Aggregate Mortality Research Articles

Mortality Deceleration and Mortality Selection: Three Unexpected Implications of a Simple Model

Unobserved heterogeneity in mortality risk is pervasive and consequential. Mortality deceleration-the slowing of mortality's rise with age-has been considered an important window into heterogeneity that otherwise might be impossible to explore. In this article, I argue that deceleration patterns may reveal surprisingly little about the heterogeneity that putatively produces them. I show that even in a very simple model-one that is composed of just two subpopulations with Gompertz mortality-(1) aggregate mortality can decelerate even while a majority of the cohort is frail; (2) multiple decelerations are possible; and (3) mortality selection can produce acceleration as well as deceleration. Simulations show that these patterns are plausible in model cohorts that in the aggregate resemble cohorts in the Human Mortality Database. I argue that these results challenge some conventional heuristics for understanding the relationship between selection and deceleration; undermine certain inferences from deceleration timing to patterns of social inequality; and imply that standard parametric models, assumed to plateau at most once, may sometimes badly misestimate deceleration timing-even by decades.

Programmatic Impact of 5 Years of Mortality Surveillance of New York City Homeless Populations

A homeless mortality surveillance system identifies emerging trends in the health of the homeless population and provides this information to key stakeholders in a timely and ongoing manner to effect evidence-based, programmatic change. We describe the first 5 years of the New York City homeless mortality surveillance system and, for the first time in peer-reviewed literature, illustrate the impact of key elements of sustained surveillance (i.e., timely dissemination of aggregate mortality data and real-time sharing of information on individual homeless decedents) on the programs of New York City's Department of Homeless Services. These key elements had a positive impact on the department's programs that target sleep-related infant deaths and hypothermia, drug overdose, and alcohol-related deaths among homeless persons.

Non-Linearities in the Relationship between Aggregate Income and Mortality Rates

We investigate the relationship between mortality rates and income per capita in Mexico. We employ a state-level panel approach and analyze overall and disaggregated age-adjusted mortality rates. The use of a fixed-effect estimator allows us to reduce potential bias from unobserved effects that may be present in cross-sectional studies of multiple countries. We do not find a statistically significant relationship between the overall mortality rate and income per capita. However, there is significant variation in the relationship for mortality categories and subcategories. Our results may provide insight into the mechanisms that drive the relationship between mortality and income.

Forecasting Mortality Trends Allowing for Cause-of-Death Mortality Dependence

Longevity risk is among the most important factors to consider for pricing and risk management of longevity products. Past improvements in mortality over many years, and the uncertainty of these improvements, have attracted the attention of experts, both practitioners and academics. Since aggregate mortality rates reflect underlying trends in causes of death, insurers and demographers are increasingly considering cause-of-death data to better understand risks in their mortality assumptions. The relative importance of causes of death has changed over many years. As one cause reduces, others increase or decrease. The dependence between mortality for different causes of death is important when projecting future mortality. However, for scenario analysis based on causes of death, the assumption usually made is that causes of death are independent. Recent models, in the form of Vector Error Correction Models (VECMs), have been developed for multivariate dynamic systems and capture time dependency with common stochastic trends. These models include long-run stationary relations between the variables and thus allow a better understanding of the nature of this dependence. This article applies VECMs to cause-of-death mortality rates to assess the dependence between these competing risks. We analyze the five main causes of death in Switzerland. Our analysis confirms the existence of a long-run stationary relationship between these five causes. This estimated relationship is then used to forecast mortality rates, which are shown to be an improvement over forecasts from more traditional ARIMA processes, which do not allow for cause-of-death dependencies.

The Impact of Aggregate Mortality Risk on Defined Benefit Pension Plans

We calculate the risk faced by defined benefit plan providers arising from uncertain aggregate mortality — the risk that the average participant will live longer than expected. First, comparing the widely cited Lee-Carter model to industry benchmarks, we show that plan providers appear to substantially underestimate the longevity of their employees. The resultant understatement of liabilities is 15.2 percent, when weighted by the characteristics of typical male participants in defined benefit plans, and reaches as much as 25.2 percent for male workers aged 22. Next, we consider the substantial mortality risk that arises even if plan providers were to use the Lee-Carter model or other unbiased forecasts of mortality reductions. We calculate the consequences for plan liabilities if aggregate mortality declines unexpectedly faster than is predicted by an unbiased projection. There is a 5 percent chance that liabilities of a terminated plan would be 2.9 to 5.1percent higher than what is expected, depending on the mix of workers covered. Lastly, we explain how longevity bonds might be used to transfer mortality risk from defined benefit plans to the capital markets, and we calculate a risk premium for a hypothetical frozen plan.

