For each article provide a one paragraph critique addressing the following below: For each article critique the study (how was it a good or bad study)Provide suggestions on how each study could be imp

Methods: This study adopted the use of a One-way Analy- sis of Variance (ANOVA) to evaluate any significant differ- ence in the under-five mortality rate of five major developing countries namely Brazil, Bangladesh, Turkey, South Africa and Nigeria owing to their similarity in economy potentials and population. The significant level of 0.05 was consid- ered for the statistical test. The under-five mortality rate figures were secondary data obtained from the UNICEF database for the period between 1990 through 2017.

Results: The test result shown that there was a significant difference in the under-five mortality rates of the five coun- tries under considerations (p < 0.05). A post-hoc analysis us- ing Tukey’s method revealed significant pairwise tests com- parison between Bangladesh VS. Brazil, Bangladesh VS.

Nigeria, Bangladesh VS. Turkey, Brazil VS. Nigeria, Brazil VS. South Africa, Nigeria VS. South Africa, Nigeria VS. Tur- key, and South Africa VS. Turkey.

Conclusion: Nigeria has the highest under-five mortality rate (163.17 ± 38.42) while Brazil has the lowest under-five mortality rate (21.92 ± 15.27). Nigeria’s average under-five mortality rate is more than twice the average rate of Brazil and almost twice of Turkey’s under-five mortality rate within the period under review (1990-2017).

Child Mortality: A Comparative Study of Some Developing Countries in the World İlker Etikan *, Ogunjesa Babatope, Kabiru Bala and Savaş İlgi Department of Biostatistics, Near East University, Cyprus *Corresponding author: İlker Etikan, Department of Biostatistics, Faculty of Medicine, Near East University; Nicosia- TRNC, Cyprus, Tel: +90-392-223-64-64 RESEaRch aRticlE Check for updates one of the important constituents of the vulnerable population. According to the 2017 World Bank report on population growth, there are about 1,953 billion people between ages 0-14 years accounting for 26% of the global population [1]. Even though there is a report- ed demographic shift that is increasing the aged 65 + cohort, the ages 0-14 years cohort is facing an increas- ing risk with an estimated decline to 21% of the global population by 2050.

Child mortality is a central case study among many researchers that revolves around children health outcome. Hence, different national and internation- al stakeholders, as well as policymakers, continual- ly strive to see to the global reduction of childhood deaths worldwide. It is also in this vein the United Nations (UN) developed the United Nations Develop- ment Goals (MDGs) which was later revamped into the Sustainable Development Goals with the number three (3) agenda still prominently dedicated to im- proving maternal and child health [2 ]. Though consid- erable milestone progress has been made in regards to reducing child mortality rate globally, however, the rate is still higher in Southern Asia and Sub-Saharan Africa with an average of 4 dead cases among every 5 deaths of children before their 5 th year birthday [3 ,4 ].

There are several types of indices that are used to describe child mortality. They include prenatal, perina- tal, neonatal, infancy and under-5 child mortality [5].

Prenatal mortality refers to the death of a child before delivery. The perinatal mortality is defined as the death of a child that occurs within the first week of delivery.

The neonatal death describes the death of a child be- fore the 28 days after delivery. The infant mortality rate Introduction The study on the vulnerable population has contin - ued to remain an important research theme in both health and social economic researches. Children remain Etikan et al. Int J Clin Biostat Biom 2019, 5:022 ISSN: 2469-5831 International Journal of Clinical Biostatistics and Biometrics Volume 5 | Issue 2 Open Access Etikan et al. Int J Clin Biostat Biom 2019, 5:022 DOI: 10.23937/2469-5831/1510022 • Page 2 of 6 • ran Region still recorded the highest cases of under-five mortality with a record death of 76 per 1000 live births [8].

Risk Factors of Child Mortality There are several factors that are responsible for child mortality. Ria, et al. [9 ] in their study on caus- es and contributors to infant mortality in North India identified social and verbal autopsy drivers as a major contributor to child death. The social autopsy borders on social, health models and behavioral agents. These include factors stemming from transportations, living conditions of people as well as health systems that are operational. According to Suwal [10], malaria, malnutrition,diarrhea, and respiratory infections are major risk factors responsible for children fatality.

