21 results on '"Kopasker, Daniel"'
Search Results
2. Incidence of diabetes after SARS-CoV-2 infection in England and the implications of COVID-19 vaccination: a retrospective cohort study of 16 million people
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Al Arab, Marwa, Almaghrabi, Fatima, Andrews, Colm, Badrick, Ellena, Baz, Sarah, Beckford, Chelsea, Berman, Samantha, Bolton, Tom, Booth, Charlotte, Bowyer, Ruth, Boyd, Andy, Bridger-Staatz, Charis, Brophy, Sinead, Campbell, Archie, Campbell, Kirsteen C, Carnemolla, Alisia, Carpentieri, Jd, Cezard, Genevieve, Chaturvedi, Nishi, Cheetham, Nathan, Costello, Ruth, Cowling, Thomas, Crane, Matthew, Cuitun Coronado, Jose Ignacio, Curtis, Helen, Denaxas, Spiros, Denholm, Rachel, Di Gessa, Giorgio, Dobson, Richard, Douglas, Ian, Evans, Katharine M, Fang, Chao, Ferreira, Vanessa, Finnigan, Lucy, Fisher, Louis, Flaig, Robin, Folarin, Amos, Forbes, Harriet, Foster, Diane, Fox, Laura, Freydin, Maxim, Garcia, Paz, Gibson, Andy, Glen, Fiona, Goldacre, Ben, Goncalves Soares, Ana, Greaves, Felix, Green, Amelia, Green, Mark, Green, Michael, Griffith, Gareth, Hamill Howes, Lee, Hamilton, Olivia, Herbet, Annie, Herrett, Emily, Hopcroft, Lisa, Horne, Elsie, Hou, Bo, Hughes, Alun, Hulme, William, Huntley, Lizzie, Ip, Samantha, Jacques, Wels, Jezzard, Peter, Jones, Louise, Kanagaratnam, Arun, Karthikeyan Suseeladevi, Arun, Katikireddi, Vittal, Kellas, John, Kennedy, Jonathan I, Kibble, Milla, Knight, Rochelle, Knueppel, Anika, Kopasker, Daniel, Kromydas, Theocharis, Kwong, Alex, Langan, Sinead, Lemanska, Agnieszka, Lukaschuk, Elena, Mackenna, Brain, Macleod, John, Maddock, Jane, Mahalingasivam, Viyaasan, Mansfield, Kathryn, McArdle, Fintan, McCartney, Daniel, McEachan, Rosie, McElroy, Eoin, McLachlan, Stela, Mitchell, Ruth, Moltrecht, Bettina, Morley, Jess, Nab, Linda, Neubauer, Stefan, Nigrelli, Lidia, North, Teri, Northstone, Kate, Oakley, Jacqui, Palmer, Tom, Park, Chloe, Parker, Michael, Parsons, Sam, Patalay, Praveetha, Patel, Kishan, Perez-Reche, Francisco, Piechnik, Stefan, Piehlmaier, Dominik, Ploubidis, George, Rafeti, Elena, Raman, Betty, Ranjan, Yatharth, Rapala, Alicja, Rhead, Rebecca, Roberts, Amy, Sampri, Alexia, Sanders, Zeena-Britt, Santorelli, Gillian, Saunders, Laura C, Shah, Anoop, Shah, Syed Ahmar, Sharp, Steve, Shaw, Richard, Sheard, Laura, Sheikh, Aziz, Silverwood, Richard, Smeeth, Liam, Smith, Stephen, Stafford, Jean, Steptoe, Andrew, Sterne, Jonathan, Steves, Claire, Stewart, Callum, Taylor, Kurt, Tazare, John, Teece, Lucy, Thomas, Richard, Thompson, Ellen, Tilling, Kate, Timpson, Nicholas, Tomlinson, Laurie, Toms, Renin, Tunnicliffe, Elizabeth, Turner, Emma L, Walker, Alex, Walker, Venexia, Walter, Scott, Wang, Kevin, Wei, Yinghui, Whitehorn, Rebecca, Wielgoszewska, Bozena, Wild, James M, Willan, Kathryn, Willans, Robert, Williams, Dylan, Wong, Andrew, Wood, Angela, Woodward, Hannah, Wright, John, Yang, Tiffany, Zaninotto, Paola, Zheng, Bang, Zhu, Jingmin, Eastwood, Sophie, Horne, Elsie M F, Massey, Jon, Hopcroft, Lisa E M, Cuitun Coronado, Jose, Davy, Simon, Dillingham, Iain, Morton, Caroline, and Sterne, Jonathan A C
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- 2024
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3. Evaluating the influence of taxation and social security policies on psychological distress: A microsimulation study of the UK during the COVID-19 economic crisis
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Kopasker, Daniel, Bronka, Patryk, Thomson, Rachel M., Khodygo, Vladimir, Kromydas, Theocharis, Meier, Petra, Heppenstall, Alison, Bambra, Clare, Lomax, Nik, Craig, Peter, Richiardi, Matteo, and Katikireddi, Srinivasa Vittal
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- 2024
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4. Short-term impacts of Universal Basic Income on population mental health inequalities in the UK: A microsimulation modelling study
