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Purpose <p>Prostate disorders pose an increasing public health burden due to their prevalence, clinical complexity, and economic impact. This study examines 10 years of prostate-related hospitalizations within a universal public healthcare system, using data visualization and statistical modeling to characterize epidemiological patterns, care pathways, and costs, and to generate evidence to support data-driven decision-making (DDDM).</p> Methods <p>More than 130 million hospital records (2015–2024) were filtered using International Classification of Diseases (ICD) codes related to prostate diseases. Statistical analyses included chi-square tests, Moran’s I for spatial autocorrelation, Poisson and negative binomial regressions to assess temporal trends, and logistic regression models, among others. These approaches were combined with data visualization techniques to improve pattern detection and interpretability.</p> Results <p>Hospitalizations increased across demographic groups, predominantly affecting individuals aged 60–70 years. Spatial analysis revealed significant clustering nationwide. Intensive Care Units utilization rose significantly, reaching &#xa0;8% in 2024, and was associated with case complexity. Mortality showed a slight decreasing trend despite a pandemic-related increase. Costs remained stable or declined in real terms, though expenditures for malignant cases increased.</p>

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Data-driven epidemiological insights into prostate-related hospitalizations in a universal public health system: a ten-year analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月20日

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  • Eduardo Paraiso,
  • Alexei Machado,
  • Cristiane Nobre

摘要

Purpose

Prostate disorders pose an increasing public health burden due to their prevalence, clinical complexity, and economic impact. This study examines 10 years of prostate-related hospitalizations within a universal public healthcare system, using data visualization and statistical modeling to characterize epidemiological patterns, care pathways, and costs, and to generate evidence to support data-driven decision-making (DDDM).

Methods

More than 130 million hospital records (2015–2024) were filtered using International Classification of Diseases (ICD) codes related to prostate diseases. Statistical analyses included chi-square tests, Moran’s I for spatial autocorrelation, Poisson and negative binomial regressions to assess temporal trends, and logistic regression models, among others. These approaches were combined with data visualization techniques to improve pattern detection and interpretability.

Results

Hospitalizations increased across demographic groups, predominantly affecting individuals aged 60–70 years. Spatial analysis revealed significant clustering nationwide. Intensive Care Units utilization rose significantly, reaching  8% in 2024, and was associated with case complexity. Mortality showed a slight decreasing trend despite a pandemic-related increase. Costs remained stable or declined in real terms, though expenditures for malignant cases increased.