Forecasting Mortality Trends Allowing for Cause-of-Death Mortality Dependence

Longevity risk is amongst the most important factors to consider for pricing and risk management of longevity products. Past improvements in mortality over many years, and the uncertainty of these improvements, have attracted the attention of experts, both practitioners and academics. Since aggregate mortality rates reflect underlying trends in causes of death, insurers and demographers are increasingly considering cause-of-death data to better understand risks in their mortality assumptions. The relative importance of causes of death has changed over many years. As one cause reduces, others increase or decrease. The dependence between mortality for different causes of death is important when projecting future mortality. However, for scenario analysis based on causes of death, the assumption usually made is that causes of death are independent. Recent models, in the form of Vector Error Correction Models (VECM), have been developed for multivariate dynamic systems and capture time dependency with common stochastic trends. These models include long-run stationary relations between the variables, and thus allow a better understanding of the nature of this dependence. This paper applies VECM to cause-of-death mortality rates in order to assess the dependence between these competing risks. We analyze the five main causes of death in Switzerland. Our analysis confirms the existence of a long-run stationary relationship between these five causes. This estimated relationship is then used to forecast mortality rates, which are shown to be an improvement over forecasts from more traditional ARIMA processes, that do not allow for cause-of-death dependencies.

Modelling Cause-of-Death Mortality and the Impact of Cause-Elimination

Changes in underlying mortality rates significantly impact insurance business as well as private and public pension systems. Individual mortality studies have data limitations; aggregate mortality studies omit many relevant details. The study of causal mortality represents the middle ground, where population data is used while cause-of-death information is retained. We use internationally classified cause-of-death categories and data obtained from the World Health Organization. We model causal mortality simultaneously in a multinomial logistic framework. Consequently, inherent dependence amongst the competing causes is accounted for. This framework allows us to investigate the effects of improvements in, or the elimination of, cause-specific mortality in a sound probabilistic way. This is of particular interest for scenario-based forecasting purposes. We show the multinomial model is more conservative than a force-of-mortality approach. Finally, we quantify the impact of cause-elimination on aggregate mortality using residual life expectancy and apply our model to a French case study.

Estado epiléptico refractario en niños: caracterización de las epilepsias, monitorización electroencefalográfica continua y respuesta al tratamiento

Patients with refractory status epilepticus (RSE) have high morbidity and mortality rates, are hospitalised for longer periods of time, suffer greater neurological damage and progress to symptomatic epilepsy. Continuous electroencephalogram (cEEG) monitoring is a valuable aid in the early detection of RSE, especially in the case of non-convulsive status epilepticus (NCSE). In this study we describe the clinical characteristics, treatment and use of cEEG in paediatric patients with RSE. A retrospective study was conducted at the Hospital Clinico de la Pontificia Universidad Catolica de Chile between November 2005 and March 2011 in patients aged between 1 month and 15 years diagnosed with RSE and cEEG. Demographic characteristics, baseline and final conditions, and therapy were recorded. A total of 15 patients, 12 of whom were males, with a mean age of 4 years (1.5 months-13 years) were identified. Eight patients had a history of epilepsy. The most frequent aetiologies were progressive symptomatic and acute symptomatic. Convulsive epileptic status (CSE) was present in 11 patients and NCSE in the other four. During the cEEG, six of the 11 patients with CSE later progressed to NCSE. The mean amount of time with RSE was 10.2 days. Of the 15 patients, 13 responded to anticonvulsive drugs and the main secondary complications were respiratory depression and hypotension. Patients with CSE tended to evolve in a more torpid manner than patients with NCSE. On discharge from hospital, 13 patients (86.6%) presented new neurological deficit or difficult-to-manage epilepsy, one still had RSE and one died (6%). The aggregate neurological morbidity and mortality rates of RSE were high. The use of cEEG monitoring should be considered for use in the management of such cases of status epilepticus.

