Time Trend of Pneumonia in under Five Children of Nepal

Brijesh Sathian, Asis De, Padam Simkhada, Kalpana Malla, Arnab Ghosh, Sahisnuta Basnet, Bedanta Roy, Indrajit Banerjee, H S Supram, Suresh Devkota

American Journal of Public Health Research OPEN ACCESSPEER-REVIEWED

Time Trend of Pneumonia in under Five Children of Nepal

Brijesh Sathian1,, Asis De1, Padam Simkhada2, Kalpana Malla1, Arnab Ghosh1, Sahisnuta Basnet1, Bedanta Roy1, Indrajit Banerjee1, H S Supram1, Suresh Devkota1

1Faculty, Manipal College of Medical Sciences, Pokhara, Nepal

2Faculty, Centre for Public Health, Liverpool John Moores University, UK

Abstract

Globally, Pneumonia is the leading communicable disease which is the reason of fatality in children. In 2013, there was approximately 935 000 child death in less than 5 years old because of Pneumonia, which was 15% of all the deaths in children. The scenario is more or less same in sub-Saharan Africa and South Asia. The objective of the study was to collate information from existing data and chart out the trends of the incidence of Pneumonia (mild + severe) per 1,000 children under five years (new visits) in the future. A secondary data analysis of the incidence of Pneumonia (mild + severe) per 1,000 children under five years (new visits) in Nepal was done between 2005 to 2014. The survey was conducted under the administrative supervision of the population division of the Ministry of Health and Population (MOHP). Curve fitting method was used to find out the convenient model. The data was analysed using Statistical Package for the Social Sciences (SPSS) for Windows Version 16.0 (SPSS Inc; Chicago, IL, USA). A p-value of < 0.05 (two-tailed) was used to establish statistical significance. Excluding the constant term in the equation, the best fitted model was cubic, for the prediction of incidence of Pneumonia (mild + severe) per 1,000 children under five years (new visits). It is estimated that there will be 331 with 95% CI (0,1000) cases of Pneumonia (mild + severe) per 1,000 children under five years during 2020 in Nepal. The year wise incidence of Pneumonia (mild + severe) per 1,000 children under five years (new visits) in Nepal is having an increasing trend. The result provides reference data for organizing, planning, and evaluation of childhood pneumonia control program. Strengthening the health care delivery system and community-based prevention strategies and case management will facilitate to trim down pneumonia cases and the overall burden of this public health threat.

Cite this article:

  • Brijesh Sathian, Asis De, Padam Simkhada, Kalpana Malla, Arnab Ghosh, Sahisnuta Basnet, Bedanta Roy, Indrajit Banerjee, H S Supram, Suresh Devkota. Time Trend of Pneumonia in under Five Children of Nepal. American Journal of Public Health Research. Vol. 3, No. 4A, 2015, pp 27-30. http://pubs.sciepub.com/ajphr/3/4A/5
  • Sathian, Brijesh, et al. "Time Trend of Pneumonia in under Five Children of Nepal." American Journal of Public Health Research 3.4A (2015): 27-30.
  • Sathian, B. , De, A. , Simkhada, P. , Malla, K. , Ghosh, A. , Basnet, S. , Roy, B. , Banerjee, I. , Supram, H. S. , & Devkota, S. (2015). Time Trend of Pneumonia in under Five Children of Nepal. American Journal of Public Health Research, 3(4A), 27-30.
  • Sathian, Brijesh, Asis De, Padam Simkhada, Kalpana Malla, Arnab Ghosh, Sahisnuta Basnet, Bedanta Roy, Indrajit Banerjee, H S Supram, and Suresh Devkota. "Time Trend of Pneumonia in under Five Children of Nepal." American Journal of Public Health Research 3, no. 4A (2015): 27-30.

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At a glance: Figures

1. Introduction

Globally, Pneumonia is the leading communicable disease which is the reason of fatality in children. During 2013, it was reported that approximately 935 000 children less than 5 years old died due to Pneumonia, which was 15% of all the deaths in children. The scenario is more or less same in sub-Saharan Africa and South Asia. [1]. There is an increase of the incidence of Pneumonia (mild + severe) per 1,000 children under five years (new visits) in Nepal. It is reported in the 2014 Nepal annual report of the Ministry of Health and Population (MoHP) and Department of Health Services (DoHS) that the incidence of Pneumonia (mild + severe) per 1,000 children under five years (new visits) is 244 [2]. Dorringston et al conducted a study in South Africa to project communicable disease and his model was appropriate, reliable and useful [3]. Another study done by Hall et al in the United States also provided a reliable model to estimate HIV incidence [4]. Allen et al also studied in England the use of Markov model for HIV disease progression [5]. Statistical modelling and forecasting techniques can be used in the prediction of the incidence of Pneumonia in under five years children.

2. Aim and Objectives

The objective of the study was to collate information from existing data and chart out the trends of the incidence of Pneumonia (mild + severe) per 1,000 children under five years (new visits) in the future.

