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Journal articles on the topic 'Population Attributable Fraction'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Khosravi, Ahmad, Maryam Nazemipour, Tomohiro Shinozaki, and Mohammad Ali Mansournia. "Population attributable fraction in textbooks: Time to revise." Global Epidemiology 3 (November 2021): 100062. http://dx.doi.org/10.1016/j.gloepi.2021.100062.

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2

Brooks-Pollock, Ellen, and Leon Danon. "Defining the population attributable fraction for infectious diseases." International Journal of Epidemiology 46, no. 3 (May 4, 2017): 976–82. http://dx.doi.org/10.1093/ije/dyx055.

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3

Williamson, D. F. "The population attributable fraction and confounding: buyer beware." International Journal of Clinical Practice 64, no. 8 (June 16, 2010): 1019–23. http://dx.doi.org/10.1111/j.1742-1241.2010.02443.x.

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4

Mishra, Sharmistha, and Stefan D. Baral. "Rethinking the population attributable fraction for infectious diseases." Lancet Infectious Diseases 20, no. 2 (February 2020): 155–57. http://dx.doi.org/10.1016/s1473-3099(19)30618-8.

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5

Davis, Caroline H., David P. MacKinnon, Amy Schultz, and Irwin Sandler. "Cumulative Risk and Population Attributable Fraction in Prevention." Journal of Clinical Child & Adolescent Psychology 32, no. 2 (May 2003): 228–35. http://dx.doi.org/10.1207/s15374424jccp3202_7.

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6

Di Maso, Matteo, Francesca Bravi, Jerry Polesel, Eva Negri, Adriano Decarli, Diego Serraino, Carlo La Vecchia, and Monica Ferraroni. "Attributable fraction for multiple risk factors: Methods, interpretations, and examples." Statistical Methods in Medical Research 29, no. 3 (May 10, 2019): 854–65. http://dx.doi.org/10.1177/0962280219848471.

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The attributable fraction is the candidate tool to quantify individual shares of each risk factor on the disease burden in a population, expressing the proportion of cases ascribable to the risk factors. The original formula ignored the presence of other factors (i.e. multiple risk factors and/or confounders), and several adjusting methods for potential confounders have been proposed. However, crude and adjusted attributable fractions do not sum up to their joint attributable fraction (i.e. the number of cases attributable to all risk factors together) and their sum may exceed one. A different approach consists of partitioning the joint attributable fraction into exposure-specific shares leading to sequential and average attributable fractions. We provide an example using Italian case–control data on oral cavity cancer comparing crude, adjusted, sequential, and average attributable fractions for smoking and alcohol and provide an overview of the available software routines for their estimation. For each method, we give interpretation and discuss shortcomings. Crude and adjusted attributable fractions added up over than one, whereas sequential and average methods added up to the joint attributable fraction = 0.8112 (average attributable fractions for smoking and alcohol were 0.4894 and 0.3218, respectively). The attributable fraction is a well-known epidemiological measure that translates risk factors prevalence and disease occurrence in useful figures for a public health perspective. This work endorses their proper use and interpretation.

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Shin, A., S. Park, H. R. Shin, E. H. Park, S. K. Park, J. K. Oh, M. K. Lim, B. Y. Choi, M. Boniol, and P. Boffetta. "Population attributable fraction of infection-related cancers in Korea." Annals of Oncology 22, no. 6 (June 2011): 1435–42. http://dx.doi.org/10.1093/annonc/mdq592.

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8

Simons, Elinor, Teresa To, and Sharon Dell. "The Population Attributable Fraction of Asthma Among Canadian Children." Canadian Journal of Public Health 102, no. 1 (January 2011): 35–41. http://dx.doi.org/10.1007/bf03404874.

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9

Taguri, Masataka, and Aya Kuchiba. "Decomposition of the population attributable fraction for two exposures." Annals of Epidemiology 28, no. 5 (May 2018): 331–34. http://dx.doi.org/10.1016/j.annepidem.2018.02.012.

