Robberstad, Bjarne, et al. “Economic Evaluation of Second Generation Pneumococcal Conjugate Vaccines in Norway.” Vaccine, vol. 29, no. 47, 2011, pp. 8564–8574., doi:10.1016/j.vaccine.2011.09.025.
The researchers gave background on a seven valent pneumococcal conjugate vaccine also known as PCV7 that was part of a childhood immunization program and it decreased the infection rate substantially. They then added that two new vaccines have become available and the researchers want to study health effects, incremental costs, and cost-effectiveness of all three vaccines. They started with the older vaccine and explained its history, to which it was well received. According to the scientists, the vaccine decreased the infection rate from 47.1 per 100,000 children to 13.7 per 100,000 in just one year. They stated that the new vaccine was supported because of its cost-effectiveness. The first of the two new vaccines, PHiD-CV is a 10-valent vaccine. The second vaccine, PCV13 is a 13-valent vaccine and has recently replaced the old vaccine in the childhood immunization program. The researchers said that there is no current economic analysis for the two new vaccines. They did a good job explaining that the vaccines provide direct effects to vaccinated people and indirect effects to unprotected people. They used a Markov model to estimate costs and epidemiological burden of pneumonia for a specific birth cohort and they even provided the application they used to make it which was Microsoft Excel. They divided the model into 3 periods: pre-vaccination age, vaccination age, and post vaccination age. During the vaccination age they measured direct and indirect benefits. They found that the two new vaccines were even more effective than the old one. They also found that the two new vaccines were more cost-effective. Overall, the scientists did a good job with explaining their methods and explaining the purpose of their study. They were very detailed and included a lot of statistics. However, I think they used too many statistics and it detracted away from the study overall. They also could have avoided using so much jargon. There were too many words that were hard to understand and there were easier substitutes for them. This made it harder to read because it became boring quickly. They also stated they found that PCV7 was a cost effective vaccine even though several previous studies confirmed this and they even mentioned it in their introduction so the information is irrelevant. I found that they also focused too much on the old vaccine and not enough on the new vaccines by not touching too deep on the fine details of how the new vaccines performed.
Williams, Derek, et al. “Predicting Severe Pneumonia Outcomes in Children.” Pediatrics, vol. 138, no. 4, 2016, pp. e20161019–e20161019.
The scientists state that pneumonia is the most common serious infection in childhood and while healthcare is advanced enough in most countries to combat the infection, the risk-stratification guide disposition and management decisions are lacking. The study was conducted in the United States and the researchers stated that 1% to 4% of all pediatric emergency visits in the United States was due to pneumonia infections. The proportion of children presenting to the emergency departments with pneumonia who are hospitalized varies from 19% to 69%. The researchers suggest that there needs to be a standardized method to improve identification of children at risk for severe outcomes. They called their study the EPIC study and it was a population-based study of community-acquired pneumonia hospitalizations among children. The research was conducted in 3 children’s hospitals in Memphis, Nashville, and Salt Lake City and children were enrolled if they were hospitalized with signs or symptoms of acute infection, respiratory illness, and evidence of pneumonia. They used a model that contained a severity scale with 3 levels: severe, moderate, and mild. The severe level included children who died, developed shock requiring vasoactive medications, or required invasive mechanical ventilation. The moderate level included children admitted to the ICU who did not meet severe criteria. The mild level were the rest of the remaining children. They used 3 primary models where the first one included 20 predictor variables, the second one was a reduced model limited to the most clinically important predictors, and the final one used 9 predictors that are normally available within electronic health records. In order to reduce predictor variables, the researchers interviewed 14 physicians who were experts in childhood pneumonia. Each of the physicians assessed the importance of each predictor with a scale of 1-5 with 5 being the most important. The predictors that had an average important or very important score were retained. The reduced model was then created with only 10 predictors. In their study, the researchers found that 7% of children had a severe outcome, 14% had a moderate outcome and most were hospitalized for less than 48 hours. They admitted that not all children had complete data for all considered predictors. They found that the presence of altered mental status, chest undraping, and multi lobar or non lobar infiltrates predicted a more severe outcome. They also found that decreasing PF ratio, systolic blood pressure, and temperature; extremes of age; and increasing heart and respiratory rates were all strong predictors of pneumonia outcomes. The reduced model included all but one of these predictors which was temperature. The scientists concluded that this model would lead to safer and more efficient care that is tailored to each patient’s risk for pneumonia outcomes. Overall, the article was well written and very easy to understand. They were able to explain different factors and what the models included. It was also helpful that they explained what certain predictors meant and how they were measured.
Rasella D, Aquino R, Barreto ML. Impact of income inequality on life expectancy in a highly unequal developing country: the case of Brazil. J Epidemiol Community Health 2013;67:661-666.
