The table below provides detailed statistical figures that show the number of live births between the year 2013 and 2014. It is vividly illustrated that the numbers of live births have increased gradually from 2013 to 2014. For instance, in the month of January 2013, the rate per 1,000 women aged between 15-44 years gave birth to 3,966,000 children (National Center for Health Statistics n.p). On the other hand, the month of December 2014, recorded a different figure. The number of live births that was recorded in December 2014 was 3,997,000; therefore, this shows that the rate of live births in the United States increases gradually with time. The United States experience a positive change in the number of children born because of the following reasons. First, the fertility rate of women is higher between the age of 15 years and 44 years. A population with high fertility rates has a greater chance of increasing. Second, the intention by most residents in the United States to have children played a key role in contributing to the increase in rate of births. The other factor that contributed the increase in live births is religion. In recent years, the population of Muslim in the United States has increased. As per the report from the National Center for Health Statistics, between the year 2014 and 2015, Muslims have shown that they have high fertility rate.
Therefore, based on the above table, we can conclude that the country experienced a rise in the number of live births because of increase in fertility among women aged between 15 and 44 years, unwanted pregnancies among the teenagers and religion.
This project entails an analysis of the statistical data from the National Center for Health Statistics and provides a forecast of the number of live births for the future year. The forecast from this project will be useful to the government and health care sectors since it will guide them with appropriate plans or strategies to reduce the uncertainty of the future events. They will be able to outline and implement relevant strategies to control the rising number of births. The paper will provide an explanation to the strategies the healthcare sectors have employed in the last three years to curb the rising rate of births in the United States. Also, it includes the factors that have contributed to the changes in the number of live births based on the events that occurred regardless of the place of residence. Lastly, this paper provides a detailed explanation about the future government plans on the healthcare sector.
The Government Strategies for the Last Three Years
In the last three years, the government through the healthcare sectors has put in place strategies to control the rising birth rates in the United States. First, the healthcare sector introduced the family planning method. The introduction of the birth control pills has ensured that families maintain a reasonable number of children. The strategy helps women and teenage girls to avoid getting pregnant(Shi and Douglas 125). Similarly, the healthcare sectors ensured that women and girls have access to contraceptives by providing community based education on the use of contraceptives. Second, the healthcare sector developed programs that provided sex education to the young generation. The youth development program’s primary objective was to prevent the unintended conceptions among teenagers by strengthening secondary preventive efforts through support, education and employment. Also, it offered sex based education that was primarily linked to contraceptive services.
Lastly, the government and healthcare sectors have been working on convincing leader to stabilize the population growth by practicing human rights and development. When leaders spread the concept of right-based population policy, the policy maker would implement the policies by ethically addressing the challenges related to population hence empowering women to make the right choices.
Factors that might have contributed to the Changes in the Number of Live Births
The forecast graph shows a rising rate of the future live births in the United States. The U.S. fertility is higher since most of the women are between the age of 20 years and 44 years. The intention by most residence in the United States to have children will contribute to the rising rate of births. The other factor that will increase the fertility of a population is the religion. According to the report from the National Center for Health Statistics, between the 2010 and 2015, Muslims have shown the highest fertility rate. They have a higher average per women than other religions. Third, due to an increase in the number of unwanted births, the future rate of births will increase (Shi and Douglas 120). The unwanted births are because of teenage and unintended pregnancies.
The Future Government and healthcare Plans
The government and healthcare sectors are working on strategies that will control the high birth rates in the future. The healthcare sectors are focusing on improving the use of contraceptives and other behaviors that might prevent pregnancy through the provision of clear and unambiguous information. The information will be disseminated through participatory teaching methods. In addition, the healthcare sectors are planning to improve on their staff selection and training programs. The programs provide long-term services that specifically meet the needs of young people who are prone to sexual abuse. The healthcare sectors are also planning to join up services that will aim at preventing unintended pregnancies by working in partnership with the community.
The primary function of forecasting is to predict the future trend by using the data collected (Lawrence, Ronald and Sheila 215). This project will assess the current statistical data on the number of live births per 1,000 population in the Unites States for the years 2013 and 2014. The analysis will provide to a forecast for the future; however, based on the data collected by the National Center for Health Statistics from 2013 to 2014, the best fitting forecasting method is winters method. For this case, our time series that we will dwell on is the monthly number of births at hospital. We shall use the winters’ method as a reliable forecasting method to determine the future values so that the healthcare sectors can have an opportunity to improve their plans for the future or prepare for the inevitable. This project is concerned with forecasting the future trends in the rates of live births in the United States.
The figure below shows a forecasting graph of the number of live births for the years 2013 and 2014 using the winter method. The forecast was based on the data of live births in the United States. Winters method was used as a reliable and best-fit method to predict the future number of live births for the year 2015. Moreover, it can be used to predict the changes in number of population for next years based on the data from previous years. Similarly, the number of population may increase or decrease depending on multiple factors such that include fertility, marriages, deaths, diseases, disasters or superior force, migration and economic situations. For instance, figure 2, which entails the winter forecasting graph, shows the demand as the blue line while the forecast is the red line. Both the forecast and the demand lines have almost the same trend. The forecast increases with the same trend as the demand for live births.