HEALTH SPENDING, OUT‐OF‐POCKET CONTRIBUTIONS, AND MORTALITY RATES

Health policies seek to achieve conflicting objectives. We argue that the objective of saving lives is best served by a careful balancing of fairness and efficiency considerations. Open, fair, and equitable access to health care for all citizens will lower overall mortality rates by enabling the very poor and chronically ill to satisfy their demand for necessary health care. But it will also result in higher costs, not least by also increasing demand for irrelevant, unnecessary, and inefficient health care. This undesirable demand and its associated costs can be reduced by increasing out‐of‐pocket contributions paid for by patients. Such payments are unpopular, though, as they are regarded as regressive and damaging to health of the relatively poor. We argue that properly enacted, no such apparent trade‐offs exist. If the freed‐up resources are used for more life‐saving measures, then higher out‐of‐pocket contributions will lower overall mortality rates. However, this beneficial effect is conditional on what happens to total health spending. Ironically, out‐of‐pocket payments are most effective as health policies if they are not or only hardly used as a means of reducing total health expenditures. Our theoretical arguments are confirmed by an econometric analysis of aggregate mortality rates in OECD countries over the period 1984 to 2007.

Life tables in actuarial models: from the deterministic setting to a Bayesian approach

The mortality dynamics experienced in the latest decades, especially at adult and old ages, has motivated the introduction of major innovations in the modeling of mortality for actuarial applications; such innovations concern, in particular, the representation of the uncertainty relating to aggregate mortality. In this paper, we first provide a description of the traditional mortality model which is deterministic but also allows quite easily for a representation of the uncertainty relating to individual mortality. Then, we discuss a stochastic approach to the modeling of the uncertainty relating to aggregate mortality. Due to the importance of mortality evolution in respect of post-retirement liabilities, we refer to a portfolio of immediate life annuities (or pension annuities). We assume that a (projected) life table which provides a best-estimate assessment of annuitants’ future mortality is available. We show that the life table, from which a deterministic description of future mortality can be obtained, can be used as the basic input of appropriate stochastic models. In particular, we consider a Bayesian-inference setting for updating the parameters of the stochastic model according to the experienced mortality.

Aggregate Income Shocks and Infant Mortality in the Developing World

Health and income are strongly correlated both within and across countries, yet the extent to which improvements in income have a causal effect on health status remains controversial. The authors investigate whether short-term fluctuations in aggregate income affect infant mortality using an unusually large data set of 1.7 million births in 59 developing countries. The authors show a large, negative association between per capita GDP and infant mortality. Female infant mortality is more sensitive than male infant mortality to negative economic shocks, suggesting that policies that protect the health status of female infants may be especially important during economic downturns.

Is video-assisted thoracoscopic surgery the best treatment for paediatric pleural empyema?: Table 1

A best evidence topic in thoracic surgery was written according to a structured protocol. The question addressed was whether video-assisted thoracic surgery (VATS) is the best treatment for paediatric pleural empyema. Altogether 274 papers were found using the reported search, of which 15 represented the best evidence to answer the clinical question. The authors, journal, date and country of publication, patient group studied, study type, relevant outcomes and results of these papers are tabulated. We conclude that early VATS (or thoracotomy if VATS not possible) leads to shorter hospitalisation. The duration of chest tube placement and antibiotic use is variable and does not correlate with treatment method. Patients who underwent primary operative therapy had a lower aggregate in-hospital mortality rate (0% vs. 3.3%), re-intervention rate (2.5% vs. 23.5%), length of stay (10.8 days vs. 20.0 days), duration of tube thoracostomy (4.4 days vs. 10.6 days), and duration of antibiotic therapy (12.8 days vs. 21.3 days), compared with patients who underwent non-operative therapy. Similar complication rates were observed for the two groups (5% vs. 5.6%). Moreover, median hospital charges for VATS were $36,320 [interquartile range (IQR), $24,814-$62,269]. The median pharmacy and radiological imaging charges were $5884 (IQR, $3142-$11,357) and $2875 (IQR, $1703-$4950), respectively, for VATS and tube drainage. Adjusting for propensity score matching, costs for primary VATS were equivalent to primary chest tube placement. Only one article found discordant results. Ninety-five children (52%) received antibiotics alone, and 87 (45%) underwent drainage procedures (21 chest tube alone, 57 VATS/thoracotomy, and eight chest tube followed by VATS/thoracotomy); only four received fibrinolytics. Mean (standard deviation) length of stay was significantly shorter in the antibiotics alone group, 7.0 (3.5) days vs. 11 (4.0) days. The strongest predictors of undergoing pleural drainage were admission to the intensive care unit and large effusion size (>1/2 thorax filled).