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Thomson, Rachel M., Kopasker, Daniel, Bronka, Patryk, Richiardi, Matteo, Khodygo, Vladimir, Baxter, Andrew J., Igelström, Erik, Pearce, Anna, Leyland, Alastair H., and Katikireddi, S. Vittal
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Mental health ,Personal income ,Employee benefits ,Gender equality ,Child care ,Simulation methods ,Mental illness ,Employee benefits ,Biological sciences - Abstract
Background Population mental health in the United Kingdom (UK) has deteriorated, alongside worsening socioeconomic conditions, over the last decade. Policies such as Universal Basic Income (UBI) have been suggested as an alternative economic approach to improve population mental health and reduce health inequalities. UBI may improve mental health (MH), but to our knowledge, no studies have trialled or modelled UBI in whole populations. We aimed to estimate the short-term effects of introducing UBI on mental health in the UK working-age population. Methods and findings Adults aged 25 to 64 years were simulated across a 4-year period from 2022 to 2026 with the SimPaths microsimulation model, which models the effects of UK tax/benefit policies on mental health via income, poverty, and employment transitions. Data from the nationally representative UK Household Longitudinal Study were used to generate the simulated population (n = 25,000) and causal effect estimates. Three counterfactual UBI scenarios were modelled from 2023: 'Partial' (value equivalent to existing benefits), 'Full' (equivalent to the UK Minimum Income Standard), and 'Full+' (retaining means-tested benefits for disability, housing, and childcare). Likely common mental disorder (CMD) was measured using the General Health Questionnaire (GHQ-12, score [greater than or equal to]4). Relative and slope indices of inequality were calculated, and outcomes stratified by gender, age, education, and household structure. Simulations were run 1,000 times to generate 95% uncertainty intervals (UIs). Sensitivity analyses relaxed SimPaths assumptions about reduced employment resulting from Full/Full+ UBI. Partial UBI had little impact on poverty, employment, or mental health. Full UBI scenarios practically eradicated poverty but decreased employment (for Full+ from 78.9% [95% UI 77.9, 79.9] to 74.1% [95% UI 72.6, 75.4]). Full+ UBI increased absolute CMD prevalence by 0.38% (percentage points; 95% UI 0.13, 0.69) in 2023, equivalent to 157,951 additional CMD cases (95% UI 54,036, 286,805); effects were largest for men (0.63% [95% UI 0.31, 1.01]) and those with children (0.64% [95% UI 0.18, 1.14]). In our sensitivity analysis assuming minimal UBI-related employment impacts, CMD prevalence instead fell by 0.27% (95% UI -0.49, -0.05), a reduction of 112,228 cases (95% UI 20,783, 203,673); effects were largest for women (-0.32% [95% UI -0.65, 0.00]), those without children (-0.40% [95% UI -0.68, -0.15]), and those with least education (-0.42% [95% UI -0.97, 0.15]). There was no effect on educational mental health inequalities in any scenario, and effects waned by 2026. The main limitations of our methods are the model's short time horizon and focus on pathways from UBI to mental health solely via income, poverty, and employment, as well as the inability to integrate macroeconomic consequences of UBI; future iterations of the model will address these limitations. Conclusions UBI has potential to improve short-term population mental health by reducing poverty, particularly for women, but impacts are highly dependent on whether individuals choose to remain in employment following its introduction. Future research modelling additional causal pathways between UBI and mental health would be beneficial., Author(s): Rachel M. Thomson 1,*, Daniel Kopasker 1, Patryk Bronka 2, Matteo Richiardi 2, Vladimir Khodygo 1, Andrew J. Baxter 1, Erik Igelström 1, Anna Pearce 1, Alastair H. Leyland [...]
