Development and application of the Demands for Population Health Interventions (Depth) framework for categorising the agentic demands of population health interventions

Kate Garrott, David Ogilvie1, Jenna Panter1, Mark Petticrew2, Amanda Sowden3, Catrin Pedder Penn-Jones, Campbell Foubister1, Emma Lawlor1, Erika Ikeda, R. M. Patterson, Dolly van Tulleken1, Roxanne Armstrong-Moore, Gokulan Vethanayakam4, Lorna Bo4, Martin White, Jean Adams
1MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
2Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine, London, UK
3Centre for Reviews and Dissemination, University of York, York, UK
4School of Clinical Medicine, University of Cambridge, Cambridge, UK

Tóm tắt

Abstract Background The ‘agentic demand’ of population health interventions (PHIs) refers to the capacity, resources and freedom to act that interventions demand of their recipients to benefit, which have a socio-economical pattern. Highly agentic interventions, e.g. information campaigns, rely on recipients noticing and responding to the intervention and thus might affect intervention effectiveness and equity. The absence of an adequate framework to classify agentic demands limits the fields’ ability to systematically explore these associations. Methods We systematically developed the Demands for Population Health Interventions (Depth) framework using an iterative approach: (1) developing the Depth framework by systematically identifying examples of PHIs aiming to promote healthier diets and physical activity, coding of intervention actors and actions and synthesising the data to develop the framework; (2) testing the Depth framework in online workshops with academic and policy experts and a quantitative reliability assessment. We applied the final framework in a proof-of-concept review, extracting studies from three existing equity-focused systematic reviews on framework category, overall effectiveness and differential socioeconomic effects and visualised the findings in harvest plots. Results The Depth framework identifies three constructs influencing agentic demand: exposure — initial contact with intervention (two levels), mechanism of action — how the intervention enables or discourages behaviour (five levels) and engagement — recipient response (two levels). When combined, these constructs form a matrix of 20 possible classifications. In the proof-of-concept review, we classified all components of 31 interventions according to the Depth framework. Intervention components were concentrated in a small number of Depth classifications; Depth classification appeared to be related to intervention equity but not effectiveness. Conclusions This framework holds potential for future research, policy and practice, facilitating the design, selection and evaluation of interventions and evidence synthesis.

Từ khóa


Tài liệu tham khảo

Rippin H, Hutchinson J, Greenwood DC, Jewell J, Breda J, Martin A, et al. Inequalities in education and national income are associated with poorer diet: pooled analysis of individual participant data across 12 European countries. PLoS ONE. 2020;15(5):e0232447.

Chastin SFM, Van Cauwenberg J, Maenhout L, Cardon G, Lambert E, Van Dyck D. Inequality in physical activity, global trends by income inequality and gender in adults. Int J Behav Nutr Phys Act. 2020;17:1–8.

Theis DR, White M. Is obesity policy in England fit for purpose? Analysis of government strategies and policies, 1992–2020. Milbank Q. 2021;99(1):126–70.

Rose G. The strategy of preventive medicine. Guildford: Oxford University Press; 1992.

Ogilvie D, Adams J, Bauman A, Gregg EW, Panter J, Siegel KR, et al. Using natural experimental studies to guide public health action: turning the evidence-based medicine paradigm on its head. J Epidemiol Community Health. 2020;74(2):203–8.

Skivington K, Matthews L, Simpson SA, Craig P, Baird J, Blazeby JM, et al. A new framework for developing and evaluating complex interventions: update of Medical Research Council guidance. BMJ. 2021;374.

White M, Adams J, Heywood P. How and why do interventions that increase health overall widen inequalities within populations? Social inequality and public health: Policy Press; 2009. p. 65–82.

Adams J, Mytton O, White M, Monsivais P. Why are some population interventions for diet and obesity more equitable and effective than others? The role of individual agency. PLoS Med. 2016;13(4):e1001990.

Sen A. Well-being, agency and freedom: the Dewey lectures 1984. J Philos. 1985;82(4):169–221.

McLaren L, McIntyre L, Kirkpatrick S. Rose’s population strategy of prevention need not increase social inequalities in health. Int J Epidemiol. 2010;39(2):372–7.

Capewell S, Graham H. Will cardiovascular disease prevention widen health inequalities? PLoS Med. 2010;7(8):e1000320.

Blankenship KM, Friedman SR, Dworkin S, Mantell JE. Structural interventions: concepts, challenges and opportunities for research. J Urban Health. 2006;83:59–72.

Backholer K, Beauchamp A, Ball K, Turrell G, Martin J, Woods J, et al. A framework for evaluating the impact of obesity prevention strategies on socioeconomic inequalities in weight. Am J Public Health. 2014;104(10):e43–50.

Hyseni L, Elliot-Green A, Lloyd-Williams F, Kypridemos C, O’Flaherty M, McGill R, et al. Systematic review of dietary salt reduction policies: evidence for an effectiveness hierarchy? PLoS ONE. 2017;12(5):e0177535.

