The global estimate of premature cardiovascular mortality: a systematic review and meta-analysis of age-standardized mortality rate

doi:10.21203/rs.3.rs-2730451/v1

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Research Article

The global estimate of premature cardiovascular mortality: a systematic review and meta-analysis of age-standardized mortality rate

https://doi.org/10.21203/rs.3.rs-2730451/v1

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Journal Publication

published 16 Aug, 2023

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Background

Cardiovascular disease (CVD) is a significant cause of premature mortality worldwide, with a growing burden in recent years. Despite this, there is a lack of comprehensive meta-analyses that quantify the extent of premature CVD mortality. Study addressed this gap by estimating the pooled age-standardized mortality rate (ASMR) of premature CVD mortality.

Methods

We conducted a systematic review of published CVD mortality studies that reported ASMR as an indicator for premature mortality measurement. All English articles published as of October 2022 were searched in four electronic databases: PubMed, Scopus, Web of Science (WoS), and the Cochrane Central Register of Controlled Trials (CENTRAL). We computed pooled estimates of ASMR using random-effects meta-analysis. We assessed heterogeneity from the selected studies using the I² statistic and performed subgroup analyses by sex, main CVD types, income country level, and study time. The analysis was performed using R software with the “meta” and “metafor” packages.

Results

A total of 15 studies met the inclusion criteria. The estimated global ASMR for premature CVD mortality was 27.0 per 100,000 people (95% CI: 20.13, 36.21), with heterogeneity among studies (I² = 99%). Subgroup analysis showed that ASMRs varied by CVD type, with the highest rate for total CVD (ASMR = 96.04, 95% CI: 67.18, 137.31), followed by ischemic heart disease (ASMR = 15.57, 95% CI: 11.27, 21.5) and stroke (ASMR = 12.36, 95% CI: 8.09, 18.91). There were also sex-specific differences, with higher ASMRs for males (37.50, 95% CI: 23.69, 59.37) than females (15.75, 95% CI: 9.61, 25.81). Middle-income countries had a significantly higher ASMR (90.58, 95% CI: 56.40, 145.48) compared to high-income countries (21.42, 95% CI: 15.63, 29.37). Stratifying by study time suggests a reducing trend in ASMR for premature CVD mortality from 2000–2019.

Conclusions

This meta-analysis synthesized a comprehensive estimate of the worldwide burden of premature CVD mortality. Our findings underscore the continued burden of premature CVD mortality, particularly in middle-income countries. Addressing this issue requires targeted interventions to mitigate the high risk of premature CVD mortality in these vulnerable populations.

Premature mortality

cardiovascular diseases

age standardized mortality rate

Premature mortality refers to deaths that occur at a younger age than expected, based on the average life expectancy [1]. The Global Burden of Disease (GBD) study reports that cardiovascular diseases (CVDs), specifically ischemic heart disease (IHD) and stroke, continue to be the primary causes of premature mortality worldwide [2]. According to the World Health Organization (WHO), low- and middle-income countries (LMICs) experience a disproportionately high burden of premature mortality compared to high-income countries (HICs) [3]. Even though CVD mortality rates have decreased dramatically over the past two decades, the burden of premature CVD mortality is on the rise in LMICs, emphasizing the need for continued efforts to prevent and manage CVD in these regions [4]. In 2015, the WHO developed an ambitious target by 2030 to reduce by one-third premature mortality from non-communicable diseases (NCDs) through the Sustainable Development Goal (SDG) [5]. The urgency needs to be focused on revising existing policies for the preventing and controlling NCDs, including CVDs [6]. In order to ensure the SDG’s target is on track, information on premature CVD mortality may assist in developing global and context-specific strategies for reducing the incidence of premature CVD mortality.

Age-standardized mortality rate (ASMR) is a commonly used measure to assess premature mortality in a population. ASMR adjusts for differences in the age distribution of populations, which can vary widely between countries or regions [7]. By controlling for these differences, ASMR allows comparisons between populations with different age structures. ASMR can be used to monitor changes in premature mortality over time and to compare mortality rates between different populations. It is a useful tool for identifying health disparities and evaluating the impact of public health interventions. The age limit for calculating ASMR for premature mortality varies depending on the individual context and purpose of the analysis. WHO considers an ASMR for premature mortality between the ages of 30 and 70 years [8], while some studies report an ASMR below 65 [9] and 75 [10].

While numerous systematic reviews and meta-analyses have explored CVD mortality, few have specifically examined premature mortality as an outcome measure. Instead, these analyses have primarily focused on identifying predictors of increased mortality, risk factors for cause-specific mortality, and relative risks associated with CVD [11–14]. The GBD study, which provides the most widely-used estimates of premature mortality globally, has faced limitations in accurately estimating premature CVD mortality due to inadequate or low-quality mortality data in some countries, particularly in impoverished regions [15, 16]. Hence, there is scarcity of a comprehensive systematic review with meta-analysis that estimates the pooled ASMR for premature CVD mortality. To address these gaps, we have undertaken a systematic review and meta-analysis to identify relevant studies and synthesize their findings on ASMR related to premature CVD mortality. Furthermore, we performed a sub-analysis to pool estimates of ASMR by sex, major types of CVD, income country level and time of study.

