Determinant of Mortality

#### **Chapter 1**

## Level and Determinant of Child Mortality Rate in Ethiopia

*Setegn Muche Fenta and Haile Mekonnen Fenta*

#### **Abstract**

**Background:** One of the objectives of the Sustainable Development Goals (SDG) is to diminish the under-five mortality rate and improvement in maternal health. This study aims to identify factors that affect under-five mortality based on the 2016 EDHS dataset using the multilevel count regression model. **Method:** The EDHS data have a two-level hierarchical structure, with 14,370 women nested within 11 geographical regions. Multilevel count models were employed to predict the outcomes. **Results:** The data were found to have excess zeros (53.7%); the variance (1.697) is higher than its mean (0.90). Among families of count models, the HNB model was found to be a better fit for the dataset than the others. The study revealed that a child of multiple births is 1.45 more likely to die as compared with a single birth. Babies delivered in the private sector are a 0.65 lower risk of under-five mortality compared to the babies delivered at home. **Conclusion:** Vaccination of child, family size, age of mother, antenatal visit, birth interval, birth order, contraceptive used, father education level, mother education level, father occupation, place of delivery, child twin, age first birth and religion were significantly associated with under-five mortality. The Ministry of Health should work properly to raise the awareness of parents for vaccination, family planning services and efforts should be made to improve the parental educational level.

**Keywords:** under five mortality, Ethiopia, hurdle negative binomial

#### **1. Introduction**

Seventeen Sustainable Development Goals (SDGs) were agreed upon by global leaders based on millennium development goals. SDG goal 3 target 3.2 is to reduce infant and under-5 mortality by 2030. The target is to drop the "neonatal mortality as low as 12 per 1000 live births and under-five Mortality to as low as 25 per thousand live births" [1]. According to UNICEF, the problem of under-five mortality requires urgent attention from the health sector. If the conditions remain as such, approximately 60 million innocent children will die until 2030 (more than half of the Ethiopian population) [2].

Every year, millions of children under 5 years of age die (WHO, 2016). In 2016, about 15,000 children still die every single day globally. The level of under-five mortality remains high in certain regions of the world. Sub-Saharan African Region continues to be the region with the highest rate of under-five mortality. In 2016, the under five-mortality rate in sub-Saharan African was 79 deaths per 1000 live births, nearly 15 times the average in developed countries [1–3].

In Ethiopia, the under-five mortality rate stands at 67 per 1000 live births, with large inequalities in her different regions. Every year, more than 257,000 children under the age of five dies [4]. If the situations continue as such, more than 3,084,000 children will die until 2030.

Most of the previous studies were done by using single-level binary logistic and survival analysis including the above studies we mentioned, but under-five mortality varies across different physical, ecological, and political structures within countries. One such contextual determinant is the regional environment [5–7]. In Ethiopia, there have been regional variations in a number of under-five mortality [8, 9]. In this study, we assume the region affects modeling the determinants of the number of under-five mortality, which may be due to the heterogeneity in regions of the study. As a result, the multilevel model approach is relatively better to determine the covariates related to under-five mortality [10, 11]. Therefore, this study was targeted to investigate the major socio-economic, demographic, health, and environmental proximate factors that might influence under-five mortality in Ethiopia with different multilevel count model approaches.

#### **2. Methodology**

The data used for this study was taken from the 2016 EDHS which is a nationally representative survey of women's age (15–49 years age) groups taken from the CSA, Ethiopia. This survey is the fourth compressive survey designed to provide estimates for the health and demographic variables of interest for the whole urban and rural areas of Ethiopia as a domain. In all of the selected households, measurements were collected from children age 0–59 months, women age 15–49 years, and men age 15–59 years old.

The main outcome variable in this study is the number of under-five death per mother. Thus, this paper attempts to include socioeconomic, demographic, health, and environmental related factors that are assumed as a potential determinant for the barriers in the number of under-five death per mother, adopted from literature reviews and their theoretical justification.

A multilevel count regression model can account for a lack of independence across levels of nested data (in this case, individual mothers nested within regions). Conventional count regression assumes that all experimental units are independent in the sense that any variable which affects the occurrence of under-five mortality has the same effect in all regions, but observations from similar environments might have shown similar behaviors as opposed to observations from a different environment. Multilevel models are used to assess whether the effects of predictors vary from region to region. The main statistical model of multilevel analysis is the HGLM, an extension of the (GLM) that includes nested random coefficients [10, 12].

The multilevel count regression model has a count outcome (number of underfive mortality). Now consider the full model equation for the two-level Poisson regression with *i th* individual mothers is nested within the *j th* region. The response variable, i.e., we let *Yij* is the *i th* individual mothers in *j th* region has under-five mortality. Using a log link function the two-level model is given by:

$$\log\left(\mu\_{\vec{\eta}}\right) = \beta\_{\vec{\alpha}} + \sum\_{l=1}^{k} \beta\_{l\vec{\eta}} \chi\_{l\vec{\eta}}; l = 1, 2, \dots, k \tag{1}$$

Where *βoj* ¼ *β<sup>o</sup>* þ *Uoj*, *βlj* ¼ *β*<sup>1</sup> þ *U*1*<sup>j</sup>*, … *:* þ *β<sup>k</sup>* þ *Ukj*. The level-two model (1) can be rewritten as:

*Level and Determinant of Child Mortality Rate in Ethiopia DOI: http://dx.doi.org/10.5772/intechopen.100482*

$$\log\left(\mu\_{\vec{\eta}}\right) = \beta\_o + \sum\_{l=1}^{k} \beta\_l \mathbb{x}\_{l\vec{\eta}} + U\_{o\vec{\eta}} + \sum\_{l=1}^{k} U\_{l\vec{\eta}} \mathbb{x}\_{l\vec{\eta}} \tag{2}$$

Where *xij* <sup>¼</sup> *<sup>x</sup>*1*ij*, *<sup>x</sup>*2*ij*, … *xkij* � � represent the first and the second level covariates, *β* ¼ *β*0, *β*1, *:* … *β<sup>k</sup>* ð Þ are regression coefficients, *Uoj*, *U*1*j*, *::* … *Ukj* are the random effect of the model parameter at level two (region level). It assumed that the *Uoj*, *U*1*j*, *::* … *Ukj* follow a normal distribution with mean zero and variance *σ<sup>u</sup>* <sup>2</sup> [13]. Without *Uoj*, *U*1*j*, *::* … *Ukj*, Eq. (2) can be considered as a single-level Poisson regression model.

#### **2.1 Empty model**

The empty two-level model for a count outcome variable refers to a population of groups (level-two units) and specifies the probability distribution for group-dependent *μij* in *Yij* ¼ *μij* þ *εij* without taking further explanatory variables into account. We focused on the model that specifies the transformed log *<sup>μ</sup>ij* � � to have a normal distribution. This is expressed, for a general link function log ð Þ *μ* , by the formula.

$$\log\left(\mu\_{\vec{\eta}}\right) = \beta\_0 + U\_{\vec{\alpha}} \tag{3}$$

Where *β<sup>o</sup>* is a fixed coefficient and *Uoj* is a random term that is independently and normally distributed with mean 0 and variance *σ<sup>u</sup>* <sup>2</sup> (random intercept variance) [14]. This model is also named as empty Poisson regression model (null model). A null model contains only a response variable, and no explanatory variables other than an intercept. Thus, *σ<sup>u</sup>* <sup>2</sup> measures regional variations of under-five mortality.