Regarding neonatal death, Adeyele and Ofoegbu [11] posited that genetically propelled malfunctions, mea- sure, and level of antennal care support, and quality of postpartum treatments play a role in the morbidity and mortality of children. Other factors responsible for child mortality include maternal literacy level, poverty, early marriage, place of abode, pneumonia, congenital abnormalities, preterm birth complica- tions, nutritional and breastfeeding practices, access to medic care support, food insecurity, early preg- nancy, poor hygiene practices, poor water access and so on [12- 14]. Methodology Data Source The data for the study was extracted from the Unit- ed Nations Children Fund (UNICEF) under-five mortality rate datasets portal [15]. The under-five mortality rate for Nigeria, India, Turkey, South Africa and Brazil be- is used to describe the likelihood of a child’s death per 1000 live birth between the time of birth and exactly a year old. The under-five year child mortality is the most common form and widely used metric to define child mortality [6]. The Under-five year child mortality is ba- sically defined as the probability of a child dying before reaching the 5 th year birthday anniversary and is usually expressed per 1000 live birth. Global Overview of Child Mortality Rate According to the UN Inter-agency Group for Child Mortality Estimation [7], an estimated 6.3 million deaths occurred among children and young adolescents. In this recorded death, 2.5 million dead cases were recorded among newborn deliveries; 1.6 million deaths occurred among age bracket 1-11 months; 1.3 million deaths oc- curred among age bracket 1-4 years; 600 deaths from age bracket 5-9 years and children between age 10-14 years recorded 400,000 thousand deaths. The report indicated that the world has indeed improved on its re - cord of child mortality compared to past records. About 5.5 million children under the age of five-years-old died in 2017 in contrast to about 12.6 million dead cases in this age cohort in 1990. This translated to a rate reduc- tion from 93 deaths per 1000 live births in 1990 to about 39 deaths per 1000 live births in 2017 Figure 1.

Geographically in 2017, the Northern America and Europe region performs better on their under-five mortality rate record by recording the lowest rate of 6 deaths per 1000 live births in 2017 [7]. The Central and Southern Asia recorded 43 deaths per 1000 live births while the Eastern and South-Eastern Asia had 16 deaths per 1000 live births. Latin America and the Caribbean reported 18 deaths per 1000 live births while Oceania recorded 23 deaths per 1000 live births. The Sub-Saha - Mortality rates 1990 1995 2000 2005 2010 2017 1990 1995 2000 2005 2010 2017 Deaths per 1,000 100 75 50 250Under-five mortality rate Neonatal mortality rate Mortality rate among children aged 5-14 years 93 3912.6 11.3 9.88.3 7.0 5.4 15 71.71.6 1.41.2 1.1 0.9 37 185.0 4.5 4.03.5 3.1 2.5 Deaths (in millions) Number of deaths Under-five deaths Neonatal deathsDeaths among children aged 5-14 years Figure 1: Child mortality decline (1990-2017) Source: [ 7].

ISSN: 2469-5831 Etikan et al. Int J Clin Biostat Biom 2019, 5:022 DOI: 10.23937/2469-5831/1510022 • Page 3 of 6 • a group classification deviation (α i) and the random ef- fect (e ij). The ANOVA makes a comparison of the varia- tion between samples (Sum of squares for groups: SSB) relative to the variation within samples (sum of squares for Error: SSE).

Mathematically, the equation for the one-way ANO - VA can be stated as follows:

( ) () () 22 2 = 1 = 1 = 1 = n nn i ii yi y yi y yi yi − −+ − ∑ ∑∑  (2) The equ (2) can be written as:

The total sum of Squares (SSTO) = Regression sum of squares (SSR) + Error sum of squares(SSE) The following degrees of freedom namely n-1, 1, and n-2 are associated with the SSTO, SSR, and SSE respec - tively.

Where n = sample size.

In general, the Table 1 below gives a summary of the One-way ANOVA F-test The p-value is derived when the comparison of the F Cal is made with the F-distribution table with their ap- propriate degrees of freedom. If the null hypothesis is rejected, a post-hoc test is required to determine a pair of groups that are responsible for the test significance.

The Tukey’s method, Bonferroni’s and Scheffe’s tests are some of the common ANOVA post-hoc tests [ 19]. Result According to Table 2 above, the mean under-five mortality rate of Bangladesh is 77.995 while for Brazil is 31.921. Nigeria have the highest a mean under-five tween 1991 through 2017 was used in this study. Method The One-way Analysis of Variance (ANOVA) statisti- cal methodology was adopted in this study. InVivoStat statistical software which was based on an r-program - ming language was used in the analysis of this study.