The scientists set out to study the effects that income inequality have on life expectancy. They used Brazil because it is a developing countries that has a large population in poverty and an extremely rich part of the population. The scientists noted that some studies had theorized that income is one of the most important determinants of health and that it represents social class especially in richer countries. Other studies find that there is no link between income inequality and life expectancy or that income inequality is negatively correlated with life expectancy. However, since Brazil is a developing country that has both poor and rich citizens, the researchers hypothesized that there is a threshold where an effect on health is only seen when inequality is high. They used a panel dataset that contained 27 Brazilian states over a 10 year period. They used a model where life expectancy was the dependent variable and the Gini index as the independent variable. They obtained life expectancy information from the Brazilian Ministry of Health’s Information System. They also looked at Brazil’s total expenditure on health as a percentage of GDP. However, they stated that values that were missing were obtained by linear extrapolation which could lead to some minor data distortion. The scientists found that the larger the income, the longer the life expectancy. For example, the Maranhão districts average income was $254, the lowest average, with a life expectancy average of 63.8 years while the Federal district had an average income of $1060, the highest average, with an average life expectancy of 73.6 years. The data shows that as income increases, life expectancy increases. Overall the way they conducted their research was good. I had a problem understanding some of the models and terms and wished they would have done a better job of explaining them. They did manage to explain the background of Brazil very well and how unique it is when it comes to other developing countries.
Barasa, E. W., Maina, T., & Ravishankar, N. (2017). Assessing the impoverishing effects, and factors associated with the incidence of catastrophic health care payments in Kenya. International Journal for Equity in Health, 16, 31. http://doi.org.ezproxy.usd.edu/10.1186/s12939-017-0526-x
Researchers conducted a study involving healthcare expenditure in Kenya where they assessed the intensity of major healthcare spending. Their objectives were to examine the intensity of health care spending, analyze the impoverishing effect of out of pocket spending, and to explore factors that are related to healthcare expenditure. Kenya’s health sector is financed by public, private, and donor resources but over the years public and donor sources have declined. Decreased public and donor sources led to higher private financing which comes in the form of out of pocket spending. Because of the lack of other sources of income, the government decided to increase the cost of healthcare which in turn affected the public negatively. Health insurance coverage is only 17.1% of the population. The Kenyan health insurance currently makes up only 5% of the total healthcare spending which means that the quality of healthcare is low. The researchers suggest that the low quality of health insurance is the main factor for out of pocket spending. The Kenyan government also under-prioritizes healthcare. The Abuja declaration recommends that governments allocate at least 15% of their budgets to healthcare but Kenya has only allocated a total of 6.1%. Out of pocket payments were so expensive that it deterred people from getting healthcare and those that received healthcare became impoverished. A previous study estimated that 1.5 million Kenyans were pushed into poverty because of out of pocket spending. The scientists used the Kenya national poverty line as a way to analyze the impoverishing effects of out of pocket healthcare payments. Healthcare payments consisted of consultations, medicines, procedures, and cost of transportation. They also used financial surveys for patients at the local public hospitals. The researchers found that households were driven to poverty if their healthcare spending exceeded 40% of their total non-food expenditure. The scientists concluded that Kenya should prioritize extending pre-payment mechanisms to the help the poor. They also concluded that the direct costs of healthcare should be reduced and that the quality of healthcare should improve. Overall this article was well written as well as being clear and concise. Each equation they used was written out and explained so others could easily understand it. The explanations were not too long so it did not become boring. Their use of a survey was effective because it covered most of the counties in Kenya.
Yim, Jun, et al. “Contribution of Income-Related Inequality and Healthcare Utilisation to Survival in Cancers of the Lung, Liver, Stomach and Colon.” Journal of Epidemiology and Community Health, vol. 66, no. 1, 2012, p. 37.
Researchers in South Korea had noticed an increase in cancer and cancer related deaths. They also noticed that cancer was not equal in populations with low-income citizens when compared to populations with higher income citizens. The researchers objective in this study was to examine the differences in survival rates of cancer patients with low incomes and moderate to high incomes. They also focused on the degree of healthcare used by the patients. They hypothesized that patients with lower incomes would utilize healthcare less and therefore would have a shorter survival time. They observed cancer patients of different socioeconomic statuses who had lung cancer, liver cancer, stomach cancer, and colon cancer. The cases were registered from five major hospitals in South Korea and data from cancer patients was collected from the Korean National Cancer Registry. The income of the doctors employed at the hospitals determines the National Health Insurance premiums paid by patients which allowed the researchers record each patients income level. The researchers used the Kaplan-Meier method for unadjusted analyses to examine differences in the rate of survival from each type of cancer. This method is used to measure the fraction of patients living for a certain amount of time after treatment. The researchers used variables such as income status, age, gender, cancer stage and family history. According to the researchers, the characteristics of the patients were not significantly different besides age. They found that while socioeconomic status did not effect the survival rate of cancer patients in the stage of diagnosis, healthcare and family history; patients with lower income were more effected by less fatal cancers like colon cancer. They concluded that survival of less fatal cancers are influenced more strongly by income-related factors like nutrition, housing, level of healthcare utilization, and social support. They also concluded that the quality of healthcare may have differed because patients could not afford it with their insurance. The basic healthcare given by the government is not enough for low-income people to survive less fatal diseases. Even though South Korea is a well developed country, they still have problems with diseases due to healthcare expenditures. Patients of a different income level may receive healthcare but its quality is noticeably lower.