{'fraction_non_alphanumeric': 0.020134228187919462, 'fraction_numerical': 0.022520507084265475, 'mean_word_length': 4.8410278745644595, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Assessing the Trend of Malnutrition among Children Under Five Years in West Gonja District, Ghana

The research paper seeks to investigate the trend of malnutrition cases among children under five years in the West Gonja District. A cross-sectional survey approach was used to recruit respondents for primary results. Desktop analysis was used to collect secondary data that included opinions of other people on the field of study. 150 sample sizes were determined with a malnutrition prevalence rate of 200 percent and a margin of 5 percent error and a confidence interval of 95 per cent. Stratified sampling was employed. The primary and secondary data were analyzed using SPSS, R program, Minitab and Gretl. The responses were put into summary statistics, figures, one-way ANOVA, trend to facilitate successful discussion. Finding revealed that underweight of the children under five years can be described by quadratic trend and ARIMA(0,1,1). The findings further revealed that, there were significant difference among children weight categories but remain same across the various years from 2014 through 2019.The findings finally revealed that the malnutrition of the children under five years of age are likely to decrease drastically for next five years if the intervention program is effectively managed and maintained. The research paper concluded that the malnutrition of the children under five years of age likely to decrease drastically for next five years if the intervention program are effectively managed and maintained as indicated on the forecasted curve. The District Health Directory should deploy more Nurses for home monitoring in order to arrest the situation at stake.

healthy diets are the main strategies implemented over the years to accomplish this. The district of West Gonja lies in the northern region. For many years this District has been combating child malnutrition. The District health directory launched multiple measures to address the problem. Many of these measures include: public education on healthy nutrition, breastfeeding, pregnancy attendance Malnutrition is a human-life deficiency that often affects children. It is killing, it is retarding human growth. It is a disease that affects a lot of people all over the world. When it comes to malnutrition problems, Ghana is no exception. According to the latest UNICEF report, in Ghana too many children suffer from health problems caused by malnutrition, which is the root cause of child death.
The Annual Report of the Damongo Health Service (2009) shows that children suffer from malnutrition in every five years. Yet the situation has been a problem for the District ever since. This is since the 2015 annual report reveals that infant mortality is the second-largest cause of child death in the district. In addition to malaria, child malnutrition is the next cause of most child deaths according to the study. This paper looks at the trend of child malnutrition in the northern part of Ghana's West Gonja District

Research Methods
The research was a descriptive analysis with a cross-sectional analytical design conducted to access malnutrition trend in the West Gonja District. A cross-sectional survey approach used to recruit respondents for primary results. Desktop analysis was used to collect secondary data that included opinions of other people on the field of study. The target population refers to the study population of interest and has been described as households in communities with children under five years of age and who have lived in the community for at least six months.
The sample frame consists of the set of elements from which actually the sample is taken. This was composed of a list of malnourished cases obtained from the District Hospital in Damongo. Saunders et al (2007) claimed that the sample size and the way it is chosen certainly indicate the trust you can have in your data and the degree to which you can generalize with a population. Using EPI STAT CALC version 4.0.3, 150 sample sizes was determined with a malnutrition prevalence rate of 200 percent and a margin of 5 percent error and a confidence interval of 95 per cent. Stratified sampling was employed. The instrument used to gather primary data for the analysis was a questionnaire. The data were analyzed to identify the trend of child malnutrition in district. Various statistical applications such as Minitab and SPSS were used for both primary and secondary data analysis. The primary data were analyzed using SPSS, R program, Minitab and Gretl. The responses were put into summary statistics, figures, one-way ANOVA, trend to facilitate successful discussion.

Results and Discussion
The findings cover the effects of the various statistical methods used in the collated and coded analysis of the data. This serves as the basis for the definition, debate and conclusion to achieve the research goal The minimum number of children underweight was found to be 9 whiles the maximum underweight was also found to be 67. The average number of children underweight was 35.5 with a standard deviation of 25.74, which denote that children weightare widely spread over the mean. For the period of 2014 to 2019total number of children underweight was recorded to be 213.The distribution of the children underweight exhibits positive skewness of 0.31 which shows that most of the children underweight are centered on the right of the mean indicating that there is an improvement malnutrition among children under five years of age within the districtand -2.45 of the kurtosis value shows that the children underweight wasless extreme to the normal peak which means that the underweight data have a platykurtic distribution. Vol 8 Issue 3 DOI No.: 10.24940/theijhss/2020/v8/i3/HS2003-092 March, 2020 The normal weight of children curve flits above both the underweight and moderate weight, this suggest that there is an improvement of malnutrition situation within the district as indicated in figure 1.  The researchers want to determine whether there is a significant difference in weight categories (below 2.5kg, moderate2.5kg and normal 2.5kg+) of children age under five in West Gonja District. As shown in table 2, there is a statistical significant difference among weight categories of children aged under five in West Gonja District as indicated by p-value=0.0000032. This implies that there is a reduction of malnutrition cases among children under five years of age within the district. Therefore, a follow-up research must be conducted to ascertain which weight categories is contribution the difference in children underweight within the district. Details of the follow-up analysis are shown in From table 3, both Tukey HSD and LSD methods were used to ascertain the weight categories that contribute the difference. It is clearly shown that the comparing normal weight to that of underweight and moderate saw a statistical significant difference among children under five years of aged within the district but below 2.5kg and moderate weight was not statistical difference from each other. These results re-affirm that there is a lot of improvement in malnutrition cases among children under five years of age within the district because of the social intervention like school feeding program, exclusion breastfeeding campaign and public health campaign about malnutrition among children within the district. Vol 8 Issue 3 DOI No.: 10.24940/theijhss/2020/v8/i3/HS2003-092 March, 2020   The researchers again wants to established whether there is a statistical significant reduction of malnutrition cases among children under five years of age within the district. It is clearly shows that there is no statistical significant reduction of malnutrition cases from 2014 through 2019. This implies that district health directory should roll out more programs to combat the situation at stake.

