ISSN: 2378-315X BBIJ

Biometrics & Biostatistics International Journal
Research Article
Volume 1 Issue 3 - 2014
Statistical Modeling of the Number of Deaths of Children in Bangladesh
Moshed Alam1,2, Manzur Rahman Farazi1,2, Joseph Stiglitz2 and Munni Begum2*
1Department of Statistics, Jahangirnagar University, Bangladesh
2Department of Mathematical Sciences, Ball State University, Indiana
Received: November 12, 2014 | Published: December 11, 2014
*Corresponding author: Munni Begum, Department of Mathematical Sciences, Ball State University, Indiana, Tel: 1-765-285-8673; Email: @
Citation: Alam M, Farazi MR, Stiglitz J, Begum M (2014) Statistical Modeling of the Number of Deaths of Children in Bangladesh. Biom Biostat Int J 1(3): 00014. DOI: 10.15406/bbij.2014.01.00014

Abstract

Efforts to reduce the number of children’s death in developing countries through health care programs focus more to the prevention and control of diseases than to determining the underlying risk factors/predictors and addressing these through proper interventions. This study aims to identify socioeconomic and demographic predictors of the number of children’s death to women aged 12-49 from the Bangladesh Health and Demographic Survey (BDHS) administered in 2011. The number of children’s death in a family is a non-negative count response variable. The average number of children’s death is found to be 28 per 100 women with a variance of 44per 100 women. Thus Poisson regression model is not a proper choice to predict the mean response from the BDHS data due to the presence of over-dispersion. In order to address over-dispersion, we fit a Negative Binomial Regression (NBR), a Zero-Inflated Negative Binomial Regression (ZINBR) and a Hurdle Regression (HR) model. Among these models, ZINBR fits the data best. We identify respondent’s age, respondent’s age at 1st birth, gap between 1st birth and marriage, number of family members, region, religion, respondent’s education, husband’s education, incidence of twins, source of water, and wealth index as significant predictors for the number of children’s death in a family from the best fitted model. Identification of the risk factors of the number of children’s death is an important public health issue and should be carried out correctly for the much needed intervention.
Keywords: Number of child deaths; Predictors; NBR; ZINBR; HR

Abbreviations

BDHS: Bangladesh Health and Demographic Survey; NBR: Negative Binomial Regression; ZINBR: Zero-Inflated Negative; Binomial Regression; HR: Hurdle Regression

Introduction

Reduction of child mortality is one of the prime objectives of the southeastern Asian nation Bangladesh. Bangladesh has made impressive progress in health and human development since its emergence as an independent nation in 1971 [1,2]. Although the country achieved significant improvement in public health and in controlling the morbidities and mortalities from preventable diseases, child mortality is still a major public health issue. Every year between 8 and 11 million children die worldwide before reaching their fifth birthday [3]. The underlying cause for 60% of the deaths of children under the age five in Bangladesh is malnutrition [3,4].The primary objective of the current study is to identify socioeconomic and demographic risk factors/predictors of the number of children’s death for women aged 12-49 from the Bangladesh Health and Demographic Survey (BDHS) administered in 2011. It is useful for the policymakers to have a set of risk factors of the number of children’s death in order to develop guidelines and address these risk factors with proper intervention. Framing proper guidelines and policies to reduce child mortality will insure the sustainability of achieving the Millennium Development Goal (MDG) [4] relating child mortality.
In terms of demographic and socioeconomic determinants, maternal age and education are found to be strongly correlated with child morality [1,5-11]. Although the relationship between parental education and the number of children’s death is complex, a number of studies have illustrated that children of less educated parents tend to have a higher mortality rate than children of well-educated ones [6,9,10-13]. Parental education, more importantly maternal education, is identified as a strong predictor of child morality [5,9-11]. Other determinants of child mortality may include rural-urban residency [1,3,11,13,14], number of children in a household [1,3,14], water source [1,12] and toilet facility [1,5,11,14]. Most of these studies related to children’s mortality and children’s survival used proportional hazards models and multivariable logistic regression [8]. For instance, Bhuiya et al. [8] applied a proportional hazard models to study strong relationships with childhood mortality in Bangladesh. Majumder et al. [9] used multivariate analysis to identify socioeconomic and environmental determinants of children survival in Bangladesh. Chowdhury et al. [11] used multivariate proportional hazards models to find covariates that associated with neonatal and post-neonatal mortality in Bangladesh. Some studies addressed the number of malnourished children [2-5]. As no literature is available to the best of our knowledge regarding total number of children’s death among the women of age group 12-49 years in Bangladesh, we attempt to explore the nature of the number of children’s deaths and to identify associated risk factors/ covariates in this study.
A number of regression models for the count response, namely, standard Poisson Regression model, Negative Binomial Regression (NBR) model and Generalized Poisson Regression (GPR) model had been addressed in the literature [3]. Applications of these models are based on the assumptions that the mean and variance of the response variable are equal under the standard Poisson model. The GPR model allows flexibility in dealing with over-dispersion or under-dispersion [3]. More specifically, an NBR model is suggestive for dealing with over-dispersion [15].
The main objective of the study is to develop a predictive model for the number of child deaths in families in Bangladesh. In this study we applied Negative Binomial Regression (NBR), Zero-Inflated Negative Binomial Regression (ZINBR) and Hurdle Regression (HR) to the count response, number of children’s death, to identify statistically significant predictors/risk factors.