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10

Raut, Janhavi R., Regina M. Simeone, Sarah C. Tinker, Mark A. Canfield, R. Sue Day, and A. J. Agopian. "Proportion of Orofacial Clefts Attributable to Recognized Risk Factors." Cleft Palate-Craniofacial Journal 56, no. 2 (May 4, 2018): 151–58. http://dx.doi.org/10.1177/1055665618774019.

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Objective: Estimate the population attributable fraction (PAF) for a set of recognized risk factors for orofacial clefts. Design: We used data from the National Birth Defects Prevention Study. For recognized risk factors for which data were available, we estimated crude population attributable fractions (cPAFs) to account for potential confounding, average-adjusted population attributable fractions (aaPAFs). We assessed 11 modifiable and 3 nonmodifiable parental/maternal risk factors. The aaPAF for individual risk factors and the total aaPAF for the set of risk factors were calculated using a method described by Eide and Geffler. Setting: Population-based case–control study in 10 US states. Participants: Two thousand seven hundred seventy-nine cases with isolated cleft lip with or without cleft palate (CL±P), 1310 cases with isolated cleft palate (CP), and 11 692 controls with estimated dates of delivery between October 1, 1997, and December 31, 2011. Main Outcome Measures: Crude population attributable fraction and aaPAF. Results: The proportion of CL±P and CP cases attributable to the full set of examined risk factors was 50% and 43%, respectively. The modifiable factor with the largest aaPAF was smoking during the month before pregnancy or the first month of pregnancy (4.0% for CL±P and 3.4% for CP). Among nonmodifiable factors, the factor with the largest aaPAF for CL±P was male sex (27%) and for CP it was female sex (16%). Conclusions: Our results may inform research and prevention efforts. A large proportion of orofacial cleft risk is attributable to nonmodifiable factors; it is important to better understand the mechanisms involved for these factors.

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11

Pfahlberg, Annette, Wolfgang Uter, and Olaf Gefeller. "Population Attributable Fraction Estimates of Familial Risk in Cutaneous Melanoma." Journal of Investigative Dermatology 122, no. 3 (March 2004): 853–54. http://dx.doi.org/10.1111/j.0022-202x.2004.22314.x.

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12

Hanley, J. A. "A heuristic approach to the formulas for population attributable fraction." Journal of Epidemiology & Community Health 55, no. 7 (July 1, 2001): 508–14. http://dx.doi.org/10.1136/jech.55.7.508.

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13

Würtz, Else, Vivi Schlünssen, Tine Malling, Jens Georg Hansen, and Øyvind Omland. "The population attributable fraction of occupational COPD among Danish women." ERJ Open Research 3, no. 2 (April 2017): 00075–2016. http://dx.doi.org/10.1183/23120541.00075-2016.

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14

Barnes, Hayley, Anna-Carin Olin, Kjell Torén, Charles McSharry, Iona Donnelly, Mona Lärstad, Carlos Iribarren, Patricia Quinlan, and Paul D. Blanc. "Occupation versus environmental factors in hypersensitivity pneumonitis: population attributable fraction." ERJ Open Research 6, no. 4 (October 2020): 00374–2020. http://dx.doi.org/10.1183/23120541.00374-2020.

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BackgroundDespite well-documented case series of hypersensitivity pneumonitis (HP), epidemiological data delineating relative contributions of risk factors are sparse. To address this, we estimated HP risk in a case-referent study of occupational and nonoccupational exposures.MethodsWe recruited cases of HP by ICD-9 codes from an integrated healthcare delivery system (IHCDS) and a tertiary medical care centre. We drew referents, matched for age and sex, from the IHCDS. Participants underwent comprehensive, structured telephone interviews eliciting details of occupational and home environmental exposures. We employed a hierarchical analytic approach for data reduction based on the false discovery rate method within clusters of exposures. We measured lung function and selected biomarkers in a subset of participants. We used multivariate logistic regression to estimate exposure-associated odds ratios (ORs) and population attributable fractions (PAFs) for HP.ResultsWe analysed data for 192 HP cases (148 IHCDS; 44 tertiary care) and 229 referents. Occupational exposures combined more than doubled the odds of developing HP (OR 2.67; 95% CI 1.73–4.14) with a PAF of 34% (95% CI 21–46%); nonoccupational bird exposure also doubled the HP odds (OR 2.02; 95% CI 1.13–3.60), with a PAF of 12% (3–21%). Lung function and selected biomarkers did not substantively modify the risk estimates on the basis of questionnaire data alone.DiscussionIn a case-referent approach evaluating HP risk, identifiable exposures accounted, on an epidemiological basis, for approximately two in three cases of disease; conversely, for one in three, the risk factors for disease remained elusive.

