The Burden of Alzheimer’s Disease Mortality in the United States,1999-2018

Abstract

Background:

The increasing prevalence of Alzheimer’s disease (AD), along with the associated burden on healthcare systems, presents a substantial public health challenge.

Objective:

This study aimed to investigate trends in AD mortality and the relevant burden across the United States (U.S.) from 1999 to 2018 and to predict mortality trends between 2019 and 2023.

Methods:

Data on AD-related deaths between 1999 and 2018 were collected from the WONDER database administered by the U.S. Centers for Disease Control and Prevention (CDC). The Joinpoint Regression Program was used to analyze mortality trends due to AD. Years of life lost (YLL) were calculated to explore the burden of AD deaths. An autoregressive integrated moving average (ARIMA) model was employed to forecast mortality trends from 2019 to 2023.

Results:

Over a recent 20-year period, the number of AD deaths in the U.S. increased from 44,536 (31,145 females and 13,391 males) to 122,019 (84,062 females and 37,957 males). The overall age-adjusted mortality rate increased from 16.5/100,000 in 1999 to 30.5/100,000 in 2018. AD mortality is projected to reach 42.40/100000 within the year 2023. Overall, AD resulted in 322,773.00 YLL (2.33 per 1000 population) in 1999 and 658,501.87 YLL (3.68 per 1000 population) in 2018.

Conclusion:

Our findings demonstrate an increase in AD mortality in the U.S. from 1999 to 2018 as well as a rapid increase from 2019 to 2023. The high burden of AD deaths emphasizes the need for targeted prevention, early diagnosis, and hierarchical management.

Keywords

Alzheimer’s disease mortality time series analysis years of life lost

INTRODUCTION

Alzheimer’s disease (AD) is a progressive neurodegenerative disease and the most common form of dementia. AD has a long asymptomatic or preclinical stage in individuals between roughly 20 and45 years old, followed by amnestic mild cognitive impairment. Most of these individuals will ultimately enter the terminal fully symptomatic phase, leading to a low quality of life [1]. Aging, together with healthcare improvements throughout the past decades, have contributed to living longer in both developed and developing countries, resulting in an increase in onsets of AD. Statistics indicate that 46.8 million patients worldwide currently have dementia and/or AD [2]. This figure is expected to rise to 65.7 million people by 2030 and 115.4 million by 2050, mainly attributable to AD [3].

In 2013, the Global Burden of Disease Study identified AD as one of the top 50 causes of years of life lost globally [4]. In a similar list of 10 leading causes of death, AD has been projected to be the seventh leading cause in high-income countries by 2030 [5]. The latest data highlight AD as the sixth leading cause of death in the United States (U.S.) and the fifth leading cause among Americans aged ≥65 [6]. As the average life expectancy continues to increase worldwide, AD-associated treatment costs are growing in kind. AD is predicted to account for more than $1.1 trillion in U.S. healthcare costs by 2050 [7].

A report based on county-level death certificate data revealed a mildly increasing trend in the AD death toll in the U.S. from 2005 to 2014 [8]. To update these figures on the precise public health burden, we conducted this study based on the WONDER database.

METHODS

Study setting and population

We designed a serial cross-sectional study and obtained the number of AD deaths in the U.S. between 1999 and 2018 from WONDER database (https://wonder.cdc.gov/), which records U.S. resident death certificates filed in all 50 states and the District of Columbia. Mortality was calculated based on population estimates obtained from the U.S. Census Bureau and the CDC’s National Center for Health Statistics. Age-adjusted mortality was calculated using the 2000 U.S. standard population. This study followed the reporting guidelines of Strengthening the Reporting of Observational Studies in Epidemiology (STROBE).

AD deaths

The International Classification of Disease-10 codes G30.0, G30.1, G30.8, and G30.9 were used to identify AD-attributed death. Other forms of dementia were not examined in this analysis. The number of deaths from AD as well as standard errors for crude and age-adjusted mortality (95%confidence intervals [CIs]) and for groups defined by specific characteristics. Crude and age-adjusted mortalities due to AD between 1999 and 2018 were also calculated. Subgroup analyses were based on age, gender, race, urbanization, and individual state.