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- 2024
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5. Ethnic differences in the indirect effects of the COVID-19 pandemic on clinical monitoring and hospitalisations for non-COVID conditions in England: a population-based, observational cohort study using the OpenSAFELY platform
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Chaturvedi, Nishi, Park, Chloe, Carnemolla, Alisia, Williams, Dylan, Knueppel, Anika, Boyd, Andy, Turner, Emma L., Evans, Katharine M., Thomas, Richard, Berman, Samantha, McLachlan, Stela, Crane, Matthew, Whitehorn, Rebecca, Oakley, Jacqui, Foster, Diane, Woodward, Hannah, Campbell, Kirsteen C., Timpson, Nicholas, Kwong, Alex, Soares, Ana Goncalves, Griffith, Gareth, Toms, Renin, Jones, Louise, Annie, Herbert, Mitchell, Ruth, Palmer, Tom, Sterne, Jonathan, Walker, Venexia, Huntley, Lizzie, Fox, Laura, Denholm, Rachel, Knight, Rochelle, Northstone, Kate, Kanagaratnam, Arun, Horne, Elsie, Forbes, Harriet, North, Teri, Taylor, Kurt, Arab, Marwa A.L., Walker, Scott, Coronado, Jose I.C., Karthikeyan, Arun S., Ploubidis, George, Moltrecht, Bettina, Booth, Charlotte, Parsons, Sam, Wielgoszewska, Bozena, Bridger-Staatz, Charis, Steves, Claire, Thompson, Ellen, Garcia, Paz, Cheetham, Nathan, Bowyer, Ruth, Freydin, Maxim, Roberts, Amy, Goldacre, Ben, Walker, Alex, Morley, Jess, Hulme, William, Nab, Linda, Fisher, Louis, MacKenna, Brian, Andrews, Colm, Curtis, Helen, Hopcroft, Lisa, Green, Amelia, Patalay, Praveetha, Maddock, Jane, Patel, Kishan, Stafford, Jean, Jacques, Wels, Tilling, Kate, Macleod, John, McElroy, Eoin, Shah, Anoop, Silverwood, Richard, Denaxas, Spiros, Flaig, Robin, McCartney, Daniel, Campbell, Archie, Tomlinson, Laurie, Tazare, John, Zheng, Bang, Smeeth, Liam, Herrett, Emily, Cowling, Thomas, Mansfield, Kate, Costello, Ruth E., Wang, Kevin, Mansfield, Kathryn, Mahalingasivam, Viyaasan, Douglas, Ian, Langan, Sinead, Brophy, Sinead, Parker, Michael, Kennedy, Jonathan, McEachan, Rosie, Wright, John, Willan, Kathryn, Badrick, Ellena, Santorelli, Gillian, Yang, Tiffany, Hou, Bo, Steptoe, Andrew, Giorgio, Di Gessa, Zhu, Jingmin, Zaninotto, Paola, Wood, Angela, Cezard, Genevieve, Ip, Samantha, Bolton, Tom, Sampri, Alexia, Rafeti, Elena, Almaghrabi, Fatima, Sheikh, Aziz, Shah, Syed A., Katikireddi, Vittal, Shaw, Richard, Hamilton, Olivia, Green, Michael, Kromydas, Theocharis, Kopasker, Daniel, Greaves, Felix, Willans, Robert, Glen, Fiona, Sharp, Steve, Hughes, Alun, Wong, Andrew, Howes, Lee Hamill, Rapala, Alicja, Nigrelli, Lidia, McArdle, Fintan, Beckford, Chelsea, Raman, Betty, Dobson, Richard, Folarin, Amos, Stewart, Callum, Ranjan, Yatharth, Carpentieri, Jd, Sheard, Laura, Fang, Chao, Baz, Sarah, Gibson, Andy, Kellas, John, Neubauer, Stefan, Piechnik, Stefan, Lukaschuk, Elena, Saunders, Laura C., Wild, James M., Smith, Stephen, Jezzard, Peter, Tunnicliffe, Elizabeth, Sanders, Zeena-Britt, Finnigan, Lucy, Ferreira, Vanessa, Green, Mark, Rhead, Rebecca, Kibble, Milla, Wei, Yinghui, Lemanska, Agnieszka, Perez-Reche, Francisco, Piehlmaier, Dominik, Teece, Lucy, Parker, Edward, Walker, Alex J., Inglesby, Peter, Curtis, Helen J., Morton, Caroline E., Morley, Jessica, Mehrkar, Amir, Bacon, Sebastian C.J., Hickman, George, Croker, Richard, Evans, David, Ward, Tom, DeVito, Nicholas J., Green, Amelia C.A., Massey, Jon, Smith, Rebecca M., Hulme, William J., Davy, Simon, Andrews, Colm D., Hopcroft, Lisa E.M., Drysdale, Henry, Dillingham, Iain, Park, Robin Y., Higgins, Rose, Cunningham, Christine, Wiedemann, Milan, Maude, Steven, Macdonald, Orla, Butler-Cole, Ben F.C., O'Dwyer, Thomas, Stables, Catherine L., Wood, Christopher, Brown, Andrew