A maximum-likelihood method for estimating natural mortality and catchability coefficient from catch-and-effort data

A simple stochastic model of a fish population subject to natural and fishing mortalities is described. The fishing effort is assumed to vary over different periods but to be constant within each period. A maximum-likelihood approach is developed for estimating natural mortality (M) and the catchability coefficient (q) simultaneously from catch-and-effort data. If there is not enough contrast in the data to provide reliable estimates of both M and q, as is often the case in practice, the method can be used to obtain the best possible values of q for a range of possible values of M. These techniques are illustrated with tiger prawn (Penaeus semisulcatus) data from the Northern Prawn Fishery of Australia.

An additive model for spatio-temporal smoothing of cancer mortality rates

In this paper, a Bayesian hierarchical model is used to anaylze the female breast cancer mortality rates for the State of Missouri from 1969 through 2001. The logit transformations of the mortality rates are assumed to be linear over the time with additive spatial and age effects as intercepts and slopes. Objective priors of the hierarchical model are explored. The Bayesian estimates are quite robustness in terms change of the hyperparamaters. The spatial correlations are appeared in both intercepts and slopes.

In-hospital mortality rates after a cemented femoral component for displaced neck of femur fractures

Aim This prospective cohort study investigated whether the use of preoperative anticoagulants is an independent risk factor for the outcomes of surgical treatment of patients with a neck of femur fracture. Methods Data was obtained from a prospectively collected database. All patients admitted for a neck of femur fracture between Nov 2010 and Oct 2011 were included. This resulted in three hundred twenty-eight patients with 330 neck of femur fractures. Four groups were defined; patients preoperatively (i) on aspirin (n = 105); (ii) on clopidogrel (n = 28); (iii) on warfarin (n = 30), and; (iv) without any anticoagulation history (n = 167, the control group). The non-warfarin group included the aspirin group, clopidogrel group and the control group. Primary outcome was the in-hospital mortality. Secondary outcomes were the postoperative complications, return to theatre and length of stay. Results Thirteen in-hospital deaths were identified, 4 deaths in the aspirin group, 1 death in the clopidogrel group, 2 deaths in the warfarin group and 6 deaths in the control group. No significant difference in the mortality rates was found between the different groups. Also in the secondary outcomes, no significant difference was found between the four groups. A trend to a higher wound complication rate for the warfarin group was detected. Conclusion The use of clopidrogel or aspirin pre operatively is not an influence on short term patient outcome for patients with a neck of femur fracture. Surgical procedures should not be delayed to reverse their influence.

Size-dependent natural mortality of juvenile banana prawns Penaeus merguiensis in the Gulf of Carpentaria, Australia

Natural mortality of marine invertebrates is often very high in the early life history stages and decreases in later stages. The possible size-dependent mortality of juvenile banana prawns, P. merguiensis (2-15 mm carapace length) in the Gulf of Carpentaria was investigated. The analysis was based on the data collected at 2-weekly intervals by beam trawls at four sites over a period of six years (between September 1986 and March 1992). It was assumed that mortality was a parametric function of size, rather than a constant. Another complication in estimating mortality for juvenile banana prawns is that a significant proportion of the population emigrates from the study area each year. This effect was accounted for by incorporating the size-frequency pattern of the emigrants in the analysis. Both the extra parameter in the model required to describe the size dependence of mortality, and that used to account for emigration were found to be significantly different from zero, and the instantaneous mortality rate declined from 0.89 week(-1) for 2 mm prawns to 0.02 week(-1) for 15 mm prawns.

Short and long term mortality rates after a lower limb amputation

Objective To determine mortality rates after a first lower limb amputation and explore the rates for different subpopulations. Methods Retrospective cohort study of all people who underwent a first amputation at or proximal to transtibial level, in an area of 1.7 million people. Analysis with Kaplan-Meier curves and Log Rank tests for univariate associations of psycho-social and health variables. Logistic regression for odds of death at 30-days, 1-year and 5-years. Results 299 people were included. Median time to death was 20.3 months (95%CI: 13.1; 27.5). 30-day mortality = 22%; odds of death 2.3 times higher in people with history of cerebrovascular disease (95%CI: 1.2; 4.7, P = 0.016). 1 year mortality = 44%; odds of death 3.5 times higher for people with renal disease (95%CI: 1.8; 7.0, P < 0.001). 5-years mortality = 77%; odds of death 5.4 times higher for people with renal disease (95%CI: 1.8; 16.0,P = 0.003). Variation in mortality rates was most apparent in different age groups; people 75–84 years having better short term outcomes than those younger and older. Conclusions Mortality rates demonstrated the frailty of this population, with almost one quarter of people dying within 30-days, and almost half at 1 year. People with cerebrovascular had higher odds of death at 30 days, and those with renal disease and 1 and 5 years, respectively.