Statistical analysis

The publicly available Joinpoint Regression Program (Version 4.7, National Cancer Institute) was used to analyze trends in mortality due to AD and identified the Joinpoint Year when trends changed significantly. In addition, the annual percentage change (APC) and 95%CIs were used to measure mortality increases per year in specific segments of the trend line.

To analyze the burden from AD deaths, we classified patients into 14 age groups (0, 1–4, 5–9, 10–14, 15–24, 25–34, 35–44, 45–54, 55–64, 65–69, 70–74, 75–79, 80–84, 85 + years) and calculated years of life lost (YLL) in accordance with the template provided by World Health Organization (WHO) [9].

The autoregressive integrated moving average (ARIMA) model for R3.6.2 (R Foundation for Statistical Computing) was applied to forecast the trend of Alzheimer’s mortalities from 2019 to 2023. The ARIMA equation is: y_t = δ + φ₁y_t-1 + ⋯ + φ_py_t-p + θ₁ ɛ_t-1 + ⋯ + θ_q ɛ_t-q + ɛ_t, where y_t means the differenced time series, the right end of the equation included lagged values of y_t and lagged errors [10]. The seasonal ARIMA model is written as ARIMA (p, d, q) (P, D, Q)_s, where, p, d, q, P, D, and Q mean the autoregressive order, the number of nonseasonal differences, the moving average order, the seasonal autoregressive order, the number of seasonal differences and the seasonal moving average order, respectively. The seasonal period length was defined as 12 in this study [11]. The stationary of sequence is checked by the Augmented Dickey-Fuller (ADF) test. After the sequence transforming to be stationary, the p, q, P, and Q are roughly determined by the figures of the autocorrelation function (ACF) and the partial autocorrelation function (PACF) [12]. In this study, we used the function of ‘auto.arima’ to develop the optimal model according to the AIC value, where the p, d, q, P, D, and Q can also be determined. Auto-ARIMA method is suitable for different ARIMA models of univariate time series data, searching for the models in the order of the constraints provided, and determining the optimal model [13]. Finally, the qqnorm plot, the qqline plot and the Box-Ljung test were implemented to judge whether the residuals of the optimal model were white noise [14]. The packages of “forecast” and“tseries” for R3.6.2 were used to establish the seasonal ARIMA models. The between-groups comparisons of mortality rates were performed with SPSS (Version 25.0, IBM; New York). A two-tailed p < 0.05 was considered statistically significant.

RESULTS

Between 1999 and 2018, the number of recorded AD deaths increased from 44,536 (31,145 females and 13,391 males) to 122,019 (84,062 females and 37,957 males). The crude mortality rates of AD grew from 16.0/100,000 in 1999 to 37.3/100,000 in 2018. The overall age-adjusted rate increased from 16.5/100,000 in 1999 to 30.5/100,000 in 2018. Trends in age-adjusted mortality rates from 1999 to 2018 consisted of four segments: 1) 1999 to 2005 (APC = 6.2, 95%CI = [4.2, 8.4]); 2) 2005 to 2013 (APC = –0.1, 95%CI = [–1.4, 1.1]); 3) 2013 to 2016 (APC = 8.4, 95%CI = [–0.3, 17.9]); and 4) 2016 to 2018 (APC = –0.3, 95%CI = [–7.4, 7.3]). However, the results varied by sex. As listed in Table 1, the age-adjusted rate increased from 17.4/100,000 in 1999 to 34.2/100,000 in 2018 for females and from 14.4/100,000 to 24.5/100,000 for males. As depicted in Fig. 1 and Supplementary Table 1, the number of male AD deaths declined significantly from 2008 to 2013, whereas the number of female AD deaths did not change significantly from 2005 to 2013. Males and females both exhibited a rapid increase from 2013 to 2017 (APC = 8.3%and 9.2%, respectively). Table 1 indicates that the unadjusted rate increased by 133.1%from 1999 to 2018, and the age-adjusted rate changed by 84.8%from 1999 to 2018. As for gender group analysis, there was a 96.6%increase in the age-adjusted rate from 1999 to 2018 among females and a 70.1%change among males.