Methods

Study participants
Women aged 12-49 from the Bangladesh Health and Demographic Survey (BDHS) administered in 2011by the National Institute of Population Research and Training (NIPORT), ICF International (USA), and Mitra and Associates. BDHS 2011 is the sixth national demographic and health survey in Bangladesh. The findings of this study can be used for evaluating the Health, Population and Nutrition Sector Development Program (HPNSDP). Sixteen trained interviewing teams administered 17,842 successful interviews of ever-married women aged 12-49. Information was collected from ever-married women of the selected households. The detailed methodology of the survey design, data collection, and data management has been described elsewhere [1]. For this study, we ignore all the missing values and exclude the subjects with missing entries assuming that observations are missing completely at random. Due to some missing observations, finally, we carry out our analysis on data collected from 15,044 married women aged 12-49 years in Bangladesh. The box plot (Figure 1) for the response shows that there is an unusual observation. However, this response value has been kept in the analysis considering its plausibility.
Data management
The response variable, the number of children’s death of ever-married women surveyed, has been derived by adding up total number of son’s death and total number of daughter’s death of a mother. The predictors were assessed by questions regarding age of the respondent (categorized in age groups in years: 13-19, 20-24, 25-29, 30-34, 35-39, 40-44 and 45-49), husband age group in years (13-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+), respondent age at 1st birth in years (13-19, 20-24, 25-29, 30-39 and 40-49), gap between marriage and 1st birth in months (0-24, 25-60, 60+), number of family members (<4, 5-7,8-10, 10+), region(seven divisions: Barisal, Chittagong, Dhaka, Khulna, Rajshahi, Rangpur, and Sylhet), place of residence (rural, urban), religion (Islam, others; where ‘others’ includes Hinduism, Buddhism, Christian and unknown religions), respondent’s education (no education, primary school: 1-5 years education, secondary: 6-10 years education, higher: 11+ years education), husband’s education (no education, primary school: 1-5 years education, secondary: 6-10 years education, higher: 11+ years education), does respondent currently work (yes, no), currently residing (living with husband, living elsewhere), incidence of twins (yes, no), has electricity (yes, no), water source (piped, tube-well, and other), toilet facility (standard or not), wealth index was calculated in quintiles (1= poorest and 5 = wealthiest) based on household asset data and access to media (yes or no).
Model justification
Poisson regression model is a natural choice for count response variables. However, in the presence of over-dispersion, Poisson regression model does not perform well in best fitting the data and for prediction. In this study, we test over-dispersion, [15,16] where the null hypothesis is that there is no over-dispersion in the data. Hence, the Poisson regression model is the null model against any other alternative model with over-dispersion. Following the notations of Deans and Lawless [15,16], we let Yi be the response from their subject with covariates Xi.. Then Yi is distributed as Poisson with mean µi = µi(Xi; β), where β is ap-dimensional vector of unknown coefficients. We denote the possible extra-Poisson variation byvi, in the presence which the standard Poisson model becomes a random or mixed effects Poisson model. Thus, for given Xi and vi, Yi~ Poisson (vii), where vi’s are continuous positive valued random variables that are independent and identically distributed with some finite mean E(vi) and variance Var(vi) =τ. If we let E(vi)= 1, then as Collings and Margolin [17], Var(Yi|Xi) = µi+ τ µi2and the null hypothesis for testing over-dispersion becomes, H0: τ = 0. Failure to reject the null hypothesis leads to the Poisson regression model. In this study we perform, Dean’s PB [15] test for over-dispersion using a R packaged DCluster [18] that generates the test statistic PB = 13.5292 with p-value <0.000. Rejection of H0 leads to the application of Negative Binomial type models. Since the variance is greater than the mean and there is about 79% zero counts for the response variable, we apply Zero-Inflated Negative Binomial Regression (ZINBR) and Hurdle Regression (HR) models along with the Negative Binomial Regression (NBR) model to analyze the data.
Data analysis
Simple summary statistics (frequency and percentages) are calculated for the selected socioeconomic and demographic risk factors. Sample mean and sample variance of the response variable are calculated to have an idea of its distribution pattern. Bivariate analysis (based on Pearson Chi-square test) has been performed to examine the association between response variable and each of the selected predictors. To validate the Chi-square test, we categorize the response variable as 0 deaths, 1-2 deaths, and ≥3 deaths. Otherwise the sell frequencies become <5 or zero violating the asymptotic Chi-square assumption. All the significant predictors are then included in the NBR, ZINBR and HR models. For HR model we exclude the predictor age at 1st birth since inclusion of the variable in the model makes the Hat matrix (X(X/X)-1>X/) singular. NBR model is better suited to the count response; number of deaths of children to women aged 12-49, due to the presence of over-dispersion. In the case of excessive number of zeros ZINBR and HR perform well in terms modeling number of children’s death. We applied these three models for estimating regression parameters (β) including p-values based on Wald statistics. Finally, we calculated incidence rate ratio (IRR) for all groups of categorical variables. The statistical software package R (Studio) is used for extracting information from BDHS 2011, recoding and model fitting including parameter estimation of the models. We compared the results from NBR, ZINBR and HR models and goodness of fit statistic Akike Information Criteria (AIC).
Figure 1: Box plot for the number of child deaths in a family.