D., Speed, Victoria, Bridges, Lucy, Schaffer, Andrea L., Walters, Caroline E., Rentsch, Christopher T., Bhaskaran, Krishnan, Schultze, Anna, Williamson, Elizabeth J., McDonald, Helen I., Tomlinson, Laurie A., Mathur, Rohini, Eggo, Rosalind M., Wing, Kevin, Wong, Angel Y.S., Grieve, Richard, Grint, Daniel J., Mansfield, Kathryn E., Douglas, Ian J., Evans, Stephen J.W., Walker, Jemma L., Cowling, Thomas E., Herrett, Emily L., Parker, Edward P.K., Bates, Christopher, Cockburn, Jonathan, Parry, John, Hester, Frank, Harper, Sam, O'Hanlon, Shaun, Eavis, Alex, Jarvis, Richard, Avramov, Dima, Griffiths, Paul, Fowles, Aaron, Parkes, Nasreen, Nicholson, Brian, Perera, Rafael, Harrison, David, Khunti, Kamlesh, Sterne, Jonathan AC., Quint, Jennifer, Henderson, Alasdair D., Carreira, Helena, Bidulka, Patrick, Warren-Gash, Charlotte, Hayes, Joseph F., Quint, Jennifer K., Katikireddi, Srinivasa Vittal, and Langan, Sinéad M.
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- 2023
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6. The effects of minimum unit pricing for alcohol on food purchases: Evaluation of a natural experiment
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Kopasker, Daniel, Whybrow, Stephen, McKenzie, Lynda, McNamee, Paul, and Ludbrook, Anne
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- 2022
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7. Public-sector resource allocation since the financial crisis
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Elliott, Robert, Kopasker, Daniel, and Skåtun, Diane
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- 2021
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8. The effects of economic insecurity on health and wellbeing
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Kopasker, Daniel
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338.4 ,Economic security ,Security (Psychology) ,Health ,Well-being - Abstract
This thesis examines the relationship between concerns regarding future economic misfortune and psychological wellbeing. Economic concerns are often highlighted as a source of distress within populations. The health outcomes resulting from this distress are likely to have economic impacts associated with lost productivity, and the cost of health and social care. The future orientated and subjective nature of this economic insecurity leads to these negative effects being potentially hidden. Through empirical analysis of longitudinal data from the UK, this thesis examines (i) trends in economic insecurity and assesses the potential for more objective measurement, (ii) how the likelihood of experiencing economic insecurity is related to the characteristics of employment relationships, and (iii) estimates the magnitude of the causal effect of economic insecurity on psychological wellbeing. The analysis suggests that despite broadly downward trends of economic insecurity in Great Britain, around one in ten of the working-age population are affected in any single year. Economic insecurity related to work appears to affect the largest proportion of the population. The likelihood of experiencing this form of insecurity is higher amongst temporary employees, those employed within industries that face international competition, and increases with firm tenure. Furthermore, this form of insecurity is shown to negatively impact on mental health, particularly amongst males. These findings have implications for both productivity and population health. The analysis lays a foundation for cross-country analysis of how labour market institutions, employer characteristics, and management practices limit the negative impact of economic insecurity on health and wellbeing.
