Demographic incidence of mortality related to Alzheimer’s and Amyotrophic Lateral Sclerosis (ALS) in Brazil

doi:10.22167/2675-6528-202603471

Article

October 19, 2026

Demographic incidence of mortality related to Alzheimer’s and Amyotrophic Lateral Sclerosis (ALS) in Brazil

Demographic Incidence of Mortality Related to Alzheimer’s and Amyotrophic Lateral Sclerosis (ALS) in Brazil

Simone Aparecida da Cruz Miquelin; Douglas Augusto de Paula

DOI: 10.22167/2675-6528-202603471

Article derived from a Course Conclusion Work (TCC), with content based on the student’s original work and adapted to the editorial format of the E&S Magazine with the support of the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege for textual synthesis and organization.

Summary

The demographic incidence of mortality related to Alzheimer’s disease and Amyotrophic Lateral Sclerosis (ALS) in Brazil represents a growing challenge for healthcare systems, given the acceleration of population aging. The study aimed to reveal relevant mortality patterns for these diseases, using analytical techniques applied to public databases. For this purpose, secondary mortality data from the Mortality Information System (SIM/DATASUS) and population data from IBGE (SIDRA) for the period 2015 to 2024 were used. The data were processed in Python, and descriptive methods, Poisson regression, and binary logistic regression were applied to analyze the distribution of deaths and their associations with demographic, socioeconomic, and occupational variables. The results indicated a significant increase in mortality from Alzheimer’s disease, predominantly in women over 75 years old and in the Southeast and South regions, and associated with low education levels and agricultural/rural occupations. Mortality from ALS remained relatively stable, with a higher incidence in men, in intermediate age groups (41-80 years), and in occupations with higher cognitive load, such as lawyers, but also in agricultural workers and bricklayers. It was concluded that Alzheimer’s disease shows an upward trend as a cause of neurological death, and ALS, although rare, maintains a relevant impact, highlighting the need for public policies for healthy aging and specialized care, with data science as a useful tool to support such actions.

Keywords: Alzheimer’s; Cognitive; ALS; Neurodegenerative.

1. Introduction

Neurodegenerative diseases, such as Alzheimer’s and Amyotrophic Lateral Sclerosis (ALS), represent a growing challenge for healthcare systems, especially in countries with rapidly aging populations, like Brazil (IBGE, 2023). The analysis of demographic data can reveal important patterns regarding the incidence, geographic distribution, and associated factors of these diseases, such as age, sex, and socioeconomic conditions.

Alzheimer’s is the most common form of dementia, characterized by gradual memory loss, behavioral changes, and decline in cognitive functions. It mainly affects people over 65 years old and is related to genetic, environmental, and lifestyle factors. In Brazil, it is estimated that 8.5% of the population aged 60 and over live with the disease, representing about 1.8 million cases, with a projection of 5.7 million by 2050 (BRASIL; UNIFESP, 2022). This exponential growth reflects not only the increase in life expectancy but also the need for public policies focused on prevention, early diagnosis, and continuous patient follow-up. National studies confirm this trend, pointing to a consistent increase in Alzheimer’s mortality in all regions of Brazil between 2000 and 2019 (PASCHALIDIS et al., 2023).

Amyotrophic Lateral Sclerosis (ALS) is a rare and progressive neurodegenerative disease that affects motor neurons, leading to loss of muscle function and, in advanced stages, respiratory failure. Although there have been advances in the genetic understanding of ALS, many unknown aspects about its origin and evolution remain, making the analysis of population data essential to identify incidence patterns and associated factors. The rarity of the disease makes it difficult to construct specific policies, but epidemiological studies show that ALS, despite being less prevalent, has a significant impact on the quality of life of patients and their families, requiring multidisciplinary support and specialized follow-up.

Although Alzheimer’s and ALS are distinct conditions, previous studies have pointed out relevant correlations between them. International research describes common neuropathological mechanisms, such as protein aggregation and cellular stress, in addition to the presence of similar biomarkers in patients with both diseases (HETZ et al., 2018; JOHNSON et al., 2025). Furthermore, patients with ALS may present cognitive impairment, bringing their clinical picture closer to typical Alzheimer’s manifestations (BORGHEAI et al., 2024). This intersection between the two conditions reinforces the importance of comparative studies that consider not only clinical aspects but also demographic and socioeconomic ones (VRILLON et al., 2021).

In recent years, data science has revolutionized the way large volumes of information are processed, offering predictive models capable of generating clinical insights and supporting personalized medical decisions. This work proposes the use of data analysis techniques to explore demographic and clinical information available in public databases, combining statistics and interactive visualizations.

In addition to contributing to the understanding of mortality from Alzheimer’s and ALS, this approach seeks to demonstrate how data science tools can be applied in public health contexts to transform raw data into useful knowledge. The integration of demographic, socioeconomic, and occupational variables allows not only for the characterization of disease incidence but also for the identification of more vulnerable population groups. This perspective broadens the scope of research, as it is not limited to the clinical description of diseases but incorporates social and economic dimensions that directly influence mortality indicators.