Olstad D, Teychenne M, Minaker L, Taber D, Raine K, Nykiforuk C, et al. Can policy ameliorate socioeconomic inequities in obesity and obesity-related behaviours? A systematic review of the impact of universal policies on adults and children. Obes Rev. 2016;17(12):1198–217.

McGowan V, Buckner S, Mead R, McGill E, Ronzi S, Beyer F, et al. Examining the effectiveness of place-based interventions to improve public health and reduce health inequalities: an umbrella review. BMC Public Health. 2021;21(1):1–17.

Reynolds JP, Scalco A, Ejebu O, Toumpakari Z, Smith A, Lu F, et al. Low agency population interventions to reduce meat consumption. Report produced for the Global Food Security Programme. . Global Food Security Programme; 2020.

McFadden A, Green JM, Williams V, McLeish J, McCormick F, Fox-Rushby J, et al. Can food vouchers improve nutrition and reduce health inequalities in low-income mothers and young children: a multi-method evaluation of the experiences of beneficiaries and practitioners of the Healthy Start programme in England. BMC Public Health. 2014;14(1):1–13.

Lowe MR, Tappe KA, Butryn ML, Annunziato RA, Coletta MC, Ochner CN, et al. An intervention study targeting energy and nutrient intake in worksite cafeterias. Eat Behav. 2010;11(3):144–51.

Moise N, Cifuentes E, Orozco E, Willett W. Limiting the consumption of sugar sweetened beverages in Mexico’s obesogenic environment: a qualitative policy review and stakeholder analysis. J Public Health Policy. 2011;32:458–75.

O’Neill J, Tabish H, Welch V, Petticrew M, Pottie K, Clarke M, et al. Applying an equity lens to interventions: using PROGRESS ensures consideration of socially stratifying factors to illuminate inequities in health. J Clin Epidemiol. 2014;67(1):56–64.

Rotondi M. _kappaSize: sample size estimation functions for studies of interobserver agreement_. R package version 1.2. 2018.

Hollands GJ, Bignardi G, Johnston M, Kelly MP, Ogilvie D, Petticrew M, et al. The TIPPME intervention typology for changing environments to change behaviour. Nat Hum Behav. 2017;1(8):1–9.

Hosking J, Macmillan A, Jones R, Ameratunga S, Woodward A. Searching for health equity: validation of a search filter for ethnic and socioeconomic inequalities in transport. Syst Rev. 2019;8:1–5.

Beauchamp A, Backholer K, Magliano D, Peeters A. The effect of obesity prevention interventions according to socioeconomic position: a systematic review. Obes Rev. 2014;15(7):541–54.

McGill R, Anwar E, Orton L, Bromley H, Lloyd-Williams F, O’Flaherty M, et al. Are interventions to promote healthy eating equally effective for all? Systematic review of socioeconomic inequalities in impact. BMC Public Health. 2015;15(1):1–15.

Ogilvie D, Fayter D, Petticrew M, Sowden A, Thomas S, Whitehead M, et al. The harvest plot: a method for synthesising evidence about the differential effects of interventions. BMC Med Res Methodol. 2008;8:1–7.

Hobin E, So J, Rosella L, Comte M, Manske S, McGavock J. Trajectories of objectively measured physical activity among secondary students in Canada in the context of a province-wide physical education policy: a longitudinal analysis. J Obes. 2014;2014.

Sturm R, Powell LM, Chriqui JF, Chaloupka FJ. Soda taxes, soft drink consumption, and children’s body mass index. Health Aff. 2010;29(5):1052–8.

Brownson RC, Smith CA, Pratt M, Mack NE, Jackson-Thompson J, Dean CG, et al. Preventing cardiovascular disease through community-based risk reduction: the Bootheel Heart Health Project. Am J Public Health. 1996;86(2):206–13.

Braithwaite J. Restorative justice & responsive regulation: Oxford University Press, USA; 2002.

Love R, Adams J, van Sluijs EM. Are school-based physical activity interventions effective and equitable? A meta-analysis of cluster randomized controlled trials with accelerometer-assessed activity. Obes Rev. 2019;20(6):859–70.

Bere E, Veierød MB, Klepp K-I. The Norwegian School Fruit Programme: evaluating paid vs. no-cost subscriptions. Prev Med. 2005;41(2):463–70.

Lowe CF, Horne PJ, Tapper K, Bowdery M, Egerton C. Effects of a peer modelling and rewards-based intervention to increase fruit and vegetable consumption in children. Eur J Clin Nutr. 2004;58(3):510–22.

Lorenc T, Petticrew M, Welch V, Tugwell P. What types of interventions generate inequalities? Evidence from systematic reviews. J Epidemiol Community Health. 2013;67(2):190–3.