Protocol and registration

We registered the protocol for this review with the International Prospective Register of Systematic Reviews (PROSPERO), [Registration number: CRD42021288415]. We conducted this review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) [17].

Search Strategy

We conducted electronic databases namely PubMed, Scopus, Web of Science (WoS), and the Cochrane Central Register of Controlled Trials (CENTRAL) to identify potential studies.

We also searched Google Scholar to identify articles not in the major electronic databases. The search was conducted in all databases up to October 18, 2022. Our search strategy included terms for "cardiovascular diseases" (e.g., coronary heart disease, cerebrovascular disorder, myocardial ischemia, or stroke), and a term for premature mortality (e.g., premature death, premature mortality, age standardized mortality rate or years of life lost). The “AND” Boolean operator was used to combine search terms across the categories and the “OR” was used to combine within the categories. We expanded the search from the reference list of included articles to further identify other eligible articles. The search was limited to studies in English-language only. The detailed search terms for each database are presented in Supplement 1.

Inclusion And Exclusion Criteria

All eligible studies had to satisfy the following criteria: (1) reported premature mortality or death from cardiovascular disease; (2) measure premature mortality using ASMR; (3) defined age limit for premature mortality (e.g., 0–64 years, 0–74 years); (4) reported ASMR per 100,000 population; (5) used primary data or observational data on cause-specific mortality from CVD; (6) follow International classification of diseases (ICD) code for CVD death (e.g., ICD-10 code for CVD: I01-I99); (7) reported number of deaths from CVD, or 95% confident interval (CI) allowing standard error (SE) to be calculated. The studies were excluded if when they; (1) reported mortality in a particular subgroup of the population or specific cohort (e.g., congenital heart disease, epilepsy or pregnant women); (2) used estimated data (e.g., data from GBD study or any global estimated data); (3) reported CVD as a combination with other diseases (e.g., NCD or all cause premature mortality; or (4) reported duplicate data (or data sets from overlapping periods at the same site).

Screening Process

One review author (W.S.R.H) removed all duplicate publication prior to titles and abstracts screening. Two authors (W.S.R.H and C.X.W) independently screened the titles and abstracts to examine the potential studies for inclusion and exclude those that were obviously irrelevant. Another two independent review authors (W.S.R.H and T.M.H) independently screened the full-text for inclusion according to the eligibility criteria, and documented reasons for exclusion for all excluded studies. We resolved any disagreements through discussion. If necessary, a third review author (N.A.M) was consulted to provide input. If consensus could not be reached, another author (K.I.M) acted as an arbiter. All references were stored, organized, and managed using Mendeley Reference Management Software [18]. To ensure transparency, we recorded our selection process and completed a flow diagram (Fig. 1) in accordance with the PRISMA guidelines [17].

Data Extraction And Management

Data extraction was performed independently by two review authors (W.S.R.H and T.M.H) following the Cochrane Handbook for Systematic Reviews of Interventions [19]. We utilized a standardized data extraction form developed using a Microsoft Excel spreadsheet to record study characteristics and outcome data. One review author (W.S.R.H) conducted a comprehensive extraction of all data, subsequently verified for accuracy by a second review author (T.M.H). For all eligible articles, we extracted information including the first author's name, year of publication, study design, country, source of data, year of data source, study population, age coverage for premature mortality, number of deaths, types of CVD death, ICD code for cause of death, the method or formula used for ASMR calculation and the value of ASMR per 100,000 population with their respective 95% CI if provided. The ASMR value was categorised separately based on sex, CVD types and study time. We contacted the author to get the exact value for ASMR if they reported it in the plot or if ASMR was reported as part of all-cause or general NCD mortality. Each study may report ASMR values for each sex or CVD type. Thus, we treated each value as separate data from each study and extracted the ASMR value separately for the summary effect size. Data from some countries might have limited quality and representativeness. Hence, in the analysis, we exerted two assumptions: a) the data from each source represents the national population, and b) data measurement was valid for all data sources.

