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Pakistan ranks third globally in terms of newborn deaths occuring within the first 24 hours of life. With a neonatal mortality rate of 42.0%, it carries the highest burden compared to neighboring countries such as Bangladesh (17%), India (22.7%) and Afghanistan (37%). While there has been a decline in neonatal mortality rates in Pakistan, the pace of this decline is slower than that of other countries in the region. Hence, it is crucial to conduct a comprehensive examination of the risk factors contributing to neonatal mortality in Pakistan over an extended period. This study aims to analyze the trends and determinants of neonatal mortality in Pakistan over three decades, providing valuable insights into this persistent issue.

The study focused on neonatal mortality as the response variable, which is defined as the death of a live-born child within 28 days of birth. Neonates who passed away during this period were categorized as “cases,” while those who survived beyond a specific timeframe were referred to as “noncases.” To conduct a pooled analysis of neonatal mortality, birth records of 39,976 children born in the five years preceding the survey were extracted from four waves (1990–2018) of the Pakistan Demographic and Household Survey. The relationship between risk factors and the response variable was examined using the Cox Proportional Hazard Model. Neonatal mortality rates were calculated through the direct method using the “syncmrates” package in Stata 15.

During the extended period in Pakistan, several critical protective factors against neonatal mortality were identified, including a large family size, improved toilet facilities, middle-aged and educated mothers, female children, singleton live births, large size at birth and longer birth intervals. These factors were found to reduce the risk of neonatal mortality significantly.

This study makes the first attempt to analyze the trends and patterns of potential risk factors associated with neonatal mortality in Pakistan. By examining a large dataset spanning several years, the study provides valuable insights into the factors influencing neonatal mortality.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-09-2022-0604

The 30 MOCHA (Models of Child Health Appraised) countries are diverse socially, culturally and economically, and differences exist in their healthcare systems and in the scope and role of primary care. An economic analysis was undertaken that sought to explain differences in child health outcomes between countries. The conceptual framework was that of a production function for health, whereby health outputs (or outcomes) are assumed affected by several ‘inputs’. In the case of health, inputs include personal (genes, health behaviours) and socio-economic (income, living standards) factors and the structure, organisation and workforce of the healthcare system. Random effects regression modelling was used, based on countries as the unit of analysis, with data from 2004 to 2016 from international sources and published categorisations of healthcare system. The chapter describes the data deficiencies and measurement conundrums faced, and how these were addressed. In the absence of consistent indicators of child health outcomes across countries, five mortality measures were used: neonatal, infant, under five years, diabetes (0–19 years) and epilepsy (0–19 years). Factors found associated with reductions in mortality were as follows: gross domestic product per capita growth (neonatal, infant, under five years), higher density of paediatricians (neonatal, infant, under five years), less out-of-pocket expenditure (neonatal, diabetes 0–19), state-based service provision (epilepsy 0–19) and lower proportions of children in the population, a proxy for family size (all outcomes). Findings should be interpreted with caution due to the ecological nature of the analysis and the limitations presented by the data and measures employed.

In recent years, a number of historians have examined the reasons for differences in the height and health of men and women in nineteenth-century Britain, often drawing on economic studies which link excess female mortality in the developing world to restrictions in women's employment opportunities. This paper re-examines this literature and summarises the existing literature on sex-specific differences in height, weight and mortality in England and Wales before 1850. It then uses two electronic datasets to examine changes in cause-specific mortality rates between 1851 and 1995. Although there is little evidence to support the view that the systematic neglect of female children was responsible for high rates of female mortality in childhood, there is rather more evidence to show that gender inequalities contributed to excess female mortality in adulthood.

The study of war has generally been neglected in sociology, with much of the discussion focusing around military spending or the organization of the military rather than war per se. Sociologists have critiqued and investigated the military-industrial complex (Mills, 1959), investigated morale in military units (Durkheim, 1951; Stouffer & DeVinney, 1955), and studied the socialization of soldiers (Cockerham & Cohen, 1980). However, the direct examination of war has been relatively rare. When war has been examined, sociological research has focused on the causes of war, often discussing the preconditions of revolutions (Goldstone, Gurr & Moshiri, 1991; Skopol, 1979), or the reasons for military interventions by core countries in the peripheral countries of the world system (Kowalewski, 1991). Examinations of the sociological impact of war on civilian populations have been even rarer.