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15

Poole, Charles. "A history of the population attributable fraction and related measures." Annals of Epidemiology 25, no. 3 (March 2015): 147–54. http://dx.doi.org/10.1016/j.annepidem.2014.11.015.

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16

Freese, Kyle E., Lisa M. Bodnar, Maria M. Brooks, Kathleen McTigue, and Katherine P. Himes. "Population-attributable fraction of risk factors for severe maternal morbidity." American Journal of Obstetrics & Gynecology MFM 2, no. 1 (February 2020): 100066. http://dx.doi.org/10.1016/j.ajogmf.2019.100066.

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17

Gorwitz, Rachel J., Harold C. Wiesenfeld, Pai-Lien Chen, Karen R. Hammond, Karen A. Sereday, Catherine L. Haggerty, Robert E. Johnson, et al. "Population-attributable fraction of tubal factor infertility associated with chlamydia." American Journal of Obstetrics and Gynecology 217, no. 3 (September 2017): 336.e1–336.e16. http://dx.doi.org/10.1016/j.ajog.2017.05.026.

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18

Browner, W. S., and T. B. Newman. "Sample size and power based on the population attributable fraction." American Journal of Public Health 79, no. 9 (September 1989): 1289–94. http://dx.doi.org/10.2105/ajph.79.9.1289.

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19

Plaß, D., M. Tobollik, B. Devleesschauwer, E. Grill, B. Hoffmann, J. Hurraß, N. Künzli, et al. "Kritik an Population Attributable Fraction bei genauerem Hinsehen nicht gerechtfertigt." Das Gesundheitswesen 81, no. 05 (May 2019): 444–47. http://dx.doi.org/10.1055/a-0915-1215.

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20

Arokiasamy, Periyanayangam, and Gopal Agrawal. "Population Attributable Risk Fraction for Selected Chronic Diseases in India." Journal of Primary Care & Community Health 1, no. 3 (September 30, 2010): 192–99. http://dx.doi.org/10.1177/2150131910378527.

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21

Tanuseputro, P. "Improving Population Attributable Fraction Methods: Examining Smoking-attributable Mortality for 87 Geographic Regions in Canada." American Journal of Epidemiology 161, no. 8 (April 15, 2005): 787–98. http://dx.doi.org/10.1093/aje/kwi093.

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22

Choi, Bernard C. K. "Population attributable fraction: comparison of two mathematical procedures to estimate the annual attributable number of deaths." Epidemiologic Perspectives & Innovations 7, no. 1 (2010): 8. http://dx.doi.org/10.1186/1742-5573-7-8.

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23

Allore, Heather. "Differences in the Racial Contribution of Dementia and Chronic Conditions to Hospitalization, SNF Admission." Innovation in Aging 5, Supplement_1 (December 1, 2021): 274. http://dx.doi.org/10.1093/geroni/igab046.1066.

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Abstract We estimate the contribution for experiencing hospitalization, skilled nursing facility admission and mortality using a measure of attributable fraction that incorporates both the prevalence, incidence and risk called Longitudinal Extension of the Average Attributable Fraction (LE-AAF). We estimate the LE-AAF for Non-Hispanic whites and Non-Hispanic Blacks for dementia and 10 chronic conditions, for three outcomes. This approach analyses the temporal relationships among conditions to estimate their population-level average attributable fractions. Unlike standard measures of attributable fraction, the sum of the contribution of each condition based on the LE-AAF will not exceed 100 percent, enabling us to compute the contribution of pairs, triads or any combination of conditions. Furthermore, in studying multimorbidity, the LE-AAF has the desirable feature of being based on all combinations of the risk factors and covariates present in the data with final values for the individual LE-AAFs obtained by averaging across these observed combinations of predictors.

