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Seasonality and multiple maternities: Comparisons between different models.

Johan Fellman1

  • 1Hanken School of Economics, Finland.

Early Human Development
|September 14, 2019
PubMed
Summary
This summary is machine-generated.

This study examines seasonality in demographic data, emphasizing the need for daily analysis and proper standardization for accurate comparisons. It compares seasonal models with exact orthogonality for improved insights into population dynamics.

Keywords:
Adjusted coefficient of determinationBirthsCircadian rhythmDeathsDegrees of freedomF testIndicesOLSOrdinary least squaresPopulation at riskTrigonometric regressionTripletsTwinst-testχ(2) test

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Area of Science:

  • Demography
  • Biostatistics
  • Epidemiology

Background:

  • Seasonality significantly influences demographic data, with findings often varying due to reliance on monthly data and climatic factors.
  • Accurate analysis requires considering the population at risk, especially for short-term events like births and deaths, necessitating daily rate calculations.

Purpose of the Study:

  • To investigate methods for analyzing seasonality in demographic data, focusing on accurate rate calculation and model comparison.
  • To compare the effectiveness of standard seasonal models against models employing exact orthogonality for demographic data analysis.

Main Methods:

  • Analyzed demographic data, adjusting for the varying lengths of months to calculate daily rates for births and deaths.
  • Developed and compared seasonal analysis models, including those with approximate and exact orthogonality, to assess their fit and accuracy.
  • Standardized data to common means (indices) for robust inter-dataset comparisons of seasonality.

Main Results:

  • Daily analysis is crucial for accurate seasonality assessment of demographic events, correcting for variations in month length.
  • Models with exact orthogonality offer a more precise measure of seasonality, unaffected by data standardization methods.
  • Poor model fit can lead to underestimation of true seasonal fluctuations in demographic data.

Conclusions:

  • Accurate seasonality analysis in demography necessitates daily data and appropriate statistical models.
  • Comparing seasonal patterns across different datasets requires careful standardization and the use of orthogonal models for reliable insights.
  • The study highlights the importance of model selection and data granularity for understanding demographic seasonality.