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- 2017
9. Home working and social and mental wellbeing at different stages of the COVID-19 pandemic in the UK: Evidence from 7 longitudinal population surveys
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Wels, Jacques, Wielgoszewska, Bozena, Moltrecht, Bettina, Booth, Charlotte, Green, Michael J., Hamilton, Olivia KL, Demou, Evangelia, Di Gessa, Giorgio, Huggins, Charlotte, Zhu, Jingmin, Santorelli, Gillian, Silverwood, Richard J., Kopasker, Daniel, Shaw, Richard J., Hughes, Alun, Patalay, Praveetha, Steves, Claire, Chaturvedi, Nishi, Porteous, David J., Rhead, Rebecca, Katikireddi, Srinivasa Vittal, and Ploubidis, George B.
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Biological sciences - Abstract
Background Home working has increased since the Coronavirus Disease 2019 (COVID-19) pandemic's onset with concerns that it may have adverse health implications. We assessed the association between home working and social and mental wellbeing among the employed population aged 16 to 66 through harmonised analyses of 7 UK longitudinal studies. Methods and findings We estimated associations between home working and measures of psychological distress, low life satisfaction, poor self-rated health, low social contact, and loneliness across 3 different stages of the pandemic (T1 = April to June 2020 -first lockdown, T2 = July to October 2020 -eased restrictions, T3 = November 2020 to March 2021 -second lockdown) using modified Poisson regression and meta-analyses to pool results across studies. We successively adjusted the model for sociodemographic characteristics (e.g., age, sex), job characteristics (e.g., sector of activity, pre-pandemic home working propensities), and pre-pandemic health. Among respectively 10,367, 11,585, and 12,179 participants at T1, T2, and T3, we found higher rates of home working at T1 and T3 compared with T2, reflecting lockdown periods. Home working was not associated with psychological distress at T1 (RR = 0.92, 95% CI = 0.79 to 1.08) or T2 (RR = 0.99, 95% CI = 0.88 to 1.11), but a detrimental association was found with psychological distress at T3 (RR = 1.17, 95% CI = 1.05 to 1.30). Study limitations include the fact that pre-pandemic home working propensities were derived from external sources, no information was collected on home working dosage and possible reverse association between change in wellbeing and home working likelihood. Conclusions No clear evidence of an association between home working and mental wellbeing was found, apart from greater risk of psychological distress during the second lockdown, but differences across subgroups (e.g., by sex or level of education) may exist. Longer term shifts to home working might not have adverse impacts on population wellbeing in the absence of pandemic restrictions but further monitoring of health inequalities is required., Author(s): Jacques Wels 1,2,*, Bozena Wielgoszewska 3, Bettina Moltrecht 3, Charlotte Booth 3, Michael J. Green 4, Olivia KL Hamilton 4, Evangelia Demou 4, Giorgio Di Gessa 5, Charlotte Huggins [...]
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- 2023
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10. Skill mix and patient outcomes: A multi-country analysis of heart disease and breast cancer patients
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Kopasker, Daniel, Islam, M. Kamrul, Gibson, Jonathan, Lau, Yiu-Shing, Sutton, Matt, Askildsen, Jan Erik, Bond, Christine, and Elliott, Robert
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- 2020
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11. An Investigation of the Stability of Patients’ Treatment Preferences Over the Course of a Clinical Trial
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Allanson, Paul F., Brown, Eric A., Kopasker, Daniel, and Kwiatkowski, Andrzej
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- 2020
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12. Economic insecurity: A socioeconomic determinant of mental health
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Kopasker, Daniel, Montagna, Catia, and Bender, Keith A.
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- 2018
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13. Causal Assessment of Income Inequality on Self‐Rated Health and All‐Cause Mortality: A Systematic Review and Meta‐Analysis.
