Geospatial and temporal disparities in Alzheimer's disease deaths in Argentina

Abstract

Background

Alzheimer's disease (AD) and other dementias are currently among the leading causes of morbidity and mortality worldwide.

Objective

To analyze the spatial and temporal behavior of AD mortality in Argentina.

Methods

This is a retrospective eco-epidemiological study based on the Death Certificate between 1997–2017 provided by the Ministry of Health. The specific death rates (SDRs) related to AD were calculated per 1000 deaths (AD*1000) by gender and age at departmental, provincial, and regional levels for the entire period. The Join Point method was applied to analyze the temporal trend, and SaTScan software was used to establish geospatial disparities.

Results

The highest SDRs were recorded in the departments, provinces, and regions located in the center of the country. At all administrative levels, female SDRs were almost twice as high as male SDRs. A positive secular trend in SDR was observed in all regions, with a significant increase between 1997–2002 and a less pronounced increase between 2003–2017. Clusters with the highest relative risks of AD death were located in the center of the country.

Conclusions

Following the global pattern, Argentina and all its regions show an increasing trend of AD deaths, with higher rates among women and older age groups. However, notable geospatial disparities attributed to population dynamics, migrations, and regional socioeconomic characteristics were identified.

Keywords

Alzheimer's disease Argentina epidemiology mortality

Introduction

Alzheimer's disease (AD) and other dementias have emerged as a primary cause of mortality and morbidity on a global scale.^1,2 Projections indicate a further increase in the number of individuals affected by these diseases in the future, driven by population growth and aging. This underscores the critical importance of addressing AD as a public health concern. The Global Burden of Disease study has documented a substantial increase in the global prevalence of AD. From 1990 to 2016, the number of individuals affected by the disease more than doubled, rising from 20.2 million in 1990 to 43.8 million in 2016. This increase can be primarily attributed to the aging population.²

In 2016, the global prevalence of AD was estimated to be 1539.4 cases per 1000 individuals (95% CI: 1353.2 to 1781.3). The percentage change between 2006 and 2016 was 41.9% (95% confidence interval: 38.6 to 45.2), indicating a significant increase in the prevalence of AD over the decade.^2,3 In Argentina, AD was the eighth leading cause of death in 2019, with a prevalence of 842.8 per 1000 individuals (95% CI: 740.4 to 945.2) and the increase between 2006 and 2016 was 50% (95% CI: 46.8 to 53.1).⁴

The care and support of people with dementia have a broad impact on families, healthcare systems and society as a whole, with significant financial and societal costs. In 2007, the annual direct cost of the disease in Argentina was estimated to increase with cognitive decline, from US$3420.4 for mild AD to US$9657.6 for severe AD, and with institutionalization (US$3189.2 ambulatory vs. US$14,447.68 institutional).⁵

In addition to temporal changes in AD prevalence due to demographic dynamics, disparities in AD mortality have also been observed due to geographic issues related to place of birth and adult residence, lifestyles, differences in health outcomes associated with AD, competencies and availability of the organizations or health care system responsible for the care of AD patients, and cultural, linguistic, and ethnic characteristics of the populations.^6–10

The literature concerning the prevalence of AD in Argentina is characterized by its fragmentation. Specifically, the majority of studies focus on specific geographic locations with varying population sizes. Moreover, these studies utilize disparate data sources and date back decades.^11–13 In order to achieve a comprehensive and contemporary understanding of the topic, it is necessary to consider the geographic characteristics and demographic changes in Argentina. The present study aims to analyze the behavior of AD mortality at spatial and temporal levels.

Methods

Study design and setting

This retrospective eco-epidemiological study was conducted using death certificate data between 1997 and 2017, provided by the Department of Health Statistics and Information (DEIS) of the Ministry of Health of Argentina. The dataset under consideration encompassed the age and year of death, primary cause of death code (according to the International Classification of Diseases, 10th Revision), gender, and the geographical location where the death was officially documented.

