Analysis of chronic kidney disease epidemiology in Kazakhstan using nationwide data for 2014-2020 and forecasting future trends of prevalence and mortality for 2030

According to the Global Burden of Disease (GBD) study, chronic kidney disease (CKD) was prevalent in 697.5 million individuals worldwide in 2017. By 2040, it is anticipated that CKD will rank as the fifth most common cause of death. This study aims to examine the epidemiology of CKD in Kazakhstan and to project future trends in CKD prevalence and mortality by 2030. The retrospective analysis was performed on a database acquired from the Unified National Electronic Health System for 703,122 patients with CKD between 2014 and 2020. During the observation period, 444,404 women and 258,718 men were registered with CKD, 459,900 (66%) were Kazakhs and 47% were older than 50. The incidence rate notably decreased: 6365 people per million population (PMP) in 2014 and 4040 people PMP in 2020. The prevalence changed from 10,346 to 38,287 people PMP, and the mortality rate increased dramatically from 279 PMP to 916 PMP. Kazakhstan's central regions, Turkestan and Kyzylorda were identified as the most burdensome ones. The ARIMA model projected 1,504,694 expected prevalent cases in 2030. The predicted mortality climbed from 17,068 cases in 2020 to 37,305 deaths in 2030. By 2030, the prevalence and mortality of CKD will significantly increase, according to the predicted model. A thorough action plan with effective risk factor management, enhanced screening among risk populations, and prompt treatment are required to lessen the burden of disease in Kazakhstan.

Short-term forecasts of expected deaths

We introduce a method for making short-term mortality forecasts of a few months, illustrating it by estimating how many deaths might have happened if some major shock had not occurred. We apply the method to assess excess mortality from March to June 2020 in Denmark and Sweden as a result of the first wave of the coronavirus pandemic; associated policy interventions; and behavioral, healthcare, social, and economic changes. We chose to compare Denmark and Sweden because reliable data were available and because the two countries are similar but chose different responses to COVID-19: Denmark imposed a rather severe lockdown; Sweden did not. We make forecasts by age and sex to predict expected deaths if COVID-19 had not struck. Subtracting these forecasts from observed deaths gives the excess death count. Excess deaths were lower in Denmark than Sweden during the first wave of the pandemic. The later/earlier ratio we propose for shortcasting is easy to understand, requires less data than more elaborate approaches, and may be useful in many countries in making both predictions about the future and the past to study the impact on mortality of coronavirus and other epidemics. In the application to Denmark and Sweden, prediction intervals are narrower and bias is less than when forecasts are based on averages of the last 5 y, as is often done. More generally, later/earlier ratios may prove useful in short-term forecasting of illnesses and births as well as economic and other activity that varies seasonally or periodically.

Short-term forecasts of expected deaths

We introduce a method for making short-term mortality forecasts of a few months, illustrating it by estimating how many deaths might have happened if some major shock had not occurred. We apply the method to assess excess mortality from March to June 2020 in Denmark and Sweden as a result of the first wave of the coronavirus pandemic; associated policy interventions; and behavioral, healthcare, social, and economic changes. We chose to compare Denmark and Sweden because reliable data were available and because the two countries are similar but chose different responses to COVID-19: Denmark imposed a rather severe lockdown; Sweden did not. We make forecasts by age and sex to predict expected deaths if COVID-19 had not struck. Subtracting these forecasts from observed deaths gives the excess death count. Excess deaths were lower in Denmark than Sweden during the first wave of the pandemic. The later/earlier ratio we propose for shortcasting is easy to understand, requires less data than more elaborate approaches, and may be useful in many countries in making both predictions about the future and the past to study the impact on mortality of coronavirus and other epidemics. In the application to Denmark and Sweden, prediction intervals are narrower and bias is less than when forecasts are based on averages of the last 5 y, as is often done. More generally, later/earlier ratios may prove useful in short-term forecasting of illnesses and births as well as economic and other activity that varies seasonally or periodically.

Modelling and forecasting adult age-at-death distributions

Age-at-death distributions provide an informative description of the mortality pattern of a population but have generally been neglected for modelling and forecasting mortality. In this paper, we use the distribution of deaths to model and forecast adult mortality. Specifically, we introduce a relational model that relates a fixed 'standard' to a series of observed distributions by a transformation of the age axis. The proposed Segmented Transformation Age-at-death Distributions (STAD) model is parsimonious and efficient: using only three parameters, it captures and disentangles mortality developments in terms of shifting and compression dynamics. Additionally, mortality forecasts can be derived from parameter extrapolation using time-series models. We illustrate our method and compare it with the Lee-Carter model and variants for females in four high-longevity countries. We show that the STAD fits the observed mortality pattern very well, and that its forecasts are more accurate and optimistic than the Lee-Carter variants.