Another relevant aspect is the possibility of using the results to support public policies and health management strategies. By identifying regions with higher incidence or more affected population groups, managers can direct resources more efficiently, promoting awareness campaigns, professional training, and investments in hospital infrastructure. Temporal analysis also allows for observing trends and projecting future scenarios, contributing to long-term planning (PASCHALIDIS et al., 2023).

Therefore, this work is inserted in an interdisciplinary context, uniting knowledge from health, statistics, and data science to offer a comprehensive view of mortality from Alzheimer’s and ALS in Brazil. By revealing patterns and correlations, the aim is not only to broaden the scientific understanding of these diseases but also to provide practical subsidies for public health policies that respond to the challenges imposed by population aging and the complexity of neurodegenerative diseases. The objective of this study is to reveal relevant patterns of mortality from Alzheimer’s and Amyotrophic Lateral Sclerosis in Brazil, using analytical techniques applied to public databases, combining demographic and clinical statistics with interactive visualization tools.

2. Material and Methods

The present study was characterized as a quantitative, descriptive, and analytical research, with a methodological approach based on data science techniques. The objective was to reveal relevant patterns of mortality from Alzheimer’s and Amyotrophic Lateral Sclerosis (ALS) in Brazil, using secondary data from public databases and combining demographic and clinical statistics with interactive visualization tools.

Data collection was carried out from two main sources. Firstly, secondary mortality data from the Mortality Information System (SIM), which is part of the Informatics Department of the Unified Health System (DATASUS), were used. Access to the files was made via file transfer protocol (FTP – File Transfer Protocol) at the official DATASUS address (ftp://ftp.datasus.gov.br/dissemin/publicos/SIM/), covering the period from 2015 to 2024.

The identification of diseases of interest in death records was carried out through the CAUSABAS variable (basic cause of death), using the codes from the International Classification of Diseases – 10th Revision (ICD-10), as established by the World Health Organization (WHO, 1992). For Alzheimer’s, codes G30, G30.0, G30.1, G30.8, and G30.9 were considered. For Amyotrophic Lateral Sclerosis (ALS), code G12.2, corresponding to motor neuron disease, was used.

Additionally, population data by Federation Unit (UF) were obtained from the IBGE Automatic Recovery System (SIDRA), maintained by the Brazilian Institute of Geography and Statistics (IBGE). Specifically, table 1209 was consulted, which provides population estimates by age groups and UF for the year 2022 (IBGE, 2022). This information was crucial for contextualizing and comparing mortality rates.

For the treatment and data cleansing, the original files, which were in DBC (Data Base Compressed) format, were converted to DBF (Data Base File) using the TABWIN software (SUS Data Tabulator), an official tool for reading and converting DATASUS data. After conversion, the data were processed and treated using the Python programming language, in the Anaconda development environment, utilizing the Spyder IDE.

In the processing stage, the DBF files were read and converted into data structures called dataframes. The SIDRA population data were standardized, with the removal of irrelevant rows and the renaming of fields. Auxiliary dataframes were also created from tables provided by DATASUS, including the Brazilian Classification of Occupations (cbo2002-ocupacao.csv), the Municipalities Registry (CADMUN.DBF), the Federation Units Table (TABUF.DBF), and the ICD-10 descriptions (CID10.DBF).

The dataframes were integrated through merge operations, allowing the association of information such as municipality, capitals, codes and acronyms of the Federative Units, in addition to the description of diagnoses and occupations. The date variables (DTOBITO and DTNASC) were converted to the standard format, enabling the calculation of individuals’ age at the time of death. For fields with missing values, specific treatments were applied: assignment of zero for numerical variables (age, schooling, race/color, marital status) and filling with “n/a” for categorical variables (occupation and municipality).

The statistical analysis was conducted in Python, using libraries such as pandas, numpy, statsmodels, and scikit-learn. Descriptive methods and statistical models were applied to evaluate the distribution of deaths by Alzheimer’s and ALS and their associations with demographic variables. Individuals were grouped into age intervals (0-20, 21-40, 41-60, 61-80, 80+ years). The distribution by sex and region was obtained through tables of absolute and relative frequency.

The incidence rate was calculated per federative unit (UF), considering the resident population according to IBGE (2022). The measure was expressed as the number of deaths per 100,000 inhabitants, allowing for the comparison of the relative occurrence of diseases among different states, adjusted for population size.

For statistical modeling, Poisson regression was used, a model suitable for analyzing count data, such as the number of deaths in epidemiology (COLOSIMO, 2011). The dependent variable corresponded to the number of deaths from Alzheimer’s and ALS, adjusted by the resident population. The model allowed for the incorporation of explanatory variables such as schooling and occupation, evaluating their influence on mortality. The interpretation of the results was carried out using the Incidence Rate Ratio (IRR), where an IRR equal to one indicated a risk equivalent to the reference group, an IRR greater than one indicated increased risk, and an IRR less than one indicated reduced risk.