Quality Assessment

The quality of the included studies was assessed through an adapted version of the Newcastle-Ottawa Scale (NOS) for cross-sectional studies and modified this version to better suit the specific characteristics of our study [20]. The NOS is a widely used tool for assessing the quality of non-randomized studies, such as observational and case-control studies [21]. The adopted version of NOS assesses the same three components (selection, comparability, and outcome) as the original version. The score for the adapted version of the cross-sectional studies is as follows: 1) very good studies: 9–10 points; 2) good studies: 7–8 points; 3) satisfactory studies: 5–6 points; and 4) unsatisfactory studies: 0–4 points. The detailed criteria for NOS assessment are represented in Supplement 2. Two review authors (W.S.R.H and N.H.M) assessed the quality of each study using our adapted NOS tool and resolved any discrepancies through discussion or by consulting a third reviewer (N.A.M). We reported the details results of the quality assessment in a separate table (presented in Supplement 2), which included the total score for each study and the scores for each item. We also presented the overall quality score for each study in Table 1, along with the characteristics of the included studies.

Statistical Analysis

We manually calculated the SE for each study's ASMR estimate before running the meta-analysis. For studies that reported a 95% CI, we used the formula SE = (upper limit of CI - lower limit of CI) / 3.92 [22]. For studies that reported the number of deaths, we used the formula SE = R/ square root of N, where R is age adjusted rate and N is numbers of death [23]. These manual calculations allowed us to standardize the SEs across studies and incorporate them into the meta-analysis using a random-effects model. We verified our manual calculations by comparing them to the SEs reported in the studies' original publications, and found them to be consistent.

To estimate the pooled ASMR from CVD across multiple studies, we used the “meta” package [24] and “metafor” [25] package in R. We first imported the data from the included studies into R and performed any necessary data cleaning and processing. We then used the metagen function to fit a random-effects model to the ASMR estimates. We opted to use a random effects model for our meta-analysis given the observed heterogeneity among the included studies, as this method accounts for both within-study and between-study variation in the effect sizes and provides a more conservative estimate of the pooled effect size. We specified the effect size measure as the log-transformed ASMR. We also used the forest function to generate a forest plot of the individual study effect sizes and their 95% CI, including a horizontal line representing the overall effect estimate. In addition, we estimated a prediction interval (PI) for the overall effect size estimate, which considers both within-study and between-study variation and gives an expected range of true effects in future studies. This is shown as a horizontal line on the forest plot, extending beyond the limits of the CI and indicating the plausible range of effects [26]. We conducted subgroup analyses by sex, CVD type, income country classification and study time to investigate potential sources of heterogeneity in the pooled ASMR estimate. We first classified the studies by sex (male or female), CVD type (IHD, stroke, or other), study time (1990–1999, 2000–2009 or after 2010) and income country level; HICs or middle-income countries, MICs (which included low and upper middle-income countries). We then fit separate random-effect models for each subgroup. The subgroup differences were assessed using the chi-squared test statistic (x²) with a significance level of p < 0.05 and presented graphically in a forest plot.

Assessment Of Heterogeneity

We used the I-squared (I²) statistic to quantify the proportion of total variation in effect sizes due to heterogeneity beyond chance. We considered I² values of 25%, 50%, and 75% to represent low, moderate, and high heterogeneity, respectively [27]. In addition to I², we visually inspected the forest plots of the individual study effect sizes and their CI to identify any outliers or clusters of effect sizes. We also used the Cochran's Q test to formally test for heterogeneity, which has a null hypothesis of homogeneity. A p-value less than 0.01 indicated significant heterogeneity [28]. In addition to the I² statistic and the Cochran's Q test, we also used the tau (τ) statistic to quantify the amount of heterogeneity in the meta-analysis. The tau statistic is a measure of between-study variance that accounts for sampling error and true heterogeneity. A larger value of tau indicates greater heterogeneity in effect sizes across studies. We also explored potential sources of heterogeneity using subgroup analyses by sex, CVD type, income country level, and study time.

Assessment Of Reporting Biases

We used the funnel plot to assess potential reporting bias in our meta-analysis. The funnel plot displays the effect size estimate (ASMR) from each study on the x-axis and its standard error or precision on the y-axis. The plot can be used to detect asymmetry, which may indicate that smaller studies with larger effect sizes are missing from the lower left corner of the plot. However, it is important to note that asymmetry in a funnel plot may suggest publication bias, but other factors, such as heterogeneity or chance, can also create asymmetry [29]. Apart from the funnel plot, we used Begg's [30] and Egger's tests [31] to assess reporting bias in our meta-analysis. Begg's test is a rank correlation test that examines the association between the effect size and its variance across studies [30]. A significant p-value indicates potential publication bias or small-study effects, where smaller studies with larger effect sizes are more likely to be published. Egger's test is a regression-based test that examines the asymmetry in the funnel plot of the effect size against its standard error [31]. A significant p-value indicates potential publication bias or small-study effects.

Sensitivity Analysis

We conducted a sensitivity analysis by excluding outliers identified using the Baujat plot. Baujat et al. (2002) [32] introduced a diagnostic plot as a tool to identify the sources of heterogeneity in meta-analytic data. The Baujat plot displays the influence of overall result versus contribution of overall heterogeneity of each study included in the meta-analysis. It can help identify potential outliers, which are studies that have a large influence on the overall results of meta-analysis and may be responsible for the heterogeneity. Based on our criteria for outlier exclusion, we re-analyzed the data with and without these outlier studies to assess the impact on the overall effect estimate, confidence intervals, and heterogeneity.