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24

Laukkanen, Päivi, Esa Läärä, Pentti Koskela, Eero Pukkala, Hanna Virkkunen, and Matti Lehtinen. "Population fraction of cervical neoplasia attributable to high-risk human papillomaviruses." Future Oncology 6, no. 5 (May 2010): 709–16. http://dx.doi.org/10.2217/fon.10.38.

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25

Domínguez, Karen. "Population attributable fraction in cross-sectional studies: proposal for data analysis." Medwave 16, Suppl6 (November 30, 2016): 6718. http://dx.doi.org/10.5867/medwave.2016.6718.

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26

Ojima, Toshiyuki, Ritei Uehara, Makoto Watanabe, Morihiro Tajimi, Izumi Oki, and Yosikazu Nakamura. "Population attributable fraction of smoking to low birth weight in Japan." Pediatrics International 46, no. 3 (June 2004): 264–67. http://dx.doi.org/10.1111/j.1442-200x.2004.01881.x.

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27

Karp, Igor, Eric Topol, and Louise Pilote. "Population attributable fraction: Its implications for genetic epidemiology and illness prevention." American Heart Journal 154, no. 4 (October 2007): 607–9. http://dx.doi.org/10.1016/j.ahj.2007.06.007.

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28

Mason, Craig A., and Shihfen Tu. "Partitioning the population attributable fraction for a sequential chain of effects." Epidemiologic Perspectives & Innovations 5, no. 1 (2008): 5. http://dx.doi.org/10.1186/1742-5573-5-5.

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29

Khosravi, Ahmad, Rasmus Oestergaard Nielsen, and Mohammad Ali Mansournia. "Methods matter: population attributable fraction (PAF) in sport and exercise medicine." British Journal of Sports Medicine 54, no. 17 (February 12, 2020): 1049–54. http://dx.doi.org/10.1136/bjsports-2020-101977.

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30

Zhao, Wei, Jiayin Zheng, Ying Qing Chen, and Li Hsu. "Adjusted time-varying population attributable hazard in case–control studies." Statistical Methods in Medical Research 29, no. 1 (February 25, 2019): 243–57. http://dx.doi.org/10.1177/0962280219831725.

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Population attributable fraction is a widely used measure for quantifying the disease burden associated with a modifiable exposure of interest at the population level. It has been extended to a time-varying measure, population attributable hazard function, to provide additional information on when and how the exposure's impact varies over time. However, like the classic population attributable fraction, the population attributable hazard is generally biased if confounders are present. In this article, we provide a natural definition of adjusted population attributable hazard to take into account the effects of confounders, and its alternative that is identifiable from case–control studies under the rare disease assumption. We propose a novel estimator, which combines the odds ratio estimator from logistic regression model, and the conditional density function estimator of the exposure and confounding variables distribution given the failure times of cases or the current times of controls from a kernel smoother. We show that the proposed estimators are consistent and asymptotically normal with variance that can be estimated empirically from the data. Simulation studies demonstrate that the proposed estimators perform well in finite sample sizes. Finally, we illustrate the method by an analysis of a case–control study of colorectal cancer. Supplementary materials for this article are available online.

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31

Shaw, Souradet Y., Deborah L. Nowicki, Erin Schillberg, Christopher G. Green, Craig P. Ross, Joss Reimer, Pierre J. Plourde, and Lawrence J. Elliott. "Epidemiology of incident chlamydia and gonorrhoea infections and population attributable fractions associated with living in the inner-core of Winnipeg, Canada." International Journal of STD & AIDS 28, no. 6 (July 10, 2016): 550–57. http://dx.doi.org/10.1177/0956462415614168.

