Research article

Trends in epidemiology: the role of denominator fluctuation in population based estimates

Running title: Denominator fluctuation in epidemiology
  • Received: 07 May 2021 Accepted: 05 July 2021 Published: 08 July 2021
  • Background

    Population estimates are of paramount importance for calculating occurrence and association measures although they can be affected by problems of accuracy and completeness. This study has performed a simulation of the impact of Italian population size variability on incidence rates.

    Methods

    Data have been obtained by the Italian National Institute of Statistics. For each year expected cases were calculated at increasing fixed rates (up to 1,000/100,000) and were considered constant in the “following year”, calculating statistical differences (P < 0.05).

    Results

    In Italy and in other regions, statistically significant higher RRs were found in 2012 vs. 2011 whereas statistically significant lower RRs were found in 2013 vs. 2012 and in 2014 vs. 2013.

    Contribution

    The simulation confirms that significant differences due to population fluctuation could be found between consecutive years when investigating diseases with medium-high rates. Researchers should be encouraged to implement actions for reducing the risk of biased population denominators.

    Citation: Emanuele Amodio, Maurizio Zarcone, Alessandra Casuccio, Francesco Vitale. Trends in epidemiology: the role of denominator fluctuation in population based estimates[J]. AIMS Public Health, 2021, 8(3): 500-506. doi: 10.3934/publichealth.2021040

    Related Papers:

  • Background

    Population estimates are of paramount importance for calculating occurrence and association measures although they can be affected by problems of accuracy and completeness. This study has performed a simulation of the impact of Italian population size variability on incidence rates.

    Methods

    Data have been obtained by the Italian National Institute of Statistics. For each year expected cases were calculated at increasing fixed rates (up to 1,000/100,000) and were considered constant in the “following year”, calculating statistical differences (P < 0.05).

    Results

    In Italy and in other regions, statistically significant higher RRs were found in 2012 vs. 2011 whereas statistically significant lower RRs were found in 2013 vs. 2012 and in 2014 vs. 2013.

    Contribution

    The simulation confirms that significant differences due to population fluctuation could be found between consecutive years when investigating diseases with medium-high rates. Researchers should be encouraged to implement actions for reducing the risk of biased population denominators.



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    Acknowledgments



    The authors are grateful to Dr. Stefano Pizzo e Alessandro Marrella for their contribution to the conception of the study and assistance in database development and management.

    Conflict of interest



    The authors declare no conflicts of interest.

    [1] United Nations World Population Prospects: The 2017 Revision (2019) .Available from: https://www.un.org/development/desa/publications/world-population-prospects-the-2017-revision.html.
    [2] Eurostat (2019) .Available from: https://ec.europa.eu/eurostat/statistics-explained/index.php/Population_and_population_change_statistics.
    [3] Italian National Institute of Statistics (2019) .Available from: https://www.istat.it/en/.
    [4] Italian National Institute of Statistics La revisione post-censuaria (2019) .Available from: https://www.istat.it/it/files/2016/09/LA-REVISIONE-POST-CENSUARIA.pdf.
    [5] Italian National Institute of Statistics (2019) .Available from: http://demo.istat.it/archive.html.
    [6] Rothman KJ, Greenland S (2008) Modern epidemiology. Philadelphia Lippincott Williams & Wilkins.
    [7]  R software. Version 3.5.2 Available from: https://www.r-project.org/.
    [8] Aickin M, Dunn CN, Flood TJ (1991) Estimation of population denominators for public health studies at the tract, gender, and age-specific level. Am J Public Health 81: 918-920. doi: 10.2105/AJPH.81.7.918
    [9] Swanson D, Tayman J (2012)  Sub-National Population Estimates New York: Springer. doi: 10.1007/978-90-481-8954-0
    [10] Baker J, Alcantara A, Ruan XM, et al. (2013) A Comparative evaluation of error and bias in census tract level age/sex-specific popuation estimates: Component I (net-migration) vs Component III (Hamilton-Perry). Popul Res Policy Rev 11: 919-942. doi: 10.1007/s11113-013-9295-4
    [11] Montori VM, Kleinbart J, Newman TB, et al. (2004) Tips for learners of evidence-based medicine: 2. Measures of precision (confidence intervals). CMAJ 171: 611-615. doi: 10.1503/cmaj.1031667
    [12] Rashid I, Giacomin A, Michiara M, et al. (2016) Intercensal reconstruction of population and descriptive epidemiological measures in Italy: what is the impact on the cancer incidence rates? Epidemiol Prev 40: 103-110.
    [13] Boscoe FP, Miller BA (2004) Population Estimation Error and Its Impact on 1991–1999 Cancer Rates. Prof Geogr 56: 516-529.
    [14] Swanson DA, Tedrow LM (1984) Improving the measurement of temporal change in regression models used for county population estimates. Demography 21: 373-381. doi: 10.2307/2061166
    [15] Tayman J, Smith SK, Rayer S (2011) Evaluating Population Forecast Accuracy: A Regression Approach Using County Data. Popul Res Policy Rev 30: 235-262. doi: 10.1007/s11113-010-9187-9
    [16] Smith SK, Sincich T (1992) Evaluating the forecast accuracy and bias of alternative population projections for states. Int J Forecast 8: 495-508. doi: 10.1016/0169-2070(92)90060-M
    [17] Khang YH, Hwang IA, Yun SC, et al. (2005) Census population vs. registration population:which population denominator should be used to calculate geographical mortality. J Prev Med Public Health 38: 147-153.
    [18] Sadeghi M, Haghdoost AA, Bahrampour A, et al. (2017) Modeling the Burden of Cardiovascular Diseases in Iran from 2005 to 2025: The Impact of Demographic Changes. Iran J Public Health 46: 506-516.
    [19] Xiao Q, Liang F, Ning M, et al. (2021) The long-term trend of PM2.5-related mortality in China: The effects of source data selection. Chemosphere 263: 127894. doi: 10.1016/j.chemosphere.2020.127894
    [20] Jung PH, Thill JC, Issel M (2019) Spatial Autocorrelation Statistics of Areal Prevalence Rates under High Uncertainty in Denominator Data. Geogr Anal 51: 354-380. doi: 10.1111/gean.12177
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