Blog 5

Summary: The article I read was “Labor migration and child mortality in Mozambique” by Scott T. Yabiku, Victor Agadjanian, and Boaventura Cau. The purpose of the article was to determine the relationship between labor migration and child mortality in Mozambique. First, they determined the variables, male migration for labor and male not migrating for labor.

Personal take on summary: This paper was interesting because it isn’t an obvious topic like other papers I read. Like I’ve never heard of Mozambique before, so it was interesting to see and read about how they still have this labor work that they must leave their family for. I also found it interesting that the researchers are comparing the labor to child mortality because at a glance I wouldn’t even have seen the correlation.

Background/ Personal take on the background: The background was hard to find so I’ll have to say that the background is that in Mozambique families that migrate for labor have a correlation to child mortality.

Rationale: The rationale is trying to find the correlation of how one single family member leaving would be associated with child mortality. Also, specifically how the prominent male figure leaving affects child mortality.

Personal take on the rationale: I really like this rationale because it is something that I have not thought about before but makes a lot of sense. I’m glad I came across this article because it made me think about other kinds of abstract and different variables that could affect child mortality.

Methods: The methods used were surveys done by married rural women in southern Mozambique. “56 villages of four districts of Gaza Province; one woman per household was interviewed. In total, 1680 women aged 18–40 were interviewed” (Scott Y, Victor A, Boaventura C).

Personal take on the methods: I feel that the researchers shouldn’t have only surveyed the women. If there were men in the villages they should have got them too, maybe had the data separated. However, that number of participants is reasonable, and I feel is acceptable.

Results: “Of the 1932 children at risk of death between the ages of 0 and 5 from 2006 to 2009, 8% died. Almost 40% of the children had a father who was a labor migrant.” (Scott Y, Victor A, Boaventura C).

Personal take on the Results: The results were a little vague and didn’t have outright results. However, from the numbers, it’s hard to conclude or not if there is a relationship between labor migrants and child mortality.

Graphs & Tables: The tables that the article had were easy to read and were straightforward so good job on the graphs.

Conclusion: To conclude the article was alright, it was interesting in the way that it tested a variable I wouldn’t have ever thought about but beyond that, the paper was standard nothing too different from what I expect from a paper. Not bad but not the most enjoyable read.
Yabiku, et al. “Labor Migration and Child Mortality in Mozambique.” Social Science &Amp; Medicine, vol. 75, no. 12, 2012, pp. 2530–2538.

Blog 4

Summary: The article I read was “Differences in cognitive ability, per capita income, infant mortality, fertility, and latitude across the states of India” written by Richard Lynn, and Prateek Yadav. In this article, they delve into how the variables of cognitive ability, GDP, infant mortality, fertility, and latitude all correlate and affect each other.

Personal take on summary: I found this article to be somewhat interesting compared to the other articles that I have read before. This was not as obvious as the previous articles, and some of the information I found to be interesting.

Background / Personal take on the background: There isn’t much background on this article since its mostly just a study on variables and their effects.

Rationale: The rationale is simply just wanting to see the effects of the various variables and how they all correlate with each other.

Personal take on the rationale: I think that the rationale is reasonable, some of the factors are obvious but then again, they must make sure to check in case of outliers. However, I was curious about why India, since I don’t think they mentioned a reason. Perhaps the researchers have different articles pertaining to other countries.

Methods: The researchers decided to test all of India, this includes the 30 states and 6 union territories. They then measured the aforementioned variables as well as different cognitive abilities. The full list is:  Language scores class III, Mathematics scores class III, Language scores class VIII, Mathematics scores class VIII, Science scores class VIII, Teachers’ Index, Infrastructure index, GDP per capita, Literacy Rate, Infant mortality rate, Child mortality rate, Life expectancy, Fertility rate, Latitude, Coastline, Percentage of Muslims.

Personal take on the methods: I really like how specific and how many variables were tested. I also praise them for giving detailed explanations on what each of the variables mean.

