Determinants of infant mortality in Pakistan: evidence from Pakistan Demographic and Health Survey 2017–18

Patel, Kamalesh Kumar; Rai, Rashmi; Kumar, Ambarish

doi:10.1007/s10389-019-01175-0

Cited by 36 publications

(46 citation statements)

References 28 publications

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“…The odds of infant death among infants from mothers who did not use ANC more likely as compared to infants born from mothers who did during their pregnancy in the same clusters. This nding is consistent with studies conducted in Ethiopia [11], Pakistan [12], and Brazil [13].…”

Section: Discussionsupporting

confidence: 92%

“…The odds of infant death among rural residents was more likely as compared to their counterpart urban residents. This nding is consistent with studies conducted in Ethiopia [15], Nigeria [25], and Pakistan [12]. The reason may be that mothers living in rural areas lack access to health institutions to have ANC follow-ups or lack of media exposure which in turn affects their knowledge and practice of care for the infant.…”

Section: Discussionsupporting

confidence: 88%

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Spatial Distribution of Infant Mortality in Ethiopia: Using Demographic Health Survey, the Ethiopia

Kefale¹,

Woya

Tekile

et al. 2022

Preprint

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Background Mortality is one of the demographic variables that affect population trends. Among mortality of children, Infant mortality contributed to more than 75% of all under-five deaths globally. It disproportionately affects those living in the different regions of countries and within the region. Exploring the spatial distribution and identifying associated factors is important to design effective intervention programs to reduce infant mortality. Thus, this study aimed to assess the spatial distribution and associated factors of infant mortality in Ethiopia using the 2016 Ethiopian Demographic and Health Survey (EDHS). Method The Data this study were used Ethiopian Demographic and Health Survey in 2016. A total of 11,023 live births from the EDHS data were included in the analysis. Spatial analysis was done to explore spatial distribution of infant mortality using ArcGIS version 10.4. Results This study revealed that the spatial distribution of infant mortality was non-random in the country with Moran’s index 0.1546 (P-value=0.0185). The Afar and Somali regions of Ethiopia were identified in this study on the hot spot of infant mortality. Conclusions The spatial distribution of infant mortality varies across the country. ANC usage, sex of a child, birth interval, birth size, birth type, birth order, wealth index, residence, region, and the spatial variable (Si) were significant predictors of infant mortality. Therefore, it needs interventions in the hot spot areas. Focusing on maternal health care services, rural residences, multiple births, infants having a smaller birth size, and male infants deserves special attention.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 88%

Spatial Distribution of Infant Mortality in Ethiopia: Using Demographic Health Survey, the Ethiopia

Kefale¹,

Woya

Tekile

et al. 2022

Preprint

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“…Child mortality is expected to be influenced by biological factors (age of mother, birth interval, birth order (male/female baby), and weight of the child at the time of birth) family structure (joint/nuclear) as well as socio-economic (wealth status of household, education and employment status of parents, etc.) [ 9 – 15 ], and cultural factors (food habits, set of values and ideologies of a particular community) [ 7 , 8 , 14 – 18 ].…”

Section: Introductionmentioning

confidence: 99%

Socio-economic determinants of child mortality in Pakistan and the moderating role of household’s wealth index

et al. 2022

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Background Child mortality is an important social indicator that describes the health conditions of a country as well as determines the country’s overall socio-economic development. The Government of Pakistan has been struggling to reduce child mortality (67.2 per thousand live births in 2019). Pakistan could not achieve the target set for Millennium Development Goals to reduce child mortality and still working to meet the target set by the Sustainable Development Goals. This study has investigated the socio-economic determinants of child mortality in Pakistan by using household-level data. Socio-economic characteristics related to women (mothers) and households have been considered as possible determinants of child mortality. The moderating role of a household’s wealth index on the association between woman’s education and child mortality has also been investigated. Methods The comprehensive dataset of the Pakistan Demographic and Health Survey 2017–18 has been used to explore the determinants of child mortality by using multivariable logistic regression. The interaction term of women’s education and household wealth index has been used to investigate the moderating role of the household’s wealth index. Results The results indicate that the likelihood of child mortality decreases with an increase in women’s education, their empowerment, their husband’s education, the wealth status of their households, access to clean drinking water, access to toilet facilities, and exposure to mass media. Whereas, an increase in unmet need for family planning increases the likelihood of child mortality. The study also identified the moderating role of a household’s wealth index on the association between woman’s education and child mortality. Conclusions Household wealth status moderates the association between women’s education and child mortality. The absolute slope of the curve showing the association of women’s education and child mortality is higher (more negative) for richer households than poorer households. It implies that a household’s wealth status strengthens the relationship between women’s education and child mortality. With the increase in the household’s wealth status, the effect of a mother’s education on child mortality becomes more pronounced.

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“…Similarly, the parent education aspect is also a known factor that has strong relationship with child death. As reported in Patel et al, 21 the infant mortality trends in Pakistan indicate that infant mortality declined slightly in the last 28 years and the education of women was a major contribution to this decline. Parents with good educational background tend to vaccinate their children and follow the routine immunization schedule which makes their children healthy as compared to the parents with low education.…”

Section: Resultsmentioning

confidence: 64%

Predictive analytics framework for accurate estimation of child mortality rates for Internet of Things enabled smart healthcare systems

Islam

Usman

Mahmood

et al. 2020

International Journal of Distributed Sensor Networks

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Globally, under-five child mortality is a substantial health problem. In developing countries, reducing child mortality and improving child health are the key priorities in health sectors. Despite the significant reduction in deaths of under-five children globally, developing countries are still struggling to maintain an acceptable mortality rate. Globally, the death rate of under-five children is 41 per 1000 live births. However, the death rate of children in developing nations like Pakistan and Ethiopia per 1000 live births is 74 and 54, respectively. Such nations find it very challenging to decrease the mortality rate. Data analytics on healthcare data plays a pivotal role in identifying the trends and highlighting the key factors behind the children deaths. Similarly, predictive analytics with the help of Internet of Things based frameworks significantly advances the smart healthcare systems to forecast death trends for timely intervention. Moreover, it helps in capturing hidden associations between health-related variables and key death factors among children. In this study, a predictive analytics framework has been developed to predict the death rates with high accuracy and to find the significant determinants that cause high child mortality. Our framework uses an automated method of information gain to rank the information-rich mortality variables for accurate predictions. Ethiopian Demographic Health Survey and Pakistan Demographic Health Survey data sets have been used for the validation of our proposed framework. These real-world data sets have been tested using machine learning classifiers, such as Naïve Bayes, decision tree, rule induction, random forest, and multi-layer perceptron, for the prediction task. It has been revealed through our experimentation that Naïve Bayes classifier predicts the child mortality rate with the highest average accuracy of 96.4% and decision tree helps in identifying key classification rules covering the factors behind children deaths.

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Determinants of infant mortality in Pakistan: evidence from Pakistan Demographic and Health Survey 2017–18

Cited by 36 publications

References 28 publications

Spatial Distribution of Infant Mortality in Ethiopia: Using Demographic Health Survey, the Ethiopia

Spatial Distribution of Infant Mortality in Ethiopia: Using Demographic Health Survey, the Ethiopia

Socio-economic determinants of child mortality in Pakistan and the moderating role of household’s wealth index

Predictive analytics framework for accurate estimation of child mortality rates for Internet of Things enabled smart healthcare systems

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