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SHIMONOVICH, MICHAL, CAMPBELL, MHAIRI, THOMSON, RACHEL M., BROADBENT, PHILIP, WELLS, VALERIE, KOPASKER, DANIEL, M c CARTNEY, GERRY, THOMSON, HILARY, PEARCE, ANNA, and KATIKIREDDI, S. VITTAL
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SELF-evaluation ,MEDICAL information storage & retrieval systems ,INCOME ,HEALTH status indicators ,RESEARCH funding ,SOCIOECONOMIC factors ,CAUSES of death ,META-analysis ,DESCRIPTIVE statistics ,SYSTEMATIC reviews ,MEDLINE ,ODDS ratio ,CAUSALITY (Physics) ,HEALTH equity ,CONFIDENCE intervals - Abstract
Policy PointsIncome is thought to impact a broad range of health outcomes. However, whether income inequality (how unequal the distribution of income is in a population) has an additional impact on health is extensively debated.Studies that use multilevel data, which have recently increased in popularity, are necessary to separate the contextual effects of income inequality on health from the effects of individual income on health.Our systematic review found only small associations between income inequality and poor self‐rated health and all‐cause mortality. The available evidence does not suggest causality, although it remains methodologically flawed and limited, with very few studies using natural experimental approaches or examining income inequality at the national level. Context: Whether income inequality has a direct effect on health or is only associated because of the effect of individual income has long been debated. We aimed to understand the association between income inequality and self‐rated health (SRH) and all‐cause mortality (mortality) and assess if these relationships are likely to be causal. Methods: We searched Medline, ISI Web of Science, Embase, and EconLit (PROSPERO: CRD42021252791) for studies considering income inequality and SRH or mortality using multilevel data and adjusting for individual‐level socioeconomic position. We calculated pooled odds ratios (ORs) for poor SRH and relative risk ratios (RRs) for mortality from random‐effects meta‐analyses. We critically appraised included studies using the Risk of Bias in Nonrandomized Studies – of Interventions tool. We assessed certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation framework and causality using Bradford Hill (BH) viewpoints. Findings: The primary meta‐analyses included 2,916,576 participants in 38 cross‐sectional studies assessing SRH and 10,727,470 participants in 14 cohort studies of mortality. Per 0.05‐unit increase in the Gini coefficient, a measure of income inequality, the ORs and RRs (95% confidence intervals) for SRH and mortality were 1.06 (1.03‐1.08) and 1.02 (1.00‐1.04), respectively. A total of 63.2% of SRH and 50.0% of mortality studies were at serious risk of bias (RoB), resulting in very low and low certainty ratings, respectively. For SRH and mortality, we did not identify relevant evidence to assess the specificity or, for SRH only, the experiment BH viewpoints; evidence for strength of association and dose–response gradient was inconclusive because of the high RoB; we found evidence in support of temporality and plausibility. Conclusions: Increased income inequality is only marginally associated with SRH and mortality, but the current evidence base is too methodologically limited to support a causal relationship. To address the gaps we identified, future research should focus on income inequality measured at the national level and addressing confounding with natural experiment approaches. [ABSTRACT FROM AUTHOR]
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- 2024
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14. Firm size distribution and employment fluctuations: Theory and evidence
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Görg, Holger, Henze, Philipp, Jienwatcharamongkhol, Viroj, Kopasker, Daniel, Molana, Hassan, Montagna, Catia, and Sjöholm, Fredrik
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- 2017
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15. Microsimulation as a flexible tool to evaluate policies and their impact on socioeconomic inequalities in health
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Kopasker, Daniel, Katikireddi, Srinivasa Vittal, Santos, João Vasco, Richiardi, Matteo, Bronka, Patryk, Rostila, Mikael, Cecchini, Michele, Ali, Shehzad, Emmert-Fees, Karl, Bambra, Clare, Hoven, Hanno, Backhaus, Insa, Balaj, Mirza, and Eikemo, Terje Andreas
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- 2023
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16. To what extent does income explain the effect of unemployment on mental health? Mediation analysis in the UK Household Longitudinal Study.