Maximum likelihood estimation of mortality and growth with individual variability from multiple length-frequency data

We consider estimation of mortality rates and growth parameters from length-frequency data of a fish stock and derive the underlying length distribution of the population and the catch when there is individual variability in the von Bertalanffy growth parameter L-infinity. The model is flexible enough to accommodate 1) any recruitment pattern as a function of both time and length, 2) length-specific selectivity, and 3) varying fishing effort over time. The maximum likelihood method gives consistent estimates, provided the underlying distribution for individual variation in growth is correctly specified. Simulation results indicate that our method is reasonably robust to violations in the assumptions. The method is applied to tiger prawn data (Penaeus semisulcatus) to obtain estimates of natural and fishing mortality.

Socio-environmental factors and suicide in Queensland, Australia

Suicide has drawn much attention from both the scientific community and the public. Examining the impact of socio-environmental factors on suicide is essential in developing suicide prevention strategies and interventions, because it will provide health authorities with important information for their decision-making. However, previous studies did not examine the impact of socio-environmental factors on suicide using a spatial analysis approach. The purpose of this study was to identify the patterns of suicide and to examine how socio-environmental factors impact on suicide over time and space at the Local Governmental Area (LGA) level in Queensland. The suicide data between 1999 and 2003 were collected from the Australian Bureau of Statistics (ABS). Socio-environmental variables at the LGA level included climate (rainfall, maximum and minimum temperature), Socioeconomic Indexes for Areas (SEIFA) and demographic variables (proportion of Indigenous population, unemployment rate, proportion of population with low income and low education level). Climate data were obtained from Australian Bureau of Meteorology. SEIFA and demographic variables were acquired from ABS. A series of statistical and geographical information system (GIS) approaches were applied in the analysis. This study included two stages. The first stage used average annual data to view the spatial pattern of suicide and to examine the association between socio-environmental factors and suicide over space. The second stage examined the spatiotemporal pattern of suicide and assessed the socio-environmental determinants of suicide, using more detailed seasonal data. In this research, 2,445 suicide cases were included, with 1,957 males (80.0%) and 488 females (20.0%). In the first stage, we examined the spatial pattern and the determinants of suicide using 5-year aggregated data. Spearman correlations were used to assess associations between variables. Then a Poisson regression model was applied in the multivariable analysis, as the occurrence of suicide is a small probability event and this model fitted the data quite well. Suicide mortality varied across LGAs and was associated with a range of socio-environmental factors. The multivariable analysis showed that maximum temperature was significantly and positively associated with male suicide (relative risk [RR] = 1.03, 95% CI: 1.00 to 1.07). Higher proportion of Indigenous population was accompanied with more suicide in male population (male: RR = 1.02, 95% CI: 1.01 to 1.03). There was a positive association between unemployment rate and suicide in both genders (male: RR = 1.04, 95% CI: 1.02 to 1.06; female: RR = 1.07, 95% CI: 1.00 to 1.16). No significant association was observed for rainfall, minimum temperature, SEIFA, proportion of population with low individual income and low educational attainment. In the second stage of this study, we undertook a preliminary spatiotemporal analysis of suicide using seasonal data. Firstly, we assessed the interrelations between variables. Secondly, a generalised estimating equations (GEE) model was used to examine the socio-environmental impact on suicide over time and space, as this model is well suited to analyze repeated longitudinal data (e.g., seasonal suicide mortality in a certain LGA) and it fitted the data better than other models (e.g., Poisson model). The suicide pattern varied with season and LGA. The north of Queensland had the highest suicide mortality rate in all the seasons, while there was no suicide case occurred in the southwest. Northwest had consistently higher suicide mortality in spring, autumn and winter. In other areas, suicide mortality varied between seasons. This analysis showed that maximum temperature was positively associated with suicide among male population (RR = 1.24, 95% CI: 1.04 to 1.47) and total population (RR = 1.15, 95% CI: 1.00 to 1.32). Higher proportion of Indigenous population was accompanied with more suicide among total population (RR = 1.16, 95% CI: 1.13 to 1.19) and by gender (male: RR = 1.07, 95% CI: 1.01 to 1.13; female: RR = 1.23, 95% CI: 1.03 to 1.48). Unemployment rate was positively associated with total (RR = 1.40, 95% CI: 1.24 to 1.59) and female (RR=1.09, 95% CI: 1.01 to 1.18) suicide. There was also a positive association between proportion of population with low individual income and suicide in total (RR = 1.28, 95% CI: 1.10 to 1.48) and male (RR = 1.45, 95% CI: 1.23 to 1.72) population. Rainfall was only positively associated with suicide in total population (RR = 1.11, 95% CI: 1.04 to 1.19). There was no significant association for rainfall, minimum temperature, SEIFA, proportion of population with low educational attainment. The second stage is the extension of the first stage. Different spatial scales of dataset were used between the two stages (i.e., mean yearly data in the first stage, and seasonal data in the second stage), but the results are generally consistent with each other. Compared with other studies, this research explored the variety of the impact of a wide range of socio-environmental factors on suicide in different geographical units. Maximum temperature, proportion of Indigenous population, unemployment rate and proportion of population with low individual income were among the major determinants of suicide in Queensland. However, the influence from other factors (e.g. socio-culture background, alcohol and drug use) influencing suicide cannot be ignored. An in-depth understanding of these factors is vital in planning and implementing suicide prevention strategies. Five recommendations for future research are derived from this study: (1) It is vital to acquire detailed personal information on each suicide case and relevant information among the population in assessing the key socio-environmental determinants of suicide; (2) Bayesian model could be applied to compare mortality rates and their socio-environmental determinants across LGAs in future research; (3) In the LGAs with warm weather, high proportion of Indigenous population and/or unemployment rate, concerted efforts need to be made to control and prevent suicide and other mental health problems; (4) The current surveillance, forecasting and early warning system needs to be strengthened, to trace the climate and socioeconomic change over time and space and its impact on population health; (5) It is necessary to evaluate and improve the facilities of mental health care, psychological consultation, suicide prevention and control programs; especially in the areas with low socio-economic status, high unemployment rate, extreme weather events and natural disasters.