Inclusion criteria

The analysis included all deaths attributed to AD. The codes G30.0 (early-onset AD), G30.1 (late-onset AD), G30.8 (other forms of AD), and G30.9 (unspecified AD) were used. Other forms of dementia were not examined.

Exclusion criteria

The only missing data in some records is the age at the time of death. These certificates were not considered.

Geographic variables

In addition to the primary administrative divisions (23 provinces) and their respective subdivisions (525 departments), Argentina can be categorized into five geographic regions based on environmental, cultural, and socioeconomic characteristics: Northwest (NWA), Northeast (NEA), Cuyo, Centro, and Patagonia (Figure 1). Tables 1 and 2 present a detailed list of provinces belonging to each region.

Figure 1.

Map of Argentina with provinces. In color, five geographic regions.

Table 1.

Specific death rates (SDRs) associated with Alzheimer's disease, as documented on the Death Certificate from 1997 to 2017. The data is disaggregated by gender, province, and geographic region to provide a comprehensive view of the mortality patterns. The highest values are highlighted in bold.

		Women			Men			Total
Region	Provinces	Deaths	Alzheimer's Deaths	SDR	Deaths	Alzheimer's Deaths	SDR	Deaths	Alzheimer's Deaths	SDR
Centro	Buenos Aires	1,224,864	4988	4.07	1,372,776	2250	1.64	2,604,061	7253	2.79
	Ciudad Autónoma de Buenos Aires	379,332	1404	3.7	320,914	527	1.64	700,390	1933	2.76
	Córdoba	265,529	2187	8.24	291,547	995	3.41	557,125	3182	5.71
	La Pampa	22,075	325	14.72	28,170	158	5.61	50,255	483	9.61
	Santa Fe	281,604	2724	9.67	308,240	1335	4.33	590,008	4059	6.88
	Total	2,173,404	11,628	5.35	2,321,647	5265	2.27	4,501,839	16,910	3.76
Cuyo	Mendoza	121,657	1122	9.22	134,565	562	4.18	256,265	1684	6.57
	San Juan	43,964	189	4.3	50,230	71	1.41	94,211	260	2.76
	San Luis	25,040	158	6.31	30,847	70	2.27	55,974	228	4.07
	Total	190,661	1469	7.70	215,642	703	3.26	406,450	2172	5.34
NEA	Chaco	60,656	211	3.48	81,534	143	1.75	142,222	354	2.49
	Corrientes	59,491	241	4.05	73,302	121	1.65	133,325	364	2.73
	Entre Rios	92,047	672	7.30	108,320	342	3.16	200,486	1015	5.06
	Formosa	27,755	180	6.49	38,123	116	3.04	65,908	296	4.49
	Misiones	51,229	230	4.49	71,509	156	2.18	123,365	387	3.14
	Total	291,178	1534	5.27	372,788	878	2.35	665,306	2416	3.63
NWA	Catamarca	20,207	73	3.61	23,720	39	1.64	44,000	113	2.57
	Jujuy	34,327	80	2.33	44,378	41	0.92	78,717	121	1.54
	La Rioja	17,172	34	1.98	21,557	16	0.74	38,742	50	1.29
	Salta	60,200	220	3.65	77,628	124	1.6	137,929	344	2.49
	Santiago del Estero	46,168	126	2.73	59,273	69	1.16	105,720	195	1.84
	Tucumán	89,192	423	4.74	104,900	200	1.91	194,202	623	3.21
	Total	267,266	956	3.58	331,456	489	1.48	599,310	1446	2.41
Patagonia	Chubut	23,580	168	7.12	33,738	79	2.34	57,331	247	4.31
	Neuquén	22,355	269	12.03	30,934	116	3.75	53,300	385	7.22
	Río Negro	31,862	321	10.07	43,178	179	4.15	75,066	500	6.66
	Santa Cruz	9414	17	1.81	15,846	11	0.69	25,286	28	1.11
	Tierra del Fuego	3330	16	4.80	5082	4	0.79	8421	20	2.38
	Total	90,541	791	8.74	128,778	389	3.02	219,404	1180	5.38
Argentina	Total	3,013,050	16,378	5.44	3,370,311	7724	2.29	6,392,309	24,124	3.77

Table 2.