Figure 3: Time Series Plot of the Children Weight below 2.5kg
The series depict an increase between 2014 through 2015 and decrease drastically from 2016, 2017, 2018 to 2019. This increasing and decreasing fluctuation over time indicated that the mean and variance are not constant over time suggesting that the series are not stationary. This implies that the underweight cases experience decreasing trend over the study period.

: Stationary Test
The stationarity test was conducted to ascertain the claim in figure 3 and the results tend to be true suggesting the series were not stationary. In order to achieve stationarity condition, the series was difference once in order to achieve the stationarity. KPSS test results in table 5 clearly shows that the series is stationary since the p-value (0.1) is greater than 0.05. However, the ADF test with a reverse null hypothesis indicates that the data is stationary with p-value 0.02. In conclusion, the two tests agreed that the series is stationary after first difference.

Trend Analysis
Determining the behaviour of the series is paramount to identifying the trend model that best describe the behavior of the series. Some of these models are linear, quadratic or exponential. In order to identify the model that best described the series, researchers often use the minimum values of the measures accuracy such as MAPE, MAD and MSD in the selection criterion.

Trend Models
 Linear Trend Model; is estimated using the Ordinary Least Square estimation with a general model of = + + Where yt is the projected value of the y variable for a selected value of t, βo is the constant intercept; β1 represents the average change from one period to the next.  Quadratic Trend Model; which accounts for a simple curve is of the form = + + +  Exponential Growth Trend Model; accounts for exponential growth or decay. Mathematically,    A follow-up analysis was conducted on Partial Autocorrelation Function (PACF) and Autocorrelation Function (ACF) plots. The plot of ACF and PACF for the series appears not be stationary at 95% confidence interval. The ACF divesup and down slowly with significant spikes at lags 1and there was no significant of lags of the PACF plotas illustrated in Figures  In order to identify the model suitable to describe the underweight of the children under five years the researchers made use of the Akaike Information Criteria (AIC), Bayesian information criterion (BIC) and Hannan-Quinn (HQ). This selection criterion would be based on the minimum values of the AIC, SIC and HQ. Thus, by careful examination of all the calculated models in Table 9the ARIMA (0, 1, 1) model has the minimum values and hence the appropriate model for estimating. coefficient std. error z p-value const âˆ'11.3006 5.15960 âˆ'2.190 0.0285 ** phi_1 âˆ'0.131055 0.522709 âˆ'0.2507 0.020 Table 8: Parameter Estimation Table 8 shows the estimations of the parameters of the ARIMA (0, 1, 1) model. AR (0) and MA(1) are significant at 5% levels with coefficients and p-values of0.0285and respectively less than 0.05 indicating the significance of the parameters.

Model Diagnosis
To determine appropriate model there is the need to perform the following diagnosis.

Residual Plot
The patterns of the residuals over time around the zero mean as seen in figure 10 indicate that the residuals are random and independent of each other, thus, indicating that the model is fit.  Figure 10 shows all autocorrelation spikes within the 99% confidence interval. This means that there is no serial correlation between residuals indicating that they are accurate and the model is adequate.      Figure 12 shows that the malnutrition cases among children under five years are likely to decrease for next five as indicated in the forecast curve.

Summary of the Findings
The finding revealed that the minimum number of children underweight was found to be 9 whiles the maximum underweight was also found to be 67. The average number of children underweight was 35.5 with a standard deviation of 25.74, which denote that children weight are widely spread over the mean. For the period of 2014 to 2019 total number of children underweight was recorded to be 213. The distribution of the children underweight exhibits positive skewness of 0.31 which shows that most of the children underweight are centered on the right of the mean indicating that there is an improvement malnutrition among children under five years of age within the district and -2.45 of the kurtosis value shows that the children underweight was less extreme to the normal peak which means that the malnutrition data have a platykurtic distribution.
Finding also revealed that underweight of the children under five years can be described by quadratic trend and ARIMA(0,1,1).
The findings further revealed that, there were significant difference among children weight categories but remain same across the various years from 2014 through 2019.
The findings finally revealed that the malnutrition of the children under five years of age are likely to decrease drastically for next five years if the intervention program is effectively managed and maintained.

Conclusion
The research paper concluded that the malnutrition of the children under five years of age likely to decrease drastically for next five years if the intervention program are effectively managed and maintained as indicated on the forecasted curve. Though there was a significant reduction in the number of malnutrition cases among the weight