What measure of temperature is the best predictor of mortality?

Hot and cold temperatures significantly increase mortality rates around the world, but which measure of temperature is the best predictor of mortality is not known. We used mortality data from 107 US cities for the years 1987–2000 and examined the association between temperature and mortality using Poisson regression and modelled a non-linear temperature effect and a non-linear lag structure. We examined mean, minimum and maximum temperature with and without humidity, and apparent temperature and the Humidex. The best measure was defined as that with the minimum cross-validated residual. We found large differences in the best temperature measure between age groups, seasons and cities, and there was no one temperature measure that was superior to the others. The strong correlation between different measures of temperature means that, on average, they have the same predictive ability. The best temperature measure for new studies can be chosen based on practical concerns, such as choosing the measure with the least amount of missing data.

Mortality, fertility, and persistent income inequality

Many developing countries are plagued by persistent inequality in income distribution. While a growing body of economic-demographic literature emphasizes differential fertility channel, this paper investigates differential child mortality--differences in child mortality across income groups--as a critical link through which income inequality persists. Using an overlapping generations model in which both child mortality and fertility are endogenously determined by parental choice, this paper demonstrates that differential child mortality and its interaction with differential fertility may generate an "income inequality trap." The trap is characterized by higher child mortality and lower degree of skill formation among the poorer households. The model can also explain the behavior of aggregate fertility and mortality rates for countries at various stages of development, consonant with patterns of demographic transition. The results indicate that provision of public health that raises the productivity of private health spending may be an effective way to reduce income inequality

Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010 : a systematic analysis for the Global Burden of Disease Study 2010

