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Chapter 1: Problem 33

Health Insurance The accompanying table gives the population (in hundred thousands) and number of people not covered by health insurance (in hundred thousands) for the United States. Find the percentage of people not covered by health insurance for each of the given years and describe the trend. (Source: 2017 World Almanac and Book of Facts \()\) $$ \begin{array}{|c|c|c|} \hline \text { Year } & \text { Uninsured } & \text { Total Population } \\ \hline 1990 & 34,719 & 249,778 \\ \hline 2000 & 36,586 & 279,282 \\ \hline 2015 & 29,758 & 316,574 \\ \hline \end{array} $$

Short Answer

Expert verified
The percentages of uninsured people in 1990, 2000, and 2015 are 13.9%, 13.1%, and 9.4% respectively. The general trend is a decrease in the percentage of uninsured individuals over this 25 year period.

Step by step solution

01

Calculating the percentage of uninsured people

To calculate the percentage of uninsured people for each year, the number of uninsured people should be divided by the total population and then multiplied by 100. This can be represented by the formula: \[ \text{Percentage of uninsured} = \left(\frac{\text{Number of uninsured people}}{\text{Total population}} \right) \times 100 \] Using this formula, the percentages for each year can be calculated as follows: \[ \text{1990:} \left(\frac{34,719}{249,778} \right) \times 100 = 13.9\% \] \[ \text{2000:} \left(\frac{36,586}{279,282} \right) \times 100 = 13.1\% \] \[ \text{2015:} \left(\frac{29,758}{316,574} \right) \times 100 = 9.4\% \]
02

Analyzing the trend

The difference in the percentages from year to year can be interpreted as a trend. If the percentage is decreasing, it would imply that fewer people are uninsured, and if it's increasing, it would imply that more people are uninsured. From the calculations, it appears that the percentage of uninsured people has generally decreased from 1990 to 2015. So, the trend is that the percentage of people not covered by health insurance in the United States has been decreasing over this 25 year period.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Percentage Calculation
Percentage calculation is a fundamental mathematical skill that is crucial for data analysis in various fields, including health insurance statistics. In essence, calculating a percentage involves determining how much a part is of a whole. The process typically involves dividing a particular quantity (the part) by another (the whole) and then multiplying the result by 100 to obtain the percentage value.

For example, if we want to find the percentage of people not covered by health insurance in a given population, we take the number of uninsured individuals, divide it by the total population, and then multiply by 100. This is represented mathematically by the formula: \[ \text{Percentage of uninsured} = \left(\frac{\text{Number of uninsured people}}{\text{Total population}} \right) \times 100 \] In our exercise, the decrease in the uninsured rate from 1990 (13.9\%) to 2015 (9.4\%) suggests an improvement in health insurance coverage over time.
Trend Analysis
Trend analysis is a method used to observe patterns over a specified period. In the context of health insurance statistics, trend analysis allows us to look at changes in the uninsured rate across different years. It is critical for spotting increases or decreases in coverage and identifying potential causes or outcomes associated with these trends.

By comparing the calculated percentages of uninsured individuals for each year, as done in our textbook problem, one can determine the direction and strength of the trend. A trend line 鈥� either charted on a graph or assessed through numerical comparison 鈥� can provide visual representation of this trend. For instance, observing the decline in the uninsured rate from nearly 14% to under 10% over a 25-year span indicates a positive trend towards greater insurance coverage, reflecting changes in policy, economic conditions, or public health initiatives.
Population Data Analysis
Population data analysis involves examining large sets of demographic data to inform decisions and policy. When dealing with health insurance statistics, analysts look at various metrics, including the overall number of people insured versus uninsured. Understanding these figures on a large scale is important for planning healthcare services, allocating resources, and evaluating the effectiveness of insurance programs.