Additionally, binary logistic regression was applied to estimate the probability of an event occurring as a function of explanatory variables. This model classified mortality incidence into two binary categories: high incidence (rate above the median) and low incidence (rate below the median). The dependent variable was constructed from aggregated information by UF and year, based on the mortality incidence rate per 100,000 inhabitants.

The independent variables used in the binary logistic regression included schooling (ESC), occupation (OCUPACAO), age (IDADE), and sex (SEXO). Occupations with more than one hundred deaths were transformed into dummy variables. These variables were selected because they represent socioeconomic factors that may be associated with mortality from neurodegenerative diseases. The quality of the model was evaluated by accuracy, which corresponded to the proportion of correct classifications, and by the confusion matrix, which detailed the distribution between true positives, true negatives, false positives, and false negatives.

3. Results and Discussion

The analysis of mortality data for Alzheimer’s disease and Amyotrophic Lateral Sclerosis (ALS) in Brazil, from 2015 to 2024, revealed distinct and relevant demographic and socioeconomic patterns. A progressive increase in the incidence of both diseases was observed over the last decade, with Alzheimer’s showing a more significant upward trend. These findings are crucial for understanding the impact of population aging and the improvement of diagnostic processes and death registration in the country, as previously indicated by earlier studies (PASCHALIDIS et al., 2023). The use of analytical techniques applied to public databases allowed for the unveiling of these dynamics, contributing to the objective of revealing relevant mortality patterns.

The geographical distribution of deaths indicated that the South and Southeast regions concentrate the highest incidence rates for both diseases. This concentration can be attributed to demographic factors, such as the larger proportion of elderly population in these regions, according to data from the 2022 Demographic Census (IBGE, 2022). The temporal analysis, in turn, demonstrated continuous growth in mortality incidence, especially for Alzheimer’s, throughout the studied period. This trend reinforces the need for public policies aimed at healthy aging and specialized care, considering the regional particularities of Brazil.

Distribution by age group, sex, and region

Regarding the age distribution, Alzheimer’s disease demonstrated a strong association with advanced aging. The highest concentration of deaths occurred in individuals over 61 years old, with a marked predominance in the age group over 80 years, which registered 174,939 deaths. In contrast, younger age groups showed significantly lower numbers, with only 1 death between 1 and 20 years old, 23 deaths between 21 and 40 years old, and 1,312 deaths between 41 and 60 years old. This pattern reinforces the disease’s character as a condition strongly linked to old age, aligning with literature that highlights aging as the main risk factor for dementia (PASCHALIDIS et al., 2023).

Amyotrophic Lateral Sclerosis (ALS), although also related to aging, exhibited a distinct mortality profile regarding age group. The highest incidence of deaths from ALS was observed between 41 and 80 years of age, with a peak in the 61 to 80 year range, which accounted for 8,499 deaths. The 41 to 60 year range recorded 4,912 deaths, while the range above 80 years presented 1,187 deaths. In the younger age groups, there were 128 deaths between 1 and 20 years and 488 deaths between 21 and 40 years. These data suggest that the lethal manifestation of ALS tends to occur at earlier stages of the life cycle compared to Alzheimer’s, implying differentiated challenges for health policies, especially regarding early diagnosis and palliative care.

Temporal evolution by sex

The analysis of the temporal evolution of deaths by sex between 2015 and 2024 revealed important patterns. For Alzheimer’s, a continuous growth in the number of deaths was observed throughout the decade, with a clear predominance in the female sex category. The female category accounted for the majority of deaths in all years analyzed, followed by the male category. This pattern of unequal distribution by sex indicates a greater vulnerability of women to mortality from Alzheimer’s, a finding that corroborates evidence from the literature associating the female sex with an increased risk of dementia (BRASIL; UNIFESP, 2022).

Regarding ALS, a gradual increase in deaths was observed in the same period, although on a smaller scale when compared to Alzheimer’s. Unlike Alzheimer’s, the higher incidence of mortality from ALS occurred in the male sex category, followed by the female category. The category of unidentified deaths showed residual values in both cases, indicating that most records contained information about the sex of the individuals. The temporal progression of ALS, even if less pronounced, maintains a growing impact on mortality, especially in intermediate and advanced age groups, reinforcing the need for continuous attention to this rare disease.

Education

The research results indicated that schooling is a factor that is distinctly associated with mortality from Alzheimer’s and ALS. For Alzheimer’s, the highest concentration of deaths was observed in individuals with low schooling. The Fundamental II (2) category registered 60,562 deaths, followed by the Ensino Médio (3) category with 51,281 deaths and Fundamental I (1) with 44,294 deaths. People without schooling (0) or with unidentified schooling (9) also showed significant numbers, with 11,071 and 26,948 deaths, respectively. Individuals with complete higher education (5) and incomplete higher education (4) showed lower incidence, with 30,472 and 14,373 deaths, respectively. This pattern suggests a direct relationship between lower educational level and greater vulnerability to mortality from Alzheimer’s, reinforcing the importance of socioeconomic factors in the progression of the disease.