Table 1

Comparison of Alzheimer’s mortalities (per 100,000) in the United States between 1999 and 2018

Classifications	1999		2018		%change from 1999 to 2018
	No.	Rate (95%CI)	No.	Rate (95%CI)
Total	44,536	NA	122,019	NA	NA
Unadjusted	NA	16.0 (15.8∼16.1)	NA	37.3 (37.1∼37.5)	133.1^a
Age-adjusted	NA	16.5 (16.3∼16.6)	NA	30.5 (30.3∼30.7)	84.8^a
Gender
Female	31,145	17.4 (17.2∼17.6)	84,062	34.2 (33.9∼34.4)	96.6^a
Male	13,391	14.4 (14.1∼14.6)	37,957	24.5 (24.3∼24.8)	70.1^a
Age groups
55∼64 y	448	1.9 (1.7∼2.1)	1,245	2.9 (2.8∼3.1)	52.6^a
65∼74 y	3,204	17.4 (16.8∼18)	7,543	24.7 (24.2∼25.3)	42.0^a
75∼84 y	15,836	129.5 (127.5∼131.6)	32,927	213.9 (211.6∼216.2)	65.2^a
≥85 y	24,980	601.3 (593.9∼608.8)	80,188	1225.3 (1216.8∼1233.8)	103.8^a
Race
Black or African American	2,342	11.3 (10.8∼11.8)	9,042	26.6 (26.1∼27.2)	135.4^a
Asian or Pacific Islander	231	4.9 (4.3∼5.6)	2,884	15.8 (15.2∼16.3)	222.4^a
American Indian or Alaska Native	86	9.5 (7.5∼11.7)	394	15.3 (13.8∼16.8)	61.1^a
Hispanic Whites	981	9.6 (9.0∼10.2)	8,021	25.5 (25.0∼26.1)	165.6^a
Non-Hispanic Whites	43,471	16.7 (16.6∼16.9)	113,842	30.9 (30.7∼31.1)	85.0^a
Urbanization classifications
Large central metro	11,582	15.3 (15.0∼15.6)	31,722	28.9 (28.5∼29.2)	88.9^a
Large fringe metro	9,570	16.2 (15.8∼16.5)	26,458	27.2 (26.9∼27.6)	67.9^a
Medium metro	9,776	17.5 (17.2∼17.9)	28,130	32.6 (32.2∼33.0)	86.3^a
Micropolitan (Non-metro)	5,019	17.4 (16.9∼17.9)	12,988	34.4 (33.8∼35.0)	97.7^a
Noncore (Non-metro)	3,773	15.5 (15.0∼16.0)	9,264	31.6 (30.9∼32.2)	103.9^a
Small metro	4,816	18.1 (17.6∼18.6)	13,457	34.0 (33.5∼34.6)	87.8^a

CI, confidence interval; NA, not applicable; ^aStatistically significant difference in rates for 1999 and 2018 using the chi-square (χ²) test.

Fig. 1

Crude and Age-Adjusted Mortality (per 100,000) due to Alzheimer’s Disease between 1999 and 2018. (a) Crude mortality rates; (b) Age-adjusted mortality rates; (c) Age-adjusted mortality rates for women; (d) Age-adjusted mortality rates for men.

As shown in Fig. 2a and Supplementary Table 2, the AD-attributed mortality rate was highest among people aged 85 and older; this rate grew rapidly from 601.3/100,000 in 1999 to 1225.3/100,000 in 2018, equal to a 103.8%change. The mortality rate among individuals aged 55 to 64 increased from 1.9/100,000 to 2.9/100,000 from 1999 to 2018, representing a 52.6%change. Individuals from 75 to 84 years old demonstrated a 65.2%change, whereas those aged 65 to 74 exhibited the least change (42.0%). Notably, a dramatic increase was observed in AD mortality among adults aged ≥75 between 2013 and 2017.