Results and Discussion

Sample characteristics
The average age of the surveyed women was 31.44 years while the average age of the husband was 40.97 years. The average age of the women at their first experience of childbirth was 17.89 years. On average a woman gave 2.85 births in their lifetime and the average family size is 5.6.Women gave birth to their first child approximately 3 years after their marriage. About 21% of the women experienced child death and 1.3% women experienced three or more child deaths. 17.47% of women are from the lowest wealth index category and 23.37% are from the highest wealth category. About twenty-six percent of the women had no education. Few of them (7.5%) had 11+ year of education. On the other hand, 29% of the husbands were illiterate but 14% had at least11+ year of education. The majority of the women were Muslims (88.71%). About 66% of the respondents were from urban area. In case of family water sources, 80.52% of families depend on tube-well. About 57% of the families use standard toilets. The percentage of women reported that they had no access to any kind of media is 35%.Box plot (Figure 1) for the number of deaths in a family depicts only one unusual observation. It reveals that one mother experienced 15 child deaths. Figure 2 shows that the distribution of number of children’s death in a family is highly positively skewed. Incidence of more than three child death is almost negligible. Scatter plot in (Figure 3) shows that number of child deaths increases as the respondent’s age increases. The distribution of the number of children’s death with respect to mother’s education (Figure 4) postulates the high incidence of child death for the mothers having no education. Figure 5 also indicates that families with no education of the household head experience more child death. Proportion of deaths for male children and female children are 0.53 and 0.47 respectively. However, the difference of the proportion is not statistically significant (p-value=0. 54).
Figure 2: Histogram for the number of child deaths in a family.
Figure 3: Scatter plot for the number of child deaths in a family vs. respondent age.
Figure 4: Distribution of the number of child deaths in a family with respect to respondent’s (mother’s) education level.
Simple association: Response versus Predictor
The associations between the response variable and each of risk factors considered have been examined with Pearson Chi-square at 5% significance level (Table 1) with the following hypothesis H0i: There is no association between the number of children’s death and their risk factor. Table 2 shows that all the risk factors except respondent’s current work status are significantly associated with the number of deaths of children.
Figure 5: Distribution of the number of child deaths in a family with respect to respondent’s husband education level.
Model fitting
We compare the fitted models with respect to AIC. Although NBR and ZINBR produce very close results, we see ZINBR model acquires the lowest AIC (17,690) among the three fitted models (Table 3). Accordingly, we assert that ZINBR is the best predictive model for the number of deaths of children of women aged 12-49 in Bangladesh and present results from this model. According to the results of ZINBR model (Table 4), one or more of the categories of the predictors (respondent’s age, respondent’s age at 1st birth, gap between 1st birth and marriage, number of family members, region, religion, respondent’s education, husband’s education, incidence of twins, source of water, wealth index)are statistically significantly associated with the number of children’s death in a family. The NBR model also suggests almost similar findings [results not shown].On the other hand, HR model[results not shown] shows gap between 1st birth and marriage, number of household members, region, religion, incidence of twins, and toilet type are significantly related with the response variable. The ZINBR model (Table 4) shows that as the age of mothers increase, they experience higher rate of incidence of child death during their childbearing ages. Mothers having first birth between 20 to 24year’s experiences 35.90% lower child death incidence than the mothers having first birth between 13 to19 years.