In contrast, ALS presented a more balanced distribution of deaths in relation to schooling, without the same direct correlation with lower levels of education observed in Alzheimer’s. The highest incidence of deaths from ALS was identified in individuals with Complete Higher Education (5), with 3,900 deaths, and High School (3), with 3,196 deaths. The Middle School category (2) registered 2,577 deaths, while Elementary School (1) had 874 deaths. People with no schooling (0) and with unidentified schooling (9) presented 748 and 1,450 deaths, respectively. This data indicates that ALS may not have the same relationship with schooling as Alzheimer’s, suggesting that other factors may be more influential in its incidence.

Occupation

The application of the Poisson regression model to correlate occupation and deaths, considering 87 occupations with more than 100 deaths, revealed striking differences between the two neurodegenerative diseases. For Alzheimer’s, occupations related to agricultural and rural work showed extremely high risk ratios (RR). Flying agricultural workers registered an RR of 21,019.00, and agricultural workers in general presented an RR of 17,378.00. Other occupations with high risk included general service domestic employees (RR of 7,601.00) and retail merchants (RR of 8,346.00). Professions requiring higher education, such as lawyers (RR of 2,956.00) and university professors (RR of 2,503.00), showed a relatively low risk compared to agricultural occupations, although they still represent significant numbers of deaths.

Regarding ALS, the occupation with the highest risk was lawyer, with a risk ratio of 6.09. This finding is particularly noteworthy, as it suggests an association with occupational conditions involving mental stress and high cognitive load. Next, agricultural workers (RR of 3.242 for agricultural itinerant workers and 3.069 for agricultural workers in general) and bricklayers (RR of 2.314) also presented elevated risks. Professions linked to the service and commerce sector, such as retail merchants (RR of 2.531) and self-employed commercial representatives (RR of 1.976), also indicated an increased risk. This distinction suggests that ALS may be associated not only with social factors such as low education, but also with cognitive demands and occupational stress, a point that finds support in international literature (RHODES et al., 2024; BORGHEAI et al., 2024; VRILLON et al., 2021).

Binary logistic regression

The binary logistic regression models applied to classify the incidence of mortality as high and low for Alzheimer’s and ALS presented moderate accuracy, with 65% for Alzheimer’s and 63% for ALS. This reasonable ability to distinguish between incidence contexts reinforces the utility of the approach. The analysis demonstrated that variables such as schooling, occupation, age, and sex exert a significant influence on mortality, contributing to the identification of vulnerability patterns in the Brazilian population. The confusion matrix, which compares predicted and observed values, corroborated the model’s performance, indicating a balance in class classification.

Despite the relevance of these explanatory variables, the models also showed that these factors are not sufficient to capture the full complexity of neurodegenerative diseases. This suggests the need to incorporate additional variables, such as clinical conditions and individual health history, in future studies to enhance the predictive power of the models. However, logistic regression proved to be a valuable tool for highlighting population groups more exposed to risk, providing important subsidies for the development of effective public policies aimed at reducing mortality from Alzheimer’s and ALS and promoting healthy aging with specialized support.

In summary, the research revealed that mortality from Alzheimer’s and ALS in Brazil presents distinct, yet equally challenging, demographic and socioeconomic patterns. Alzheimer’s shows a strong association with advanced age, the female sex, and low education levels, in addition to a high incidence in agricultural occupations. ALS, in turn, although rarer, impacts intermediate age groups, the male sex, and is associated with occupations of high cognitive load, such as lawyers, but also with agricultural workers. These findings reinforce the need for differentiated public policies and the application of data science as an essential tool to support effective and targeted public health actions.

4. Conclusion

The present study aimed to reveal relevant patterns of mortality from Alzheimer’s and Amyotrophic Lateral Sclerosis (ALS) in Brazil, using analytical techniques applied to public databases. An expressive increase in Alzheimer’s mortality was observed in the period from 2015 to 2024, predominantly in women over 75 years old and in the Southeast and South regions of the country. An association was observed with low schooling and occupations related to agricultural and rural work. In contrast, mortality from ALS, although with gradual growth on a smaller scale, predominantly affected men, in intermediate age groups (41-80 years), and in occupations with higher cognitive load, such as lawyers, but also in agricultural workers and bricklayers. These findings demonstrate the capacity of data science to transform raw information into useful knowledge, identifying vulnerable population groups and providing subsidies for the direction of public policies and health management strategies, aiming for healthy aging and specialized care.

Despite the relevance of the identified patterns, the applied binary logistic regression models showed moderate accuracy, indicating that demographic, socioeconomic, and occupational variables, although significant, do not capture the full complexity of these neurodegenerative diseases. This limitation suggests the need for future studies that incorporate additional variables, such as clinical conditions and individual health history, to enhance predictive power and understanding of the phenomena. However, the analytical approach employed reinforces the role of data science as an essential tool to support effective and targeted public health actions, contributing to long-term planning and mitigation of the challenges posed by population aging and the growing incidence of neurodegenerative diseases in Brazil.