Using data from the Survey of Income and Program Participation (SIPP) matched to administrative records, we examine mortality risk and participation in the Disability Insurance (DI) and Supplemental Security Income (SSI) disability programs from a long-term perspective. Over a period of 14 years, we analyze the effect of self-reported health and disability on the probability of death and disability program entry among individuals aged 18–48 in 1984. We also assess DI and SSI programs from a life-cycle perspective. Self-reported poor health and severe disability at baseline are strongly correlated with death over the 14-year follow-up period. These variables also are strong predictors of disability program participation over the follow-up period among non-participants at baseline or before, with increasing marginal probabilities in the out-years. Our cross-sectional models are consistent with recent studies that find that the work-prevented measure is useful in modeling DI entry. However, once self-reported health and functional limitations are accounted for, the longitudinal entry models provide conflicting DI results for the work-prevented measure, suggesting that, contrary to claims based on cross-sectional or short-time horizon application models, the work-prevented measure is an unreliable indicator of severity. The risk of SSI and DI participation is significantly greater for individuals who die, suggesting that future mortality captures the effect of case severity and deterioration of health during the follow-up period. From a life-cycle perspective, a substantially greater proportion of individuals participate in SSI or DI at some point in their lives compared to typical cross-sectional estimates of participation, especially among minorities, people with less than a high school education, and those with early onset of poor health and/or disabilities. Cross-sectional estimates for the Social Security area population indicate SSI and DI participation rates of no more than 5% combined in 2000. In contrast, for individuals aged 43–48 in 1984, we observe a cumulative lifetime SSI and/or DI participation rate of 14%. The corresponding figure is 32% for individuals in that age group who did not graduate from high school, suggesting the need for human capital investments and/or improved work incentives.

This chapter proposes that efforts to improve our understanding of factors affecting migrant health and longevity in the United States must consider migrants’ labor market incorporation and the structural conditions under which they work. I use public-use death certificate data to examine whether there is a mortality penalty for foreign-born workers in the secondary sector industries of agriculture and construction. I focus on the decade of the 1990s for two contextual and empirical reasons: (1) the decade was characterized by economic restructuring, restrictive immigration policy, increased migration, and dispersion of migrants to new geographic destinations; and (2) the 1990s is an opportunistic decade because 19 states coded the industry and occupation of the decedent during this time. These numerator mortality data and Census denominator data are used to compare all-cause mortality rates between working-age (16–64 years) US-born and foreign-born agricultural and construction workers, the overall foreign-born population, and foreign-born workers in health care – an industry where the foreign-born tend to work in well-paid occupations that are well-regulated by the state. The results show a clear mortality penalty for foreign-born workers in agriculture and construction compared to the overall foreign-born population and foreign-born healthcare workers. The results also show the mortality penalty for foreign-born secondary sector workers varies by industry. These findings support the argument that bringing work into our analyses is critical to understanding the contextual and structural factors affecting migrant health and survival.

Increases in deaths of despair in the USA have been associated with economic conditions and drug availability. In the state of Illinois, deaths of despair represent a significant public health issue. This study aims to examine the relationship between county-level economic distress, drug availability and mortality from deaths of despair collectively and for each contributing cause of death individually in the state of Illinois to better understand drivers of mortality locally.

Two cross-sectional analyses were conducted for 2010–2014 and 2015–2019. Correlations, regression analyses and relative weight analyses were applied to assess the relationship between deaths of despair mortality rate and the individual variables.

Deaths of despair mortality collectively and for each cause of death individually increased significantly from 2010–2014 to 2015–2019 in Illinois. Suicide mortality was higher in rural counties and was related to economic distress, while drug poisoning and alcohol-related deaths were higher in urban counties and were related to drug availability indicators.