Results: The results are best explained by the researchers “intelligence in the states (given as cognitive ability in Table 1) is positively correlated with the GDP per capita, the Teachers’ Index, the Infrastructure Index, the Literacy Rate, Life expectancy and having a coastline, and negatively correlated the Infant Mortality Rate, the Child Mortality Rate, the Fertility Rate, Latitude and the percentage of Muslims.”

Personal take on the Results: Some of the results were obvious such as intelligence and GDP having correlations. However, something that I found interesting was that the British Isles, France, the United States, Italy, Spain, China, Japan, and Portugal all have similar positive correlation values all around .25. There were also statements referring to the other variables but the results are too many to list in a blog post if you would like to see the results yourself you can check my citation of it below.

Graphs & Tables: There were many tables, however, it contained all the states and unions testing all the variables at once making the table look like a giant wall of numbers that was very hard to read.

Conclusion: In conclusion, the article was somewhat interesting. I praise the researchers with their thorough methods and many variables. Also for having detailed results with their input. Overall a decent paper.

Lynn, Richard, and Prateek Yadav. “Differences in cognitive ability, per capita income, infant mortality, fertility and latitude across the states of India.” ScienceDirect, 17 Feb. 2015,



Blog 3

Summary: The article I read was “Economic status and temperature- related mortality in Asia” by Youn-Hee Lim, Michelle L. Bell, Haidong Kan, Yasushi Honda, Yue-Liang Leon Guo, Ho Kim. The target of this study was to find the correlation of varying Socioeconomic status (SES) to temperature related mortality in Asian countries. To determine SES, GDP per capita was used as the scale.

Personal Opinion on summary: I feel that the article is on the more logical side of the spectrum compared to my past articles. However, it still has that “well duh” factor. Before finishing the article I’m already going to assume that the places with higher SES will have less mortality’s.

Background/ personal take on the background: There wasn’t an exact section within the research article but it was kind of embedded within the abstract and introduction.” few studies have examined the effect of economic status on the relationship between long-term exposure to high temperature and health.”(Youn-Hee L.) And this study tried to fill that gap.  Personally, I think that the study was ok. Like yes there should be maybe one study covering this topic just to show and justify the correlation but its defiantly a topic that doesn’t need multiple research papers on.

Rationale/ personal take on the rationale: The rationale dealing with this study is to confirm that SES to temperature related mortalities are a real thing. And my opinion like I said before is, it makes sense if this is the only study about it. However, it’s not a topic that needs multiple studies done.

Methods/ personal take on the methods: The researchers choose places in Asian of generally similar environments to minimize outside factors. “These four countries are located in East Asia and historically shared similar lifestyles and diets; thus, the comparison was based on an assumption of genetic similarity to environmental stress.” (Youn-Hee L.). Daily mortality counts, Environmental variables, and GDP were all things compared and collected. I think the researchers did a good job of choosing places of similar structure and not just in Asia.

Results/ personal take on the Results: The mortality count can’t all be due to heat so the first step of unraveling their results was to “estimated the association between temperature and mortality separately for each city” (Youn-Hee L.) The results are all shown in their graphs. But it pretty much boiled down to what I guessed, Higher GDP = less deaths, Higher temperature = more deaths.

Graphs: Like I’ve said about all the graphs so far in past articles, its hard if you don’t read the whole thing. Even I had some trouble deciphering it because the explanations for them are all in paragraphs full of numbers, equations, and calculations.

Conclusion: This definitely wasn’t one of the worse research articles I’ve read. It still has that duh factor I mentions earlier but covered a topic I guess I was more interested in.

Lim, YH., Bell, M.L., Kan, H. et al. Int J Biometeorol (2015) 59: 1405.

Blog 1

The article I read was “Income inequality and child mortality in wealthy nations” written by D. Collison, C. Dey, G. Hannah, L. Stevenson. In summary of the article, they were investigating if the correlation between income inequality and child mortality were still prevalent if they were to remove countries with higher OECD (Organization for Economic Cooperation and Development). This blog will explain the articles background, rationale, methods, major results, and major conclusions.