Study characteristics

The search strategy identified a total of 2012 records, and after removing duplicates and screening for relevance, 15 of them were included in the meta-analysis. Figure 1 illustrates the flow of information through the identification and screening phases of this systematic review. Table 1 presents the characteristics of the included studies. All studies were cross-sectional in design with representation from diverse geographic regions including the United States of America, USA (5 studies), Spain (2 studies), and one study each from Serbia, China, Brazil, Finland, Poland, Australia, Philippines, and Argentina. The source of data for the studies was vital registration systems, which represent the general population of samples. Additionally, all studies reported cause of death using ICD codes, ensuring consistency in the definition of outcomes across studies. Eight studies reported CVD as total CVD, and some [33, 37–40, 43–45, 47] reported CVD as a specific CVD type (IHD, stroke, or others). All included studies were published between 1998 and 2022, but data source covered a period from 1990 to 2019. Moreover, ten studies reported ASMR for premature CVD mortality by sex.

Quality Assessment

The NOS adopted version for cross-sectional design was used to assess the quality of the included studies. Out of the 15 studies, 13 were rated as very good, one was rated as good, and one was rated as satisfactory, with none being classified as poor. Consequently, all 15 studies were included in the analysis (Table 1).

Overall ASMR Estimates And Subgroup Analysis By CVD Types And Sex

Using a random effects model, Table 2 presents the summary of the pooled estimate of ASMR for premature CVD mortality, in overall and based on subgroup. The meta-analysis estimated the overall ASMR for premature CVD mortality to be 27.0 (95% CI: 20.13, 36.21) per 100,000 people, with a high degree of heterogeneity among the studies (I² = 99%). Subgroup analysis showed a significant difference in the ASMRs for different types of CVD, with the ASMR for total CVD being the highest (ASMR = 96.04, 95% CI: 67.18, 137.31; I² = 84%), followed by IHD (ASMR = 15.57, 95% CI: 11.27, 21.5; I² = 92) and stroke (ASMR = 12.36, 95% CI: 8.09, 18.91; I² = 97%). We also observed differences in ASMRs between sex, where males (ASMR = 37.50, 95% CI: 23.69, 59.37; I² = 96%) demonstrated a higher rate than females (ASMR = 15.75, 95% CI: 9.61, 25.81; I² = 99%). The forest plot for subgroup analysis for overall studies by CVD types, and sex was presented in the Supplement 3 (Figure S1-S2). To further explore sex-specific differences in CVD mortality, we generated forest plots for each CVD type (total CVD, IHD, and stroke) by sex. We excluded studies that only provided data for the combined male and female groups. Our analysis revealed that ASMR for all types of CVD, including IHD and stroke, was significantly higher for males than females. The test for subgroup differences yielded a p-value of less than 0.05, as shown in Fig. 2–4. When comparing specific CVD types, we found that the ASMR for males was 27.51 (95% CI: 17.89, 42.30) and for females was 9.30 (95% CI: 6.64, 13.03) for IHD (Fig. 3). Similarly, for stroke (Fig. 4), the ASMR for males was 15.18 (95% CI: 10.12, 22.77), while it was 7.23 (95% CI: 2.45, 21.29) for females.

Table 2

Summary of pooled ASMR per 100,000 population for premature cardiovascular mortality
Subgroup		Studies (n)	Random effect model ASMR per 100,000	95% Confidence interval	Test for Heterogeneity
Subgroup		Studies (n)	Random effect model ASMR per 100,000	95% Confidence interval	I² (%)	T²	p (Q test)
Overall study		15	27.00	20.13, 36.21	99	1.21	0
CVD types
	Total CVD^a	8	96.04	67.18, 137.31	84	0.33	< 0.01
	IHD^b	4	15.57	11.27, 21.51	92	0.39	< 0.01
	Stroke^c	4	12.36	8.09, 18.91	97	0.29	< 0.01
	Other types^d	3	19.79	8.41, 46.58	100	2.11	0
Sex
	Male	10	37.50	23.69, 59.37	96	0.97	< 0.01
	Female	10	15.75	9.61, 25.81	99	1.32	0
Country income level^e
	HICs	10	21.42	15.63, 29.37	99	1.16	0
	MICs	6	90.58	56.40, 145.48	77	0.31	< 0.01
Time study (year)
	1990–1999	4	16.35	8.35, 32.02	96	0.77	< 0.01
	2000–2009	5	63.84	35.34, 115.31	98	1.27	< 0.01
	2010–2019	10	19.93	13.56, 29.30	99	0.97	0

ASMR: age-standardized mortality rate; I²: I statistics; T²: Tau statistics; Q test: Cochran's Q test. p: p-value.