#### **2.2 The random intercept model**

A random intercepts model is a model in which intercepts are allowed to vary. The scores on the dependent variable for each individual observation are predicted by the intercept that varies across regions, but the relationship between explanatory and response variables cannot differ between groups. The random intercept model expresses the natural log of *μij*, as a sum of a linear function of the explanatory variables. That is,

$$\begin{aligned} \log \left( \mu\_{ij} \right) &= \beta\_{0j} + \beta\_1 \mathbf{x}\_{1ij} + \beta\_2 \mathbf{x}\_{2ij} + \dots \ &+ \beta\_k \mathbf{x}\_{kij} \\ &= \beta\_{oj} + \sum\_{l=1}^{k} \beta\_l \mathbf{x}\_{lij} \end{aligned} \tag{4}$$

Where the intercept term *βoj* is allowed to vary across the regions and is given by the sum of an average intercept *β*<sup>0</sup> and regions-dependent deviations *Uoj*, that is.

$$
\beta\_{o\dot{\jmath}} = \beta\_o + U\_{o\dot{\jmath}}
$$

As a result, we have:

$$\log\left(\mu\_{ij}\right) = \beta\_o + \sum\_{l=1}^{k} \beta\_l \mathbf{x}\_{lij} + U\_{oj} \tag{5}$$

Note that the above equation *<sup>β</sup><sup>o</sup>* <sup>þ</sup> <sup>P</sup>*<sup>k</sup> <sup>l</sup>*¼1*βlxlij* is the fixed part of the model. The remaining *Uoj* is called the random part of the model. It is assumed that the random part of *Uoj* are mutually independent and normally distributed with mean zero and variance *σuo*2.

#### **2.3 The random coefficients model**

A random slopes model is the slopes are different across regions. In other words, the relationship between an explanatory variable and the response is different across all regions. If we fit a model based on the same predictors on the response variable for all regions separately, we may obtain different intercepts and slopes for each region. Now consider a model with group-specific regressions, on a single level one explanatory variable *X*,

$$\log\left(\mu\_{i\circ}\right) = \beta\_{0j} + \beta\_{1\circ}\varkappa\_{1\circ} \tag{6}$$

The intercepts *βoj* as well as the regression coefficients or slopes, *β*1*<sup>j</sup>* are group dependent. These group dependent coefficients can be split into an average coefficient and the group dependent deviation:

$$
\beta\_{o\dot{j}} = \beta\_o + U\_{o\dot{j}}
$$

$$
\beta\_{1\dot{j}} = \beta\_1 + U\_{1\dot{j}}
$$

Substitution into (6) leads to the model

$$\log\left(\mu\_{\vec{\eta}}\right) = \left(\beta\_o + U\_{o\vec{\eta}}\right) + \left(\beta\_1 + U\_{\vec{\eta}}\right)\mathbf{x}\_{\mathbb{1}\vec{\eta}} = \beta\_o + \beta\_1\mathbf{x}\_{\mathbb{1}\vec{\eta}} + U\_{o\vec{\eta}} + U\_{\vec{\eta}}\mathbf{x}\_{\mathbb{1}\vec{\eta}}\tag{7}$$

There are two random group effects, the random intercept *Uoj* and the random slope *U*1*<sup>j</sup>*. It is assumed that the level two residuals *Uoj* and *U*1*<sup>j</sup>* have both zero mean given the value of the explanatory variable X. Thus, *β*<sup>1</sup> is the average regression coefficient like *β*<sup>0</sup> is the average intercept. The first part of Eq. (7) *β<sup>o</sup>* þ *β*1*x*1*ij* is called the fixed part of the model whereas the second part *Uoj* þ *U*1*jx*1*ij* is called the random part of the model.

The term *Uoj* þ *U*1*jx*1*ij* can be regarded as a random interaction between group and predictors (X). This model implies that the groups are characterized by two random effects: their intercept and their slope. These two groups' effects *Uoj* and *U*1*<sup>j</sup>* will not be independent. Further, it is assumed that, for different groups, the pairs of random effects *Uoj*, *U*1*<sup>j</sup>* � � are independent and identically distributed. Thus, the variances and covariance of the level-two random effects *Uoj*, *U*1*<sup>j</sup>* � � are denoted by:

$$\operatorname{Var}\left(U\_{\neq}\right) = \sigma\_{00} = \sigma\_{0}^{2}$$

$$\operatorname{Var}\left(U\_{\neq}\right) = \sigma\_{11} = \sigma\_{1}^{2}$$

$$\operatorname{Cov}\left(U\_{\neq}, U\_{\neq}\right) = \sigma\_{01}$$

The model for a single explanatory variable discussed above can be extended by including more variables that have random effects.

#### **2.4 Multilevel negative binomial regression model**

The hierarchical study design or the data collection procedure, over-desperation, and lack of independence may occur simultaneously, which render the standard NB model inadequate. To account for the over-desperation and the inherent correlation of observations, a class of multilevel NB regression models with random effects is presented. The multilevel NB model is then generalized to cope with a more complex correlation structure. The multilevel NB model derives by allowing for between regional random variation of the expected number of under-five mortality *μij*.

$$
\ln \mu\_{\vec{\eta}} = \eta\_{\vec{\eta}} + \mathfrak{e}\_{\vec{\eta}} \tag{8}
$$

Where cov *eij*, *<sup>η</sup>ij* � � <sup>¼</sup> 0 and exp *eij* � � follows a gamma probability distribution, Γð Þ*v* , with mean 1 and variance *α* ¼ *v*�1. Integrating concerning *eij* [15] the resulting probability distribution

$$p\left(Y\_{\vec{\eta}} = y\_{\vec{\eta}}\right) = \frac{\exp\left(-\exp\left(\eta\_{\vec{\eta}} + e\_{\vec{\eta}}\right)\exp\left(\eta\_{\vec{\eta}} + e\_{\vec{\eta}}\right)^{y\_{\vec{\eta}}}\right)}{y\_{\vec{\eta}}!} \tag{9}$$

One version of the multilevel negative binomial regression model is obtained;

$$p\left(Y\_{\vec{\eta}} = \boldsymbol{\nu}\_{\vec{\eta}}\right) = \frac{\Gamma\left(\boldsymbol{\nu}\_{\vec{\eta}} + \boldsymbol{\nu}\right) \boldsymbol{\nu}^{\boldsymbol{\nu}} \mu\_{\vec{\eta}} \ast \boldsymbol{\nu}\_{\vec{\eta}}}{\boldsymbol{\nu}\_{\vec{\eta}}! \Gamma(\boldsymbol{\nu}) \left(\boldsymbol{\nu} + \boldsymbol{\mu}\_{\vec{\eta}} \ast \boldsymbol{\nu}\right)^{\boldsymbol{\nu} + \boldsymbol{\eta}\_{\vec{\eta}}}} \boldsymbol{\nu}\_{\vec{\eta}} = \mathbf{0}, \mathbf{1}, 2... \tag{10}$$

With mean and variance given, respectively, as follows:

The multilevel negative binomial regression model gives the expected mean of a number of under-five mortality. *E Yij* � � <sup>¼</sup> *<sup>μ</sup>ij* <sup>∗</sup> <sup>¼</sup> log *<sup>η</sup>ij* � �. Its variance is given by var yij � � <sup>¼</sup> <sup>μ</sup>ij <sup>þ</sup> αμ<sup>2</sup>*:* ij Where *ηij* ¼ *β*0*<sup>j</sup>* þ *β*1*<sup>j</sup> x*1*ij* þ *β*2*<sup>j</sup> x*2*ij* þ *:* … þ *βkjxkij*