Frequency

Cumulative Frequency

Percentage

Cumulative Percentage

Total Children Died

<2

11880

11880

78.97

78.97

3-5

2923

14803

19.43

98.4

6+

241

15044

1.6

100

Age of Respondent

13-19

1018

1018

6.77

6.77

20-24

2899

3917

19.27

26.04

25-29

3122

7039

20.75

46.79

30-34

2505

9544

16.65

63.44

35-39

2104

11648

13.99

77.43

40-44

1907

13555

12.68

90.1

45-49

1489

15044

9.9

100

Husband Age

15-19

16

16

0.11

0.11

20-24

477

493

3.17

3.28

25-29

1710

2203

11.37

14.64

30-34

2314

4517

15.38

30.03

35-39

2543

7060

16.9

46.93

40-44

2256

9316

15

61.93

45-49

2073

11389

13.78

75.7

50+

3655

15044

24.3

100

Age at 1st Birth

13-19

11343

11343

75.4

75.4

20-24

3024

14367

20.1

95.5

25-29

556

14923

3.7

99.2

30-39

119

15042

0.79

99.99

40-49

2

15044

0.01

100

 

Frequency

Cumulative Frequency

Percentage

Cumulative Percentage

Gap Between Marriage and 1st Birth

<24 Months

8360

8360

55.57

55.57

25-60 Months

5238

13598

34.82

90.39

60+ Months

1446

15044

9.61

100

Household Members

< 4

5729

5729

38.08

38.08

5-7

751

6480

4.99

43.07

8-10

6732

13212

44.75

87.82

8+

1832

15044

12.18

100

Region

Barisal

1768

1768

11.75

11.75

Chittagong

2440

4208

16.22

27.97

Dhaka

2533

6741

16.84

44.81

Khulna

2225

8966

14.79

59.6

Rajshahi

2236

11202

14.86

74.46

Rangpur

2122

13324

14.11

88.57

Sylhet

1720

15044

11.43

100

Residence

Rural

5153

5153

34.25

34.25

Urban

9891

15044

65.75

100

Religion

Islam

13345

13345

88.71

88.71

Other

1699

15044

11.29

100

Respondent's Education

No Education

1138

1138

7.56

7.56

Primary

3960

5098

26.32

33.89

Secondary

4656

9754

30.95

64.84

Higher

5290

15044

35.16

100

 

 

 

 

 

 

Frequency

Cumulative Frequency

Percentage

Cumulative Percentage

Husband Education

No Education

2157

2157

14.34

14.34

Primary

4474

6631

29.74

44.08

Secondary

4159

10790

27.65

71.72

Higher

4254

15044

28.28

100

Is Respondent Currently working?

