Disaggregating proportional multistate lifetables by population heterogeneity to estimate intervention impacts on inequalities

Background Simulation models can be used to quantify the projected health impact of interventions. Quantifying heterogeneity in these impacts, for example by socioeconomic status, is important to understand impacts on health inequalities. We aim to disaggregate one type of Markov macro-simulation model, the proportional multistate lifetable, ensuring that under business-as-usual (BAU) the sum of deaths across disaggregated strata in each time step returns the same as the initial non-disaggregated model. We then demonstrate the application by deprivation quintiles for New Zealand (NZ), for: hypothetical interventions (50% lower all-cause mortality, 50% lower coronary heart disease mortality) and a dietary intervention to substitute 59% of sodium with potassium chloride in the food supply. Methods We developed a disaggregation algorithm that iteratively rescales mortality, incidence and case-fatality rates by time-step of the model to ensure correct total population counts were retained at each step. To demonstrate the algorithm on deprivation quintiles in NZ, we used the following inputs: overall (non-disaggregated) all-cause mortality & morbidity rates, coronary heart disease incidence & case fatality rates; stroke incidence & case fatality rates. We also obtained rate ratios by deprivation for these same measures. Given all-cause and cause-specific mortality rates by deprivation quintile, we derived values for the incidence, case fatality and mortality rates for each quintile, ensuring rate ratios across quintiles and the total population mortality and morbidity rates were returned when averaged across groups. The three interventions were then run on top of these scaled BAU scenarios. Results The algorithm exactly disaggregated populations by strata in BAU. The intervention scenario life years and health adjusted life years (HALYs) gained differed slightly when summed over the deprivation quintile compared to the aggregated model, due to the stratified model (appropriately) allowing for differential background mortality rates by strata. Modest differences in health gains (HALYs) resulted from rescaling of sub-population mortality and incidence rates to ensure consistency with the aggregate population. Conclusion Policy makers ideally need to know the effect of population interventions estimated both overall, and by socioeconomic and other strata. We demonstrate a method and provide code to do this routinely within proportional multistate lifetable simulation models and similar Markov models.

A method for small-area estimation of population mortality in settings affected by crises

Background Populations affected by crises (armed conflict, food insecurity, natural disasters) are poorly covered by demographic surveillance. As such, crisis-wide estimation of population mortality is extremely challenging, resulting in a lack of evidence to inform humanitarian response and conflict resolution. Methods We describe here a ‘small-area estimation’ method to circumvent these data gaps and quantify both total and excess (i.e. crisis-attributable) death rates and tolls, both overall and for granular geographic (e.g. district) and time (e.g. month) strata. The method is based on analysis of data previously collected by national and humanitarian actors, including ground survey observations of mortality, displacement-adjusted population denominators and datasets of variables that may predict the death rate. We describe the six sequential steps required for the method’s implementation and illustrate its recent application in Somalia, South Sudan and northeast Nigeria, based on a generic set of analysis scripts. Results Descriptive analysis of ground survey data reveals informative patterns, e.g. concerning the contribution of injuries to overall mortality, or household net migration. Despite some data sparsity, for each crisis that we have applied the method to thus far, available predictor data allow the specification of reasonably predictive mixed effects models of crude and under 5 years death rate, validated using cross-validation. Assumptions about values of the predictors in the absence of a crisis provide counterfactual and excess mortality estimates. Conclusions The method enables retrospective estimation of crisis-attributable mortality with considerable geographic and period stratification, and can therefore contribute to better understanding and historical memorialisation of the public health effects of crises. We discuss key limitations and areas for further development.

Features of the course of coronavirus infection in patients with diabetes mellitus

Diabetesmellitus, overweight and the age of a patient over 65 years old are identified by clinicians as themain factors that can complicate the course of the coronavirus infection and increase the likelihood of fatal outcome. Although in the general human population mortality from coronavirus fluctuateswithin 3–5 %, sometimes very significantly differing in individual countries, this level can reach 15–25 % among patientswith diabetes, especially for those receiving insulin therapy. Diabetes mellitus as a concomitant disease in COVID-19 is considered one of the most significant risk factors for the development of adverse outcomes due to a more severe course of infection in conditions of hyperglycemia and other aggravating factors.

Study of the impact of available healthcare resources on the population’s mortality rate using the hierarchical linear modelling

Introduction. The uneven development of the medical material and technical base and resources is observed worldwide. At the same time, healthcare resource availability is associated with the territorial characteristics of the population’s mortality rate. In order to reduce mortality, a better understanding of this relationship is needed. The purpose of the study is to assess the impact of healthcare resource availability on mortality, taking into account the hierarchical nesting of municipalities in subjects of the Russian Federation with further funding for health care and demographic indicators. Material and methods. For these purposes, hierarchical linear modelling is used. The assessment was carried out on the data of 265 municipalities attributed to 6 constituent entities of the Russian Federation. The data sources are the Territorial Bodies of the Federal State Statistics Service and the Unified Interdepartmental Information and Statistical System (www.fedstat.ru). Results. As a result of modelling, the health care resources (doctors, medical personnel, beds) at the municipal level were determined to reduce the population mortality rate positively. At the same time, an ambiguous influence of the actual cost of the territorial compulsory medical insurance program was revealed at the regional level. Conclusion. The results obtained correspond to studies devoted to the regional diversity of the population mortality rate and the available healthcare resources. However, they make it possible to determine the influence of factors taking into account the level of their formation (regional, municipal). The proposed models make it possible to improve the quality of managerial decision-making in the health care system since, taking into account the hierarchical nesting, they share the influence of regional and local factors on the variation of municipalities in terms of the mortality rate of the population.