#### **2.5 Multilevel ZIP regression model**

ZIP regression is useful for modeling count data with excess zeros, but because of hierarchical study design or the data collection procedure, zero-inflation and correlation may occur simultaneously [16]. Multilevel ZIP regression is used to overcome these problems. Let *Yij* be a count say, the number of under-five mortalities the *i th* mother in the *j th* region follows a multilevel ZIP distribution:

$$p\left(Y\_{\vec{\eta}} = y\_{\vec{j}}\right) = \begin{cases} \pi\_{\vec{\eta}} + \left(1 - \pi\_{\vec{\eta}}\right) \exp\left(-\mu\_{\vec{\eta}}\right), & \text{if } y\_{\vec{\eta}} = \mathbf{0} \\\\ \left(1 - \pi\_{\vec{\eta}}\right) \frac{\exp\left(-\mu\_{\vec{\eta}}\right) \mu\_{\vec{\eta}}\nu\_{\vec{\eta}}}{y\_{\vec{\eta}}!}, & \text{if } y\_{\vec{\eta}} = \mathbf{1}, 2, \dots \text{...} \end{cases} \tag{11}$$

Recently, the ZIP regression model has been extended to the random effects setting, where by random components *w <sup>j</sup>* and *u <sup>j</sup>* are incorporated within the logistic and Poisson linear predictors to account for the dependence of observations within *j th* region [16]. These random effects ZIP models are region-specific in the

sense that the random effects *w <sup>j</sup>* and *u <sup>j</sup>* so introduced are specific to the *j th* region. In the following, a multi-level ZIP regression model is developed to handle correlated count data with extra zeros.

Without loss of generality, consider the two-level hierarchical situation where *Yij* represents the *i th* observation of under-five mortality the *j th* individual region ð Þ *i* ¼ 1, 2, *::* … , *n* and ð Þ *j* ¼ 1, 2, *::* … , *m* . Let *m* be the total number of individuals in each region and P*<sup>m</sup> j*¼1 P*ni <sup>i</sup>*¼1*ni* gives the total number of observations. The observations may be taken to be independent between regions, but certain withinhousehold and within-individual correlations are anticipated, which can be modeled explicitly through random effects attached to the linear predictors:

$$\log\left(\mu\_{\vec{\eta}}\right) = \beta\_o + \sum\_{l=1}^{k} \beta\_l \mathbb{1}\_{l\vec{\eta}} + U\_{o\vec{\eta}} + \sum\_{l=1}^{k} U\_{l\vec{\eta}} \mathbb{1}\_{l\vec{\eta}} \tag{12}$$

$$\log \text{it}(\pi\_{\vec{\eta}}) = \log \left(\frac{\pi\_{\vec{\eta}}}{1 - \pi\_{\vec{\eta}}}\right) = \chi\_o + \sum\_{l=1}^{k} \chi\_l \mathbf{z}\_{l\vec{\eta}} + W\_{o\vec{\eta}} + \sum\_{l=1}^{k} W\_{l\vec{\eta}} \mathbf{z}\_{l\vec{\eta}} \tag{13}$$

Here, the covariates *Xij* and *Zij* appearing in the respective Poisson and logistic components are not necessarily the same, *β* and *γ* are the corresponding vectors of regression coefficients [17, 18]. For simplicity of presentation, the random effect *u* and *w* assumed to be independent and normally distributed with mean zero and variance *σ<sup>u</sup>* <sup>2</sup> and *σw*<sup>2</sup> respectively.

#### **2.6 Multilevel ZINB regression model**

Multilevel ZINB regression model is proposed for over-dispersed count data with extra zeros. A multilevel ZINB regression incorporating random effects to account for data dependency and over-dispersion is used [17]. Let *Yij*ð Þ *i* ¼ 1, 2, *::* … , *n*; *j* ¼ 1, 2, *::* … , *m* be a count say, the under-five mortality of the *i th* mother in *j th* region follows a ZINB distribution:

$$p\left(\mathbf{Y}\_{\vec{\eta}}=\mathbf{y}\_{\vec{\eta}}\right)=\begin{cases} \pi\_{\vec{\eta}}+\frac{\left(\mathbf{1}-\pi\_{\vec{\eta}}\right)}{\left(\mathbf{1}+a\mu\_{\vec{\eta}}\right)^{-\frac{1}{a}}}, & \text{if }\boldsymbol{\mathcal{y}}\_{\vec{\eta}}=\mathbf{0} \\\\ \mathbf{1}-\pi\_{\vec{\eta}}\frac{\Gamma\left(\boldsymbol{\mathcal{y}}\_{\vec{\eta}}+\boldsymbol{\mathcal{y}}\_{\vec{\omega}}\right)}{\boldsymbol{\mathcal{y}}\_{\vec{\eta}}\Gamma\left(\boldsymbol{\mathcal{y}}\_{\vec{\alpha}}\right)}\left(\mathbf{1}+a\mu\_{\vec{\eta}}\right)^{-\frac{1}{a}}\left(\mathbf{1}+\frac{\mathbf{1}}{a\mu\_{\vec{\eta}}}\right)^{-\boldsymbol{\mathcal{y}}\_{\vec{\eta}}}, & \text{if }\boldsymbol{\mathcal{y}}\_{\vec{\eta}}>\mathbf{0} \end{cases}$$

In my study, mothers are nested in regions and the number of under-five mortality is taken to be the response variable. Let n be the total number of individuals in each region and P*<sup>m</sup> j*¼1 P*ni <sup>i</sup>*¼<sup>1</sup>*ni* gives the total number of observations. Hence the responses of under-five mortality which belong to the different regions are independent, while they are correlated for those who live in the same region. This dependence can be modeled explicitly by considering suitable random effects in the linear predictor.

Negative binomial models for counts permit μ to depend on explanatory variables. Then the two-level ZINB regression model can be expressed in vector form as:

$$\log\left(\mu\_{\vec{\eta}}\right) = \beta\_o + \sum\_{l=1}^{k} \beta\_l \mathbb{x}\_{l\vec{\eta}} + U\_{o\vec{\eta}} + \sum\_{l=1}^{k} U\_{l\vec{\eta}} \mathbb{x}\_{l\vec{\eta}} \tag{14}$$

*Level and Determinant of Child Mortality Rate in Ethiopia DOI: http://dx.doi.org/10.5772/intechopen.100482*

$$\log \text{it} \left( \pi\_{\vec{\eta}} \right) = \log \left( \frac{\pi\_{\vec{\eta}}}{1 - \pi\_{\vec{\eta}}} \right) = \chi\_o + \sum\_{l=1}^{k} \chi\_l \text{z}\_{l\vec{\eta}} + W\_{o\vec{j}} + \sum\_{l=1}^{k} W\_{l\vec{\eta}} \text{z}\_{l\vec{\eta}} \tag{15}$$

Here, the covariates *Xij* and *Zij* appearing in the respective negative binomial and logistic components are not necessarily the same, *β* and *γ* are the corresponding vectors of regression coefficients [17, 18]. The vectors *w <sup>j</sup>* and *u <sup>j</sup>* denote the regionspecific random effects for simplicity of presentation. The random effect *u* and *w* assumed to be independent and normally distributed with mean zero and variance *σu* <sup>2</sup> and *σw*<sup>2</sup> respectively.