No

13264

13264

88.17

88.17

Yes

1780

15044

11.83

100

Husband’s Working Status

Businessman

3645

3645

24.23

24.23

Labor Intensive

10292

13937

68.41

92.64

Service/Professional

1107

15044

7.36

100

Respondent Currently Residing with

Living Elsewhere

1646

1646

10.94

10.94

Living with Husband

13398

15044

89.06

100

Have any Twin

No

14686

14686

97.62

97.62

Yes

358

15044

2.38

100

Electricity

No

6097

6097

40.53

40.53

Yes

8947

15044

59.47

100

Water Source

Others

1323

1323

8.79

8.79

Piped Water

1607

2930

10.68

19.48

Tube-well Water

12114

15044

80.52

100

Type of Toilet

Not Standard

6488

6488

43.13

43.13

Standard

8556

15044

56.87

100

 

Frequency

Cumulative Frequency

Percentage

Cumulative Percentage

Wealth Index

Lowest

2628

2628

17.47

17.47

Second

2826

5454

18.78

36.25

Third

2918

8372

19.4

55.65

Fourth

3156

11528

20.98

76.63

Highest

3516

15044

23.37

100

Media Access

Access

9837

9837

65.39

65.39

No Access

5207

15044

34.61

100

Table 1: Prevalence of the number of child deaths with predictors, BDHS, 2011.
Among the divisions, all but Dhaka is significantly associated with the number of children’s death (Barisal division is a reference category). The incidence rate ratio of children’s death in Sylhet is 1.303 times higher than that of Barisal. Conversely, mothers living in Khulna experience 0.328 times lower death incidence than the mothers living in Barisal. Mothers having no education experience 2.06 times higher incidence of death than that of mothers having more than 11 years education. The results (Table 4) also show that the women having twin births are subject to 3.53 time’s higher child death incidence. The mothers who have availability of tube-well and piped water possess lower rate of child death incidence than those of who do not have the facility. Deriving a predictive model for the number of children’s death of women is in general a hard task. Numerous factors must be taken into account. It is not always feasible to consider these issues in modeling the number of children’s death. Thus the results should be interpreted with caution.

Conclusion

Our study suggests that ZINBR is the right model to identify the risk factors of the number of children’s death in families in Bangladesh. Respondent’s age, respondent’s age at 1st birth, gap between 1st birth and marriage, number of family members, region, religion, respondent’s education, husband’s education, incidence of twins, source of water, wealth index are statistically significantly associated with the number of children’s death in a family. The number of children’s death affects about 28% of all ever-married women aged 12–49 years in Bangladesh, and it indicates a poor health system. A number of strategies are reported in studies for the reduction of child mortality [5,6]. From this study, we identify some of the well-established demographic and socioeconomic risk factors for the number of children’s death to women aged 12-49 in Bangladesh. Among these the most important one, in our opinion, is the education of a mother. Increase in the number of years of education for women delays the age at marriage, age at first birth and perhaps gap between successive births, all of which are identified as significant predictors for the number of children’s death in the current study. Education of a mother is also strongly correlated with nutrition status of the family. Intervention for improving the nutrition status is important since malnutrition is one of the major causes of child mortality in Bangladesh [3].

Chi Square

df

p-value

Respondent Age

1524.181

12

0.00000

Husband Age

1282.327

14

0.00000

Respondent Age at 1st Birth

111.307

8

0.00000

Gap between Marriage and 1st Birth

14.24685

4

0.00655

Number of Members in the Family

19.21245

6

0.00382

Region

65.18007

12

0.00000

Residence

67.39046

2

0.00000

Religion

18.33311

2

0.00010

Respondent Education

1117.853

6

0.00000

Husband Education

526.0294

6

0.00000

Is Respondent Currently Working

1.158246

2

0.56039

Husband Occupation

66.90243

4

0.00000

With Whom Respondent Currently Living

43.05137

2

0.00000

Any Incidence of Twins in the Family

564.2712

2

0.00000

Having Electricity

121.0506

2

0.00000

Source of Water

78.77498

4

0.00000

Type of Toilet

68.68566

2

0.00000

Wealth Index

283.5333

8

0.00000

Access to Media

314.0064

2

0.00000

Table 2: The association between number of child deaths and predictors, BDHS, 2011.

AIC

Log-Likelihood

NBR

17756.00

-8825.992

ZINBR

17690.00

-8826

Hurdle

20118.00

-10040.00

Table 3: Model Comparison.