Bibliographic References

BORGHEAI, Seyyed Bahram; ACHORN, Brie E.; ZISK, Alyssa H.; et al. 2024. A Comprehensive Overview of Neurophysiological Correlates of Cognitive Impairment in Amyotrophic Lateral Sclerosis. Cells, v. 15, n. 1: p. 1-37. DOI: 10.3390/cells15010037.

BRASIL. Ministério da Saúde; UNIVERSIDADE FEDERAL DE SÃO PAULO (UNIFESP). 2024. Relatório Nacional sobre a Demência: Epidemiologia, (re)conhecimento e projeções futuras. Brasília: Ministério da Saúde.

COLOSIMO, E. A. 2011. Métodos Estatísticos Avançados em Epidemiologia – Modelo de Poisson e Análise de Dados Longitudinais. Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brasil.

HETZ et al., 2018 [Referência completa não encontrada no documento original]

INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA – IBGE. 2022. Sistema IBGE de Recuperação Automática – SIDRA. Tabela 1209: População por grupos de idade e Unidade da Federação. Rio de Janeiro: IBGE. Disponível em: <https://sidra.ibge.gov.br>. Acesso em: 20 jan. 2026.

INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA – IBGE. 2023. Censo Demográfico 2022: número de pessoas com 65 anos ou mais de idade cresceu 57,4% em 12 anos. Rio de Janeiro: IBGE. Disponível em: <https://www.ibge.gov.br>. Acesso em: 20 jan. 2026.

JOHNSON et al., 2025 [Referência completa não encontrada no documento original]

ORGANIZAÇÃO MUNDIAL DA SAÚDE – OMS. 1992. Classificação Internacional de Doenças 10ª Revisão (CID-10). Genebra: OMS.

PASCHALIDIS, M.; KONSTANTYNER, T. C. R. O.; MARTINS, C. B.; SIMON, S. S. 2023. Trends in mortality from Alzheimer’s disease in Brazil, 2000–2019. SciELO Brasil, v. 57: p. 1-12.

RHODES et al., 2024 [Referência completa não encontrada no documento original]

VRILLON, Agathe; DERAMECOURT, Vincent; LOZERON, Pierre; et al. 2021. Association of Alzheimer’s disease and amyotrophic lateral sclerosis: A series of cases and review of the literature. Alzheimer’s & Dementia, v. 17, n. 12: p. 2084-2098. DOI: 10.1002/alz.045814.

Article originating from the Final Course Project of the Specialization in Data Science and Analytics of the MBA USP/Esalq

To learn more about the course, click here and access the MBX Academy platform

Most recent

Economy, Investments And Banking

October 23, 2026

Financial Education, Behaviors and Biases in Financial Decision Making

October 22, 2026

Challenges and Possibilities of the First School Year of Children with ASD for Teaching Work

October 22, 2026

Consumer Journey in the Decision to Purchase Infoproducts in the Digital Environment

October 22, 2026

Psychosocial Risks and Mental Health at Work in a Banking Institution

October 22, 2026

Digital Observatory of the Amazon: Data Science Applied to the Analysis of Social, Economic, Environmental, and Health Indicators

October 22, 2026

Continuous Learning as a Factor of Organizational Growth in the Perception of Collaborators

October 22, 2026

Statistical Regression in Microgrids for Operational Performance Estimation: Campusgrid Case

October 22, 2026

Forecast of Aggregate Default in the Brazilian Financial System by Sarimax Model with Macroeconomic Variables

October 22, 2026

Stakeholder Engagement Factors in International Technology Transfer to a Public Hospital

Personal financial management represents a growing challenge, influenced by cognitive biases that affect decision-making, even in individuals with high knowledge. The present study analyzed the relationship between the level of financial education and the occurrence of behavioral biases in the financial decision-making of adults residing in Brazil. An applied, quantitative, and descriptive-correlational field survey was conducted by administering a structured questionnaire to 163 respondents. The instrument included sociodemographic variables, financial knowledge and habits, and hypothetical situations to identify cognitive biases. The results indicated that higher levels of financial education were associated with better financial control, saving, and investment practices, with significant correlations with income and schooling. However, the presence of behavioral biases (anchoring, loss aversion, scarcity, optimism/negativity, present bias, status quo, overconfidence, and mental accounting) persisted even among individuals with higher schooling and income, with no statistically significant association with the analyzed sociodemographic variables. It was concluded that financial education, although essential for financial organization and control, was not sufficient to neutralize the influence of cognitive biases. The incorporation of behavioral approaches into financial education programs is suggested.

Keywords: Financial behavior; Behavioral economics; Personal finance; Cognitive heuristics; Cognitive biases.

October 22, 2026

Challenges and Possibilities of the First School Year of Children with ASD for Teaching Work

The inclusion of children with Autism Spectrum Disorder (ASD) in Early Childhood Education presents significant challenges, given the difficulty of adapting to new environments and the intensity of stimuli. The objective was to analyze the teaching performance in Early Childhood Education in the initial schooling process of children with ASD. A case study was conducted, using narrative interview as a data production technique. The results revealed that the teaching performance was characterized by mediation and affection practices, with the flexibility of routine and the use of playful resources as essential strategies for the child’s development and sensory organization. The importance of the school support professional for the success of the pedagogical work was also highlighted. The child’s integral development was promoted through intentional teaching actions that prioritized environmental adaptation, strengthening of bonds, and attention to the specific needs of ASD. As a contribution, a guide of pedagogical strategies aimed at welcoming children with ASD in public daycare centers was developed.