ANOVA is multiple comparisons and parametric method used to test a mean effect difference for more than two groups. It could be considered as an advanced exten- sion of the t-test independent sample distribution. Orig- inally invented by Sir Ronald A. Fisher; a versatile and renowned Statistician to investigate treatment effects in agricultural experiments, the method is now widely used in Economics, Medicine and social sciences field [16]. This statistical methodology assumes that obser- vations sets under consideration are independent, nor- mally distributed and the condition of homogeneity of variance (equal variance) is satisfied [17, 18]. This meth- od generally seeks to test the null hypothesis that:

H 0: µ1 = µ 2 = µ3 =……..= µk versus the alternative hypothesis that:

H 1: k ∃ 1 ≤ i, l ≤ k: µ i ≠ µ l ( at least one of the pair mean is not equal) The linear model for the One-way ANOVA is given as follows: X = i ij ij e µα++ (1) The “I” entails the group membership while the “j” subscript denotes class membership (from the value of 1 to n). The Xij is equal to three distinct components namely; the overall mean of the experimental units( µ), Table 1: Summary of one-way ANOVA test. Sources of VariationDFSum of Squares (SS) Mean Squares (MS)F Regression 1 ( ) 2 1 = n i SSR yi y = − ∑  = 1 SSR MSR = MSR Fcal MSE Residual Error n-2 ( ) 2 1 = n i SSE yi yi = − ∑  = 2 SSE MSE n− Total n-1 () 2 1 = n i SSTO yi y = − ∑ The F Cal is the test statistic. Table 2: Descriptive statistics of the countries under-five mortality rate.

MeanNStd Dev Std error Categorization Factor levels Bangladesh 77.992834.42 6.5 Brazil 31.922815.27 2.89 Nigeria 163.172838.95 7.36 South Africa 63.912815.17 2.87 Turkey 35.112819.1 3.61 ISSN: 2469-5831 Etikan et al. Int J Clin Biostat Biom 2019, 5:022 DOI: 10.23937/2469-5831/1510022 • Page 4 of 6 • model is significant which entails that the means of the under-five mortality of at least a pair of the countries under evaluation are significantly different.

Thus, a post-hoc test using the Tukey’s Method will be used to further evaluate this significant difference as a result of not accepting the null hypothesis of equal means the under-five mortality rate of the countries.

The post-hoc comparison indicated that the means of the under-five mortality rate between Bangladesh versus Brazil; Bangladesh versus Nigeria; Bangladesh mortality rate of 163.171, South Africa have 63.913 and Turkey have mean under-five mortality rate of 35.107 Figure 2.

The normality test using the Shapiro Wilk statistic shown that the under-five mortality rate of the coun - tries is normally distributed (p > 0.05). This normality property of the data can be visualized as indicated in Figure 3 below.

Table 3 gave the F statistic of the ANOVA distribu - tion. Since the p-value < 0.0001, it show that the ANOVA Under-Five Mortality Rate BANGLADESH BRAZILNIGERIA CountrySOUTH AFRICA TURKEY 200 150 100 50 Figure 2: Box-plot of the countries under-five mortality rate. Normal probability plot Theoretical Quantiles Sample Quantiles -2 -1 0 1 \ 2 50 0 -50 Figure 3: Probability plot of the under-five mortality rate.

ISSN: 2469-5831 Etikan et al. Int J Clin Biostat Biom 2019, 5:022 DOI: 10.23937/2469-5831/1510022 • Page 5 of 6 • 6. Ahmad OB, Lopez AD, Inoue M (2000) The decline in child mortality: A reappraisal. Bulletin of the World Health Orga- nization 78: 1175-1191.

7. United Nations Inter-Agency Group for Child Mortality Esti- mation [UN IGME] (2018) Levels & trends in child mortality.

8. Bereka SG, Habtewold FG, Nebi TD (2017) Under-five mortality of children and its determinants in Ethiopian So- mali regional state, Eastern Ethiopia. Health Science Jour- nal 11.

9. Rai SK, Kant S, Srivastava R, Gupta P, Misra P, et al.

(2017) Causes of and contributors to infant mortality in a rural community of North India: Evidence from verbal and social autopsy. BMJ Open 7.

10. Suwal JV (2001) The main determinants of infant mortality in Nepal. Social Science and Medicine Journal 53: 1667- 1681.

11. Adeyele IT, Ofoegbu DI (2013) Infant and child mortality in Nigeria: An impact analysis. International Journal of Eco- nomics Practices and Theories 3: 122-132.