#### **2.7 Multilevel hurdle regression model**

The hurdle model [19] has mostly been adopted to conduct an economic analysis of healthcare utilization. The hierarchical study design or the data collection procedure, zero-inflation, and lack of independence may occur simultaneously, which the standard Hurdle Poisson regression model is inadequate. To account for the preponderance of zero counts and the inherent correlation of observations, a class of multilevel Hurdle Poisson regression models with random effects is presented. In this study, suppose that *Yij* is the number of under-five mortality in *i th* mother in the *j th* region. Then multilevel Poisson Hurdle model can be written as follows

$$p\left(Y\_{\vec{\eta}} = y\_{\vec{\eta}}\right) = \begin{cases} \pi\_{\vec{\eta}} & \text{if } \mathfrak{y}\_{\vec{\eta}} = \mathbf{0} \\\\ \left(1 - \pi\_{\vec{\eta}}\right) \frac{\exp\left(-\mu\_{\vec{\eta}}\right)\mu\_{\vec{\eta}}{\vec{\eta}}}{\left(1 - \exp\left(-\mu\_{\vec{\eta}}\right)y\_{\vec{\eta}}!} & \text{if } \mathfrak{y}\_{\vec{\eta}} = 1, 2, \dots & \mathbf{0} \le \mathfrak{x}\_{\vec{\eta}} \le \mathbf{1} \end{cases} \right) \tag{16}$$

In the regression setting, both the mean *μij* and zero proportion *πij* parameters are related to the covariate vectors *xij* and *zij* respectively. Moreover, responses within the same region are likely to be correlated. To accommodate the inherent correlation, random effects *u <sup>j</sup>* and *w <sup>j</sup>* are incorporated in the linear predictors *ηij* for the Poisson part and *ξij* for the zero part. The

Hurdle Poisson mixed regression model is

$$\eta\_{\vec{\eta}} = \log \left( \mu\_{\vec{\eta}} \right) = \varkappa\_{\vec{\eta}}{}^T \beta + u\_j \tag{17}$$

$$\xi\_{\vec{\imath}\vec{\jmath}} = \log\left(\frac{\pi\_{\vec{\imath}\vec{\jmath}}}{1 - \pi\_{\vec{\imath}\vec{\jmath}}}\right) = z\_{\vec{\imath}\vec{\jmath}}{}^T \chi + w\_{\vec{\jmath}} \tag{18}$$

Where *β* and *γ* are the corresponding ð Þ� *p* þ 1 1 and ð Þ� *q* þ 1 1 vector of regression coefficients. The random effects *u <sup>j</sup>* and *w <sup>j</sup>* are assumed to be independent and normally distributed with mean 0 and variance *σ<sup>u</sup>* <sup>2</sup> and *σw*2, respectively [12].

#### **3. Result and discussion**

#### **3.1 Result**

A total of 14,370 women from all the 11 regions of the country were included and 7720 (53.3%) of the mothers have not faced any under-five death and only 78 (0.5%) of them lost 7 of their under-five children. Since there is a large number of zero outcomes. Additional screening of the number of under-five death calculated showed that the variance (1.697) is greater than the mean (0.9) indicating overdispersion. This is an indication that the data could be fitted better by count data models which takes into account excess zeroes (**Table 1**).

The mean numbers under-five death for uneducated fathers (1.1063) are higher than fathers with secondary and above education (0.353) and the mean number of under-five death that children who are delivered at home (1.0995) have highest than those delivered in health institutions (0.2507). Moreover, the highest and lowest mean number of child death are observed for a child of birth order of four and above and first birth order (1.1189 and 0.6167) respectively.

The result also showed that the breastfeeding mothers have a lower mean number of under-five deaths (0.6254) and the highest mean number of under-five death is occurred children born less than or equal to 24 months (1.0568) (**Table 2**)

#### **3.2 Multilevel count analysis of the data**

#### *3.2.1 Test of heterogeneity*

Comparisons of multilevel models with their single-level count model, with LRT statistic given in **Table 3**. The values of LRT's for each model are larger than the critical value X2 <sup>α</sup>ð Þ¼ 2 5*:*99 with p-value <0.05. Thus, there is evidence of heterogeneity of under-five death across regions. It also observed that multilevel count regression model is best fit over the single level count regression models (**Table 3**).

#### *3.2.2 The goodness of fit and model selection criteria*

The multilevel HNB regression model has a smaller value in deviance, AIC, and BIC than the other model. Consequently, we conclude that in this study multilevel HNB regression model is better than the other model (**Table 4**).


#### **Table 1.**

*Frequency distribution of the number of under-5 deaths per mother.*


#### *Level and Determinant of Child Mortality Rate in Ethiopia DOI: http://dx.doi.org/10.5772/intechopen.100482*


#### **Table 2.**

*Summary statistics of predictor variables related to under-five death in Ethiopia.*


#### **Table 3.**

*Likelihood ratio test value for multilevel and ordinary count model.*


#### **Table 4.**

*Model selection criteria for the multilevel count regression models.*


#### **Table 5.**

*Summary results of multilevel HNB model selection criteria.*

#### *3.2.3 Model comparisons in multilevel HNB model*

The smallest deviance, AIC, and BIC is the better to fit. The result indicated that the random coefficient model is a better fit as compared to the empty model with random intercept and the random intercept and fixed-effect model (**Table 5**).

#### *3.2.4 Results of random coefficient HNB model*

From **Table 6** in the random effect for truncated count part, estimates for intercepts and the slopes vary significantly at 5% significance level, which implies that there is a considerable variation in the effects of family size, age of mother and




#### **Table 6.**

*The results of random coefficients HNB model.*

mother's education these variables differ significantly across the regions. The value of 0.45, 0.175, 0.100, and 0.102 are the estimated variance of intercept (region), family size, age of mother, and mother's education respectively.

The fixed part of **Table 6** shows vaccination of children has a significant impact on the number of non-zero under-five death per mother. The expected number of non-zero under-five death for vaccinated children are decreased by a factor of 0.73 as compared with non-vaccinated children.

Unexpectedly, the findings of this study also showed that family size is a significant determinant of under-death. The risk of under-five death increases as family size decreases. For a unit increased family size, then the expected number of nonzero under-five death per mother is decreased by 0.04%. And also, mother's current age is a significant positive association with under-five mortality. Particularly, with each yearly increase in the age of mother, the expected number of non-zero underfive death is increased by 0.06%.

The result also revealed that the expected number of non-zero under-five death whose mothers visited at least 4 times during pregnancy is 0.79 times lower compared to child whose mothers who have not received any antenatal check during pregnancy.

This study found that preceding birth interval has a significant negative association with under-five mortality. The expected number of non-zero under-five death with children born more than 36 months after the previous birth decreased by 31 percent relative to children born less than 2 years after the previous birth. In addition to this as birth order increases the under-five mortality shown an increase. The expected number of non-zero under-five deaths with children's birth order 4 and above is increased by 59% as compared to the first order.

The finding of this study also revealed that mother's and father's levels of education have a significant factor in the number of under-five death. The expected number of non-zero under-five death for mothers with primary education is 0.724 times lower as compared to those with non-educated. Likewise, the expected number of non-zero under-five death for fathers with secondary and above education is 0.716 times lower as compared to those with non-educated (**Table 6**).

