents’ education, and income affect children’s respiratory diseases. According to the results of Chi square test, children of parents who have higher educational level are healthier. Mothers’ education was a factor of highest importance, while the income of a family didn’t correlate with reports on respiratory diseases.
In 2000, Laxminarayan and Deolalikar decided to study the influence of socioeconomic factors on how diseases transmit in Cambodia. They used information from the Cambodia Socioeconomic Survey (1997). According to obtained statistics, infectious diseases were the most common cause of death in this country. At the same time, younger citizens were infected less often. Such a risk increased as people got older. Among the most important factors that affected morbidity were the availability of a doctor and the level of income.
Obesity demonstrated a strong correlation with education and occupational positions in different countries. Under conditions of increasing obesity, people tend to eat cheaper, low-quality food. Among men, the most significant factors that increase obesity were education and high levels of occupational positions. Meantime, women demonstrated almost equal chances to become obese, regardless of their income.
In 2011, Yin et al analyzed information from the China Chronic Disease Risk Factor Surveillance (2007). They considered Chinese people of both genders, aged 15-69, in order to study the correlation between various socioeconomic factors and chronic obstructive pulmonary disease (COPD). They used multivariable logistic regression method of modeling and found out that COPD was more common among people with low educational and income levels.
Siponen et al (2011) studied health condition of Finnish children less than 12 years old. They considered such socioeconomic factors as working status, family income, and educational level. According to a survey that was based on Pearson’s Chi-Square tests, there was no correlation between such factors and health of children.
In 2007, Washington State Departments of Health conducted a survey among adult people of Washington, considering effects of obesity, smoking, lack of fruits and vegetables, low income and low education. According to results, women from groups where smoking is unacceptable for women died less often than women in social groups where smoking isn’t denounced. The lack of proper medication was related to increased problems with health. Moreover, people of higher social status had much better medical support, and more ways to get a qualitative medical care, while lower social positions received health care of bad quality. Low social classes also demonstrated an increased level of mortality. These negative factors increase among people who experience racism.
In 2012, Hosseinpoor et al published data on about 232,000 adults in 48 countries, considering sex and the level of income. They used cross tabulations, robust variance, and a Poisson regression model. According to such data, men were smoking and drinking alcohol more often than women, while women demonstrated less physical activity. Low consumption of vegetables and fruits was another significant factor that affected health statistics.
According to Braveman (2011), there is a stable relationship between health, income, and education. Health goes better as education and income increase. Along with the increase in income, people experience less stressful situations.
In 1997, Lee studied how mortality and diseases among Union army recruits are related to such factors as occupation, age, household wealth, and nativity. Results were obtained with the use of logistic regression. According to the results of the study, factors of mortality among recruits were different than those among civilians. Only diseases determined by nutrition were related to wealth. Along with this, migration and disease environments had a significant impact on mortality and morbidity rates.
In 2012, Ghias and other researchers studied HCV-positive patients living in Pakistan. Researchers studied various social and demographic risk factors, using neural networks and logistical regression. According to their study, HCV infection was spreading under the influence of migration, education, hepatitis C, injections, family size, surgery, tattooing, and blood transfusion.