Life Tables in Actuarial Models: From the Deterministic Setting to a Bayesian Approach

The mortality dynamics experienced in the latest decades, especially at adult and old ages, has motivated the introduction of major innovations in the modeling of mortality for actuarial applications; such innovations concern, in particular, the representation of the uncertainty relating to aggregate mortality. In this paper, we first provide a description of the traditional mortality model, which is deterministic, but also allows quite easily for a representation of the uncertainty relating to individual mortality. Then, we discuss a stochastic approach to the modeling of the uncertainty relating to aggregate mortality. Due to the importance of mortality evolution in respect of post-retirement liabilities, we refer to a portfolio of immediate life annuities (or pension annuities). We assume that a (projected) life table which provides a best-estimate assessment of annuitants' future mortality is available. We show that the life table, from which a deterministic description of future mortality can be obtained, can be used as the basic input of appropriate stochastic models. In particular, we consider a Bayesian-inferential setting for updating the parameters of the stochastic model according to the experienced mortality.

Study on drug related hospital admissions in a tertiary care hospital in South India

Background and objectives Accidental and intentional poisonings or drug overdoses constitute a significant source of aggregate morbidity, mortality, and health care expenditure. Studies evaluating drug related hospitalization have estimated that approximately 5–10% of all hospital admissions are drug related. The present study was carried out to investigate type, nature and incidence of drug related admissions in our hospital settings. Method A hospital based retrospective study was conducted in 575 cases of drug related admissions. The case records of patients admitted in various clinical departments of Amrita Institute of Medical Sciences (AIMS) Hospital during last 8 years (January 2002–December 2009) were collected and analyzed. Results During the retrospective study, the total number of drug related cases reported were 575. Out of these, 35.5% cases were induced by central nervous system (CNS) drugs, 19.8% were by cardiovascular system (CVS) drugs, 12.3% were by NSAIDs, 11.3% were by antibiotics and 9.9% were by anticoagulants, 11.3% by other drugs which includes hormones, cytotoxic drugs, hypolipidemics, etc. Four hundred and forty cases were admitted to emergency department. Common drug related problems resulting in hospital visits were due to intentional, accidental and overdose. The incidence of drug related hospital admissions was found to be 0.20%. Conclusion The most of the accidental and suicidal cases were reported are by CNS drugs. Psychiatric patients intentionally taking medicines as suicidal attempt and as a part of their illness. As drug related problems are so significant, increased awareness and enhanced collaborative efforts among patients, physicians, pharmacists and caregivers within community and hospital have the potential to minimize the impact of this problem.

Health Spending, Out-of-Pocket Contributions, and Mortality Rates

Health policies seek to achieve conflicting objectives. We argue that the objective of saving lives is best served by a careful balancing of fairness and efficiency considerations. Open, fair and equitable access to health care for all citizens will lower overall mortality rates by enabling the very poor and chronically ill to satisfy their demand for necessary health care. But it will also result in higher costs, not least by also increasing demand for irrelevant, unnecessary and inefficient health care. This undesirable demand and its associated costs can be reduced by increasing out-of-pocket contributions paid for by patients. Such payments are unpopular, though, as they are regarded as regressive and damaging to health of the relatively poor. We argue that properly enacted, no such apparent trade-offs exist. If the freed-up resources are used for more life-saving measures, then higher out-of-pocket contributions will lower overall mortality rates. However, this beneficial effect is conditional on what happens to total health spending. Ironically, out-of-pocket payments are most effective as health policies if they are not or only hardly used as a means of reducing total health expenditures. Our theoretical arguments are confirmed by an econometric analysis of aggregate mortality rates in OECD countries over the period 1984 to 2007.

Variability in the Measurement of Hospital-wide Mortality Rates

Several countries use hospital-wide mortality rates to evaluate the quality of hospital care, although the usefulness of this metric has been questioned. Massachusetts policymakers recently requested an assessment of methods to calculate this aggregate mortality metric for use as a measure of hospital quality. The Massachusetts Division of Health Care Finance and Policy provided four vendors with identical information on 2,528,624 discharges from Massachusetts acute care hospitals from October 1, 2004, through September 30, 2007. Vendors applied their risk-adjustment algorithms and provided predicted probabilities of in-hospital death for each discharge and for hospital-level observed and expected mortality rates. We compared the numbers and characteristics of discharges and hospitals included by each of the four methods. We also compared hospitals' standardized mortality ratios and classification of hospitals with mortality rates that were higher or lower than expected, according to each method. The proportions of discharges that were included by each method ranged from 28% to 95%, and the severity of patients' diagnoses varied widely. Because of their discharge-selection criteria, two methods calculated in-hospital mortality rates (4.0% and 5.9%) that were twice the state average (2.1%). Pairwise associations (Pearson correlation coefficients) of discharge-level predicted mortality probabilities ranged from 0.46 to 0.70. Hospital-performance categorizations varied substantially and were sometimes completely discordant. In 2006, a total of 12 of 28 hospitals that had higher-than-expected hospital-wide mortality when classified by one method had lower-than-expected mortality when classified by one or more of the other methods. Four common methods for calculating hospital-wide mortality produced substantially different results. This may have resulted from a lack of standardized national eligibility and exclusion criteria, different statistical methods, or fundamental flaws in the hypothesized association between hospital-wide mortality and quality of care. (Funded by the Massachusetts Division of Health Care Finance and Policy.).