12. Dawit G Ayele, Temesgen T Zewotir, Hemry Mwambi (2017) Survival analysis of under-five mortality using cox and frailty models in Ethiopia. Journal of Health, Population and Nutrition 36. 13. Adewemimo Adeyinka, Henry D Kalter, Jamie Perin, Alain K Koffi, John Quinley, et al. (2017) Direct estimates of cause-specific mortality fractions and rates of under-five deaths in the northern and southern regions of Nigeria by verbal autopsy interview. PLoS One 12: e0178129. 14. Akinyemi JO, Bamgboye EA, Ayeni O (2015) Trends in neonatal mortality in Nigeria and effects of bio-demograph - ic and maternal characteristics. BMC Pediatrics 15: 36. 15. https://data.unicef.org/country/bgd 16. Aczel AD (1989) Complete business statistics, (7 th edn), Mc Grawill Irwin, USA.

17. Yoosun Jamie Kim, Cribbie Robert (2018) ANOVA and the variance homogeneity assumption: Exploring a better gatekeeper. British Journal of Mathematical and Statistical Psychology 71: 1-12. versus Turkey; Brazil versus Nigeria; Brazil versus South Africa; Nigeria versus South Africa; Nigeria versus Tur- key; and South Africa versus Turkey were all statistically significant (p < 0.05) [ 20].

Conclusion It can, therefore, be inferred that among the five countries under consideration, Nigeria has the highest under-five mortality rate (163.17 ± 38.42) followed by Bangladesh (77.99 ± 34.42). However, Brazil has the lowest under-five mortality rate (21.92 ± 15.27) close- ly followed by Turkey (35.12 ± 19.10). Nigeria’s average under-five mortality rate between 1990 through 2017 is more than twice the average rate of Brazil and almost twice of Turkey’s under-five mortality rate within the period under review. Therefore, there is a need for the Nigeria government to improve the country’s health care systems by implementing policies and regulations to cater for maternal and child health. This improvement is urgently needed if the country will be able to fulfill the United Nations Sustainable Development Goals in the area of child health by the year 2030. References 1. The World Bank (2018) A changing world population.

2. https://www.un.org/sustainabledevelopment/sustain- able-development-goals 3. Kazembe L, Clarke A, Kandala N (2012) Childhood mor- tality in sub-Saharan Africa: Cross-sectional insight into small-scale geographical inequalities from census data.

BMJ Open 2.

4. United Nations Children’s Fund (2018). Under-five mortal- ity.

5. Ali Kazemi Karyani, Zhila Kazemi, Faramarz Shaahmadi, Zohreh Arefi, Zahra Meshkani (2015) The main determi- nants of under 5 mortality rate (U5MR) in OECD countries:

A cross-sectional study. Int J Pediatr 3: 14. Table 3: ANOVA table.

Sums of squares Degrees of freedom Mean square F-value p-value Country 317843.7 479460.92112.56< 0.0001 Residuals 95304.83 135705.962 Difference Lower 95% CI Upper 95% CI Std error p-value Comparison Bangladesh vs. Brazil 46.0732.03 60.12 7.10.0001 *** Bangladesh vs. Nigeria -85.18-99.22 -71.13 7.10.0001 *** Bangladesh vs. South Africa 14.080.04 28.13 7.10.2799 Bangladesh vs. Turkey 42.8928.85 56.93 7.10.0001 *** Brazil vs. Nigeria -131.25-145.29 -117.21 7.10.0001 *** Brazil vs. South Africa -31.99-46.04 -17.95 7.1 0.0001 *** Brazil vs. Turkey -3.19-17.23 10.86 7.10.9915 Nigeria vs. South Africa 99.2685.21 113.3 7.1010.0001 *** Nigeria vs. Turkey 128.06114.02 142.11 7.10.0001 *** South Africa vs. Turkey 28.8114.76 42.85 7.10.0008 ** Note: *p < .05; **p < .01; ***p < .001 indicates a statistical significance.

ISSN: 2469-5831 Etikan et al. Int J Clin Biostat Biom 2019, 5:022 DOI: 10.23937/2469-5831/1510022 • Page 6 of 6 • 20. MO Jaiyeola, SO Oyamakin, JO Akinyemi, SA Adebowale, AU Chukwu, et al. (2016) Assessing infant mortality in Ni- geria using artificial neural network and logistic regression models. British Journal of Mathematics & Computer Sci- ence 19: 1-14.

18.

Cochran WG (1947) Some consequences when the as- sumptions for the analysis of variance are not satisfied.

Biometrics 3: 22-38.

19. Eva Ostertagová, Oskar Ostertag (2013) Methodology and application of one-way ANOVA. American Journal of Me- chanical Engineering 1: 256-261.

ISSN: 2469-5831 Etikan et al. Int J Clin Biostat Biom 2019, 5:022