Data Science x Public Health

BJANALYTICS
  • 0,0 (0)
  • So geht’s
  • Halbwöchentlich

This podcast discusses the concepts of data science and public health, and then delves into their intersection, exploring the connection between the two fields in greater detail.

  1. Everyone Uses Censoring Assumptions… But They Fail When Leaving the Study Is Part of the Outcome

    13. Mai

    Everyone Uses Censoring Assumptions… But They Fail When Leaving the Study Is Part of the Outcome

    Censoring is one of the most common assumptions in epidemiology and survival analysis. It is often treated as a routine technical step for handling people who leave observation before the study ends. But what if leaving the study is not random noise—and is actually part of the outcome process itself?  In this episode, we break down why censoring assumptions often fail, how loss to follow-up can distort longitudinal research, and why disappearing from the dataset is not the same thing as disappearing from risk. 👉 Enjoyed the episode? Follow the show to get new episodes automatically. If you found the content helpful, consider leaving a rating or review—it helps support the podcast. For business and sponsorship inquiries, email us at: 📧 contact@bjanalytics.com Youtube: https://www.youtube.com/@BJANALYTICS Instagram: https://www.instagram.com/bjanalyticsconsulting/ Twitter/X: https://x.com/BJANALYTICS Threads: https://www.threads.com/@bjanalyticsconsulting

    4 Min.
  2. In Theory, Model Averaging Works. In Reality… It Doesn’t

    13. Mai

    In Theory, Model Averaging Works. In Reality… It Doesn’t

    Model averaging is often presented as a more careful and uncertainty-aware alternative to choosing one model specification. It is supposed to reduce overconfidence and make analysis more robust. But what if all the models being averaged share the same blind spots from the start?  In this episode, we break down why model averaging often overpromises, how shared structural weaknesses survive the averaging process, and why uncertainty cannot be handled simply by blending similar models.  👉 Enjoyed the episode? Follow the show to get new episodes automatically. If you found the content helpful, consider leaving a rating or review—it helps support the podcast. For business and sponsorship inquiries, email us at:  📧 contact@bjanalytics.com Youtube: https://www.youtube.com/@BJANALYTICS Instagram: https://www.instagram.com/bjanalyticsconsulting/ Twitter/X: https://x.com/BJANALYTICS Threads: https://www.threads.com/@bjanalyticsconsulting

    4 Min.
  3. This Is Why Resource Allocation Models Don’t Work (And Nobody Talks About It)

    13. Mai

    This Is Why Resource Allocation Models Don’t Work (And Nobody Talks About It)

    Resource allocation models are supposed to help public health systems distribute scarce resources more intelligently. They promise better targeting, more efficient deployment, and stronger impact under constraint. But what if the model is optimizing inside a system whose deepest constraints should never have been treated as fixed? In this episode, we break down why resource allocation models often fail in practice, how optimization can normalize structural scarcity, and why better public health modeling has to question the system—not just distribute within it. 👉 Enjoyed the episode? Follow the show to get new episodes automatically. If you found the content helpful, consider leaving a rating or review—it helps support the podcast. For business and sponsorship inquiries, email us at: 📧 contact@bjanalytics.com Youtube: https://www.youtube.com/@BJANALYTICS Instagram: https://www.instagram.com/bjanalyticsconsulting/ Twitter/X: https://x.com/BJANALYTICS Threads: https://www.threads.com/@bjanalyticsconsulting

    5 Min.
  4. In Theory, External Validation Works. In Reality… It Doesn’t

    6. Mai

    In Theory, External Validation Works. In Reality… It Doesn’t

    External validation is often presented as the gold standard for proving that a predictive model works beyond its original dataset. It is supposed to show that the model can generalize to the real world. But what if one external dataset is still far too small a test of the outside world?  In this episode, we break down why external validation often overpromises, how “different” datasets can still be too similar, and why transportability is a much harder claim than validation language suggests. 👉 Enjoyed the episode? Follow the show to get new episodes automatically. If you found the content helpful, consider leaving a rating or review—it helps support the podcast. For business and sponsorship inquiries, email us at: 📧 contact@bjanalytics.com Youtube: https://www.youtube.com/@BJANALYTICS Instagram: https://www.instagram.com/bjanalyticsconsulting/ Twitter/X: https://x.com/BJANALYTICS Threads: https://www.threads.com/@bjanalyticsconsulting