The random effect for the logit part also shows that estimates for intercepts and slopes vary significantly, which suggests that there is considerable variation in the effects of vaccination of child, family size, age of mother, place of delivery, and child twins, these variables differ significantly across the regions. The value of 0.688, 0.161, 0.146, 0.042, 0.333, and 0.463 are the estimated variance of intercept (region), vaccination of child, family size, age of mother, place of delivery, and child twins respectively (**Table 6**).

The fixed part of zero-inflated HNB model indicted that the estimated odds that the number of under-five death becomes zero with vaccinated children is 1.70 times as compared to non-vaccinated children. An increase in family size by 1 result, the estimated odds that the number of under-five death becomes zero is increased by 1.25. Similarly, the age of mother increase in by a year, the estimated odds that the number of under-five death becomes zero is decreased by 17%.

The result also revealed that the estimated odds that the number of under-five death becomes zero with mothers visit 4 and above is 2.28 times higher as compared to mothers who have not received any antenatal visit during pregnancy. In addition to, the estimated odds that the number of under-five death becomes zero for those children born with preceding birth intervals of more than 36 months is 3.58 times children born with preceding birth intervals of fewer than 24 months. The study also found that the employment status of a father is found to have an association with under-five mortality. The odds of the number of under-five death becomes zero with children born from fathers who have to work is 0.72 times fathers without work. Moreover, estimated odds that the number of under-five death among mothers who are used contraceptives is 1.30 times more than as compared to mothers who were not used contraceptive (**Table 6**).

#### **3.3 Discussion of the results**

In this study, we have examined the influence of particular social, economic, and demographic characteristics of mothers on under-five mortality in Ethiopia. Results showed that several factors are implicated in under-five mortality.

The level of parental education emerged as a strong predictor of under-five mortality, that is, the mortality rate decreases with an increase in parental education level. This result is in line with the previous study that, the higher the level of maternal and father education, the lower child mortality [20–23]. The risk of underfive death associated with multiple births is very high relative to single births and this study is similar to the previous studies that birth type to be linked with under-five child death as multiple births are associated with a higher risk of child mortality [9, 21, 23, 24]. The result also showed that under-five mortality is decreased as the length of preceding birth interval increased. This finding is similar to Gebretsadik and Gabreyohannes [21], Bereka and Habtewold [24], and Getachew and Bekele [25].

The finding of the study revealed that the death of under-five children from mothers using contraceptive is significantly less than children from non-contraceptive methods using mothers. The result is in accordance with Getachew and Bekele [25] and Bedada [26]. Those vaccinated children are lower risk of mortality than that of non-vaccinated children. Similar result was observed in another study done by Berhie and Yirtaw [9].

Mother's age at first birth is negatively correlated with under-five mortality that decreased the risk of under-five mortality as increase mother's age at first birth. The estimated result also shows that mothers age at first birth increases reduced the risk of under-five mortality and mothers born their first child at a younger age face high under-five mortality risk which is similar to the previous studies conducted by different scholars [22–26]. In addition to this, the study reported that for every unit increase in the ages of mother, the risk of under-five mortality increases, and this is similar to the findings of Yaya et al. [22] and Alam et al. [23]. Further, the result of this study indicated that the religion of respondents has significantly associated with under-five death with those who practice Islamic and those who practice other religions having higher chances of experiencing under-five death compared to those who practice Orthodox Christianity religions. This is consistent with the study of Yaya et al. [22].

The study showed that children born from working fathers have a higher risk of mortality than non-working fathers. This finding is consistent with Getachew and Bekele [25] additionally, increase the number of antenatal visits during pregnancy is *Level and Determinant of Child Mortality Rate in Ethiopia DOI: http://dx.doi.org/10.5772/intechopen.100482*

reduce the risk of under-five mortality and this finding is confirmed by the previous researches [25]. Children born in the public and private sectors are at lower risk than those born at home. This might be due to the proper health care and attention they received during and after delivery. This has been confirmed by different studies [24, 25, 27].

The study also revealed that household size is an important variable that affects the number of under-five mortality. Amazingly, as household size increases the risk of under-five mortality significantly decreased. This result is consistent with Berhie and Yirtaw [9], Alam et al. [23], Bedada [26], and Ahmed et al. [28]. Birth order increases the under-five mortality also increases and this result is consistent with the literature reviewed and contribution from different studies on birth order [9, 24, 28].

#### **4. Conclusions and recommendations**

The purpose of this study was to identify, socioeconomic demographic, health, and environmental related determinants and to assess regional variation of a number of under-five mortality per mother in Ethiopia. The descriptive results showed that 53.7% of mothers have not experienced under-five death and only 0.5% of them lost 7 of their under-five children.

In multilevel count regression analysis, individual mothers are considered as nested within the various regions in Ethiopia. As a first step in the multilevel approach, the likelihood ratio test is applied to see if there are differences in the number of under-five death among the regions. The test suggested that, the number of under-five death varies among regions and multilevel count model fit better than the single level count model. Among the six multilevel count regression model, multilevel HNB model is the best to account for the heterogeneity of the number of under-five mortalities per mother among regions of Ethiopia.

From the three multilevel HNB regressions models, the random coefficients model provided the best fit for a number of under-five death per mother. In fixed part of random coefficients HNB model the variables like mother's age, education level of father, father's occupation, family size, age of mother at first birth, religion, vaccination of child, contraceptive use, birth order, preceding birth interval, child twin, place of delivery and antenatal visit have statistically significant effect on under-five mortality. The random part of multilevel HNB model also revealed that under-five deaths per mother differ among regions of the country in terms of mother's education, family size, ages of mother, place of delivery, vaccination of child, and type of birth. As a result, this study proposes that all regions need to have separate estimates of HNB regressions for all 11 geographical regions.

Based on the findings of the study, we forward the following possible recommendations:


interval, vaccination of child, and place of delivery to reduce under-five mortality.


#### **Author details**

Setegn Muche Fenta1,2\* and Haile Mekonnen Fenta1,2


\*Address all correspondence to: setegn14@gmail.com

<sup>© 2022</sup> The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Level and Determinant of Child Mortality Rate in Ethiopia DOI: http://dx.doi.org/10.5772/intechopen.100482*

#### **References**

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[2] Hug L, Sharrow D, You D. Levels & trends in child mortality: Report 2017. In: Estimates Developed by the UN Inter-agency Group for Child Mortality Estimation. 2017

[3] WHO. Children: Reducing Mortality. 2017

[4] USAID. Maternal, Neonatal and Child Health in Ethiopia. 2018

[5] Antai D. Regional inequalities in under-5 mortality in Nigeria: A population-based analysis of individualand community-level determinants. Population Health Metrics. 2011;**9**(1):6

[6] Montgomery MR, Hewett PC. Urban poverty and health in developing countries: Household and neighborhood effects. Demography. 2005;**42**(3):397-425

[7] Wang L. Health Outcomes in Lowincome Countries and Policy Implications: Empirical Findings from Demographic and Health Surveys. Vol. 2831. World Bank, Environment Department; 2002

[8] EDHS. Ethiopian Demographic and Health Survey. 2016

[9] Berhie KA, Yirtaw TG. Statistical analysis on the determinants of under five mortality in Ethiopia. American Journal of Theoretical and Applied Statistics. 2017;**6**(1):10-21

[10] Goldstein H. Multilevel Statistical Models. Vol. 922. John Wiley & Sons; 2011 [11] Hox JJ, Moerbeek M, van de Schoot R. Multilevel Analysis: Techniques and Applications. Routledge; 2010