Annual percentage change (APC) in specific death rates (SDRs) associated with Alzheimer's trends, as documented on the Death Certificate from 1997 to 2017. Data disaggregated by gender, and geographic region.

Region		APC	Years	APC	Years
Centro	Women	10.27	1997–2002	0.67	2004–2017
	Men	8.4		−0.62
	Total	9.79		0.35
Cuyo	Women	20.47	1997–2002	0.52	2004–2017
	Men	28.07	1997–2001	0.9	2003–2017
	Total	26.04	1997–2001	1.19	2003–2017
NEA	Women	17.74	1997–2003	1.95	2005–2017
	Men	18.55	1997–2000	1.83	2002–2017
	Total	16.91	1997–2002	2.15	2004–2017
NWA	Women	6.04	1997–2017
	Men	8.86	1997–2009	−1.03	2011–2017
	Total	8.05	1997–2009	1.33	2011–2017
Patagonia	Women	15.65	1997–2006	0.21	2008–2017
	Men	3.35	1997–2017
	Total	22.13	1997–2001	3.21	2003–2017
Argentina	Women	12.14	1997–2002	1.24	2004–2017
	Men	9.88	1997–2002	−0.11	2004–2017
	Total	11.44	1997–2002	0.97	2004–2017

Statistical analysis

Mortality rates

The specific death rates (SDRs) related to AD was calculated on a national scale and disaggregated by province, geographical region, and department. The formula used was: number of deaths due to AD over total deaths * 1000. This calculation was performed separately for males and females. Additionally, the SDR was calculated for deaths within each age group, categorized as follows: less than 64 years, 65 to 74 years, 75 to 84 years, and more than 85 years.

For spatial distribution

SaTScan software was used to detect spatial disease clusters of SDR, and to determine if they are statistically significant. SaTScan uses a Poisson-based model, where the number of events in a geographical area is Poisson-distributed, according to a known underlying population at risk for the Poisson model (https://www.satscan.org/). In the context of the null hypothesis, the prevalence of deaths associated with AD is hypothesized to be distributed randomly.

For temporal trends

In order to identify temporal changes in AD mortality, a joint point regression was estimated using the Join Point Regression program, version 4.5.0.1 (Statistical Research and Applications Branch, National Cancer Institute). This method identifies the year(s) or period(s) in which a change in trend occurs, and it calculates the annual percentage change (APC) in rates between the trend change points. The p-values are determined using Monte Carlo methods. The overall asymptotic significance level is maintained using a Bonferroni correction.

Ethics approval

This study is in accordance with the bioethical guidelines established by the Ministry of Health of Argentina, which exempts epidemiological studies that utilize public or publicly accessible records from the requirement of obtaining informed consent.¹⁴

Results

Specific death rates spatial distribution

In the period under analysis, the total number of deaths was 6,392,309, of which 24,124 (3.7%) were attributable to AD (Table 1). The specific death rate (SDR) (Women + Men) exhibited significant spatial heterogeneity. At the provincial level, the highest SDR was observed in La Pampa (9.6) and the lowest in Santa Cruz (1.11). At the regional level, Patagonia exhibited the highest SDR (5.38), and NWA the lowest (2.41). When considering deaths among individuals aged 65 and above, the total number of deaths was 4,438,987, of which 23,365 (5.2%) were attributable to AD (Supplemental Table 1). The SDRs higher values in all cases, when compared to those observed in Table 1. However, the heterogeneity by provinces and regions is maintained. The departmental SDRs for women and men are presented in Supplemental Figure 1, as well as for both genders. The highest values were found in departments belonging to Centro, Cuyo and Northern Patagonia regions.

The spatial analysis reveals the presence of four distinct clusters characterized by elevated risk, and a separate six clusters that demonstrate low risk of AD deaths. These groupings were statistically significant, rejecting the null hypothesis of random distribution. The two low-risk clusters with the highest numbers of departments are located within the NWA region and comprise part of the NEA (ID 6 and 10 in Figure 2), while the high-risk clusters are concentrated in the central part of the country, encompassing the Cuyo, Centro, and parts of the Patagonia and NEA regions (ID 7, 4, 1, and 9 in Figure 2).