    5 Min.
  5. In Theory, Real-Time Health Alerts Work. In Reality… They Don’t

    6. Mai

    In Theory, Real-Time Health Alerts Work. In Reality… They Don’t

    Real-time health alerts are supposed to detect danger faster and trigger earlier intervention. They promise speed, precision, and smarter public health response. But what if the alert is fast and the system behind it is still slow? In this episode, we break down why real-time health alerts often fail in practice, how organizational bottlenecks override detection speed, and why early warning only matters when the response pathway is built to act. 👉 Enjoyed the episode? Follow the show to get new episodes automatically. If you found the content helpful, consider leaving a rating or review—it helps support the podcast. For business and sponsorship inquiries, email us at: 📧 contact@bjanalytics.com Youtube: https://www.youtube.com/@BJANALYTICS Instagram: https://www.instagram.com/bjanalyticsconsulting/ Twitter/X: https://x.com/BJANALYTICS Threads: https://www.threads.com/@bjanalyticsconsulting

    5 Min.
  6. This Is Why Competing Risks Don’t Work (And Nobody Talks About It)

    6. Mai

    This Is Why Competing Risks Don’t Work (And Nobody Talks About It)

    Competing risks methods are often presented as a more realistic way to analyze time-to-event data in epidemiology and public health. They promise to handle situations where other events prevent the outcome of interest from ever occurring. But what if the method becomes more sophisticated while the interpretation becomes less clear?  In this episode, we break down why competing risks analyses are often overtrusted, how the choice of estimand quietly changes what the result means, and why better methods do not remove the need for sharper scientific thinking. 👉 Enjoyed the episode? Follow the show to get new episodes automatically. If you found the content helpful, consider leaving a rating or review—it helps support the podcast. For business and sponsorship inquiries, email us at: 📧 contact@bjanalytics.com Youtube: https://www.youtube.com/@BJANALYTICS Instagram: https://www.instagram.com/bjanalyticsconsulting/ Twitter/X: https://x.com/BJANALYTICS Threads: https://www.threads.com/@bjanalyticsconsulting

    5 Min.
  7. Everyone Uses Attack Rates… But They Fail When Exposure Isn’t Shared

    29. Apr.

    Everyone Uses Attack Rates… But They Fail When Exposure Isn’t Shared

    Attack rates are one of the most common tools in outbreak epidemiology. They seem to offer a quick answer to a simple question: how many exposed people got sick? But what if the exposed group was never truly sharing the same exposure in the first place?  In this episode, we break down why attack rates often fail when exposure is uneven, how denominator assumptions distort outbreak interpretation, and why summary measures can hide the real structure of transmission. 👉 Enjoyed the episode? Follow the show to get new episodes automatically. If you found the content helpful, consider leaving a rating or review—it helps support the podcast. For business and sponsorship inquiries, email us at: 📧 contact@bjanalytics.com Youtube: https://www.youtube.com/@BJANALYTICS Instagram: https://www.instagram.com/bjanalyticsconsulting/ Twitter/X: https://x.com/BJANALYTICS Threads: https://www.threads.com/@bjanalyticsconsulting

    5 Min.
  8. Everyone Uses Public Health Scorecards… But They Fail When the Incentive Is the Metric

    29. Apr.

    Everyone Uses Public Health Scorecards… But They Fail When the Incentive Is the Metric

    Public health scorecards are supposed to improve accountability and make system performance easier to track. They promise clarity, targets, and faster decision-making. But what if the scorecard starts changing behavior in the wrong direction? In this episode, we break down why public health scorecards often fail, how metrics become incentives, and why better-looking numbers can still hide weaker real-world performance. 👉 Enjoyed the episode? Follow the show to get new episodes automatically. If you found the content helpful, consider leaving a rating or review—it helps support the podcast. For business and sponsorship inquiries, email us at: 📧 contact@bjanalytics.com Youtube: https://www.youtube.com/@BJANALYTICS Instagram: https://www.instagram.com/bjanalyticsconsulting/ Twitter/X: https://x.com/BJANALYTICS Threads: https://www.threads.com/@bjanalyticsconsulting

    4 Min.
Alle anzeigen (166)

Info

This podcast discusses the concepts of data science and public health, and then delves into their intersection, exploring the connection between the two fields in greater detail.

Informationen

  • Erstellt von
    BJANALYTICS
  • Jahre aktiv
    2025 - 2026
  • Folgen
    166
  • Bewertung
    Unbedenklich
  • Copyright
    © 2026 Data Science x Public Health
  • Sendungswebsite
    Data Science x Public Health

Das gefällt dir vielleicht auch