[12] Harvey G. Multilevel Statistical Models. 2003

[13] Snijders T, Bosker R. Multilevel Modeling: An Introduction to Basic and Advanced Multilevel Modeling. 1999

[14] Sturman MC. Multiple approaches to analyzing count data in studies of individual differences: The propensity for type I errors, illustrated with the case of absenteeism prediction. Educational and Psychological Measurement. 1999;**59**(3):414-430

[15] Cameron AC, Trivedi PK. Econometric models based on count data. Comparisons and applications of some estimators and tests. Journal of Applied Econometrics. 1986;**1**(1):29-53

[16] Lee AH et al. Multi-level zeroinflated Poisson regression modelling of correlated count data with excess zeros. Statistical Methods in Medical Research. 2006;**15**(1):47-61

[17] Moghimbeigi A et al. Multilevel zeroinflated negative binomial regression modeling for over-dispersed count data with extra zeros. Journal of Applied Statistics. 2008;**35**(10):1193-1202

[18] Meng XL, Van Dyk D. The EM algorithm—An old folk‐song sung to a fast new tune. Journal of the Royal Statistical Society, Series B: Statistical Methodology. 1997;**59**(3):511-567

[19] Mullahy J. Specification and testing of some modified count data models. Journal of Econometrics. 1986;**33**(3): 341-365

[20] Khan JR, Awan N. A comprehensive analysis on child mortality and its

determinants in Bangladesh using frailty models. Archives of Public Health. 2017; **75**(1):58

[21] Gebretsadik S, Gabreyohannes E. Determinants of under-five mortality in high mortality regions of Ethiopia: An analysis of the 2011 Ethiopia Demographic and Health Survey Data. International Journal of Population Research. 2016;**2016**

[22] Yaya S et al. Prevalence and determinants of childhood mortality in Nigeria. BMC Public Health. 2017;**17**(1): 485

[23] Alam M et al. Statistical modeling of the number of deaths of children in Bangladesh. Biometrics & Biostatistics International Journal. 2014;**1**(3):00014

[24] Bereka SG, Habtewold FG. Underfive mortality of children and its determinants in Ethiopian Somali Regional State, Eastern Ethiopia. Health Science Journal. 2017;**11**(3)

[25] Getachew Y, Bekele S. Survival analysis of under-five mortality of children and its associated risk factors in Ethiopia. Journal of Biosensors and Bioelectronics. 2016;**7**(213):2

[26] Bedada D. Determinant of underfive child mortality in Ethiopia. American Journal of Statistics and Probability. 2017;**2**(2):12-18

[27] Muriithi DM, Muriithi DK. Determination of infant and child mortality in Kenya using Cox-Proportional Hazard Model. American Journal of Theoretical and Applied Statistics. 2015;**4**(5):404-413

[28] Ahmed Z, Kamal A, Kamal A. Statistical analysis of factors affecting child mortality in Pakistan. Journal of College of Physicians and Surgeons Pakistan (JCPSP). 2016;**26**(6):543

#### **Chapter 2**

## Mortality Rate in Pakistan - among Low and Middle-Income Countries

*Umar Bacha and Naveed Munir*

#### **Abstract**

Age-specific and sex-specific cause of death determination is becoming very important task particularly for low- and middle-income countries (LMICs). Therefore, consistent openly accessible information with reproducibility may have significant role in regulating the major causes of mortality both in premature child and adults. The United Nations (UN) reported that 86% deaths (48 million deaths) out of 56 million globally deaths occurred in the LMICs in 2010. The major dilemma is that most of the deaths do not have a diagnosis of COD in such countries. Despite of the allocation of a large portion of resources to decrease the devastating impacts of chronic illnesses, their prevalence as well as the health and economic consequences remains staggeringly high. There are multiple levels of interventions that can help in bringing about significant and promising improvements in the healthcare system. Currently, Pakistan is facing double burden of malnutrition with record high prevalence rates of chronic diseases. Pakistan spends only a marginal of its GDP (1.2%) versus the recommended 5% by World Health Organization. On average, there are eight hospitals per district, with people load per hospital being 165512.452 and poor data management in the country, and we lack a consistent local registry on all-cause of mortality. This article was planned to compile the data related to major causes and disease specific mortality rates for Pakistan and link these factors to the social-economic determinants of health.

**Keywords:** age-specific and sex-specific, cause of death, accessible information, Pakistan, malnutrition, chronic diseases

#### **1. Introduction**

The healthcare system practiced in Pakistan is a composite of two major fields of practice, i.e., modern medicine and Unani medicine. The former one is based on practicing the evidence-based modern medicine (allopathic medicine) delivered through public sector as well as private sector healthcare facilities. The latter field of practice for therapy is traditional treatment known as *Unani* medicine (*Unani Tibb*). This system has rooted in Perso-Arabic traditional medicine or more, precisely the Greek/Greece medicine (Greek Urdu translation is *Younan or Unan or Unani*). The government of Pakistan formally recognized (through an Act passed in 1965) the Yunani medicine, along with modern medicine in 1965. Overall, Pakistan has 36,000 practitioners related to this


#### **Table 1.**

*Province-wise computed people load per tertiary care hospital in Pakistan.*

branch of professionals. Another study shows around 52600 registered Unani medical practitioners in Pakistan [1]. According to WHO, about 70% or more of the population in developing countries uses traditional medicine and about 30% pharmaceutical preparations are derived from herbal medicines [2, 3].

The more vast healthcare system in Pakistan is the practice of modern medicine and hospitals, including both public and private sectors. Data from 2020 statistics show that there are almost 1282 public hospitals working throughout the country as general hospitals and specialized hospitals. Among the healthcare providers, the numbers of registered doctors, dentists, and nurses are 2,45,987; 27,360; and 1,16,659, respectively. Till the end of 2021, total numbers of districts in Pakistan are 160, which mean that there are approximately eight hospitals per district (1282/160). The total of Pakistani population as of 2020 is approximately 21,18,55,939 reported by United Nations report data that make about 2.83% of the total world population (Woldometers population report regarding Pakistan).

**Table 1** represents people load per hospital calculated using the statistics tools on the basis of currently available data on the website of Pakistan Bureau of statistics, Government of Pakistan. **Table 1** explores that province Punjab has the highest patient load per hospital versus as comparative to other provinces of Pakistan. The average life expectancy of the Pakistani population is reportedly increased to 67.3 years (2019 estimate), while the population growth rate showed a decline from 2% to 1.9%. Regarding the health expenditure in Pakistan, it is shown that Pakistan spends only a marginal of its GDP (1.2%) that is below the recommendation of the World Health Organization (5%) (https://www.geo.tv/latest/354581-pakistans-health-care-system-in-2020-hospitals-doctors-increase).

#### **2. Mortality**

Identifying the gaps and evaluating factors related to mortality should be prioritized as the first step for dissecting the threshold of diseases, allocating appropriate human and monetary resources, and designing health policies. The mortality rate estimates the number of deaths in a particular time and population [4]. These rates

*Mortality Rate in Pakistan - among Low and Middle-Income Countries DOI: http://dx.doi.org/10.5772/intechopen.105770*

are an indirect measure of nutritional status as well as healthcare facilities of a region. Death rates are expressed per 1000 individuals; for example, a mortality rate of 5.5 per 1000 persons means 5.5 deaths or 0.55% out of the studied population. Reports explored that in Pakistan, life expectancy in males aged 1–4 years is better (41% lower death rate), while the death rate of males aged 35–39 years is higher as compared with other low- and middle-income countries (LMICs). It was well reported that a wide range of factors might expedite the death rates, for example, natural disasters, health conditions, environmental pollution, conflicts, humanmade disasters in case of deadly infections, which rapidly propagate in response to higher population density [5, 6].