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Population attributable fractions help to convey public health significance of differential disease risk for chlamydia and gonorrhoea. Geographical residence serves as a useful proxy for complex processes creating ill health. Using population-based data, Poisson regression models were used to examine factors associated with chlamydia and gonorrhoea incidence. Population attributable fractions due to residency in the Winnipeg Health Region’s inner-core were determined for chlamydia/gonorrhoea infections among 15–59-year olds (2005–2013), stratified by age group. For both chlamydia and gonorrhoea, it was found that the 15–24-year old age group had the highest incidence rates. There was also a stronger association between residency in the inner-core and incidence for gonorrhoea, compared to chlamydia. Overall, 24% (95% CI: 12–34%) of chlamydia infections were attributable to residency in the inner-core, compared to 46% (95% CI: 35–54%) for gonorrhoea ( p < .05). Within chlamydia/gonorrhoea, no statistically significant differences in population attributable fraction were observed by age group. The conclusion was that a concentration of efforts towards inner-core residents with gonorrhoea infections may result in a relatively larger decrease in incidence.

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32

Martini, Alberto. "On the use of the “Population Attributable Fraction”: application in cost estimation." Translational Andrology and Urology 10, no. 1 (January 2021): 22–23. http://dx.doi.org/10.21037/tau-20-1146.

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33

Sinha, P., L. F. White, N. S. Hochberg, and J. P. Cegielski. "Avoiding pitfalls in calculating the population attributable fraction of undernutrition for TB." International Journal of Tuberculosis and Lung Disease 26, no. 1 (January 1, 2022): 80. http://dx.doi.org/10.5588/ijtld.21.0634.

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34

taheri, moslem, mahmood lotfi, mohammad Tabatabaei, morteza mohammadzadeh, and morteza dolatian. "Application of population attributable fraction in prevention of cardiovascular disease (Review Article)." Pars of Jahrom University of Medical Sciences 13, no. 3 (October 1, 2015): 7–13. http://dx.doi.org/10.29252/jmj.13.3.7.

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35

Ramakrishnan, Viswanathan, and Leroy R. Thacker. "Population Attributable Fraction as a Measure of Heritability in Dichotomous Twin Data." Communications in Statistics - Simulation and Computation 41, no. 3 (March 2012): 405–18. http://dx.doi.org/10.1080/03610918.2011.592246.

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36

Freese, Kyle, Lisa M. Bodnar, Maria M. Brooks, and Katherine P. Himes. "754: Population attributable fraction of modifiable risk factors of severe maternal morbidity." American Journal of Obstetrics and Gynecology 218, no. 1 (January 2018): S452. http://dx.doi.org/10.1016/j.ajog.2017.11.285.

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37

Dahlqwist, Elisabeth, Zoltán Kutalik, and Arvid Sjölander. "Using instrumental variables to estimate the attributable fraction." Statistical Methods in Medical Research 29, no. 8 (October 23, 2019): 2063–73. http://dx.doi.org/10.1177/0962280219879175.

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In order to design efficient interventions aimed to improve public health, policy makers need to be provided with reliable information of the health burden of different risk factors. For this purpose, we are interested in the proportion of cases that could be prevented had some harmful exposure been eliminated from the population, i.e. the attributable fraction. The attributable fraction is a causal measure; thus, to estimate the attributable fraction from observational data, we have to make appropriate adjustment for confounding. However, some confounders may be unobserved, or even unknown to the investigator. A possible solution to this problem is to use instrumental variable analysis. In this work, we present how the attributable fraction can be estimated with instrumental variable methods based on the two-stage estimator or the G-estimator. One situation when the problem of unmeasuredconfounding may be particularly severe is when assessing the effect of low educational qualifications on coronary heart disease. By using Mendelian randomization, a special case of instrumental variable analysis, it has been claimed that low educational qualifications is a causal risk factor for coronary heart disease. We use Mendelian randomization to estimate the causal risk ratio and causal odds ratio of low educational qualifications as a risk factor for coronary heart disease with data from the UK Biobank. We compare the two-stage and G-estimator as well as the attributable fraction based on the two estimators. The plausibility of drawing causal conclusion in this analysis is thoroughly discussed and alternative genetic instrumental variables are tested.

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38

Onyango, Dickens O., Marianne A. B. van der Sande, Paul Musingila, Eunice Kinywa, Valarie Opollo, Boaz Oyaro, Emmanuel Nyakeriga, et al. "High HIV prevalence among decedents received by two high-volume mortuaries in Kisumu, western Kenya, 2019." PLOS ONE 16, no. 7 (July 1, 2021): e0253516. http://dx.doi.org/10.1371/journal.pone.0253516.