Keywords: Early Childhood Education; School Inclusion; Teacher Narratives; Inclusive Pedagogical Practices; Autism Spectrum Disorder.

October 22, 2026

Consumer Journey in the Decision to Purchase Infoproducts in the Digital Environment

The expansion of e-commerce and technological transformation have driven the need to understand consumer behavior regarding infoproducts, whose perceived experience has become crucial for professional development. The study aimed to analyze the factors that influenced the purchase decision of infoproducts in the digital environment, considering the stages of the consumer journey. An applied, quantitative, and descriptive research was conducted through a field survey with 100 consumers from Serra Gaúcha. Data collection occurred via a structured questionnaire, using non-probabilistic sampling. The results revealed a qualified audience with strong “mobile-first” behavior. It was found that the availability of detailed information and social approval were the main attraction triggers in the pre-purchase phase. In the transactional and subsequent stages, the minimization of operational friction, the agility of immediate access, and system security proved crucial for conversion and satisfaction, although gaps in human support efficiency were identified. Retention was based on the trust generated by positive past experiences, overcoming the price factor. It was concluded that the infoproduct consumer journey operates as an integrated system, requiring alignment between technological infrastructure, content curation, and support humanization to ensure engagement.

Keywords: E-commerce; Consumer behavior; Consumer experience; Loyalty; Virtual business.

October 22, 2026

Psychosocial Risks and Mental Health at Work in a Banking Institution

Mental health in the workplace is a topic of growing relevance, impacting professional development and employee well-being. A study was conducted to identify aspects and situations that can influence absenteeism levels in a banking institution. The research involved 108 participants and used a structured questionnaire, based on the short version of COPSOQ II by Gonçalves (2021), which contained 45 closed Likert scale questions and two open-ended questions. Data were collected via an online platform and analyzed with a descriptive and quantitative approach. The results revealed a scenario that indicated the need for adjustments in the distribution of activities and in managers’ perception of behaviors and signs of disorders. Important evidence was identified in the participants’ open-ended responses. It was concluded that the study contributed to promoting the perception of a safe environment for mental health in the organization.

Keywords: Absences due to disorders; Professional development; Operational leadership; Medical leave; Mental health.

October 22, 2026

Digital Observatory of the Amazon: Data Science Applied to the Analysis of Social, Economic, Environmental, and Health Indicators

The state of Amazonas faced historical challenges in consolidating the Unified Health System (SUS), stemming from geographical barriers, socioeconomic inequalities, and structural limitations. This study described the development and implementation of a health information geoportal, designed to support monitoring, evaluation, and planning in the Amazonian context. A descriptive case study approach was adopted, based on documentary analysis and secondary data, covering the mapping of functionalities, technical infrastructure, and validation steps of the tool. The Portal was developed with open technologies and integrated multiple national databases, offering interactive dashboards, thematic maps, and a digital document repository. Among the results, highlighted were the georeferenced visualization of key indicators, the possibility of temporal comparative analyses, and the use of the platform in reviewing health plans and redirecting strategic actions. The portal can represent an advance in digital governance and evidence-based management, with potential for replication in other highly complex territories, provided it is accompanied by investments in technological infrastructure, professional training, and inter-institutional coordination.

Keywords: Amazon; Health Evaluation; Digital Health; Health Information Systems.

October 22, 2026

Continuous Learning as a Factor of Organizational Growth in the Perception of Collaborators

The impact of continuous learning and knowledge management on organizational growth was studied in a context of globalization and constant changes. The study’s objective was to analyze employees’ perception of learning culture and its importance for personal and professional development. Data were obtained through an anonymous survey, with a quantitative approach, using a form with “survey” type questions applied to 87 professionals from the technology, health, education, commerce, and services sectors. The results indicated that employees had a positive perception of the contribution of continuous learning to personal and organizational growth, recognizing its benefits for the development of new skills, innovation, and competitiveness. However, it was observed that the incentive for internal knowledge exchange and the offering of practical training still presented moderate levels of agreement. It was concluded that continuous learning significantly contributes to individual and organizational development, suggesting that companies should develop strategic policies for learning focused on new skills, aiming for greater competitive advantage and innovation.

Keywords: Continuous learning; Self-development; Organizational growth; Knowledge Management; Innovation.