Background Non-fatal health outcomes from diseases and injuries are a crucial consideration in the promotion and monitoring of individual and population health. The Global Burden of Disease (GBD) studies done in 1990 and 2000 have been the only studies to quantify non-fatal health outcomes across an exhaustive set of disorders at the global and regional level. Neither effort quantified uncertainty in prevalence or years lived with disability (YLDs). Methods Of the 291 diseases and injuries in the GBD cause list, 289 cause disability. For 1160 sequelae of the 289 diseases and injuries, we undertook a systematic analysis of prevalence, incidence, remission, duration, and excess mortality. Sources included published studies, case notification, population-based cancer registries, other disease registries, antenatal clinic serosurveillance, hospital discharge data, ambulatory care data, household surveys, other surveys, and cohort studies. For most sequelae, we used a Bayesian meta-regression method, DisMod-MR, designed to address key limitations in descriptive epidemiological data, including missing data, inconsistency, and large methodological variation between data sources. For some disorders, we used natural history models, geospatial models, back-calculation models (models calculating incidence from population mortality rates and case fatality), or registration completeness models (models adjusting for incomplete registration with health-system access and other covariates). Disability weights for 220 unique health states were used to capture the severity of health loss. YLDs by cause at age, sex, country, and year levels were adjusted for comorbidity with simulation methods. We included uncertainty estimates at all stages of the analysis. Findings Global prevalence for all ages combined in 2010 across the 1160 sequelae ranged from fewer than one case per 1 million people to 350 000 cases per 1 million people. Prevalence and severity of health loss were weakly correlated (correlation coefficient −0·37). In 2010, there were 777 million YLDs from all causes, up from 583 million in 1990. The main contributors to global YLDs were mental and behavioural disorders, musculoskeletal disorders, and diabetes or endocrine diseases. The leading specific causes of YLDs were much the same in 2010 as they were in 1990: low back pain, major depressive disorder, iron-deficiency anaemia, neck pain, chronic obstructive pulmonary disease, anxiety disorders, migraine, diabetes, and falls. Age-specific prevalence of YLDs increased with age in all regions and has decreased slightly from 1990 to 2010. Regional patterns of the leading causes of YLDs were more similar compared with years of life lost due to premature mortality. Neglected tropical diseases, HIV/AIDS, tuberculosis, malaria, and anaemia were important causes of YLDs in sub-Saharan Africa. Interpretation Rates of YLDs per 100 000 people have remained largely constant over time but rise steadily with age. Population growth and ageing have increased YLD numbers and crude rates over the past two decades. Prevalences of the most common causes of YLDs, such as mental and behavioural disorders and musculoskeletal disorders, have not decreased. Health systems will need to address the needs of the rising numbers of individuals with a range of disorders that largely cause disability but not mortality. Quantification of the burden of non-fatal health outcomes will be crucial to understand how well health systems are responding to these challenges. Effective and affordable strategies to deal with this rising burden are an urgent priority for health systems in most parts of the world. Funding Bill & Melinda Gates Foundation.

Principle components analysis with covariance matrix on regional differences for mortality and percentages of cancers in Shandong province [山东省恶性肿瘤死亡率和构成比地区差异协方差矩阵主成分分析]

Objective To explore the characteristics of regional distribution of cancer deaths in Shandong Province with the principle components analysis. Methods The principle components analysis with co-variance matrix for age-adjusted mortality rates and percentages of 20 types of cancer in 22 counties （cities） were carried out using SAS Software. Results Over 90% of the total information could be reflected by the top 3 principle components and the first principle component alone represented more than half of the overall regional variances. The first component mainly reflected the area differences of esophageal cancer. The second component mainly reflected the area differences of lung cancer， stomach cancer and liver cancer. The value of the first principal component scores showed a clear trend that the west areas possessed higher values and the east the lower values. Based on the top two components，the 22 counties （cities） could be divided into several geographical clusters. Conclusion The overall difference of regional distribution of cancers in Shandong is dominated by several major cancers including esophageal cancer， lung cancer， stomach cancer and liver cancer. Among them，esophageal cancer makes the largest contribution. If the range of counties （cities） analyzed could be further widened， the characteristics of regional distribution of cancer mortality would be better examined. Abstract in Chinese 目的 利用主成分分析探讨山东省恶性肿瘤死亡的地区分布特征. 方法 利用SAS软件对山东省22个县市区2004～2006午的20种恶性肿瘤标化死亡率和构成比分别进行协方差矩阵主成分分析. 结果 前3个主成分就反映了总体差异90%以上的信息,其中仅第1主成分就提供了总体差异一半以上的信息.第1主成分主要反映了食管癌的地区差异,第2主成分主要反映肺癌的地区差异,兼顾胃癌和肝癌.各地区第1主成分得分呈现西高东低的趋势,根据第1和第2主成分可以将调查地区分为若干类别,表现为明显的地理聚集性. 结论 山东省各地区恶性肿瘤死亡的总体差异主要取决于少数高发肿瘤,包括食管癌、肺癌、胃癌、肝癌等,其中以食管癌地位最为突出.如能进一步扩大分析范围,可更好地查明恶性肿瘤死亡的地区特征.

