Fig. 1 Death rate for all causes and years
Fig. 2 Table of observations
Fig. 3 Death rates for all age groups
Fig. 4 Cause of death analysis
Fig. 5 Death rate by age group across all years
Result
This study presents a comprehensive analysis of mortality rates in Japan and Switzerland from 1960 to 2020, examining multiple dimensions, including age, sex, cause of death, and years.

Year: 1960-2019 (60 years)

Sex: Female, Male, All

Age Group: 0-85+ (divided every 5 years), All

Cause: All causes, Communicable diseases, Noncommunicable diseases, Injuries, Ill-defined diseases

Country: Japan, Switzerland

Death rate: For each variables above exists a single death rate per 100,000 population

Percentage of cause-specific deaths out of total deaths: 0-100

Accepted on
March 9, 2024
(see peer reviews)
Endorsed by
This needs updates to CMS. Plan to create a new CMS for claims, then pull claim items from there. This will allow you to create a new multi-field reference inside article CMS (limit 5). This is kind of stupid, revisit CMS limits to see if can avoid this.
Contributing Authors
Micropub

Background:  

The choice of Switzerland and Japan for this analysis stems from their contrasting healthcare systems and unique locations. Switzerland has a universal private healthcare system, while Japan has a universal public one. We focus on the period 1960-2019, a turning point in medicine post-WWII, marked by advancements in healthcare. Our approach considers age and gender disparities to avoid overgeneralization. The chosen timeframe avoids skewing results due to the COVID-19 pandemic, providing a multifaceted view of mortality trends [1-5].

Research overview:

This study presents a comprehensive analysis of mortality rates in Japan and Switzerland from 1960 to 2019. The study looks at mortality patterns across age, cause of death, and gender factors. Using different descriptive, statistical and visualization tools [6-9], our research uncovers trends, nuances and factors influencing mortality rates. These findings carry significant implications for public health policies and practices, providing a multifaceted perspective on mortality dynamics in these two nations.

Results:

The current study examined trends for specific causes of death over the entire period and identified variations in mortality rates between genders in both countries. Additionally, the study investigated how death rates change with age, analyzed the primary reasons for death based on age and country, assessed the dominant causes for each age group and country.

Analysis of the data [Data 1] from 1960 to 2020 reveals that Switzerland's mortality rates exhibit a consistent downward trend, while Japan's rates declined until 1985 but subsequently increased, surpassing Switzerland's rates by 2020 [Figure 1].

After confirming the non-normal distribution of the variable death rate per 100,000 population for each cause (Shapiro-Wilk test, p-value < 0.05), the Mann-Whitney test examined gender differences [10, 11]. For all causes, except communicable and ill defined diseases, the p-value exceeded alpha (0.05), supporting gender differences (men's mean value was higher than women's). Similar results were found for both countries.

The Mann-Whitney test for communicable diseases revealed p-values below alpha (0.05) in Switzerland and Japan, indicating no conclusive evidence of gender differences. No significant gender disparity was found for ill defined diseases for Switzerland. Notably, for Japan, differences were indicated (p-value > 0.05), with women showing a higher average mortality rate than men.

It is also worth mentioning that the populations in both countries have a different age structure, which is probably the reason for the significant increase in mortality in Japan in the period 1980-1990. This increase lasted until 2020 (i.e., the end of the entire selected period). Japan experienced a surge in births in the aftermath of World War II, occurring from 1947 to 1949. This was followed by an extended period of reduced fertility, which led to the problem of an aging population and an increase in mortality [12]. Switzerland experienced less pronounced demographic challenges. Other differences and observations for specific causes of death are shown in the table [Figure 2].

Plotting mortality rates by different age groups revealed an exponential distribution for both Japan and Switzerland. We performed a log-linear least-squares fit, yielding high R-squared values exceeding 0.99. We did not consider the first two age groups (0, and 1 - 4) in our fit. In these age groups, the dependence of mortality rate on age is not described by a single exponential function [Figure 3]. More advanced models that take all age groups into account have been introduced by Gompertz, Makeham, and Siler [13,14].

Analyzing mortality data by cause and age, we identified the most and least frequent causes of deaths in both countries [Figure 4].

After age 15, average mortality rates increase with age in both countries. The mortality rates for all age groups in both countries demonstrate a downward trend over the course of 60 years. However, the overall mortality rate for the total population for all age groups in Japan demonstrated an upward trajectory [Figure 5]. The log-linear model employed to fit mortality rate variations across diverse age groups in both countries has exhibited a satisfactory level of conformity, with correlation coefficients exceeding 0.9 for the majority of age cohorts. However, the exponential model does not capture the increase of mortality with age in the age range of 20 to 39 years.

References
  1. https://onlinelibrary.wiley.com/doi/10.1002/9781118521373.wbeaa074
  2. https://medschool.ucla.edu/blog-post/medical-advances-in-technology-of-the-past-50-years
  3. https://www.bag.admin.ch/bag/en/home.html
  4. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(11)60828-3/fulltext
  5. https://www.numbeo.com/cost-of-living/
  6. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350423/
  7. https://onlinelibrary.wiley.com/doi/10.1002/9780470473900.ch7
  8. https://www.sciencedirect.com/science/article/pii/S0266435607004378?casa_token=sxLaMOPP45MAAAAA:hy8gPw0rpOcHx37GuHG6PjeXIC0bZq900S0FhTb_oUKTFX84YfKJL-vth9WfUJpHEYBu4_Ub
  9. https://onlinelibrary.wiley.com/doi/10.1002/9781405186407.wbiecc148
  10. Shapiro, S. S., & Wilk, M. B. (1965). An Analysis of Variance Test for Normality (Complete Samples). Biometrika, 52(3/4), 591-611.
  11. Fay, M. P., & Proschan, M. A. (2010). Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Statistical Surveys, 4, 1-39. DOI: 10.1214/09-SS051.
  12. Li, C. (1981). Transition of demographic pattern in Japan since World War II and its population problems. Renkou Yanjiu, (1), 35-8. [In Chinese] PMID: 12263427.
  13. https://en.wikipedia.org/wiki/Gompertz%E2%80%93Makeham_law_of_mortality
  14. Cohen, J. E., Bohk-Ewald, C., & Rau, R. (2018). Gompertz, Makeham, and Siler models explain Taylor’s law in human mortality data. Demographic Research, 38, 773-841.
Protocols
Data sets
Permanently stored in the library at arweave.org
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