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Thomson, Rachel M., Kopasker, Daniel, Leyland, Alastair, Pearce, Anna, and Katikireddi, S. Vittal
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UNEMPLOYMENT , *ECONOMIC status , *MENTAL health , *FACTOR analysis , *QUESTIONNAIRES , *DESCRIPTIVE statistics , *DISEASE prevalence , *ODDS ratio , *POVERTY , *LONGITUDINAL method - Abstract
Background: Employment and income are important determinants of mental health (MH), but the extent that unemployment effects are mediated by reduced income is unclear. We estimated the total effect (TE) of unemployment on MH and the controlled direct effect (CDE) not acting via income. Methods: We included adults 25–64 years from nine waves of the UK Household Longitudinal Study (n = 45 497/ obs = 202 297). Unemployment was defined as not being in paid employment; common mental disorder (CMD) was defined as General Health Questionnaire-12 score ≥4. We conducted causal mediation analysis using double-robust marginal structural modelling, estimating odds ratios (OR) and absolute differences for effects of unemployment on CMD in the same year, before (TE) and after (CDE) blocking the income pathway. We calculated percentage mediation by income, with bootstrapped standard errors. Results: The TE of unemployment on CMD risk was OR 1.66 (95% CI 1.57–1.76), with 7.09% (6.21–7.97) absolute difference in prevalence; equivalent CDEs were OR 1.55 (1.46–1.66) and 6.08% (5.13–7.03). Income mediated 14.22% (8.04–20.40) of the TE. Percentage mediation was higher for job losses [15.10% (6.81–23.39)] than gains [8.77% (0.36–17.19)]; it was lowest for those 25–40 years [7.99% (−2.57 to 18.51)] and in poverty [2.63% (−2.22 to 7.49)]. Conclusions: A high proportion of the short-term effect of unemployment on MH is not explained by income, particularly for younger people and those in poverty. Population attributable fractions suggested 16.49% of CMD burden was due to unemployment, with 13.90% directly attributable to job loss rather than resultant income changes. Similar analytical approaches could explore how this differs across contexts, by other factors, and consider longer-term effects. [ABSTRACT FROM AUTHOR]
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- 2023
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17. Systems science methods in public health: what can they contribute to our understanding of and response to the cost-of-living crisis?
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Höhn, Andreas, Stokes, Jonathan, Pollack, Roxana, Boyd, Jennifer, Chueca Del Cerro, Cristina, Elsenbroich, Corinna, Heppenstall, Alison, Hjelmskog, Annika, Inyang, Elizabeth, Kopasker, Daniel, Sonthalia, Shreya, Thomson, Rachel M., Zia, Kashif, Vittal Katikireddi, Srinivasa, and Meier, Petra
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RESEARCH ,SOCIAL determinants of health ,RESEARCH methodology ,COST of living ,SOCIAL theory ,PUBLIC health ,SYSTEM analysis ,FORECASTING ,RESEARCH funding ,PSYCHOLOGICAL adaptation ,ECONOMICS - Published
- 2023
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18. Effects of poverty on mental health in the UK working-age population: causal analyses of the UK Household Longitudinal Study.
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Thomson, Rachel M, Kopasker, Daniel, Leyland, Alastair, Pearce, Anna, and Katikireddi, S Vittal
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STATISTICAL models , *MENTAL health , *INCOME , *POVERTY , *LONGITUDINAL method , *ODDS ratio - Abstract
Background Addressing poverty through taxation or welfare policies is likely important for public mental health; however, few studies assess poverty's effects using causal epidemiology. We estimated the effect of poverty on mental health. Methods We used data on working-age adults (25–64 years) from nine waves of the UK Household Longitudinal Survey (2009–19; n = 45 497/observations = 202 207 following multiple imputation). We defined poverty as a household equivalized income <60% median, and the outcome likely common mental disorder (CMD) as a General Health Questionnaire-12 score ≥4. We used double-robust marginal structural modelling with inverse probability of treatment weights to generate absolute and relative effects. Supplementary analyses separated transitions into/out of poverty, and stratified by gender, education, and age. We quantified potential impact through population attributable fractions (PAFs) with bootstrapped standard errors. Results Good balance of confounders was achieved between exposure groups, with 45 830 observations (22.65%) reporting poverty. The absolute effect of poverty on CMD prevalence was 2.15% [%-point change; 95% confidence interval (CI) 1.45, 2.84]; prevalence in those unexposed was 20.59% (95% CI 20.29%, 20.88%), and the odds ratio was 1.17 (95% CI 1.12, 1.24). There was a larger absolute effect for transitions into poverty [2.46% (95% CI 1.56, 3.36)] than transitions out of poverty [–1.49% (95% CI –2.46, –0.53)]. Effects were also slightly larger in women than men [2.34% (95% CI 1.41, 3.26) versus 1.73% (95% CI 0.72, 2.74)]. The PAF for moving into poverty was 6.34% (95% CI 4.23, 8.45). Conclusions PAFs derived from our causal estimates suggest moves into poverty account for just over 6% of the burden of CMD in the UK working-age population, with larger effects in women. [ABSTRACT FROM AUTHOR]
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- 2023
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19. Firm size distribution and employment fluctuations: Theory and evidence
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Görg, Holger, Henze, Philipp, Jienwatcharamongkhol, Viroj, Kopasker, Daniel, Molana, Hassan, Montagna, Catia, and Sjöholm, Fredrik
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Employment ,L20 ,ddc:330 ,Fluctuations ,E23 ,E20 ,Firm distribution ,Firm size - Abstract
This paper studies the effect of the firm-size distribution on the relationship between employment and output. We construct a theoretical model, which predicts that changes in demand for industry output have larger effects on employment in industries characterised by a distribution that is more skewed towards smaller firms. Industry-specific shape parameters of the firm size distributions are estimated using firm-level data from Germany, Sweden and the UK, and used to augment a relationship between industry-level employment and output. Our empirical results align with the predictions of the theory and confirm that the size distribution of firms is an important determinant of the relationship between changes in output and employment.