3. Materials and Methods

A secondary data analysis of the incidence of Pneumonia (mild + severe) per 1,000 children under five years (new visits) in Nepal was done for the period between 2005 to 2014. The survey was conducted under the administrative supervision of the population division of the Ministry of Health and Population (MOHP) [2,6-12].

The data was analysed using Statistical Package for the Social Sciences (SPSS) for Windows Version 16.0 (SPSS Inc; Chicago, IL, USA) and GraphPad Prism 6. A p-value of < 0.05 (two-tailed) was used to establish statistical significance. The incidence of Pneumonia (mild + severe) per 1,000 children under five years (new visits) from 2005 to 2014 plotted in y-axis against the corresponding year in the x-axis. Curve fitting method was applied to choose the proper curve for a given data points. The models used were Compound, Linear, Power, Logarithmic, S, Inverse, Growth, Quadratic, Exponential and Cubic.

1. Liner Model

2. Logarithmic Model

3. Inverse Model

4. Quadratic Model

5. Cubic Model

6. Compound Model

7. Power Model

8. S-curve Model

9. Growth Model

10. Exponential Model

To select the best fitting curve for the testing of hypothesis F-test was used. P-value was taken as significant when < 0.05 (two-tailed). R2 value > 0.90 was considered significant for prediction. The Cubic model was the best fitted model for incidence of Pneumonia (mild + severe) per 1,000 children under five years (new visits) from 2005 to 2014 (Figure 2). In the cubic model, m0 is the constant term and m1 and m2 are coefficient terms. Where Y is the incidence of Pneumonia (mild + severe) per 1,000 children under five years (new visits) and X is the corresponding year; 1=2005, 2=2006, 3=2007, 4=2008 and so on.

4. Results

Table 1, Table 2 and Graph 1 illustrate the parameter estimates and the model summary for different models including the constant term. Considering the constant term in the model, none of the model was the best fit, for the prediction of incidence of Pneumonia (mild + severe) per 1,000 children under five years (new visits).

Table 1. Model Summary with Constant Term in The Model

Table 2. Parameter Estimates with Constant Term in The Model

Figure 1. Fitted Curves with Constant Term in The Model Year Wise Incidence of Pneumonia (Mild + Severe) Per 1,000 Children Under Five years (New Visits) from 2005 to 2014

Table 3. Model Summary without Constant Term in The Model

Table 3, Table 4 and graph 2 portray the parameter estimates and the model summary for different models, excluding the constant term. Excluding the constant term in the equation, the best fitted was the cubic model, for the prediction of the incidence of pneumonia (mild + severe) per 1,000 children under five years (new visits).

Table 4. Parameter Estimates without Constant Term in The Model

Figure 2. Fitted Curves without Constant Term in The Model in Year Wise Incidence of Pneumonia (Mild + Severe) Per 1,000 Children Under Five Years (New Visits) From 2005 to 2014

Table 5. Forecasted Year Wise Incidence of Pneumonia (Mild + Severe) Per 1,000 Children Under Five Years (New Visits) from 2005 to 2020

Figure 3. Year Wise Incidence of Pneumonia (Mild + Severe) Per 1,000 Children Under Five Years (New Visits) From 2005 to 2020

5. Discussions

In the Curve fitting method, collected data were plotted in a graph to find out the relationship between dependent variable and time by connecting the `points' with a line. Then the next step to find out the best fitted model to the observed data. Once the model is selected then it would be used to forecast the trend of the dependent variable for a time variable [13, 14]. Sathian B et al. has done several studies using curve fitting method to predict the trends in non communicable and communicable diseases in Nepal [13-18][13]. This study hereby launches the suitability of statistical modelling in forecasting the year wise incidence of Pneumonia (mild + severe) per 1,000 children under five years (new visits) in the Nepalese context.

This study showed a increasing trend in pneumonia in under five children which is supported by the studies done in Bangladesh [19] and Taiwan [20]. The main reason for this is because children are more vulnerable to general and viral infections [21, 22, 23].

The WHO and UNICEF started a program to hasten pneumonia control with a blend of interventions to treat, prevent and protect Pneumonia in children and named it as Integrated Global Action Plan for Pneumonia and Diarrhea (GAPPD) [1]. Prevention program should focus mainly on Immunization against Hib, Measles, Pneumococcus and Whooping Cough (Pertussis).

6. Conclusion

Year wise incidence of Pneumonia (mild + severe) per 1,000 children under five years (new visits) in Nepal is having an increasing trend. The findings of this study will be helpful for the policy makers, stakeholders and all health care providers to recognize the circumstances of Pneumonia among children under five years. Furthermore, the result provide reference data for organizing, planning, and evaluation of childhood pneumonia control program. Strengthening health care delivery system and community-based prevention strategies and case management will facilitate to trim down pneumonia cases and the overall burden of this public health threat.

Declaration of Conflicting Interests

The authors declare that there is no potential conflicts of interest with respect to the research, authorship and /or publication of this article

Funding

The authors received no financial support for the research, authorship and/or publication of this article

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