Coeff

SE

Wald

P-Val

RR

(Intercept)

-3.040

1.028

-2.957

0.003

0.048

Respondent Age (Year)

20-24

0.375

0.145

2.584

0.010

1.455

25-29

0.743

0.154

4.835

0.000

2.103

30-34

1.264

0.161

7.844

0.000

3.539

35-39

1.470

0.166

8.857

0.000

4.349

40-44

1.845

0.169

10.951

0.000

6.330

45-49

2.152

0.171

12.598

0.000

8.599

Husband Age  (Year)

20-24

0.052

1.027

0.050

0.960

1.053

25-29

-0.052

1.021

-0.050

0.960

0.950

30-34

0.076

1.022

0.075

0.940

1.079

35-39

0.058

1.023

0.057

0.954

1.060

40-44

-0.041

1.024

-0.040

0.968

0.960

45-49

0.060

1.024

0.059

0.953

1.062

50+

0.174

1.025

0.170

0.865

1.190

Respondent Age at 1st Birth  (Year)

20-24

-0.444

0.053

-8.421

0.000

0.641

25-29

-0.633

0.116

-5.458

0.000

0.531

30-39

-1.155

0.283

-4.080

0.000

0.315

40-49

-10.946

201.965

-0.054

0.957

0.000

The time interval between marriage and 1st month(Birth)

25-60

-0.149

0.037

-3.986

0.000

0.862

60+

-0.035

0.063

-0.547

0.584

0.966

Number of Members in the Family

 

5-7

-0.095

0.038

-2.506

0.012

0.910

8-10

-0.266

0.059

-4.484

0.000

0.766

10+

-0.126

0.089

-1.411

0.158

0.881

 

Coeff

SE

Wald

P-Val

RR

Region (Division)

 

Chittagong

0.066

0.064

1.035

0.301

1.069

Dhaka

-0.061

0.065

-0.940

0.347

0.941

Khulna

-0.397

0.068

-5.821

0.000

0.672

Rajshahi

-0.232

0.066

-3.488

0.000

0.793

Rangpur

-0.194

0.066

-2.920

0.003

0.824

Sylhet

0.264

0.067

3.921

0.000

1.303

Residence

Urban

-0.016

0.044

-0.359

0.719

0.984

Religion

Others

-0.226

0.060

-3.798

0.000

0.798

Respondent educational status

No Education

0.723

0.146

4.952

0.000

2.060

Primary

0.596

0.143

4.176

0.000

1.814

Secondary

0.228

0.137

1.661

0.097

1.256

Husband Educational Status

 

No Education

0.452

0.098

4.607

0.000

1.572

Primary

0.383

0.096

4.003

0.000

1.466

Secondary

0.251

0.091

2.745

0.006

1.285

Is Respondent Currently Working

Yes

0.006

0.054

0.119

0.906

1.006

 

Husband Occupational Status

Labor

-0.075

0.042

-1.779

0.075

0.928

Professional

0.046

0.097

0.475

0.634

1.047

Respondent Currently Living with

Husband

0.116

0.065

1.788

0.074

1.123

Incidence of Twin

Yes

1.263

0.064

19.604

0.000

3.536

 

Having Electricity

Yes

-0.083

0.049

-1.703

0.089

0.921

Source of Water

Piped water

-0.192

0.101

-1.907

0.056

0.825

Tube-well

-0.096

0.071

-1.356

0.175

0.908

Toilet Type

Standard

-0.067

0.039

-1.721

0.085

0.935

Wealth Index

Poorest

0.008

0.051

0.156

0.876

1.008

3rd Group

-0.074

0.055

-1.342

0.180

0.929

4th Group

-0.130

0.064

-2.039

0.041

0.878

Wealthiest

-0.323

0.081

-3.971

0.000

0.724

Access to Media

No

0.057

0.041

1.406

0.160

1.059

Table 4: Results of multivariate Zero-Inflated Negative Binomial (ZINBR) and Negative Binomial regression (NBR) and Hurdle Model (HR) to study the total number of deaths of children in Bangladesh.
Increasing parental educational facilities can improve child nutrition and child mortality as well. Facilities to safe drinking water and safe sanitation contribute much to reduce malnutrition. However, due to unavailability of malnutrition data on children this study could not address how malnutrition would contribute to the number of children’s death. The findings of this study on the socio-demographic risk factors /determinants of the number of children’s death to women aged 12-49 will provide the policymakers proper insight and guidance toward implementation of the needed intervention to reduce child mortality in Bangladesh and in other countries around the world. Reducing child mortality through intervening its significant determinants will insure the sustainability of the MDG 4 achievement program in Bangladesh. As mentioned earlier, child death depends on a diverse number of factors including socio-demographic and physiological factors in a complex pattern. The current study explores the role of socio-demographic factors in predicting the number of children’s death in women aged 12-49 in Bangladesh. More extensive studies focusing on the interplay between socio-demographic factors and other relevant factors on the child death should be carried out in order to fully understand the risk factors of child mortality.