Figure 2.

Clusters of specific death rates (SDRs) related with Alzheimer's disease. Low risk: ID 6 (NWA region), 10 (NEA region), 2, 3, 5 and 8 (Centro region, specific in Eastern province of Buenos Aires). High risk: ID 7 (Cuyo region), 1, 4 (Centro region) and 9 (NEA region, specific in Eastern Formosa province).

Gender distribution

A significant increase in the SDRs related to AD has been observed across both genders and various age groups (Supplemental Table 2). The highest SDRs was observed among individuals aged >85. It is noteworthy that AD deaths prior to the age of 65 constitute a mere ≈3% of all deaths. Throughout the entirety of the time series under consideration, this particular age group exhibits the highest incidence of male deaths.

Temporal trends

The joinpoint analysis revealed a positive secular trend of SDR values across all regions (Figure 3). However, when the data were disaggregated by gender, this trend was non-existent among men from the Central Region and among the country as a whole.

Figure 3.

Joinpoint analysis of specific death rates (SDRs) related to Alzheimer's disease values disaggregated by sex and across all regions. Color patterns for each region are the same as in Figure 1.

The APC was divided into two periods: 1997–2002 and 2004–2017. The largest change was observed in the first period, across all regions and both genders (Table 2).

The relative risk in high-risk regions is found to be three times the value of the low-risk regions, while the relative risk in high-risk regions is found to be five times the value of the low-risk regions. The results are presented in Table 3, accompanied by their respective p-values, all of which are less than 0.001.

Table 3.

Demographic, spatial and statistical characteristics of low- and high-risk clusters.

Cluster	ID	Coordinates	Radius (Km)	Number of departments therein	Population	Number of cases	Expected cases	Relative risk
High risk	1	35.227242 S, 63.660911 W	355.67	77	406,981	3423	1703.63	2.26*
	4	31.576884 S, 58.486373 W	270.75	31	396,828	2422	1661.12	1.53*
	7	31.686632 S, 68.467595 W	194.71	32	195,640	1183	818.95	1.48*
	9	25.370038 S, 58.062606 W	70.71	3	27,720	202	116.04	1.75*
Low risk	2	34.835619 S, 58.367373 W	19.43	10	406,731	696	1702.58	0.41*
	3	34.448160 S, 58.903333 W	33.82	15	376,998	852	1578.12	0.52*
	5	34.617370 S, 58.420572 W	4.22	6	307,370	804	1286.65	0.61*
	6	25.829089 S, 66.174478 W	527.68	119	411,290	1180	1721.66	0.66*
	8	37.498641 S, 57.643266 W	53.22	4	89,231	167	373.52	0.44*
	10	27.138890 S, 59.346783 W	153.56	28	120,360	360	503.83	0.71*

* All values of relative risk with p-values <0.001.

Discussion

A salient finding is the location in the central region of the country of the highest rates of AD deaths. This phenomenon is thought to be associated with the characteristics of the settling patterns in the territory currently known as Argentina, the genetic background of the populations involved (particularly the high prevalence in certain regions of genomes native to the American continent), and the socioeconomic inequality between the different regions.

Human settlement in Argentina

The presence of human groups has been documented from the end of the Pleistocene.¹⁵ Three phases can be distinguished: (a) pre-colonial; (b) colonial; (c) formation of the national state. In the pre-colonial phase, prior to the European conquest, Argentina was populated by multiple native societies, with a remarkable linguistic diversity, developing a wide range of economic strategies and resource use, including seasonal hunting and gathering, agriculture, and the domestication of mammals.¹⁶ The Inca expansion, initiated around the year 1480 and spanning 50 years, profoundly influenced the NWA region, leaving a tangible legacy in material, sociopolitical, and biological spheres.¹⁷ According to estimations, when the European conquest began, around the middle of the sixteenth century, the total indigenous population of Argentina exceeded 300,000 individuals, with higher population density in the Northwest.¹⁸ The NWA region has been determined to be one with the lowest rates of SDR of AD.