^aTotal CVD death was based on ICD − 10 code: I00-I99 or ICD-9 codes: 350–459.

^bIschemic heart disease (IHD) death based on ICD-10 (I20-I25) or ICD-9 (410–414)

^cCerebrovascular disease or stroke death based on ICD-10 (I60-I69) or ICD-9 (430–438)

^dOther types of CVD death including heart disease (ICD-10: I00-I09, I11, I13, I20- I51), heart failure (ICD-10: I50) and cardiac death ICD-10 (I21, I25, I40, I34, I35, I42, I45-I49, R96, Q20-Q24, Q87)

^eAccording to World Bank’s classification. HICs = high income countries, MICs = middle-income countries (including upper middle-income countries and low middle-income countries)

Subgroup Analysis By Income Country Classification

Furthermore, stratifying by income country classification (Table 2) revealed a significant higher overall ASMR for premature CVD mortality in MICs (ASMR = 90.58, 95% CI: 56.40, 145.48; I² = 77%) compared to HICs (ASMR = 21.42, 95% CI: 15.63, 29.37; I² = 99%) (see forest plot Figure S3 in Supplement 3). In addition, we did the specific subgroup analysis for selected studies that reported total CVD (ICD-10 codes I00-I99 or ICD-9 codes 350–459) by country income level (see Forest plot Figure S4, in Supplement 3), where this analysis also demonstrated a higher ASMR estimate in MICs than HICs (ASMR = 111.11 vs. 78.21 respectively).

Subgroup Analysis By Study Time

The conducted a subgroup analysis of ASMR for premature CVD mortality based on the study time showed undulated pattern from, 1990–1999, 2000–2009, and 2010–2019. ASMR for premature CVD mortality was 16.35 (95% CI: 8.35, 32.02; I² = 96%) for the 1990–1999 period, increased to 63.84 (95% CI: 35.34, 115.31; I² = 98%) for the 2000–2009 period, and then declined to 19.93 (95% CI: 13.56, 29.30; I² = 99%) for the 2010–2019 period (see Table 2 and Forest plot in Supplement 3, Figure S5). Similar undulated pattern was revealed when we conducted a subgroup analysis of premature CVD mortality rate by income country level based on the study time. We separately analysed the ASMR for HIC and MIC by study time, as shown in Table 3 (see comparing with original data in specific subgroup analysis) and Forest plots in Supplement 3, Figure S6-7. Our results showed that the trend in premature CVD mortality was similar in both HICs and MICs, with an increase from 1990–1999 to 2000–2009, followed by a decrease from 2000–2009 to 2010–2019. Specifically, in HICs, the ASMR increased from 16.35 (95% CI: 8.35, 32.02; I² = 96%) in 1990–1999 to 40.47 (95% CI: 16.19, 101.22; I² = 99%) in 2000–2009, and then decreased to 18.47 (95% CI: 11.89, 28.67; I² = 99%) in 2010–2019. In MICs, the ASMR decreased from 172.55 (95% CI: 109.64, 271.55; I² = 12%) in 1990–1999 to 50.89 (95% CI: 45.62, 56.77; I² = 0%) in 2010–2019. It should be noted that MICs data during the period 1990–1999 were not available in any of the studies included in our meta-analysis. Our findings suggest that the trend of premature CVD mortality is consistent across income levels.

Table 3

Sensitivity analysis of pooled ASMR per 100,000 population from premature CVD mortality by excluding the studies outlier
		After removed outlier			Comparison with original data
Subgroup		n	ASMR (95% CI)	I² (%)	n	ASMR (95% CI)	I² (%)
Overall study		13	31.20 (23.81, 40.89)	97	15	27.00 (20.13, 36.21)	99
CVD types
	Total CVD^a	6	46.69 (28.63, 76.15)	29	8	96.04 (67.18, 137.31)	84
	IHD^b	3	20.67 (12.41, 34.45)	74	4	15.57 (11.27, 21.51)	92
	Stroke^c	3	12.23 (10.55, 14.17)	86	4	12.36 (8.09, 18.91)	97
Sex
	Male	8	46.23 (31.95, 66.90)	93	10	16.12 (9.9, 26.25)	99
	Female	8	18.87 (11.99, 29.70)	97	10	37.83 (24.10, 59.40)	96
Country income classification^e
	HIC	9	26.53 (19.61, 35.91)	97	10	21.42 (15.63, 29.37)	99
	MIC	5	121.56 (70.05, 210.95)	40	6	90.58 (56.40, 145.48)	77
Time study (year)
	1990–1999	3	21.40 [14.68, 31.19)	68	4	16.35 (8.35, 32.02)	96
	2000–2009	4	112.85 (64.87, 196.32)	85	5	63.84 (35.34, 115.31)	98
	2010–2019	9	23.25 (16.76, 32.24)	97	10	19.93 (13.56, 29.30)	99
Specific subgroup analysis
HIC by study time
	1990–1999	3	21.40 (14.68, 31.19)	68	4	16.35 (8.35, 32.2)	96
	2000–2009	3	40.47 (16.19, 101.22)	97	3	40.47 (16.19, 101.22)	99
	2010–2019	5	21.84 (14.99, 31.81)	96	6	18.47 (11.89, 28.67)	99
MIC by study time
	2000–2009	3	172.55 (109.64, 271.55)	12	3	172.55 (109.64, 271.55)	12
	2010–2019	3	22.10 (3.29, 148.74)	0	4	50.89 (45.62, 56.77)	0
Total CVD^a by sex
	Male	4	81.68 (23.63, 282.35)	0	5	181.86 (112.98, 292.76)	0
	Female	4	32.36 (11.30, 92.68)	40	5	72.69 (30.04, 175.91)	85
IHD^b by sex
	Male	3	45.07 (24.44, 82.13)	0	4	27.51 (17.89, 42.30)	68
	Female	3	11.18 (7.42, 16.86)	46	4	9.30 (6.64, 13.03)	83
Stroke^c by sex
	Male	2	14.40 (0.65, 316.68)	11	3	15.18 (10.12, 22.77)	28
	Female	2	10.54 (8.22, 13.50)	0	3	7.23 (2.45, 21.29)	99