Fig. 2

Mortality rates of Alzheimer’s disease by age, race, and urbanization between 1999 and 2018. (a) Crude mortality rates by age groups; (b) Age-adjusted mortality rates by race; (c) Age-adjusted mortality rates by urbanization.

With regard to race classification, an increased trend was identified in each subgroup from 2014 to 2018 (Fig. 2b). As shown in Supplementary Table 3, non-Hispanic Whites had the highest age-adjusted mortality rates (30.9 per 100,000) in 2018, while the lowest were found in American Indian or Alaska Native (15.3 per 100,000). Asian or Pacific Islander increased from 4.9/100,000 in 1999 to 15.8/100,000 in 2018, with a change of 222.4%. Hispanic White increased from 9.6/100,000 in 1999 to 25.5/100,000 in 2018, with an increase of 165.6%. However, the Black or African American and non-Hispanic White had a slight decrease in AD mortalities from 2005 to 2013 (APC, –0.4%& –0.1%, respectively).

In terms of urbanization classification, all subgroups (i.e., large central metro, large fringe metro, medium metro, micropolitan, noncore, and small metro areas) exhibited similar trends in AD mortality from 1999 to 2018 (Fig. 2c and Supplementary Table 4). During this period, AD death rates increased significantly in all 50 states and the District of Columbia (Supplementary Table 5). Across all national areas, age-adjusted rates ranged from 7.0 to 29.8 per 100,000 in 1999 and from 13.9 to 46.5 per 100,000 in 2018. All states experienced increased mortality except Maine, where the rate declined from 29.6/100,000 in 1999 to 28.4/100,000 in 2018. Notably, Mississippi witnessed a 245.9%change from 13.3/100,000 in 1999 to 46.0/100,000 in 2018. The age-adjusted AD mortality of each state was shown in Fig. 3.

Fig. 3

Mortality rates of Alzheimer’s disease (per 100,000) by states in 1999 and 2018.

We performed a seasonal decomposition of the data and found a seasonal trend (Supplementary Figure 1). The highest mortality of AD was observed between December and January, while the lowest mortality was observed between June and July (Supplementary Figures 2–4, Supplementary Table 6). A similarity of seasonal trends was also observed for both women and men.

The analysis on the burden of early loss of life showed that AD deaths were responsible for 322,773.00 person-years of life lost (2.33 per 1000 population) in 1999 and 658,501.87 person-years of life lost (3.68 per 1000 population) in 2018 (Table 2 and Supplementary Figure 5). The burden of AD death was higher among women (454,015.18 YLL; 4.88 per 1000 population) than men (204,486.69 YLL; 2.38 per 1000 population) in 2018. Stratified by age, the increase in YLL from AD was accompanied by the increase of age. Although varied by age and gender, an increased in AD-related burden was identified in each subgroup between 1999 and 2018.

Table 2

Years of Life Lost (YLL) from Alzheimer’s Deaths in the United States in 1999 and 2018