The figure below is an excel sheet that entails the data for the number of live births for 2013 and 2014, level estimate, trend estimate, seasonality factor and the forecast. Similarly, we have the values for Mean Absolute Deviation (MAD), Mean Standard Deviation (MSD) and BIAS.
On the hand, figure 4 illustrates how I arrived to the correct alpha and beta. The table exhibit different values for MAD, MSD and BIAS since I used multiple combinations of alpha and beta to try to come up with right value to use as alpha and beta. For instance, when you want to minimize MAD and MSD, it would be appropriate to use 0.3 alpha and 0.1 beta. Similarly, if you want to minimize BIAS, the appropriate coefficient to use is 0.1 alpha and 0.5 beta. However, it is difficult to find the coefficient that would best fit by minimizing MAD, MSD and BIAS. For this forecasting project I picked 0.2 as alpha and 0.3 as beta because the two value in the MAD column that are almost close to one another are 3,425 and 3,433. Therefore, between the two number, I chose 3,425 because it is the minimum hence giving us the overall alpha and beta values as 0.2 and 0.3 respectively.
Figure 5 shows the basic time series output from JMP. It is a combination of time series demand and time series basic diagnostics. I arrived at the illustrations depicted in figure 5 after forecasting the data using the JMP software. The graph for the time series demand in figure 5 shows the 2013 and 2014 demand and the forecast for 2015. The graph portrays an upward trend whereby the data increases according to the non-linear curve. For instance, the number of live births kept on fluctuating from 2013 to 2015. Moreover, the forecasted number of live births for 2015 is approximately 340,000.
Figure 6 (a) provides an illustration of the JMP output using the Winters method additive. Also, it encompasses the model summary, parameter estimates and forecast. When I used the JMP software to forecast the data, it predicted the future number of live births that the country is expecting. The JMP software enabled me to get the forecast of the number of live births for the years 2015 and 2016. The JMP winters method in the figure below has a detailed illustration of the exact averages from the 2013 and 2014 data. Precisely, figure 6(b) has the value for actual demand, demand, prediction formula and the predicted demand. The importance of Winters method additive is that, it gives the exact averages.
Figure 6 (a)
Figure 7, is a model comparison that shows different statistics from the JMP output. The comparison model has the statistics for the winter method (additive), seasonal ARIMA, and ARIMA. In addition, we have figure 7(b) which entails figures for ARIMA specification. Figure 7(c), shows the output for seasonal ARIMA specification. For ARIMA model, I used 2 for autoregressive order p1, 0 for differencing order d1, and 3 for moving average order q1. For seasonal ARIMA I used 1 for autoregressive order p1, 0 for differencing order d1, and 0for moving average order q1. The ARIMA specification has a confidence interval of 0.95.
Figure 7(e) provides an illustration for the final summary of output from JMP.
Figure 7 (a)
Figure 7 (b)
Figure 7 (e)
Mean Absolute Percentage Error (MAPE) illustrated as figure 8 is a formula that is used to measure the statistical size of the error in a percentage form. It is used when you are assessing the accuracy of the forecast (Lawrence, Ronald and Sheila 215). Similarly, it can give information in situation when you do not have an idea about the demand volume of an item. In situation when the actual demand is zero, then MAPE would be undefined. When the actual demand is small, MAPE would have an extreme value.
Figure 9 provides a detailed illustration of the everage birth values for two models that include ARIMA and seasonal ARIMA. The average values in the two models differ because I used different values in auto-regression order, differencing order and moving average order.
This project explained a time series forecast of the monthly number of live births at a hospital in the United States. The reliable and most appropriate method that was proposed was winters method. The project used the JMP model to provide clear illustrations by using Winters method additive, ARIMA and seasonal ARIMA. All the output from JMP have been explained in this project. The ARIMA model was used to forecast a single time series. It is important to note that, the winter forecasting method used in this project predicted the future trend of live births for the years 2015 and 2016. The forecasting method predicted an increase in the number of live births for the years 2015 and 2016. Therefore, it would be important for both the healthcare and government to use my data as a stepping-stone for their endeavors in controlling the rising rates of births. My data would be useful to the healthcare sector since it will help them prepare for the future by forecasting. In case when the healthcare sectors experience calamities or diseases, my data would not be useful since the forecast would predict a decrease in live births. Lastly, in the last three years, the health facilities employed certain strategies to curb the rising rates of live births. They proposed the use of family planning methods that ensured women and girls avoid unwanted pregnancies. In addition, they also ensured the young generations enroll in sex education programs.
Lawrence, Kenneth D, Ronald K. Klimberg, and Sheila M. Lawrence. Fundamentals of Forecasting Using Excel. New York, N.Y: Industrial Press, 2009. Print.
National Center for Health Statistics. Retrieved from http://www.cdc.gov/nchs/data/dvs/provisional_tables/Provisional_Table01_2014Dec.pdf
Shi, Leiyu, and Douglas A. Singh. Delivering health care in America. Jones & Bartlett Learning, 2014.
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