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Background Accurate data on HIV-related mortality are necessary to evaluate the impact of HIV interventions. In low- and middle-income countries (LMIC), mortality data obtained through civil registration are often of poor quality. Though not commonly conducted, mortuary surveillance is a potential complementary source of data on HIV-associated mortality. Methods During April-July 2019, we assessed HIV prevalence, the attributable fraction among the exposed, and the population attributable fraction among decedents received by two high-volume mortuaries in Kisumu County, Kenya, where HIV prevalence in the adult population was estimated at 18% in 2019 with high ART coverage (76%). Stillbirths were excluded. The two mortuaries receive 70% of deaths notified to the Kisumu East civil death registry; this registry captures 45% of deaths notified in Kisumu County. We conducted hospital chart reviews to determine the HIV status of decedents. Decedents without documented HIV status, including those dead on arrival, were tested using HIV antibody tests or polymerase chain reaction (PCR) consistent with national HIV testing guidelines. Decedents aged less than 15 years were defined as children. We estimated annual county deaths by applying weights that incorporated the study period, coverage of deaths, and mortality rates observed in the study. Results The two mortuaries received a total of 1,004 decedents during the study period, of which 95.1% (955/1004) were available for study; 89.1% (851/955) of available decedents were enrolled of whom 99.4% (846/851) had their HIV status available from medical records and post-mortem testing. The overall population-based, age- and sex-adjusted mortality rate was 12.4 per 1,000 population. The unadjusted HIV prevalence among decedents was 28.5% (95% confidence interval (CI): 25.5–31.6). The age- and sex-adjusted mortality rate in the HIV-infected population (40.7/1000 population) was four times higher than in the HIV-uninfected population (10.2/1000 population). Overall, the attributable fraction among the HIV-exposed was 0.71 (95% CI: 0.66–0.76) while the HIV population attributable fraction was 0.17 (95% CI: 0.14–0.20). In children the attributable fraction among the exposed and population attributable fraction were 0.92 (95% CI: 0.89–0.94) and 0.11 (95% CI: 0.08–0.15), respectively. Conclusions Over one quarter (28.5%) of decedents received by high-volume mortuaries in western Kenya were HIV-positive; overall, HIV was considered the cause of death in 17% of the population (19% of adults and 11% of children). Despite substantial scale-up of HIV services, HIV disease remains a leading cause of death in western Kenya. Despite progress, increased efforts remain necessary to prevent and treat HIV infection and disease.

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39

Smith, Gary. "Estimating the population attributable fraction for schizophrenia when Toxoplasma gondii is assumed absent in human populations." Preventive Veterinary Medicine 117, no. 3-4 (December 2014): 425–35. http://dx.doi.org/10.1016/j.prevetmed.2014.10.009.

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40

Okosun, Ike S., Terrence E. Forrester, Charles N. Rotimi, Babatunde O. Osotimehin, Walinjom F. Munu, and Richard S. Cooper. "Abdominal Adiposity in Six Populations of West African Descent: Prevalence and Population Attributable Fraction of Hypertension." Obesity Research 7, no. 5 (September 1999): 453–62. http://dx.doi.org/10.1002/j.1550-8528.1999.tb00433.x.

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41

Zhao, Wei, Ying Qing Chen, and Li Hsu. "On estimation of time-dependent attributable fraction from population-based case-control studies." Biometrics 73, no. 3 (January 18, 2017): 866–75. http://dx.doi.org/10.1111/biom.12648.

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42

von Cube, Maja, Martin Schumacher, and Martin Wolkewitz. "Causal inference with multistate models—estimands and estimators of the population attributable fraction." Journal of the Royal Statistical Society: Series A (Statistics in Society) 183, no. 4 (July 2019): 1479–500. http://dx.doi.org/10.1111/rssa.12486.

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43

Zapata-Diomedi, Belen, Jan J. Barendregt, and J. Lennert Veerman. "Population attributable fraction: names, types and issues with incorrect interpretation of relative risks." British Journal of Sports Medicine 52, no. 4 (March 8, 2016): 212–13. http://dx.doi.org/10.1136/bjsports-2015-095531.