Year	Sex	Age	Population, No.	Deaths, No.	YLL, No.	YLL, No./1000 Population
1999	Female
		35–39	11,474,154	2	49.85	0.00
		40–44	11,195,450	3	70.75	0.01
		45–49	9,987,734	20	441.46	0.04
		50–54	8,644,571	32	653.51	0.08
		55–59	6,765,034	123	2,270.62	0.34
		60–64	5,613,729	275	4,503.55	0.80
		65–69	5,141,988	601	8,429.23	1.64
		70–74	4,983,436	1,689	19,461.18	3.91
		75–79	4,331,221	4,626	41,810.55	9.65
		80–84	3,079,977	10,133	68,323.00	22.18
		85+	2,960,708	19,184	76,548.37	25.85
		Total	74,178,002	36,688	222,562.07	3.00
1999	Male
		20–24	9,492,814	1	27.47	0.00
		40–44	10,998,678	3	68.11	0.01
		45–49	9,666,473	10	210.24	0.02
		50–54	8,279,041	18	345.02	0.04
		55–59	6,319,787	68	1,161.36	0.18
		60–64	5,079,476	154	2,274.20	0.45
		65–69	4,399,510	389	4,796.59	1.09
		70–74	3,893,975	1,048	10,436.98	2.68
		75–79	3,011,385	2,332	18,096.50	6.01
		80–84	1,802,331	3,572	20,721.76	11.50
		85+	1,593,236	11,479	42,072.70	26.41
		Total	64,536,706	19,074	100,210.93	1.55
1999	Overall
		20–24	9,492,814	1	27.47	0.00
		35–39	11,474,154	2	49.85	0.00
		40–44	22,194,128	6	138.86	0.01
		45–49	19,654,207	30	651.69	0.03
		50–54	16,923,612	50	998.53	0.06
		55–59	13,084,821	191	3,431.98	0.26
		60–64	10,693,205	429	6,777.75	0.63
		65–69	9,541,498	990	13,225.82	1.39
		70–74	8,877,411	2,737	29,898.16	3.37
		75–79	7,342,606	6,958	59,907.05	8.16
		80–84	4,882,308	13,705	89,044.76	18.24
		85+	4,553,944	30,663	118,621.07	26.05
		Total	138,714,708	55,762	322,773.00	2.33
2018	Female
		30–34	10,944,147	1	26.05	0.00
		40–44	9,916,891	3	70.75	0.01
		45–49	10,483,140	12	264.87	0.03
		50–54	10,607,357	46	939.42	0.09
		55–59	11,271,658	173	3,193.63	0.28
		60–64	10,602,115	528	8,646.81	0.82
		65–69	9,052,080	1,242	17,419.46	1.92
		70–74	7,194,151	3,118	35,926.56	4.99
		75–79	5,122,392	7,082	64,008.28	12.50
		80–84	3,536,942	13,368	90,135.39	25.48
		85+	4,218,810	58,489	233,383.95	55.32
		total	92,949,683	84,062	454,015.18	4.88
2018	Male
		25–29	12,018,838	1	26.54	0.00
		40–44	9,797,410	3	68.11	0.01
		45–49	10,263,995	13	273.31	0.03
		50–54	10,277,207	36	690.05	0.07
		55–59	10,669,327	117	1,998.23	0.19
		60–64	9,729,536	427	6,305.75	0.65
		65–69	8,034,813	937	11,553.75	1.44
		70–74	6,211,272	2,246	22,367.79	3.60
		75–79	4,144,674	4,743	36,806.04	8.88
		80–84	2,590,366	7,734	44,866.20	17.32
		85+	2,325,693	21,699	79,530.93	34.20
		Total	186,768,566	193,875	204,486.69	2.38
2018	Overall
		25–29	12,018,838	1	26.54	0.00
		30–34	10,944,147	1	26.05	0.00
		40–44	19,714,301	6	138.86	0.01
		45–49	20,747,135	25	538.18	0.03
		50–54	20,884,564	82	1,629.46	0.08
		55–59	21,940,985	290	5,191.86	0.24
		60–64	20,331,651	955	14,952.56	0.74
		65–69	17,086,893	2,179	28,973.22	1.70
		70–74	13,405,423	5,364	58,294.36	4.35
		75–79	9,267,066	11,825	100,814.32	10.88
		80–84	6,127,308	21,102	135,001.59	22.03
		85+	6,544,503	80,188	312,914.88	47.81
		Total	179,012,814	122,018	658,501.87	3.68

The function of ‘auto.arima’ was used to identify the optimal model, by which the ARIMA(2,0,1)(0,1,2)₁₂ model was established for forecasting the trend of AD mortalities from 2019 to 2023. An increase was identified between 2019 and 2023, where the highest mortality was projected to reach 4.16/100000 in January 2023, and 42.40/100000 in the year 2023. As listed in Fig. 4, the ARIMA(1,0,3)(2,1,1)₁₂ and the ARIMA(2,0,1)(0,1,1)₁₂ models were determined to forecast the trend of female and male, respectively, by which the highest mortality for female would grow to 57.43/100000 in 2023. Meanwhile, the mortality for male would increase to 26.46/100000 in 2023.