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- 2016
20. Health Equity and Its Economic Determinants (HEED): protocol for a pan-European microsimulation model for health impacts of income and social security policies
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Katikireddi, Srinivasa Vittal, Kopasker, Daniel, Pearce, Anna, Leyland, Alastair H., Rostila, Mikael, and Richiardi, Matteo
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General Medicine - Abstract
IntroductionGovernment policies on taxation and social security are important determinants of population health outcomes and health inequalities. However, there is a shortage of evidence to inform policymakers of the health consequences of such policies. The Health Equity and Its Economic Determinants project aims to assess the potential impacts of different taxation and social security policies across Europe on population health and health inequalities using a computer-based simulation that provides projections over multiple health domains.Methods and analysisIn the first phase, key input parameters for the model will be estimated using estimation techniques that control for the effects of prior exposure on time-varying confounders and mediators (g-methods). The second phase will involve developing and validating the microsimulation model for the UK. Policy proposals, developed with policymakers, will be simulated in the third phase to investigate the impacts of income tax and social security changes on population health and health inequalities. In the final phase, the microsimulation model will be extended across other European countries.Ethics and disseminationThis project will use deidentified secondary data for which ethical approval and consents were received by the original data collectors. No further ethical approval will be required for our main analytical datasets. Dissemination plans include academic publications, conference presentations, accessible policy briefings, mass media engagement and a project website. Both the syntax and the underlying synthetic data for the HEED microsimulation model will be made freely available through GitHub and the project website.
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21. Longitudinal study of the effects of price and promotion incentives on purchases of unhealthy foods: evidence for restricting food promotions.
- Author
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Kopasker D, Ejebu OZ, Norwood P, and Ludbrook A
- Abstract
Objectives: Taxes and restrictions on promotions have recently been proposed as policy instruments to reduce consumption of unhealthy foods. The objective of this study is to add to the limited evidence on the comparative effectiveness of price changes, price promotions and volume promotions in changing household purchasing of unhealthy foods, using biscuits, crisps and savoury snacks as examples., Design: Longitudinal regression analysis of consumer microdata., Setting: Secondary data on itemised household purchases of biscuits, crisps and savoury snacks from 2006 to 2012., Participants: Sample of 3024 households in Scotland., Main Outcome Measures: Changes in the number of calories (kcal) purchased in the product category by a household caused by changes in the price for the product category, any temporary in-store price promotions and any temporary in-store volume promotions. Changes are measured at the mean, median, 25th percentile and 75th percentile of the household purchasing distribution for the full sample. Subgroup analyses were conducted by household income band and for households with and without children., Results: Between product categories, the scale of purchasing response to incentives varies significantly. Within product categories, the mean calories (kcal) purchased by a household are more responsive to any volume promotion than to price or any price promotion for all product categories. As the volume of items purchased increases, households are less responsive to price, less responsive to any volume promotion and more responsive to any price promotion. Statistically significant differences are observed between household income groups in their response to price and promotion incentives within the biscuits category only. In cases where statistically significant differences are observed, households with children are more responsive to promotion and price incentives than households without children., Conclusions: For all product categories analysed (biscuits, crisps and savoury snacks), household purchasing is most responsive to any volume promotion. Therefore, assuming the response of consumers to incentives remains constant following legislation, the most effective policy instrument to reduce the calorie intake from these products may be a ban on volume promotions., Competing Interests: Competing interests: This work was supported by the Scottish Government Rural and Environment Science and Analytical Services division and the Chief Scientist Office of the Scottish Government Health and Social Care Directorates., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)
- Published
- 2022
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