Changes of mortality and causes of death from 1970 to 2005 and decomposition analysis in Shandong province [山东省1970-2005年人群死亡率极死因变化与差别分解分析]

Objective To describe the trend of overall mortality and major causes of death in Shandong population from 1970 to 2005,and to quantitatively estimate the influential factors. Methods Trends of overall mortality and major causes of death were described using indicators such as mortality rates and age-adjusted death rates by comparing three large-scale mortality surveys in Shandong province. Difference decomposing method was applied to estimate the contribution of demographic and non-demographic factors for the change of mortality. Results The total mortality had had a slight change since 1970s,but had increased since 1990s.However,both the mortality rates of age-adjusted and age-specific decreased significantly. The mortality of Group Ⅰ diseases including infectious diseases as well maternal and perinatal diseases decreased drastically. By contrast, the mortality of non-communicable chronic diseases (NCDs)including cardiovascular diseases(CVDs),cancer and injuries increased. The sustentation of recent overall mortality was caused by the interaction of demographic and non-demographic factors which worked oppositely. Non-demographic factors were responsible for the decrease of Group Ⅰ disease and the increase of injuries. With respect to the increase of NCDs as a whole. Demographic factors might take the full responsibility and the non-demographic factors were the opposite force to reduce the mortality. Nevertheless, for the increase of some leading NCD diseases as CVDs and cancer, the increase was mainly due to non-demographic rather than demographic factors. Conclusion Through the interaction of the aggravation of ageing population and the enhancement of non-demographic effect, the overall mortality in Shandong would maintain a balance or slightly rise in the coming years. Group Ⅰ diseases in Shandong had been effectively under control. Strategies focusing on disease control and prevention should be transferred to chronic diseases, especially leading NCDs, such as CVDs and cancer.

Mortality following hip arthroplasty—inappropriate use of National Joint Registry (NJR) data

Mortality following hip arthroplasty is affected by a large number of confounding variables each of which must be considered to enable valid interpretation. Relevant variables available from the 2011 NJR data set were included in the Cox model. Mortality rates in hip arthroplasty patients were lower than in the age-matched population across all hip types. Age at surgery, ASA grade, diagnosis, gender, provider type, hip type and lead surgeon grade all had a significant effect on mortality. Schemper's statistic showed that only 18.98% of the variation in mortality was explained by the variables available in the NJR data set. It is inappropriate to use NJR data to study an outcome affected by a multitude of confounding variables when these cannot be adequately accounted for in the available data set.

Provincial mortality in South Africa, 2000 - priority-setting for now and a benchmark for the future

Background. Cause-of-death statistics are an essential component of health information. Despite improvements, underregistration and misclassification of causes make it difficult to interpret the official death statistics. Objective. To estimate consistent cause-specific death rates for the year 2000 and to identify the leading causes of death and premature mortality in the provinces. Methods. Total number of deaths and population size were estimated using the Actuarial Society of South Africa ASSA2000 AIDS and demographic model. Cause-of-death profiles based on Statistics South Africa's 15% sample, adjusted for misclassification of deaths due to ill-defined causes and AIDS deaths due to indicator conditions, were applied to the total deaths by age and sex. Age-standardised rates and years of life lost were calculated using age weighting and discounting. Results. Life expectancy in KwaZulu-Natal and Mpumalanga is about 10 years lower than that in the Western Cape, the province with the lowest mortality rate. HIV/AIDS is the leading cause of premature mortality for all provinces. Mortality due to pre-transitional causes, such as diarrhoea, is more pronounced in the poorer and more rural provinces. In contrast, non-communicable disease mortality is similar across all provinces, although the cause profiles differ. Injury mortality rates are particularly high in provinces with large metropolitan areas and in Mpumalanga. Conclusion. The quadruple burden experienced in all provinces requires a broad range of interventions, including improved access to health care; ensuring that basic needs such as those related to water and sanitation are met; disease and injury prevention; and promotion of a healthy lifestyle. High death rates as a result of HIV/AIDS highlight the urgent need to accelerate the implementation of the treatment and prevention plan. In addition, there is an urgent need to improve the cause-of-death data system to provide reliable cause-of-death statistics at health district level.