n = number of studies, CVD = cardiovascular disease, ASMR = age standardized mortality rate (ASMR) per 100,000. Random effect model applied pool estimate of ASMR, 95% CI = 95% confident interval of estimated ASMR.

^atotal CVD death was based on ICD − 10 code: I00-I99 or ICD-9 codes: 350–459.

^bIschemic heart disease (IHD) death based on ICD-10 (I20-I25) or ICD-9 (410–414)

^cCerebrovascular disease or stroke death based on ICD-10 (I60-I69) or ICD-9 (430–438)

^eAccording to World Bank’s classification. HICs = high income countries, MICs = middle-income counties (including upper middle-income countries and low middle-income countries)

Outlier removed for each subgroup as below;

Overall study: Jin et.al, (2020) and Gómez-Martínez et al. (2018)
Total CVD: Yang et al. (2021) and Gawryszewski & Souza (2014)
IHD: Dani et al. (2022)
Stroke: Moryson & Stawińska (2022)
Male and female: Best et al. (2018) and Gómez-Martínez et al. (2018)
Study time year 1990–1999: Gómez-Martínez et al. (2018)
Study time year 2000–2009: Moryson & Stawińska (2022)
Study time year 2010–2019: Gómez-Martínez et al. (2018)
High-income countries: Gómez-Martínez et al. (2018)
Middle-income countries: Yang et al. (2021)

Sensitivity Analysis

We conducted a sensitivity analysis by excluding outliers identified using the Baujat plot for overall and subgroup ASMR analysis. Based on the criteria for outlier exclusion as suggested by the Baujat plot, we reanalysed the data with and without these outlier studies to assess the impact on the overall effect estimate, CI, and heterogeneity. The sensitivity analysis revealed an improvement in I² for each subgroup after removing the outlier studies, indicating a reduction in heterogeneity and improved precision of the effect estimates (Table 3). Specifically, among the MIC (I² decreased from 77–40%) and specific subgroups analysis (such as female stroke), removing outliers resulted in a significant decrease in heterogeneity, with I² reducing from 99–0%. However, it is essential to note that despite the improvement in I² after removing the outlier studies, the pattern and direction of the findings remained consistent with the results obtained before outlier removal. For instance, the ASMR for males remained higher than that for females, and the ASMR for MIC remained higher than that for HIC, both before and after outlier removal. Similarly, the undulated trend observed for other subgroups also remained consistent, with increases and decreases in ASMR from 1990–2019. These results suggest that while excluding outlier studies improved the precision of the effect estimates and reduced heterogeneity, it did not substantially alter the overall conclusions of the analysis.

Publication’s Bias

To assess publication bias in the included studies, we used funnel plots, Egger's test, and Begg's test. The funnel plot for all studies showed an asymmetrical distribution (Fig. 5a), indicating the possibility of publication bias or other sources of small-study effects. However, the Begg's and Egger's tests did not reach significance (p = 0.764 and p = 0.088, respectively), suggesting no evidence of publication bias in the meta-analysis. We also examined the funnel plot for studies that reported total CVD (ICD-10: I00-I99 or ICD-9: 350–459), which exhibited an almost symmetrical distribution (Fig. 5b). Furthermore, the Egger's and Begg's tests for total CVD were not significant (p = 0.559 and p = 0.084, respectively), indicating no presence of publication bias for total CVD. Therefore, based on our comprehensive assessment, we found no strong evidence of publication bias in our meta-analysis.