The background of the article was the well-known fact that income inequality and child mortality are related. Also discussed in the background is that the “wealthier OECD countries as well as changes in their relative child mortality rankings over time” The researchers wanted to figure out if the results were influenced by nations of high individual correlation.

The rationale of the researchers makes sense because if the whole data is being brought up by a couple outliers, then the data can’t be reliable. I agree with the researcher’s opinion to do this test to add supportive data to the first initial claim. However, I feel that there was a lot of unnecessary calculations for this topic.

The data and methods section explained their process of calculation “calculated correlation coefficients for associations between the Gini coefficient of income inequality and the UNICEF child mortality data.” (D. Collison, et al).  The researchers also provided a data table and graph. The Table was easier to read and understand however the graph was a little bit harder to understand if you skimmed.

In my own words if I were to describe the article I would say it had a lot of unnecessary information. All scientific papers seem to love putting unnecessary sentences in their paper describing something unrelating to the topic. However, this paper literally put me to sleep, I sat down and decided to finish however with the paper taking so long to get into the meat and potatoes of the research I fell asleep less then halfway through. Something to complement though was the over all organization of the paper. It followed a chronological order of describing the goal, giving the data and methods, then finishing with the results.   The negatives however where things commonly found in many biology research papers so it is hard to put a lot of blame on this specific one. The graphs were technical and hard to understand if you didn’t have background knowledge and paragraph explanations.



  1. Collison, C. Dey, G. Hannah, L. Stevenson; Income inequality and child mortality in wealthy nations, Journal of Public Health, Volume 29, Issue 2, 1 June 2007, Pages 114–117,


Blog 2

Summary: Places with larger measles vaccine coverage have lower mortality rates pertaining to measles. Tested in this study are different variables which may affect the outcome such as urban population and density. Also stated was that most of the countries that were studied were middle to high income.

Personal opinion on Summary: This was the biggest waste of time I’ve ever spent on reading. Literally within the first two paragraphs I understood and could have explained the rest of what would be talked about. Of course, countries with more measles vaccines would have less measles related deaths, I did not need to read twenty plus paragraphs explaining why that was the case.

Background/personal take on the Background: I believe the article does the best job of explaining the background. “This study characterizes the historical relationship between coverage of measles containing vaccines (MCV) and mortality in children under 5 years, with a view toward ongoing global efforts to reduce child mortality.” Again its not rocket science in fact even a elementary kid could tell you the relationship between vaccines and mortality.

Rationale: I mean the rationale of wanting to know if the vaccine was working or not is ok. However, to write a whole paper confirming it seemed like a waste of time.

Methods: The researchers studied data from forty four countries and compared the relationship between vaccine coverage and measles mortality. They got an accurate reading and understanding by having countries of varying measles vaccine coverage. “We divided MCV coverage into six categories: 0%; 1–59%; 60–79%; 80–89%; 90–94%; and ≥95% coverage.”

My take on the Methods: It’s nice that they had research methods and a way to accurately determine the effect of vaccine to mortality rate. But like I’ve been saying this whole post, Its pointless. Its painfully obvious that MORE vaccines equal LESS death.

Results: and my sarcastic comments I beat from the above post that you couldn’t possible fathom the results. Turns out I was right the whole-time vaccines DO reduce deaths. I know mind blowing. It is honestly baffling that someone wrote twenty plus paragraphs about this.

Graphs: The graphs were hard to read same with the tables. They had a lot of abbreviations which made it hard to understand without diving deep into the article.

Conclusion: This article was dumb and could have been written in about five paragraphs. There was unnecessary filler. Also, it felt like a waste of time. The time spent on this could have been used on researching things that aren’t common knowledge.


Goldhaber-Fiebert, Jeremy D., et al. “Quantifying Child Mortality Reductions Related to Measles Vaccination.” PMC, PLOS one, 4 Nov. 2010, Accessed 2 Oct. 2017.