#### **2.1 Classification of mortality**

Mortality is classified into various categories, such as mortality due to various chronic diseases, age, and gender, to name a few (**Table 2**). Collectively, irrespective of the cause, mortality is expressed as crude death rate (CDR). Eq. (1) is a generic formula for the determination of the crude death rate CDR.

$$\text{CDR} = \text{Overall number of death in a time point} / \text{}$$

$$\text{Population under observation} \times \text{(10}^\circ\text{)}^\circ \tag{1}$$

The global burden of CDR was plus seven deaths out of 1000 people per year (C.I.A., 2020). According to the World Health Organization report [7], major ten (10)


*\* Mortality rate per 1,000 persons.*

**Table 2.** *Classification of the frequently used measures of mortality.* global causes of death in the year 2019 were ischemic heart disease, stroke, pulmonary disease, respiratory infections, neonatal conditions, trachea, bronchus, lung cancers, neurological issues, diarrhea, hyperglycemia, and kidney diseases. Furthermore, World Health Organization (2016) showed around 56.9 million deaths globally due to various causes. Loss of life due to chronic disorders such as ischemic heart disease and stroke was the leading cause of 15.2 million deaths (2016) worldwide.

#### **3. The burden associated with chronic diseases**

The burden associated with chronic diseases, particularly heart-related issues, on mortality and morbidity is overwhelming. Its gravity is, however, more momentous in lower and middle-income countries probably because of relatively unstable economy and inadequate allocation of national budget for healthcare sectors. It has been reported that approximately 50% of Pakistani people face at least one of the chronic disorder [8]. The prevalence of cardiovascular diseases (CVD) in Asian countries is reportedly high in Pakistan, India, Bangladesh, Sri Lanka, and Nepal versus Chinese and Canadian subjects [9]. As of 2020, the overall death rate in Pakistan was 6.8/1,000 people, and Afghanistan was 13.89 deaths/100,000 population [10], Bangladesh 5.526/1000 person (2019). As mentioned, a wide array of factors could expedite this death rate associated with the chronic illnesses. For example, disproportionately high intake of salt and lipid consumption showed a positive association with mortality. It is, therefore, crucial to make education programs as part of the curriculum aiming to mitigate consumption of salt and *trans*fatty acids intake as they are highly associated with the high prevalence of CVDs and hypertension in Pakistan. WHO and Food and Agriculture Organization of the United Nations strongly recommend *trans*-fat content to be less than 4% in dietary fat. The consumption of vanaspati ghee (which contains 14.2–34.3% of *trans* fat) might be a leading factor the development of cardiovascular diseases in Pakistan and other Asian countries along with many other risk factors. A significantly appreciable work on *trans*-fat reduction in food items has been done by Denmark, where the mortality due to CVD is declined by 50% over two decades [11]. Such policies need to be kept as a gold standard and amended in local contexts too.

Tobacco consumption in Pakistan is very common. It was also reported that tobacco usage is significantly associated with various types of cancers, particularly with lung cancer. It increases the risk of mortality by almost 12 times, smokers are 2–4 times more prone to develop coronary heart disorder and two times at more risk to develop stroke [12–14]. Ahmed & Colleagues [15] during a survey conducted in Pakistan reported that tobacco usage is 36% and 9% among males and females, respectively. Moreover, it was also found that out of 36% almost 15% were young adult university students [16, 17]. Therefore, efforts are needed to reduce smoking tendency for reducing the overall burden of chronic diseases and mortality rates.

At the same time, appropriate measures should be taken to motivate the public for physical activity [18], which is reportedly linked with a decrease in health related issues and mortality rates in 17 countries (Asia and Western nations) [19]. Zhou and others [20] reported that regular workout is associated with reduced risk of mortality from all causes (46%), circulatory diseases (56%), and respiratory disorders (49%). It is noted that high workout is a relatively simple and highly recommended intervention strategy for the attenuation of mortality and CVDs across all age groups [18, 21].

Population growth rate determines the availability of health facilities to general public. If the medical facilities are not increased at rates to match the growing

#### *Mortality Rate in Pakistan - among Low and Middle-Income Countries DOI: http://dx.doi.org/10.5772/intechopen.105770*

population, morbidity and mortality rates will ultimately rise. A recent meta-analysis recommended availability and access to hospitals and surgical care in developing countries [22]. It has been suggested in literature that changing the fertility and education rate and increasing human resources in medical care [23] should be one of the focus areas to address health-related issues.

The age-standardized cancer mortality rate in Pakistan is 48600 in male and 52500 in female reported by WHO cancer country profiles (2014), the death rate due to cancer in India was 0.44 million [24], Afghanistan (2015 data) 15,211, and the United States (2015 data) 667,333 cancer-related death [10].

Pakistan's relatively high death rate (Global burden of diseases, 2010) is attributed to infections that affect the lower respiratory system, neonatal encephalopathy, and diarrheal diseases affecting all age groups and gender. However, after 1990, diarrheal diseases showed a declining trend in Pakistan; a 35% reduction was reported in 2010.

Air pollution in general and polluted in-house air from solid fuels affect the vulnerable segment of the Pakistani population. The rural–urban health disparities are also common in Pakistan. The majority of the population (60%) lives in a rural area where solid wood is burnt to generate energy. Further, poverty, accident, dietary insufficiency, sedentary lifestyle, higher carbohydrate have driven energy intake at the expense of the protein, negligible health insurance, and constrained access to hospitals are the major factors that accelerate the death rate. The actual mortality rate in Pakistan could be high as death records in the big city are maintained, but it is not usually reported in rural areas. It is, therefore, needed to consider more effective ways of registering death numbers.

Further, rural area where the majority of the population resides in Pakistan also faces a shortage of medical physicians. This scenario set the stage for an undiagnosed or unidentified cause of mortality. In conclusion, the death record, cause, and an appropriate number of medical physicians should be a national health policy priority.

#### **4. Regional distribution of mortality rates**

**Figure 1** shows major causes of death in LMICs. Some of the middle-income countries work on continuously improving their healthcare services and provision. For example, Malta et al. [25] reported a significant decline in mortality (35.3%) in Brazil, demonstrating the remarkable achievement in health sector reforms in this country, particularly when deaths related to neoplasms and diabetes have been reduced. On the contrary, some economically emerging nations displayed record-high mortality rates; for instance, 6 years of data from Nigeria show 2,198 deaths in 49,287 participants who were admitted to the hospital [26] (**Figure 2**).

Abegunde et al. [27] published the burden of costs associated with chronic diseases in LMICs. The diseases burden due to chronic disorders in 23 LMICs was accountable for half (50%) of the total disease burden for the year 2005. Moreover, the death rates for men (54% higher) and women (86% higher) in 15 out of the 23 LMICs were higher versus the burden of the disease in men and women in high-income countries. Moreover, chronic diseases hardly hit women than men in the LMICs. Kassebaum et al. [28] documented that mother death ratios are 100 times more in LMICs than in developed nations, while the neonatal and fetal death rates are 10 times more than in high-income nations [29].