October 22, 2026

Statistical Regression in Microgrids for Operational Performance Estimation: Campusgrid Case

A multiple statistical regression model was proposed to estimate the operational performance of UNICAMP’s CampusGrid microgrid, based on internal electrical quantities. The microgrid integrates photovoltaic generation, batteries, and a gas generator. Energy management system data were collected at five-minute intervals over one month. The Operational Reliability Index, defined as the ratio between supplied and demanded energy, was established as the dependent variable. The model was adjusted for the islanded operation mode, evaluating the impact of demand, state of charge, solar generation, and supplied energy. The results revealed high explanatory power, indicating that operational reliability depends on the interaction between supply and demand. It was observed that the nighttime period concentrated the highest rate of partial supply, attributed to the reduction in solar generation. It was concluded that multiple regression offers an interpretable and useful framework for microgrid monitoring and control strategy support.

Keywords: Battery storage; Photovoltaic generation; Reliability Index; Islanded operation; Energy Management System.

October 22, 2026

Forecast of Aggregate Default in the Brazilian Financial System by Sarimax Model with Macroeconomic Variables

Aggregate non-payment in the Brazilian financial system is a crucial indicator of credit risk, the prediction of which is regulated by CMN Resolutions n.° 4.557/2017 and 4.966/2021 for the prospective measurement of expected losses. Aggregate non-payment was modeled and predicted using the SARIMAX model, projecting its trajectory until December 2026 under optimistic, base, and pessimistic scenarios. 164 monthly observations (July/2012 to February/2026) and macroeconomic variables such as unemployment, Selic, IPCA, IBC-Br, and household debt were used. The specification with contemporaneous variables was selected based on information criteria and coefficient stability, and residual diagnostics confirmed statistical adequacy, with a MAPE of 3.12%. Historical non-payment ranged between 4.33% and 7.64%. The estimation identified unemployment (β = 0.2534; p < 0.001) and Selic (β = 0.2128; p < 0.001) as the main positive determinants, with strong temporal persistence (AR(1) = 0.8965). Selic showed increasing correlation with non-payment (r = 0.62 at lag 12), while unemployment demonstrated a contemporaneous effect (r = 0.33). Projections indicated non-payment between 6.30% and 7.98% in December 2026, with a range of 1.68 percentage points, highlighting direct implications for provisioning in financial institutions.

Keywords: Unemployment; Predictive modeling; Monetary policy; Loss provisioning; Time series.

October 22, 2026

Stakeholder Engagement Factors in International Technology Transfer to a Public Hospital

This study described the critical factors perceived for stakeholder engagement in international technology transfer in a public hospital, with a focus on planning. The objective was to describe these conditioning factors, according to the perception of professionals. A single case study was conducted, applying a questionnaire to 25 professionals and using content analysis, complemented by descriptive tabulations. The main obstacles identified were resistance to change (72%), funding (64%), lack of communication (60%), and bureaucracy (40%). Only 28% of respondents recognized the existence of adequate tools for stakeholder management, while 48% denied it and 24% did not know. Exploratory cross-tabulations indicated variations in the assessment of influence according to time of practice, but did not establish causal relationships. Lack of engagement was associated with delays, low adherence, waste of resources, communication failures, compromised knowledge absorption, and risks to patient safety. The conclusions were limited to the perceptions of the researched group and supported preliminary recommendations for intersectoral communication and coordination.

Keywords: Case study; Project Management; Health innovation; Stakeholders; Public sector.

Economy

October 19, 2026

End of the 6×1 scale: what do the numbers allow us to conclude?

Following the promulgation of the end of the 6×1 shift system, the debate on the shorter workday moves to implementation and requires distinguishing social benefits from economic costs. The end of the 6×1 shift system transitioned from legislative discussion to a constitutional change in Brazil. On October 16, 2026, the National Congress promulgated Constitutional Amendment No. 141/2026, originating from Proposal […]

Artificial Intelligence

October 16, 2026

Why study if AI exists?

When answers become easy, learning comes to mean more than just arriving at them. Why do I need to learn this if I can ask an artificial intelligence? This type of question has accompanied education for decades. Why learn an operation if a calculator exists? Why memorize information if it can be found […]

Tax Management

October 15, 2026

Pink tax and the gender of price

How different marketing treatments for men and women are incorporated by consumption taxation Imagine a shelf on which two razors of the same brand and intended for the same purpose are displayed side by side: one has dark packaging and promises strength, precision, and efficiency; the other comes in shades of pink or […]

Health

October 13, 2026

The missing link in health transformation

Why is innovation not enough to drive changes in organizations and health systems Digital transformation has become a strategic priority for health systems worldwide. Electronic health records, artificial intelligence, telemedicine, remote monitoring, and data analysis are now part of the strategic agenda of governments and health organizations. Paradoxically, […]

Marketing

October 09, 2026

Bitcoin or pets: what Artificial Intelligence would not see

A real-life consumer intelligence case shows what gets left out of the brief A few years ago, an American insurance company hired a specialized consumer intelligence firm to answer three questions: The insurer was also considering launching paid insurance in bitcoin. At the time, the consultancy’s workflow was as follows: the analyst created a query […]

School Management

October 06, 2026

The school is not a casino: when trading cards become currency in the playground

Why, for certain students, does school manage to offer so few experiences of achievement? The World Cup is over. Brazil left the competition, the school holidays came, the final was played and the football calendar moved on. It would be reasonable to imagine that, with the end of the tournament and the return to classes, […] would also begin

Energy Transition

September 30, 2026

Renewable energies: the Brazilian advantage

Brazil needs to invest in the capacity to transform natural resources into innovation, productivity, and development. Talking about energy is talking about development. Behind industry, transportation, services, cities, and increasingly artificial intelligence, there is an energy infrastructure that sustains the economy. Understanding a country’s energy matrix is understanding […]

Data Analysis

September 30, 2026

When the data changes, has reality changed?