During the colonial period, the location of cities was preferential in the NWA, Cuyo, and Centro regions, forming the geographic and political colonial space.¹⁹ The number of individuals with European ancestry grew, predominantly from Spain and Portugal. The transatlantic slave trade, which originated in Angola, Congo, Mozambique, and Southeast Africa, played a pivotal role in the introduction of the African population to the country.

The nineteenth century witnessed the war for independence, which ultimately led to Argentina's separation from the Spanish royal domain. However, the consolidation of the new country and its borders was achieved only after decades of internal struggles. With the industrial expansion of Europe and the subsequent need for raw material and new lands, vast territories were effectively annexed to the national political control and introduced into the global market 15. The geopolitical term “desert” was used to denote vast expanses that had yet to be conquered, characterized by a prevalence of indigenous social structures. The so-called “conquest of the desert”, which commenced in 1879 and concluded in 1881, involved the total occupation of the Patagonian and NEA regions. The defeated indigenous peoples were massacred, expelled to territories with difficult survival conditions and no economic interest, taken to urban areas as servile labor, and family units were fragmented.²⁰ The borders of modern states disrupted the indigenous nations and imposed the same pattern of domination over their ancestral territories.²¹

Furthermore, during the period of the formation of the national state, between 1857 and 1914, the country received an incoming population of 4,666,723 migrants, a phenomenon that has been termed “alluvional Argentina”.²² These individuals came mainly from Europe, 80% from Spain or Italy. According to the 1914 National Census, foreigners constituted 44% of the population in the Centro and Patagonia region, 15% in Cuyo, 11% in NEA, and a mere 3% in NWA.²³ The disparities in demographic dynamics and spatial occupancy have persisted over the decades, rendering each region a distinct unit of analysis. The mortality rates for AD, as determined by region, demonstrate a concurrence with patterns of European-origin migration across the territory.

Ethnic and genetic background of the Argentine population

In the 1994 reform of the National Constitution, Argentina formally acknowledged the ethnic and cultural pre-existence of indigenous peoples. Subsequent censuses have incorporated an objective to enumerate and geolocate these ethnic groups through individual self-identification. The results of the Complementary Survey of Indigenous Peoples indicate that the number of inhabitants who identified themselves as members and/or first-generation descendants reached 600,329.²⁴ By 2010, this number had increased to 955,032. In the 2022 Census, the question regarding self-recognition was incorporated into the base questionnaire and asked to all inhabitants equally. According to recent demographic data, 2.9% of the total population of the country (1,306,730 individuals) identified as indigenous and/or descendants.²⁵ Over 40% of this population resided in provinces belonging to the NWA region (the most densely populated at the time of European conquest) or southwestern Patagonia (which was annexed to the national state at a relatively later stage).

Consequently, the genomic diversity of Argentina identifies three parental lineages (Native, European, and African) whose presence in the current mestizo population is not homogeneous throughout the territory.¹⁹^26–28 Avena and colleagues,²⁹ based on 100 ancestry informative markers (AIMs) in a sample of 558 individuals observed that Centro region exhibited a notably high percentage of European ancestry, reaching 76% (95%CI: 73– 79%), Patagonia 54% (95%CI: 49–59%), NEA 54% (95%CI: 49–58%), and NWA 33% (95%CI: 21–41%). Muzzio and colleagues¹⁹ provide evidence that Native American ancestry is significantly associated with distance to Buenos Aires (r = 0.99; p < 0.0001), with the highest percentages residing at the greatest distances from the capital city. Luisi and colleagues²⁸ demonstrate that individuals from the Argentine province of Misiones (NEA region) have the highest proportion of central/north European ancestry, which corresponds with the historical record of settlement by Polish, German, Danish and Swedish colonies in the area. The population substructuring produced by these dynamics is a subject of recent interest,³⁰ particularly given its association with the prevalence of complex diseases such as AD. Surnames can serve as a proxy for geographic origin, constituting a valuable instrument in population studies. A methodological approach combining diachronic (surnames from the 2015 Electoral Registry) and synchronous (death certificates between 2005 and 2017) data was utilized for study the spatial distribution of deaths related to AD.³¹ According to earlier reports on familial forms of early-onset AD among descendants of Volga Germans, this particular population, whose migration to Argentina is well-documented, was the focal point of the analyses. The frequency of Volga surnames accounted for 43.53% of the variation in deaths from AD, and three clusters of high non-random frequency were identified in the Centro and North Patagonia regions.