**Figure 1.** *Major 10 chronic disorders associated with causes of death in LMICs reported by WHO [7].*

#### **Figure 2.**

*Crude death rate (per 1,000 people) in lower-income countries in selected lower-income countries during the year 2019 [data generated from https://data.worldbank.org/] on 28-Nov 2021].*

### **5. Adult mortality trend in LMICs and their comparison in Pakistan**

Crude death rate/1,000 people for various middle-income countries is presented in **Figure 3**. Serbia and Bulgaria have the highest mortality rate for all age groups and genders, followed by Russian Federation and Romania. World Health Organization demographic data show a death rate of 15.4/1,000 people. Compared with neighboring nations in the European States, the death rate in Bulgaria is attributed to chronic noninfectious diseases such as cardiovascular disorders and cancer diseases. Other leading factors might be contagious diseases, malnutrition, inadequate healthcare, violence, poverty, and accidents. Other countries with undesirable ranking concerning their high death rate in the European sides were Montenegro, Kosovo, and Albania. Three countries in the Asian continent, such as Pakistan, India, and Bangladesh, occupy a similar position on the ranking.

**Figure 3.**

*Crude death rate (per 1,000 people) in LMICs and their comparison in Pakistan.*

In contrast, small countries such as Malaysia, Nepal, and the Philippines where death rates were comparatively low show significant positive progress in their healthcare system.

#### **6. Maternal mortality rate (MMR) in LMICs and their comparison in Pakistan**

Bangladesh shows maternal mortality as 176/100,000 live births during 2015 [30]. Compared with other countries (India, Congo, Guatemala, Kenya, and Zambia have 456,276 births), Pakistan reported 91,076 births with MMR 319 per 100,000 live births versus the average 124/100,000 live births in the other five countries. Regarding per 1,000 live births death rate, Pakistan's performance on the ranking is not satisfactory, 49.4 compared with the average 20.4 in the other five countries. [31]. Likewise, Afghanistan has reportedly recorded high MMR at 400/100,000 live births versus other countries on this side of the world [32]. Iran's neighboring country has reduced MMR from 48/100,000 to 16/100,000 over 17 years, meaning an annual decrease of 6.3% in MMR [33]. Associated factors for high mortality may include the following: Challenges include prolonged conflicts, political instability, high blood pressure due to persistent stress, infection, bleeding, obstructed labor, unsafe abortion, dietary deficiency, low education, and poor maternal and birth in health facilities with a skilled birth attendant and newborn, postpartum care.

#### **7. Under-5 mortality rate in LMICs and their comparison in Pakistan**

The Sustainable Development Goals (SDGs) of the United Nations aim to attenuate neonatal mortality to 12 deaths/1,000 live births and under-5 mortality rates to 25 deaths/1,000 live births by 2030 [34]. African countries show a negative ranking regarding under-5 mortality 77.5 per 1000 live births and neonatal mortality 27.7 deaths/1000 live births compared with their Asian counterparts. South Asian nations present under-5 mortality as 42.1 per 1000 live births and neonatal mortality as 25.8 deaths per 1000 live births [35]. The under-5 death rate for Bangladesh was 133 in 1990, which reduced significantly to 30.2 deaths/1000 live births [36, 37], while that of Pakistan was 69.3/ 1000 live births and India 36.6 per 1000 live births [38, 39]. Baqui et al. [40] documented a comprehensive 7 years' (2007–2013) work on neonatal mortality that involved 149570 live births. The data collection was carried out from six countries, such as rural areas of Bangladesh, Ghana, India, Pakistan, the United Republic of Tanzania, and Zambia. The overall neonatal mortality in the studied countries was 30.5 /1000 live births. Overall, neonatal mortality in Pakistan from the selected population was 47.4 versus Zambia 13.6. Regarding the total mortality rate within 24 hours for the selected nation, it was 14.1 /1000 live births. The country-wise trend showed 5.1 in Zambia versus 20.1 in India. Likewise, the first 24 hours were crucial as 46.3% of all neonatal deaths followed within this time (36.2% in Pakistan compared with 65.5% in Tanzania). In parallel, in the first 6 hours mortality was less, i.e., 8.3 deaths/1000 live births for the selected countries (31.9%). Another study reported the stillbirth rate in Pakistan as 53.5/1000 births compared with the average 23.2 in India, Pakistan, the Democratic Republic of Congo, Guatemala, Kenya, Zambia [31].

Based on the data presented, it is desirable to lower neonatal mortality within the first 24 hours by adopting high standard medical care for mothers and babies *Mortality Rate in Pakistan - among Low and Middle-Income Countries DOI: http://dx.doi.org/10.5772/intechopen.105770*

before pregnancy, during, and after birth. A study from Brazil conducted (Foz do Iguassu city, from 2012 to 2016) demonstrates the high rate of neonatal mortality under the age of 5 (61%) versus average neonatal mortality in Brazil. Some of the countries showed exceptional performance in reducing neonatal mortality. For example, Bangladesh has shown remarkable improvement in attenuating the national neonatal mortality rate (1993) as 52 per 1000 live births versus 28 per 1000 live births in 2014, reflecting a 46% reduction [30]. The associated factors were a congenital fetal anomaly and low birth weight [41]. Overall, it is documented that 27.8 million neonates could lose their lives across the nation (2018–2030) due to poor neonatal and maternal care [42]. Moreover, this study associated neonatal mortality with respiratory and cardiovascular disorders (43%) and low birth weight and preterm (33%); other key factors that could accelerate neonatal deaths were placenta, cord, and pregnancy complications. Likewise, poor-quality medical and surgical conditions were lead factors. Low birth weight and preterm (42%) were the factors leading to neonatal death after discharge [42]. Kruk et al. [43] recommend universal health coverage, bringing innovation to quality healthcare that could prevent 8·6 million deaths per year. Nutrition policies such as taking care of maternal nutrition, antenatal care, and promotion of breastfeeding can prevent many of the cases of neonatal mortality.

#### **8. Conclusion**

Age-specific and sex-specific cause of death (COD) determination is becoming very important task particularly for low- and middle-income countries (LMICs). It was reported that such countries lack the proper system to record COD in such countries. Therefore, a valid policy must be adopted for assessment and reporting of up-to-date health-related data so that mitigation policies may be implemented. Currently, Pakistan is facing double burden of malnutrition in addition to the high prevalence rates of chronic diseases. There is a lack of data management in the country, and we lack a consistent local registry on all-cause of mortality. However, on reviewing the mortality rate in LMICs, it could be suggested that 1) cheap, easily accessible healthcare system should be available to all levels of the population; 2) integrating human resources for the health promotion should work together for the common goal, i.e., uplift public health; 3) most of the hospitals in rural area face huge shortage of financial resources. Timely allocation and management of resources could bring positive changes in healthcare. 4) Openly accessible data for local population should be available and 5) healthcare facilities should be centralized using modern means to update the COD nationwide.

#### **Acknowledgements**

We really appreciate the School of Health Sciences, University of Management and Technology, Lahore, Pakistan administration to give free access for downloading and compiling the reference data for the write-up of this article.

#### **Conflict of interest**

Authors have not any type of conflict of interest regarding the publication of data.

*Mortality Rates in Middle and Low-Income Countries*

#### **Author details**

Umar Bacha\* and Naveed Munir School of Health Sciences, University of Management and Technology, Lahore, Pakistan

\*Address all correspondence to: umar.bacha@umt.edu.pk

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Mortality Rate in Pakistan - among Low and Middle-Income Countries DOI: http://dx.doi.org/10.5772/intechopen.105770*

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