Premature mortality from CVD has significant socio-economic consequences and its ASMR varies widely across countries and regions, making it essential to understand the global burden of this disease. This meta-analysis aimed to estimate the global ASMR for premature CVD mortality. Based on our review of 15 included studies that utilized national vital registration data, the pooled estimate of ASMR for premature CVD mortality was 27.0 per 100,000 individuals. This estimate increased to 90.0 per 100,000 population when we included studies that used specific ICD codes for total CVD (ICD-10: I00-I99 or ICD-9: 350–459). Our analysis shows that the estimated ASMR for premature mortality related to total CVD (90.0 per 100,000 population) is comparable to the global estimate reported by Ji Zhang et al. [48]. The authors reported a global ASMR of 82.9 per 100,000 population for premature mortality from CVD in 2016 using data from the WHO's Global Health Estimates program. Therefore, our analysis provides further evidence to support the global estimation of premature CVD mortality and underscores the need for continued efforts to prevent and manage CVD.

Subgroup analysis by type of CVD and sex revealed significant disparities in the ASMRs. The ASMR for IHD was higher than that for stroke, consistent with the GBD 2019 study [49], which identified IHD as the leading cause of premature CVD mortality worldwide and accounted for approximately 7.8 million deaths in individuals under 70 years of age. In comparison, stroke was the second leading cause, responsible for approximately 3.6 million premature deaths. These findings emphasise the need for targeted public health interventions to reduce the burden of CVD, particularly for IHD and stroke, which remain major causes of premature mortality globally. Furthermore, our analysis revealed that premature CVD mortality rates were higher in males than females. These findings align with a prior study that employing global data estimated from the GBD and the WHO, in which male individuals had higher rates of premature CVD mortality than their female counterparts [48–50]. Further exploration of sex-specific differences in ASMRs by CVD type showed that males had a significantly higher ASMR for IHD and stroke than females. These findings highlight the importance of sex-specific interventions for reducing the burden of CVD mortality, where males are at a higher risk than females. Over the past few decades, there has been increasing recognition of sex differences in the presentation, treatment, and outcomes of CVD. Studies have also demonstrated the variations in the effectiveness of risk factor control between sexes [51], propounding future research to explore the extent and underlying factors contributing to these disparities.

Present study revealed MICs had a significantly higher ASMR compared to HICs, suggesting that the burden of premature CVD mortality is disproportionately higher in MICs. This is in line with previous studies indicating a higher burden of CVD and other NCDs in MICs [52, 53], where healthcare resources and access to preventative interventions may be limited [54, 55]. Looking forward, the global health players and payers shall examine strategically to reduce the burden of premature CVD mortality by prioritizing healthcare resources and conducting interventions in middle-income countries.

Additionally, our trend analysis by study time period revealed a significant increase in ASMR for premature CVD mortality between the periods 1990–1999 and 2000–2009, followed by a significant decrease in the period 2010–2019. While the reasons for this pattern are not entirely clear, it is possible that changes in behavioural lifestyle risk factors such as smoking, diet, and physical inactivity, as well as improvements in medical treatment and management of CVD, may have contributed to this trend. Over and beyond, our analysis of subgroups by income level uncovered a noteworthy reduction in ASMR from 2000–2009 to 2010–2019, for both MICs and HICs. According to the GBD study, there has been a global decline in premature CVD mortality rates over the past few decades with the greatest decline observed in HICs [52, 56]. These findings suggest that efforts to improve the prevention and management of CVD have been successful in reducing premature mortality in recent years, particularly in HICs. However, the burden of premature CVD mortality remains high in MICs, urging the need to ensure universal access to timely and affordable treatment for people living with CVD in these countries.

Present meta-analysis could put forward several recommendations. Firstly, prevention strategies for premature CVD mortality should be reinforced, particularly for ischemic heart disease. Secondly, prevention strategies should be tailored to specific populations, taking into account sex and income level. Thirdly, trends in ASMR over time should be monitored to evaluate the effectiveness of prevention strategies and identify areas where more resources are needed. Fourthly, more research is needed to understand the complex factors contributing to premature CVD mortality, in particular structural determinants or social determinants of health inequities. These recommendations provide important information for the global players and policymakers in intervening modifiable determinants and conducting effective strategies to reduce premature CVD mortality globally.

Strengths of this study include the inclusion of studies from both HICs and MICs, providing a more comprehensive and up-to-date estimate of the global prevalence of premature CVD mortality. In addition, the use of standardized study rating instruments and compliance with relevant guidelines for systematic reviews and meta-analyses enhanced the rigor and validity of the study. However, this study has some limitations that should be considered when interpreting the results. The high heterogeneity observed among the included studies may have lowered the quality of evidence, and the different characteristics of the studies included in this review may have introduced biases. Furthermore, although meta-analysis can provide a more comprehensive estimate of the mortality rate of premature CVD mortality, it is limited by the quality of the included studies. Finally, although subgroup analyses were conducted to explore potential sources of heterogeneity, these methods are not without limitations and should be interpreted with caution. Despite these limitations, our study provides valuable insights into the global burden of premature CVD mortality and highlights the need for continued efforts to address this important public health issue.