Dalmasso and colleagues³² conducted the inaugural AD genome-wide association study (GWAS) on populations from Argentina and Chile. A cross-ethnic meta-analysis identified four novel loci implicating lysosomal function in AD. The AD genetic risk score, developed from European samples, was evaluated in these admixed South American populations, exhibiting comparable performance, though the score underwent a reduction when the Native American ancestry increased. There is a broad consensus on the differential effect of variants of Apolipoprotein E (APOE), a protein involved in fat and cholesterol metabolism in mammals. The three main alleles (ɛ2, ɛ3, ɛ4) of APOE gene affect the risk of AD and cardiovascular disease. The APOE ɛ2 allele exerts a protective effect;³³ conversely, the APOE ɛ4 allele—in its heterozygous form—is associated with an elevated probability of developing late-onset AD, with a range of odds ratios spanning from 2- to 3-fold. In the homozygous state, the risk is 14.9-fold higher compared to carriers of the more prevalent APOE ɛ3 allele.^34,35 As demonstrated by Belloy and colleagues,³³ there is a general and graded pattern of APOE genotypes and the risk of AD among groups in a large cohort of individuals from the United States. East Asian individuals present the maximum risk of association between APOE ɛ4 and AD, followed by Non-Hispanic White, Non-Hispanic Black, and Hispanic individuals. While these racial and ethnic classifications may be too broad, this study offers the most comprehensive overview to date on the association of APOE with AD risk across a large sample of 68,756 individuals. Strong epidemiological evidence suggests that various lifestyle habits and environmental factors may interact with APOE alleles to synergistically affect the risk of developing AD.³⁶ Recently, Trigo and colleagues³⁷ reported the allele frequencies of the APOE system in mestizo Andean populations and compared them with those of other South American native groups. APOE ɛ3 was the most common allele, followed by APOE ɛ4 and APOE ɛ2. The observed allele distribution pattern aligns with the findings reported in previous studies of Native American populations.^38,39

Socioeconomic and geographic disparities

Cognitive outcomes are influenced by factors that emerge during both early and late life.^40,41 A number of studies have examined the so-called “place effects over the course of life” through cohort analysis, examining the health outcomes of the subjects involved (especially cognitive problems and/or dementia), their places of birth, and their current places of residence.^42,43 The local environment itself is likely to be a contributing factor to dementia risk, particularly in cases of AD. The increasing trend in AD mortality seen in Argentina mirrors that observed in middle- and low-income countries. In contrast to the situation in developed countries, more pronounced increases in this cause of death have been detected. Lifestyle-related factors (i.e., diabetes, obesity, physical inactivity, depression, smoking, low educational level, and diet) would play a preponderant role in this difference.⁴⁴ According to the findings of the Fourth National Survey of Risk Factors,⁴⁵ 61.6% of individuals exhibited excess weight (defined as overweight and/or obesity). The prevalence of diabetes or high blood glucose escalated from 9.8% in 2013 to 12.7% in 2018, and a mere 6% of individuals adhered to the recommended consumption of five or more servings of fruits or vegetables daily. The data indicates a downward trend in tobacco use from 29.7% in 2005 to 22.2% in 2018. Furthermore, the prevalence of physical activity decreased to 44.2%, indicating a reduction in comparison to the 2013 value of 54.7%.