O nefrologista como consultor ante a intoxicação aguda: epidemiologia das intoxicações graves no Rio Grande do Sul e métodos de aumento da depuração renal

Accidental and intentional poisonings or drug overdoses constitute a significant cause of aggregate morbidity and mortality, and health care expenditures. The nephrologist is frequently called to the emergency room and ICU as a consultant to help with the indication of measures to enhance renal depuration of toxic agents. This study reviews the use of dialysis in acute poisonings due to medications or pesticides, whose specialized toxicological support was provided via telephone by the poison control center of the state of Rio Grande do Sul (CIT-RS from Portuguese). The correlation between need for dialysis and death was assessed in a retrospective cohort (1998-2000). Of the 36,055 cases registered, 337 were identified as severe, and 245 met the inclusion criteria required. Mean age was 30 ± 18 years, and 53% of the patients were women. The most commonly involved medications were anticonvulsants and antidepressants, and the pesticides were organophosphates, bipyridyl compounds, and glyphosate. Techniques to enhance elimination included urinary alkalinization (n = 37) and dialysis. In severe poisonings, dialysis was performed in 4.5% of the cases (n = 11), 3.67 procedures/year (1/22.7 reports of severe cases). In the group undergoing dialysis, 91% involved a suicide attempt (mainly phenobarbital and paraquat). Two cases required hemoperfusion (chloramphenicol and paraquat). Death among non-dialyzed severely ill patients occurred in 25.6%, versus 36.3% of dialyzed patients (RR = 0.89; 95% CI = 0.54-1.35). The findings can be explained by the statistic power associated with the number of procedures performed. The nephrologist should be aware of situations requiring the use of dialysis, even if not necessarily aimed at renal replacement, but at enhancing depuration of a toxic agent.

Modeling Long-Run Cause of Death Mortality Trends

This paper models relationships between trends in cause of death mortality rates for five main causes of death (circulatory system, cancer, respiratory system, external causes, infectious and parasitic diseases) across nine major countries (USA, Australia, Switzerland, Japan, Singapore, Italy, Norway, Sweden, UK). Trends and relationships between mortality rates for causes of death are important since these trends are hidden in aggregate data. Vector Error Correction Models (VECM) are used to model the common trends in causes of death by country. A VECM is a multivariate dynamic system allowing for long-run relationships between variables and common stochastic trends. The paper demonstrates that mortality rates by causes of death have common stochastic trends in many countries but these also differ across countries highlighting the potential for geographical diversification of mortality trends. The results confirm long-run relationships exist between the five main causes of death, indicating dependence between these competing risks. Cause of death analysis provides valuable information that can improve the estimation of aggregate mortality trends.

Societal Heavy Drinking and Suicide Mortality among Russian Youth

Research has found an aggregate association between heavy drinking and suicide rates in general, and in Russia specifically. Alcohol is one of the most serious drug problems facing Russian youth, yet the aggregate alcohol-suicide association has not been tested for this population. Aggregate mortality data for Russian regions (n = 78) for the year 2000 were used to measure youth (age 10–19) suicides and a proxy for societal heavy drinking. We employed the negative binomial estimator to test the heavy societal drinking-youth suicide hypothesis, controlling for other structural factors thought to influence suicide rates. The results showed a positive and significant association between the two for overall, male, and female rates. These results are consistent with studies of alcohol and suicide in Russia, suggesting youth suicide rates are influenced by levels of heavy drinking among youth and those close to them.

Equity and the child health Millennium Development Goal: the role of pro-poor health policies

BackgroundThere are substantial disparities in mortality between rich and poor children in developing countries. As a result, there is a call for explicitly pro-poor health programming in efforts to reach...