By scrutinizing population data, we can identify disparities in coverage and target areas that require attention. Specialists use statistical techniques to analyze complex data, understand the characteristics of different demographics, and make forecasts about future trends. To put it simply, population data analysis is like piecing together a health coverage puzzle; looking closely at each piece (data) gives a clearer picture of the overall health insurance landscape.

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Most popular questions from this chapter

Incarceration Rates (Example 7) The table gives the prison population and total population for a sample of states in 2014-15. (Source: The 2017 World Almanac and Book of Facts) $$ \begin{array}{|l|c|c|} \hline \text { State } & \text { Prison Population } & \text { Total Population } \\ \hline \text { California } & 136,088 & 39,144,818 \\ \hline \text { New York } & 52,518 & 19,795,791 \\ \hline \text { Illinois } & 48,278 & 12,859,995 \\ \hline \text { Louisiana } & 38,030 & 4,670,724 \\ \hline \text { Mississippi } & 18,793 & 2,992,333 \\ \hline \end{array} $$ Find the number of people in prison per thousand residents in each state and rank each state from the highest rate (rank 1\()\) to the lowest rank (rank 6). Compare these rankings of rates with the ranks of total numbers of people in prison. Of the states in this table, which state has the highest prison population? Which state has the highest rate of imprisonment? Explain why these two answers are different.

Indicate whether the study is an observational study or a controlled experiment. A group of boys is randomly divided into two groups. One group watches violent cartoons for one hour, and the other group watches cartoons without violence for one hour. The boys are then observed to see how many violent actions they take in the next two hours, and the two groups are compared.

Milk and Cartilage (Example 10) Cartilage is a smooth, rubber-like padding that protects the long bones in the body at the joints. A study by Lu et. al. in Arthritis Care \& Research found that women who drank one glass of milk daily had \(32 \%\) thicker, healthier cartilage than women who did not. Researchers obtained information on milk consumption through questionnaires and measured cartilage through x-rays. In the article, researched conclude, "Our study suggested that frequent milk intake may be associated with reduced OA progression in women." (Source: Lu et al., "Milk consumption and progression of medial tibiofemoral knee osteoarthritis: Data from the osteoarthritis initiative," Arthritis Care \& Research, vol. 66 [June 2014]: 802-809, https://doi.org/10.10002/acr.22297 Does this study show drinking milk causes increased cartilage production? Why or why not?

The projected U.S. population is given for different decades. The projected number of people 65 years of age or older is also given. Find the percentage of people 65 or over and comment on the trend over time. Numbers are in millions of people (Source: 2017 World Almanac and Book of Facts) $$ \begin{array}{|c|c|c|} \hline \text { Year } & \text { Population } & \text { Older Population } \\ \hline 2020 & 334 & 54.8 \\ \hline 2030 & 358 & 70.0 \\ \hline 2040 & 380 & 81.2 \\ \hline 2050 & 400 & 88.5 \\ \hline \end{array} $$

A study reported by Griffin et al. compared the rate of pneumonia between 1997 and 1999 before pneumonia vaccine (PCV7) was introduced and between 2007 and 2009 after pneumonia vaccine was introduced. Read the excerpts from the abstract, and answer the question that follows it. (Source: Griffin et al., "U.S. hospitalizations for pneumonia after a decade of pneumococcal vaccination," New England Journal of Medicine, vol. \(369[\) July 11,201\(]: 155-163\) ) We estimated annual rates of hospitalization for pneumonia from any cause using the Nationwide Inpatient Sample database..... Average annual rates of pneumonia-related hospitalizations from 1997 through 1999 (before the introduction of PCV7) and from 2007 through 2009 (well after its introduction) were used to estimate annual declines in hospitalizations due to pneumonia. The annual rate of hospitalization for pneumonia among children younger than 2 years of age declined by \(551.1\) per 100,000 children \(\ldots\) which translates to 47,000 fewer hospitalizations annually than expected on the basis of the rates before PCV7 was introduced. Results for other age groups were similar. Does this show that pneumonia vaccine caused the decrease in pneumonia that occurred? Explain.
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