Argentina has a universal and free healthcare system, but there are significant regional variations in access to medical coverage and primary prevention, diagnosis, and long-term treatment programs. The observed disparities in SDRs among different geographical regions may be attributed to the presence of socioeconomic and demographic heterogeneities. The NWA and NEA regions have historically exhibited elevated levels of unsatisfied basic needs, higher infant mortality rates, lower human development indices, and reduced life expectancies.^46–49

Gender differences in deaths from AD

The rate of AD mortality is higher in women, a finding consistent with existing literature on the subject.⁵⁰ This phenomenon can be partly explained by a higher female life expectancy, as advanced age remains the greatest risk factor for AD.^51,52 Projections based on the results of the 2010 National Population, Household and Housing Census estimate that life expectancy at birth in Argentina in 2020 was 74.9 for males and 81.44 for females.⁵³

Secular mortality trend in Argentina

According to the World Bank database,⁵⁴ the positive secular trend of SDR between 1997–2017 in all regions and in Argentina as a whole follows the same behavior as the Crude Mortality Rate*1000 Persons (Males + Females), the Adult Male Mortality Rate*1000 Adult Males and the Adult Female Mortality Rate*1000 Adult Females. These rates exhibited a progressive decline during the observed period, with a higher number of deaths recorded between 1997 and 2002 compared to the period between 2004 and 2017. The period from 1990 to 2002 is regarded as one of the most critical in Argentina's socioeconomic history. The year 2001 marked a significant breakpoint, resulting in profound socio-sanitary consequences for the population, as evidenced by several health indicators, particularly adult mortality.^55,56

Conclusion

Our research primarily focuses on calculating AD death rates and their spatial variation. This focus stems from the inherent limitations of death certificates. By their very design, these documents limit our ability to investigate and explore other clinical aspects of AD. It is impossible to determine the vital characteristics (e.g., lifestyle, level of education, place of birth) of each individual or whether AD was a preexisting condition in cases where death occurred due to other causes. It is acknowledged that discrepancies may exist in the issuance of death certificates throughout the time series. However, due to the data series’ considerable temporal depth and substantial geographical coverage—encompassing the entire country—the observed spatial disparities cannot be attributed to this bias. The health statistics system collected these databases over the course of several decades. These records are important for understanding diseases and associated demographic phenomena. In accordance with the global pattern, Argentina exhibits an escalating trend in AD deaths, with a higher frequency among women and older age groups. Significant geospatial disparities have been observed, attributed to historical population dynamics, migration patterns, and regional socioeconomic characteristics. The methodology and approach employed in this study enabled the identification and geolocation of vulnerable groups in high-risk, that require proactive intervention within health systems. Such intervention may include the implementation of preventive programs, prioritization in mental illness diagnosis, and global care.

Supplemental Material

sj-docx-1-alr-10.1177_25424823251391705 - Supplemental material for Geospatial and temporal disparities in Alzheimer's disease deaths in Argentina

Supplemental material, sj-docx-1-alr-10.1177_25424823251391705 for Geospatial and temporal disparities in Alzheimer's disease deaths in Argentina by Arturo L Morales, Lautaro D Andrade, Rubén Bronberg, Virginia Ramallo, Marcelo I Figueroa and José E Dipierri in Journal of Alzheimer's Disease Reports

Footnotes

Acknowledgements

The authors would like to express their gratitude to the DEIS (Health Statistics and Information, Ministry of Health, Argentina) authorities, for enabling access to the death database utilized in this research study.

ORCID iDs

Arturo L Morales

Lautaro D Andrade

Virginia Ramallo

Marcelo I Figueroa

Ethical considerations

Formal approval from the local medical ethics committee was not required due to the retrospective nature of the study.

Author contribution(s)

Arturo L Morales: Data curation; Formal analysis.

Lautaro D Andrade: Formal analysis; Writing – review & editing.

Rubén Bronberg: Formal analysis.

Virginia Ramallo: Resources; Writing – original draft; Writing – review & editing.

Marcelo I Figueroa: Data curation; Writing – review & editing.

José E Dipierri: Conceptualization; Investigation; Resources; Writing – original draft; Writing – review & editing.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

Data supporting the findings are available from the corresponding author upon request.

Supplemental material

Supplemental material for this article is available online.

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