Fig. 4

The forecast of Alzheimer’s disease mortality rates (per 100,000) from 2019–2023 through ARIMA(2,0,1)(0,1,2)_12 . (a) Total; (b) Women; (c) Men; The blue line, the dark grey shade, and the light grey shade represent the point estimate, the 95%confidence interval, and the 80%confidence interval, respectively.

DISCUSSION

Our findings, based on CDC WONDER database, show an obvious increase in AD mortality in the U.S. from 1999 to 2018, along with a notable projected increase from 2019 to 2023. The increase in the trend of AD mortality has occurred in each subgroup of gender, race, and urbanization classifications. Women and non-Hispanic Whites have a higher mortality of AD. The burden of AD deaths also has been increasing during the recent 20 years. And individuals with higher age experienced higher burden from AD deaths.

AD is the most common type of dementia around the world. The clinical manifestation of the disease includes gradual loss of cognitive function, which seriously damages the body and severely compromises patients’ independence. And it is now considered to be a pathological continuum. According to the type and severity of symptoms, AD could be divided into three main stages [15]. Abnormal biomarkers are observed early, with little or no cognitive impairment or memory difficulties. Studies have reported that subjective cognitive decline in individuals who showed no impairment on cognitive tests may be the first observable symptom of AD [16]. Then the stage is the middle stage, which is characterized by abnormal pathophysiological biomarkers and situational cognitive and memory impairment. The final stage manifests as abnormal pathological biomarkers and severe cognitive and memory dysfunction. People with AD may have problems identifying relatives or friends or performing multi-step tasks such as dressing. In advanced stages, AD patients may be bedridden, have difficulty communicating properly, have difficulty swallowing, or become incontinent [6]. AD is incurable due to irreversible dysfunction and loss of neurons; nevertheless, early detection and targeted intervention at the preclinical stage can improve patients’ quality of life [17]. Due to functional and cognitive decline in the disease’s final stages, patients with AD often require continuous care, placing a burden upon paid and unpaid caregivers.

One study has reported an increase in AD-related mortality as of 2014 [8]. In this study, we identified a rapid increase in deaths due to AD between 2014 and 2017 as well as a remarkable projected increase from 2019 to 2023, highlighting an ongoing increase in AD-associated deaths. As age-adjusted rates have also been increasing, we suppose that life expectancy is not the only reason behind the high mortality rate associated with AD; this trend is also due to an increase in premorbid diagnosis or detection, which may occur when patients seek healthcare for early symptoms of cognitive impairment. Diagnosis is usually made through interviews using standardized tests, taking into account parameters such as memory, advanced thinking, and other abilities. Patients may also receive examinations using magnetic resonance imaging (MRI) or computer tomography (CT) to help assess the degree of atrophy of the hippocampus or other brain regions. Social attention has shifted to identifying individuals with dementia in earlier stages, particularly in the U.S. Research identifying new biomarkers has also improved AD detection rates. Over the last decade, studies on diagnostic approaches to AD have expanded rapidly: the International Working Group (IWG) and the National Association on Aging and Alzheimer’s Disease (NIA-AA) are each working to integrate novel biomarkers into diagnostic criteria [18, 19]. These efforts can help practitioners distinguish disease stages, from asymptomatic to severe dementia. In addition, both plasma and cerebrospinal fluid assays may contribute to differential diagnosis of patients with AD from other dementia cases [20, 21]. These improvements make diagnosis increasingly reliable and accurate, especially in early disease stages [22, 23].

Although early diagnosis of AD may help improve clinical outcomes and enhance the quality of life for both patients and caregivers, it does not change the course of illness or the rate of decline [24, 25]. Over time, the number of AD deaths will inevitably increase, and the barn door will be obvious.