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44

Cube, Maja, Martin Schumacher, Hein Putter, Jéan‐François Timsit, Cornelis Velde, and Martin Wolkewitz. "The population‐attributable fraction for time‐dependent exposures using dynamic prediction and landmarking." Biometrical Journal 62, no. 3 (June 19, 2019): 583–97. http://dx.doi.org/10.1002/bimj.201800252.

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45

Gruenwald, Talor, Brady A. Seals, Luke D. Knibbs, and H. Dean Hosgood. "Population Attributable Fraction of Gas Stoves and Childhood Asthma in the United States." International Journal of Environmental Research and Public Health 20, no. 1 (December 21, 2022): 75. http://dx.doi.org/10.3390/ijerph20010075.

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Indoor gas stove use for cooking is associated with an increased risk of current asthma among children and is prevalent in 35% of households in the United States (US). The population-level implications of gas cooking are largely unrecognized. We quantified the population attributable fraction (PAF) for gas stove use and current childhood asthma in the US. Effect sizes previously reported by meta-analyses for current asthma (Odds Ratio = 1.34, 95% Confidence Interval (CI) = 1.12–1.57) were utilized in the PAF estimations. The proportion of children (<18 years old) exposed to gas stoves was obtained from the American Housing Survey for the US, and states with available data (n = 9). We found that 12.7% (95% CI = 6.3–19.3%) of current childhood asthma in the US is attributable to gas stove use. The proportion of childhood asthma that could be theoretically prevented if gas stove use was not present (e.g., state-specific PAFs) varied by state (Illinois = 21.1%; California = 20.1%; New York = 18.8%; Massachusetts = 15.4%; Pennsylvania = 13.5%). Our results quantify the US public health burden attributed to gas stove use and childhood asthma. Further research is needed to quantify the burden experienced at the county levels, as well as the impacts of implementing mitigation strategies through intervention studies.

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46

Deng, Changyu, Zan Ding, Liujiu Li, Yanfang Wang, Pi Guo, Shaoyi Yang, Ju Liu, Yue Wang, and Qingying Zhang. "Burden of non-accidental mortality attributable to ambient temperatures: a time series study in a high plateau area of southwest China." BMJ Open 9, no. 2 (February 2019): e024708. http://dx.doi.org/10.1136/bmjopen-2018-024708.

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ObjectiveTo examine the total non-accidental mortality burden attributable to ambient temperatures and assess the effect modification of the burden by specific causes of death and individual characteristics in a high plateau area in southwest China.MethodsUsing daily mortality and meteorological data from 2009 to 2016, we applied a quasi-Poisson model combined with a distributed lag non-linear model to estimate the temperature–mortality association with the assessment of attributable fraction and number. We calculated attributable fractions and deaths with 95% empirical CIs (eCIs), that were due to cold and heat, defined as temperatures below and above the median temperature, and for mild and extreme temperatures, defined by cut-offs at the 2.5th and 97.5th temperature percentiles.ResultsWe analysed 89 467 non-accidental deaths; 4131 were attributable to overall temperatures, with an attributable fraction of 4.75% (95% eCI 2.33% to 6.79%). Most of the mortality burden was caused by cold (4.08%; 0.86% to 7.12%), whereas the burden due to heat was low and non-significant (0.67%; −2.44% to 3.64%). Extreme cold (1.17%; 0.58% to 1.69%) was responsible for 24.6% (ie, 1.17% divided by 4.75%) of the total death burden. In the stratification analyses, attributable risk due to cold was higher for cardiovascular than respiratory disease (6.18% vs 3.50%). We found a trend of risk of increased death due to ambient temperatures with increasing age, with attributable fractions of 1.83%, 2.27% and 6.87% for age ≤64, 65–74 and ≥75 years old, respectively. The cold-related burden was slightly greater for females, farmers, ethnic minorities and non-married individuals than their corresponding categories.ConclusionsMost of the burden of death was attributable to cold, and specific causes and individual characteristics might modify the mortality burden attributable to ambient temperatures. The results may help make preventive measures to confront climate change for susceptible population in this region.