References

  1. Mitra and Associates, ICF International (2013) National Institute of Population Research and Training (NIPORT). Bangladesh Demographic and Health Survey 2011.
  2. Das S, Hossain MZ, Islam MA (2008) Predictors of child chronic malnutrition in Bangladesh. Proc. Pakistan Acad. Sci 45(3): 137-155.
  3. Islam MM, Alam M, Tariquzaman M, Kabir MA, Pervin R, et al. (2013) Predictors of the number of under-five malnourished children in Bangladesh: application of the generalized Poisson regression model. BMC Public Health 13: 11.
  4. Khan MMH, Kraemer A, Khandoker A, Pruefer-Kramer L, Islam A (2011) Trends in sociodemographic and health-related indicators in Bangladesh, 1993- 2007: will inequities persist? Bull World Health Organ 89(8): 583-592.
  5. Black RE, Allen LH, Bhutta ZA, Caulfield LE, De Onis M, et al. (2008) Maternal and child under nutrition: global and regional exposures and health consequences. Lancet 371(9608): 243-260.
  6. Faruque ASG, Ahmed AMS, Ahmed T, Islam MM, Hossain MI, et al. (2008) Nutrition: basis for healthy children and mothers in Bangladesh. J Health PopulNutr 26(3): 325-339.
  7. Murray CJ, Lopez AD (1997) Global mortality, disability, and the contribution of risk factors: global burden of disease study. Lancet 349(9063): 1436-1442.
  8. Schroeder DG, KH Brown (1994) Nutritional Status as a predictor of child survival: summarizing the association and quantifying its global impact. Bull World Health Organ 72(4): 569-579.
  9. Bhuiya A, Streatfield K (1992) A hazard logit model analysis of covariates of childhood mortality in Matlab, Bangladesh. J BiosocSci 24(4): 447-462.
  10. Majumder AK, Islam SM (1993) Socioeconomic and environmental determinants of child survival in Bangladesh. J BiosocSci 25(3): 311-318.
  11. Kayode GA, Adekanmbi VT, Uthman OA (2012) Risk factors and a predictive model for under-five mortality in Nigeria: evidence from Nigeria demographic and health survey. BMC Pregnancy Childbirth 12: 10.
  12. Chowdhury AH, Shafiqul Islam SM, Karim A (2013) Covariates of Neonatal and Post-Neonatal Mortality in Bangladesh. Global Journal of Human Social Science Research 13(4): 7-14.
  13. BimalKanti Paul (1997) Changes in Reproductive Behavior in Bangladesh. Geographical Review 87(1): 100-104.
  14. AbulKashem Majumder, Marian may, Prakash Dev Pant (1997) Infant and child mortality determinants in Bangladesh: are they changing. J BiosocSci 29: 385-399.
  15. Mturi AJ, Curtis SL (1995) The determinants of infant and child mortality in Tanzania. Health Policy Plan 10(4): 384-394.
  16. Dean C, Lawless JF (1989) Tests for detecting over dispersion in Poisson Regression Models. Journal of the American Statistical Association 84(406).
  17. Lawless JF (1987) Negative Binomial and Mixed Poisson Regression. The Canadian Journal of Statistics 15(3): 209-225.
  18. Collings BJ, Margolin BH (1985) Testing Goodness of Fit for the Poisson Assumption When Observations are not Identically Distributed. Journal of American Statistical Association 80(390): 411-418.
  19. Virgilio Gomez-Rubio, Juan Ferrandiz-Ferragud, AntonioLopez-Quilez, Roger Bivand (2014) ‘DCluster’ version 0.2-6, R Package. 
© 2014-2016 MedCrave Group, All rights reserved. No part of this content may be reproduced or transmitted in any form or by any means as per the standard guidelines of fair use.
Creative Commons License Open Access by MedCrave Group is licensed under a Creative Commons Attribution 4.0 International License.
Based on a work at http://medcraveonline.com
Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version | Opera |Privacy Policy