AD mortality rates are significantly higher in women and are growing faster than in men. Also, we found that women experienced a higher burden from AD deaths than men. As evidenced by numerous studies, AD develops gradually [26, 27]. Given men’s shorter lifespan, the clinical manifestations of AD may not have fully developed at the time of death among cases of premature death. This characteristic can partly explain differences in prevalence based on sex or gender. A follow-up study of 2,611 cognitively intact participants illustrated that 65-year-old women have a remaining life risk of 12%, nearly twice that of men (6.3%) over a 20-year period [28]. Clinical and preclinical epidemiological studies have also confirmed that women are at higher risk of developing AD than men [29]. In addition, clinical and neurodegenerative symptoms appear faster in women than in men during the process of AD development [30, 31]. Generally, women with AD fare far worse than men in terms of cognition, memory, and visual spatial abilities [32, 33].

Our study shows that the rates of AD-attributed death increase dramatically with age, especially at 65 years and older. The mortality rate of individuals aged≥85 is even more harrowing and continues to soar. It is undeniable that age is the most important factor among the constellation of AD-related features [34, 35]. Positron emission tomography (PET) imaging analysis of brain amyloid-β peptide (Aβ) showed that positive rates increased from 10%for those aged 50–55 to 44%for those over 90 [36]. The notable increase in AD mortality over the age of 85 can be partially attributed to declines in physiological function as well as the high baseline risk of death [8, 37]. In response to this challenge, some studies have proposed a strategy advocating for the importance of focusing on patients in high-risk age stratifications [38].

Targeted population screening of AD is commonly linked to positive symptoms, such as memory loss and difficulty in planning or solving problems. Biomarker-based estimates may also influence the detectable rates of AD patients. Although the number of newly-onset patients is expected to decline, the total number of individuals along the AD spectrum will grow [6]. In the U.S., AD is considered a cause of death and can be compared to other major causes of death such as cancer and heart disease; however, deaths from other types of clinically diagnosed dementia are not listed as deaths in this way [6 , 40]. If a death certificate lists AD as a potential cause of death, then the CDC believes that a person had AD; that is, the disease is defined as “causing a series of diseases or injuries that directly lead to death.” However, not every death certificate is a conclusion drawn from a postmortem evaluation of brain tissue. Moreover, the distinction between AD and other types of dementia is sometimes difficult to make clear. In addition to other dementias, cardiovascular disease and other vascular-related diseases in AD patients may contribute to death, which is common in elderly AD patients [41 –43]. These might result in underestimation of deaths attributed to AD. Meanwhile, increasing risk factors for AD (e.g., hypertension, diabetes, and obesity) and improved diagnosis in early stages could partially explain the upward trend in the AD mortality rate [44 –48].

With regard to seasonality, we observed higher mortality from AD in winter (December and January) and lower in summer (June and July). It is known that the elderly have better cognitive abilities in late summer and early autumn than in winter and spring, showing an approximate 4-year difference in cognitive level [49]. As seasons change, so do the level of brain activity, attention, and memory [50].

Limitations

This study used a serial cross-sectional analysis on the WONDER database, in which repeated cross-sectional data were collected to determine the number of AD deaths. Data are based on death certificates for U.S. residents. However, the causes of death are not identified in a consolidated approach over time. The number of AD deaths might be underestimated between 1999 and 2018 [39, 51]. Second, the determination of cause of death is managed at the state level, leading to differential ascertainment of AD deaths across the U.S. [52].

CONCLUSIONS

Our findings demonstrate an obvious increase in reported AD mortality in the U.S. over a recent 20-year period, along with a predictable increase in the future. The high burden of AD death emphasizes the need for targeted prevention, early diagnosis, and hierarchical management.

Footnotes

ACKNOWLEDGMENTS

This study was supported by the Natural Science Foundation of Shandong Province, China (ZR2017MH100), European Commission Horizon 2020 (PRODEMOS-779238), and National Key Research and Development Program in China (2017YFE0118800).

The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Authors’ disclosures available online ().

The supplementary material is available in the electronic version of this article: .

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