Incidence and mortality of uveal melanoma in Australia (1982–2014)

AimsWe aimed to estimate the incidence and mortality of uveal melanoma (UM) in Australia from 1982 to 2014.MethodsDeidentified unit data for all cases of ocular melanoma were extracted from the Australian Cancer Database from 1 January 1982 to 31 December 2014. UM cases were extracted and trends in incidence and disease-specific mortality were calculated. Incidence rates were age-standardised against the 2001 Australian Standard Population. Mortality was assessed using Cox regression.ResultsFrom 1982 to 2014, there were 5087 cases of ocular melanoma in Australia, of which 4617 were classified as UM. The average age-standardised incidence rate of UM was 7.6 (95% CI 7.3 to 7.9) per million. There was an increase (p=0.0502) in the incidence of UM from 1982 to 1993 with an annual percent change (APC) of +2.5%, followed by a significant decrease in the incidence of UM from 1993 to 2014 (APC −1.2%). The average 5-year survival from 1982 to 2011 did not significantly change from an average of 81%, with an average APC (AAPC) of +0.1%. A multivariate Cox regression revealed that residence in Western Australia (p=0.001) or Tasmania (p=0.05), age ≥60 years (p<0.001) and histological classification as mixed (p<0.001) or epithelioid cells (p<0.001) were significantly associated with reduced survival.ConclusionIn conclusion, we found that the incidence of UM peaked in the 1990s. Although treatment for primary UM has improved in the last 30 years, overall survival did not change significantly in the last 30 years.

Common Factor Cause-Specific Mortality Model

Recent pension reforms in Europe have implemented a link between retirement age and life expectancy. The accurate forecast of life tables and life expectancy is hence paramount for governmental policy and financial institutions. We developed a multi-population mortality model which includes a cause-specific environment using Archimedean copulae to model dependence between various groups of causes of death. For this, Dutch data on cause-of-death mortality and cause-specific mortality data from 14 comparable European countries were used. We find that the inclusion of a common factor to a cause-specific mortality context increases the robustness of the forecast and we underline that cause-specific mortality forecasts foresee a more pessimistic mortality future than general mortality models. Overall, we find that this non-trivial extension is robust to the copula specification for commonly chosen dependence parameters.

The mechanism between mortality, population growth and ageing of the population in the European lower and upper middle income countries

This paper analyses the effect of mortality rates (under-five and adult mortality) and population growth on the population ageing in a pooled sample of nine lower and upper middle European countries. Therefore, the main goal of this research is to investigate the ageing process of the population in the context of mortality mechanisms (under five and adult mortality) and of population growth in nine European LUMIs. The analysis is implemented in terms of Pooled least squares with cross-section fixed effects methodology. The novelty used within this research is White two-way cluster standard errors & covariance. This study is based on a database from the World Bank and UN covering the period 1995–2019. The expected results are making available quantitative analysis and insights in the context of mechanisms between the ageing process of population, mortality and population growth across these European LUMIs. Results are consistent with the notion that the increasing ageing process within these countries may be a consequence of the negative impact of population growth and from the influence of adult mortality for both sexes. The research results confirm the presence of solid ties of the mechanism between mortality, population growth and population ageing. Therefore, a clear point was provided that mortality acceleration will depend primarily on the level of population growth.

Mortality and Alcohol as Its Cause—Comparative Characteristics of the Two Neighboring Countries: Ukraine and Poland

This paper presents a comparative assessment of mortality in Poland and Ukraine, including due to alcohol consumption, by sex, place of residence, and age groups. Mortality from alcohol consumption is and remains one of the health problems of the state’s population. The aim of this study was to establish the difference in mortality, including due to alcohol consumption, in the two neighboring countries. The analysis was conducted in 2008 and 2018 according to statistical institutions in Poland and Ukraine. Data from the codes of the International Statistical Classification of Diseases of the 10th edition: F10, G31.2, G62.1, I42.6, K70, K86.0, and X45 were used to calculate mortality due to alcohol consumption. The share of mortality caused by alcohol consumption in Ukraine in 2008 was 3.52%, and 1.83% in 2018. At the same time, in Poland, there is an increase in this cause of death from 1.72% to 2.36%. Mortality caused by alcohol consumption is the main share of mortality in the section “Mental and behavioral disorders” in both Ukraine, at 73–74%, and Poland, at 82–92%. Changes in the mortality rate in the cities and villages of Ukraine and Poland showed different trends: Poland nated, a significant increase in mortality, while in Ukraine it has halved on average. Overall and alcohol mortality rates in both countries were higher among the male population. The analysis of mortality among people of working age showed that the highest proportion of deaths from alcohol consumption in both countries was among people aged 25–44. Despite the geographical proximity, and similarity of natural and climatic characteristics and population, mortality rates in each country reflect the difference in the medical and demographic situation, and the effectiveness of state social approaches to public health.