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Maguire, Frances B., Ani S. Movsisyan, Cyllene R. Morris, Arti Parikh-Patel, Theresa H. M. Keegan, and Elisa K. Tong. "Evaluation of Cancer Deaths Attributable to Tobacco in California, 2014-2019." JAMA Network Open 5, no. 12 (December 14, 2022): e2246651. http://dx.doi.org/10.1001/jamanetworkopen.2022.46651.

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ImportanceCalifornia’s tobacco control efforts have been associated with a decrease in cancer mortality, but these estimates are based on smoking prevalence of the general population. Patient-level tobacco use information allows for more precise estimates of the proportion of cancer deaths attributable to smoking.ObjectiveTo calculate the proportion (smoking-attributable fraction) and number (smoking-attributable cancer mortality) of cancer deaths attributable to tobacco use using patient-level data.Design, Setting, and ParticipantsThe smoking-attributable fraction and smoking-attributable cancer mortality were calculated for a retrospective cohort of patients whose cancer was diagnosed from 2014 to 2019 with at least 1 year of follow-up using relative risks from large US prospective studies and patient-level smoking information. Follow-up continued through April 2022. A population-based cohort was identified from the California Cancer Registry. Participants included adults aged 20 years and older with a diagnosis of 1 of the 12 tobacco-related cancers (oral cavity or pharynx, larynx, esophagus, lung, liver, stomach, pancreas, kidney, bladder, colon or rectum, cervix, and acute myeloid leukemia).ExposuresTobacco use defined as current, former, or never.Main Outcomes and MeasuresThe primary outcomes were the smoking-attributable fraction and smoking-attributable cancer mortality for each of the 12 tobacco-related cancers over 2 time periods (2014-2016 vs 2017-2019) and by sex.ResultsAmong 395 459 patients with a tobacco-related cancer, most (285 768 patients [72.3%]) were older than 60 years, the majority (228 054 patients [57.7%]) were non-Hispanic White, 229 188 patients were men (58.0%), and nearly one-half (184 415 patients [46.6%]) had lung or colorectal cancers. Nearly one-half of the deaths (93 764 patients [45.8%]) in the cohort were attributable to tobacco. More than one-half (227 660 patients [57.6%]) of patients had ever used tobacco, and 69 103 patients (17.5%) were current tobacco users, which was higher than the proportion in the general population (11.7%). The overall smoking-attributable fraction of cancer deaths decreased significantly from 47.7% (95% CI, 47.3%-48.0%) in 2014 to 2016 to 44.8% (95% CI, 44.5%-45.1%) in 2017 to 2019, and this decrease was seen for both men and women. The overall smoking-attributable cancer mortality decreased by 10.2%.Conclusions and RelevanceCalifornia still has a substantial burden of tobacco use and associated cancer. The proportion of cancer deaths associated with tobacco use was almost double what was previously estimated. There was a modest but significant decline in this proportion for overall tobacco-associated cancers, especially for women.

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Pirani, Narges, and Farzad Khiavi. "Population Attributable Fraction for Cardiovascular Diseases Risk Factors in Selected Countries: A comparative study." Materia Socio Medica 29, no. 1 (2017): 35. http://dx.doi.org/10.5455/msm.2017.29.35-39.

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Chan, Sze Ling, Chen Suo, Kee Seng Chia, and Yik Ying Teo. "The population attributable fraction as a measure of the impact of warfarin pharmacogenetic testing." Pharmacogenomics 13, no. 11 (August 2012): 1247–56. http://dx.doi.org/10.2217/pgs.12.104.

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Menzler, Susanne, Georges Piller, Martha Gruson, Angelika Schaffrath Rosario, H. Erich Wichmann, and Lothar Kreienbrock. "POPULATION ATTRIBUTABLE FRACTION FOR LUNG CANCER DUE TO RESIDENTIAL RADON IN SWITZERLAND AND GERMANY." Health Physics 95, no. 2 (August 2008): 179–89. http://dx.doi.org/10.1097/01.hp.0000309769.55126.03.

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