While all three causes of death increased in the state, suicide mortality was inversely related to deaths of despair mortality. This may be because of different individual risk factors in rural versus urban areas or issues with coding cause of death. The findings of this study point to a rising public health challenge of deaths of despair mortality, particularly from substance use, in urban counties and from suicide in rural counties.

Adopting digital technology could facilitate the public health response to the COVID-19 pandemic. Some analysts argue that countries that adopted digital technology in their health sector have managed to control the virus better (Whitelaw et al., 2020). For instance, countries with more comprehensive contact tracing have significantly lower fatality rates (Yalaman et al., 2021). Moreover, World Health Organization (WHO) believes this technology is a crucial enabler for countries to meet the current challenge (WHO. Regional Office for the Western Pacific & University of Melbourne, 2021). In this regard, this study aims to quantitatively find the relationship between the technological advancement of countries and COVID-19 health outcomes, using seven technological indices that measure technological advancement.

The authors used the multiple linear regression method to answer the research questions. The first analysis focuses on a cross section of all countries worldwide, and the second focuses on European countries for which weekly death statistics exist after the pandemic.

The findings support those countries with more technological abilities managed to control the virus’s mortality better, as evidenced by the negative link between the mortality rate of COVID-19 and the technological factors at the national level. Results also reveal that technology adoption decreases the death risk due to COVID-19 in countries with more elderly people. The authors may argue that technological advancement positively correlates with the number of deaths and diagnosed cases because the authors can better collect data or because the virus spreads due to higher economic and business activities. However, such technological advancement significantly decreases the death risk (lower mortality rate in the first analysis and lower mortality rate for elderly people in the second analysis).

Three important conclusions could be made from the results: a lower mortality rate is generally expected for countries adopting advanced technology; technological advancement significantly decreases the death risk for elderly people; and a higher technology adoption level does not necessarily result in fewer diagnosed cases of/death due to COVID-19.

Although some studies have focused on e-health applications in the public health response to the COVID-19 pandemic, no studies, to the best of the authors’ knowledge, have tried to quantify its efficacy, most especially on the global level.

The purpose of this paper is to investigate the effect of flood occurrence on mortality rate and life expectancy amongst 53 African countries.

The study utilizes panel data from the period 2000–2018 on 53 African countries and system generalized method of moments (system GMM) for the analysis.

The result indicates that flood occurrence causes the destruction of health facilities and the spread of diseases which reduces life expectancy. In addition, flood occurrence increases mortality rate amongst 53 African countries.

The study recommends that governments amongst African countries should implement strategies being enshrined in Conference of Parties (COP, 2021) on climate change. This will help to reduce the level of climate change and flood occurrence.

Previous studies focussed on the adverse effect of flood occurrence without considering the issue of life expectancy amongst African countries. This study contributes to existing empirical studies by examining the effect of flood occurrence on mortality rate and life expectancy amongst African countries.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-07-2022-0508.

The purpose of the paper is to investigate the potential relationships between emergency-care flow, patient mortality and healthcare costs using a patient-flow model.

The researchers used performance data from one UK NHS trust collected over three years to identify periods where patient flow was compromised. The delays’ root causes in the entire emergency care system were investigated. Event time-lines that disrupted patient flow and patient mortality statistics were compared.

Data showed that patient mortality increases at times when accident and emergency (A&E) department staff were struggling to admit patients. Four delays influenced mortality: first, volume increase and mixed admissions; second, process delays; third, unplanned hospital capacity adjustments and finally, long-term capacity restructuring downstream.

This is an observational study that uses process control data to find times when mortality increases coincide with other events. It captures contextual background to whole system issues that affect patient mortality.

Managers must consider cost-decisions and flow in the whole system. Localised, cost-focused decisions can have a detrimental effect on patient care. Attention must also be paid to mortality reports as existing data-presentation methods do not allow correlation analysis.

Previous studies correlate A&E overcrowding and mortality. This method allows the whole system to be studied and increased mortality root causes to be understood.