An analysis of almost 20 million teaching positions-year between 2020 and 2025 shows how changes in population composition and record production can alter the interpretation of historical series Between 2020 and 2025, the number of formal teaching employment positions identified in RAIS (Annual Relation of Social Information), from the Ministry of Labor, […]

Artificial Intelligence

September 28, 2026

Artificial intelligence and the trends

Why do so many people join in on the fun with ChatGPT? I was never the trend person. I still am not. Some pass through my timeline, I see them, I find them interesting but not to the point of trying them out. This was the case, inclusive, with some recent waves of images made by artificial intelligence, like the 80s trend, the one that […]

Tax Management

September 01, 2026

Tax reform and estate planning in the countryside

Efficiency in managing taxes facilitates governance and allows for the organization of family succession. Rural wealth organization has become a strategic measure in light of the tax reform in Brazil, which came into effect this year. The planning of activities and rural wealth management, through tools such as the transition from an Individual to […]

Renata de Gaspari Valdejão Almeida

Artificial Intelligence

August 05, 2026

Inside the company and off the radar: the risks of Shadow AI

According to Microsoft, 75% of professionals already use artificial intelligence at work without company approval. The use of generative artificial intelligence is already part of the corporate routine. Tools like ChatGPT, Copilot, and other platforms help professionals produce texts, analyze data, create presentations, and accelerate tasks in practically all sectors. But this advance […]

Renata de Gaspari Valdejão Almeida

Strategy

July 02, 2026

The future as a competence

Academic interest reinforces the projection of scenarios as an essential tool for decision-making. While some companies are still trying to get the next step right, others are already working with multiple possible futures — and preparing for all of them. But after all, what does it mean to “think about the future”? Is it a trend, a strategy, or a new competence that […]

Renata de Gaspari Valdejão Almeida

Innovation

Technology

May 21, 2026

Agro SP Corridor: innovation, institutional articulation and future

Pecege commands the executive operation of the 400 km ecosystem activities that became known as the Brazilian agro Silicon Valley In 2025, in addition to taking over the management of the Piracicaba Technology Park, the Pecege Institute was also designated to command the executive operation of the State Agribusiness Innovation Corridor activities, the so-called […]

Education

April 27, 2026

How to turn a TCC into a business

New management of the Piracicaba Technology Park offers consulting to MBA graduates The new project director of the Piracicaba Technology Park (PTP), Alexandre Barreto, often says that one of the greatest rewards he has ever had in life was seeing his MBA USP/Esalq students structure companies from their final projects […]

Technology

March 20, 2026

The human repertoire in the age of AI

Critical thinking and creativity are irreplaceable in the work of a marketing professional Artificial Intelligence (AI) is capable of incredible feats, but it does not yet possess the judgment, empathy, intuition, and strategic creativity necessary to do the job of a good marketing professional. In this area, it is important to know how to use the tool to automate […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

February 23, 2026

ESG as a growth strategy in a cooperative in RS

Santa Clara balances social and environmental responsibility with innovation Founded in 1911 by Italian immigrants in Carlos Barbosa, in the Serra Gaúcha region, Cooperativa Santa Clara is an example that it is possible to balance economic growth with social and environmental responsibility. From a small cheese production company, the organization has evolved into a […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

January 22, 2026

Brazil free of Foot-and-Mouth Disease: what changes for Brazilian beef?

With a herd of over 200 million head of cattle, the country is currently the leader in exports. Brazil recently received, for the first time, the certification of country free of foot -and-mouth disease without vaccination, conferred by the World Organisation for Animal Health (WOAH). The seal strengthens the country’s position as a reliable supplier of meat for the […]

Renata de Gaspari Valdejão Almeida

Innovation

December 09, 2025

Innovation: the new cycle of the Piracicaba Technology Park

Instituto Pecege assumes management, betting on the creation of new businesses Starting in 2026, Instituto Pecege will assume the management of the Piracicaba Technology Park (PTP). Among its responsibilities will be the administration, operation, maintenance, and expansion of the park, in addition to fostering activities in innovation, entrepreneurship, and science and technology. The choice was made […]

Renata de Gaspari Valdejão Almeida

July 06, 2026

Large-Scale Architecture: DSR-SCRUM Change Management

Mônica Alessandra Guerios; Maria do Carmo Assis Todorov Abstract generated by ResumeAI, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing. Architecture and urbanism are configured as essential bases in an increasingly complex and challenging urban space, where the intrinsic connection between social, environmental, and technological issues […]