In conclusion, our review and meta-analysis of 15 studies provides estimates of the global age-standardized mortality rate for premature CVD mortality. The overall ASMR for premature CVD mortality was estimated to be 27.0 per 100,000 people, with significant heterogeneity among studies. Our subgroup analyses revealed that ASMRs varied by CVD type, sex, income level, and study time. The highest ASMR was observed for total CVD, followed by IHD and stroke. ASMRs were also higher for males than females, and middle-income countries had a significantly higher ASMR than high-income countries. Furthermore, our study suggests a reducing trend in ASMR for premature CVD mortality from 2000–2019. Overall, this study provides important insights into the global trends and disparities in premature CVD mortality. These findings can inform the development of targeted interventions and policies to reduce the burden of CVD and improve health outcomes worldwide.

ASMR

Age-standardized mortality rate

CVD

Cardiovascular disease

GBD

Global Burden of Disease

IHD

Ischemic heart disease

CBVD

Cerebrovascular disease

LMICs

low- and middle-income countries

HICs

High-income countries

MICs

Middle-income countries

NCD

non-communicable diseases

SDG

Sustainable Development Goal

PROSPERO

Prospective Register of Systematic Reviews

PRISMA

Preferred Reporting Items for Systematic Reviews and Meta-analysis

WOS

Web of Science

CENTRAL

Cochrane Central Register of Controlled Trials

NOS

Newcastle-Ottawa Scale. ICD:International Classification of Disease

Standard error

Confidence interval

prediction interval

NMRR

National Medical Research Register

USM

Universiti Sains Malaysia.

Ethics approval and consent to participate

We registered this systematic review with the National Medical Research Register (NMRR), Ministry of Health Malaysia (NMRR ID-22-00231-MOX) and the Human Research Ethics Committee of USM (USM/JEPeM/22030181). There will be no concerns about privacy.

Consent for publication

All authors approve the manuscript for publication.

Availability of data and materials

The datasets analysed during the current study are available from thecorresponding author on reasonable request.

Competing interests

All authors declare that they have no conflicts of interest.

Funding

No funding was obtained for this study.

Authors’ contributions

WSRH originated the research idea, carried out the study and prepared the manuscript. Study design: WSRH, NAM, TMH and KIM. Literature research and screen: WSRH, NHM, CXW, TMH and NAM. Data extraction: WSRH and TMH. Critical appraisal: WSRH, NAM, TMH, NHM, CXW and KIM. Evidence assessment: WSRH, NHM and TMH. Statistical analysis: WSRH and TMH. Manuscript writing WSRH, NAM, TMH, CXW, NHM, MAO, KYC, ZAK, MRAH and KIM. The author(s) read and approved the final manuscript

Acknowledgement

We would like to thank the Director-General of Health Malaysia for his permission to publish this article.

Authors’ information

¹Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, 16150 Kelantan, Malaysia. ²Institute for Public Health, National Institutes of Health, Ministry of Health Malaysia, Setia Alam 40170, Selangor, Malaysia. ³Sector for Evidence-based Healthcare, National Institutes of Health, Ministry of Health, Shah Alam, Selangor, Malaysia. ⁴Department of Public Health Medicine, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, 47000 Selangor, Malaysia. ⁵Sector for Biostatistics and Data Repository, National Institutes of Health, Ministry of Health Malaysia, Setia Alam 40170, Selangor, Malaysia. ⁶Unit of Biostatistics and Research Methodology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, 16150 Kelantan, Malaysia. ⁷Office of The Manager, National Institutes of Health, Ministry of Health Malaysia, Setia Alam 40170, Selangor, Malaysia. ⁸Office of Deputy Director-General, Ministry of Health Malaysia, Putrajaya 62590, Malaysia

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Table 1 is available in the Supplementary Files section.

No competing interests reported.

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The global estimate of premature cardiovascular mortality: a systematic review and meta-analysis of age-standardized mortality rate

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Abstract

Background

Methods

Results

Conclusions

Figures

Background

Methods

Protocol and registration

Search Strategy

Inclusion And Exclusion Criteria

Screening Process

Data Extraction And Management

Quality Assessment

Statistical Analysis

Assessment Of Heterogeneity

Assessment Of Reporting Biases

Sensitivity Analysis

Results

Study characteristics

Quality Assessment

Overall ASMR Estimates And Subgroup Analysis By CVD Types And Sex

Subgroup Analysis By Income Country Classification

Subgroup Analysis By Study Time

Sensitivity Analysis

Publication’s Bias

Discussion

Strengths And Limitations

Conclusions

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

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