IDEAS home Printed from https://ideas.repec.org/a/sae/evarev/v25y2001i3p267-287.html
   My bibliography  Save this article

Salt and Blood Pressure

Author

Listed:
  • David A. Freedman

    (University of California, Berkeley)

  • Diana B. Petitti

    (Kaiser Permanente Southern California)

Abstract

The salt hypothesis is that higher levels of salt in the diet lead to higher levels of blood pressure, increasing the risk of cardiovascular disease. Intersalt, a cross-sectional study of salt levels and blood pressures in 52 populations, is often cited to support the salt hypothesis, but the data are somewhat contradictory. Four of the populations (Kenya, Papua, and 2 Indian tribes in Brazil) do have low levels of salt and blood pressure. Across the other 48 populations, however, blood pressures go down as salt levels go up, contradicting the hypothesis. Experimental evidence suggests that the effect of a large reduction in salt intake on blood pressure is modest, and health consequences remain to be determined. Funding agencies and medical journals have taken a stronger position favoring the salt hypothesis than is warranted, raising questions about the interaction between the policy process and science.

Suggested Citation

  • David A. Freedman & Diana B. Petitti, 2001. "Salt and Blood Pressure," Evaluation Review, , vol. 25(3), pages 267-287, June.
  • Handle: RePEc:sae:evarev:v:25:y:2001:i:3:p:267-287
    DOI: 10.1177/0193841X0102500301
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0193841X0102500301
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0193841X0102500301?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Goldthorpe, John H., 1998. "Causation, Statistics and Sociology," Research Series, Economic and Social Research Institute (ESRI), number GLS29.
    2. Freedman, David & Lane, David, 1983. "A Nonstochastic Interpretation of Reported Significance Levels," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(4), pages 292-298, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stefano Bonnini & Michela Borghesi, 2022. "Relationship between Mental Health and Socio-Economic, Demographic and Environmental Factors in the COVID-19 Lockdown Period—A Multivariate Regression Analysis," Mathematics, MDPI, vol. 10(18), pages 1-15, September.
    2. Purevdorj Tuvaandorj, 2021. "Robust Permutation Tests in Linear Instrumental Variables Regression," Papers 2111.13774, arXiv.org, revised Jul 2024.
    3. Hu, Yuan & Behrman, Jere R. & Zhang, Junsen, 2021. "The causal effects of parents’ schooling on children's schooling in urban China," Journal of Comparative Economics, Elsevier, vol. 49(1), pages 258-276.
    4. Kherad-Pajouh, Sara & Renaud, Olivier, 2010. "An exact permutation method for testing any effect in balanced and unbalanced fixed effect ANOVA," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1881-1893, July.
    5. Daly, Michael & Delaney, Liam & Doyle, Orla & Fitzpatrick, Nick & O'Farrelly, Christine, 2014. "Can Early Intervention Policies Improve Well-being? Evidence from a randomized controlled trial," SIRE Discussion Papers 2015-03, Scottish Institute for Research in Economics (SIRE).
    6. Gill,Indermit S., 1990. "Does the structure of production affect demand for schooling in Peru?," Policy Research Working Paper Series 468, The World Bank.
    7. Pinto, Rodrigo, 2010. "Evaluation of Small-sample Compromised Randomization: Long-term Effects of Early Childhood Intervention on Health and Addictive Behavior," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 30(2), December.
    8. Sara Kherad-Pajouh & Olivier Renaud, 2015. "A general permutation approach for analyzing repeated measures ANOVA and mixed-model designs," Statistical Papers, Springer, vol. 56(4), pages 947-967, November.
    9. Pini, Alessia & Stamm, Aymeric & Vantini, Simone, 2018. "Hotelling’s T2 in separable Hilbert spaces," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 284-305.
    10. Mittelhammer, Ron C. & Judge, George G., 2005. "Combining estimators to improve structural model estimation and inference under quadratic loss," Journal of Econometrics, Elsevier, vol. 128(1), pages 1-29, September.
    11. Orla Doyle, 2024. "Can Early Intervention Have a Sustained Effect on Human Capital?," Journal of Human Resources, University of Wisconsin Press, vol. 59(5), pages 1599-1636.
    12. Nathaniel E. Helwig, 2022. "Robust Permutation Tests for Penalized Splines," Stats, MDPI, vol. 5(3), pages 1-18, September.
    13. Karel Hron & Jitka Machalová & Alessandra Menafoglio, 2023. "Bivariate densities in Bayes spaces: orthogonal decomposition and spline representation," Statistical Papers, Springer, vol. 64(5), pages 1629-1667, October.
    14. Soo Hong Chew & Junjian Yi & Junsen Zhang & Songfa Zhong, 2016. "Education and anomalies in decision making: Experimental evidence from Chinese adult twins," Journal of Risk and Uncertainty, Springer, vol. 53(2), pages 163-200, December.
    15. David Dekker & David Krackhardt & Tom Snijders, 2007. "Sensitivity of MRQAP Tests to Collinearity and Autocorrelation Conditions," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 563-581, December.
    16. Veronika Římalová & Alessandra Menafoglio & Alessia Pini & Vilém Pechanec & Eva Fišerová, 2020. "A permutation approach to the analysis of spatiotemporal geochemical data in the presence of heteroscedasticity," Environmetrics, John Wiley & Sons, Ltd., vol. 31(4), June.
    17. Jiří Dvořák & Tomáš Mrkvička & Jorge Mateu & Jonatan A. González, 2022. "Nonparametric Testing of the Dependence Structure Among Points–Marks–Covariates in Spatial Point Patterns," International Statistical Review, International Statistical Institute, vol. 90(3), pages 592-621, December.
    18. Orla Doyle, 2017. "The First 2,000 Days and Child Skills: Evidence from a Randomized Experiment of Home Visiting," Working Papers 201715, School of Economics, University College Dublin.
    19. Laurin Charles & Boomsma Dorret & Lubke Gitta, 2016. "The use of vector bootstrapping to improve variable selection precision in Lasso models," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(4), pages 305-320, August.
    20. Zhao, Anqi & Ding, Peng, 2021. "Covariate-adjusted Fisher randomization tests for the average treatment effect," Journal of Econometrics, Elsevier, vol. 225(2), pages 278-294.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:evarev:v:25:y:2001:i:3:p:267-287. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.