The Nueva Ecija Cardiovascular Risk Experiment (NECVaRE)

The Nueva Ecija Cardiovascular Risk Experiment: An Evaluation of the Impact of Risk Information and Screening on Primary Prevention of Cardiovascular Disease

This study seeks to assess how beliefs about health risks, specifically the risk of cardiovascular disease (CVD), affect health lifestyles and the demand for preventive care in a low-income setting. It also aims to establish the effectiveness of the Package of Essential Noncommunicable Disease Interventions in the Philippines (PhilPEN) in delivering primary prevention of CVD. To meet these objectives, the study is designed as a randomized parallel experiment with two separate, non-overlapping treatment groups and one control group. The experiment will be implemented in Nueva Ecija province, Philippines.

Study Overview

• Status: Unknown
• Conditions: Cardiovascular Diseases; Cardiovascular Risk Factor
• Intervention / Treatment: Behavioral: Information on CVD Risk; Behavioral: Lottery Incentive

Detailed Description

This study seeks to assess how beliefs about health risks, specifically the risk of cardiovascular disease (CVD), affect health lifestyles and the demand for preventive care in a low-income setting. It also aims to establish the effectiveness of the Package of Essential Noncommunicable Disease Interventions in the Philippines (PhilPEN) in delivering primary prevention of CVD.To realize the first objective, the investigators will measure the accuracy of beliefs about exposure to CVD risk and, subsequently, randomly provide information on personal CVD risk based on measured risk factors. This will allow assessment of the extent to which biased beliefs constrain demand for primary prevention and sustain unhealthy lifestyles. In addition, the investigators will test whether beliefs about susceptibility to CVD are responsive to the receipt of information on personal risk, and whether health behaviors and the demand for CVD screening and medication are affected by any revision of beliefs.

To meet the second objective the investigators will randomly encourage uptake of the PhilPEN program's risk screening by offering entry to a money prize lottery conditional on attending a health clinic where the program operates. The induced random variation in clinic attendance will be used to estimate the program's impact on exposure to risk factors, medication of hypertension, the predicted risk of CVD and awareness of this risk.

Meeting both objectives will allow the investigators to distinguish between scenarios. One is that PhilPEN is effective in preventing CVD of patients who access the program but its impact on population health is muted because poor information on susceptibility to CVD reduces the demand for primary prevention. Another is that even if improved information is effective in raising this demand, this will have little impact on population health through PhilPEN because of deficiencies in the operation of the program in health facilities.

Within the Nueva Ecija province, the investigators will randomly sample barangays (N=304), subsequently households (n=5019) and, finally, one person aged 40-70 within each household. At the barangay level, the investigators will randomly allocate to a treatment group receiving the lottery incentive to attend a health clinic (n=2261), another treatment group receiving information on personal CVD risk (n=497) and a control group (n=2261). A baseline survey (January-April 2018) will record data on initial health, health behavior, health knowledge, risk perceptions, risk attitudes, time preferences, health care utilization and expenditure and socioeconomic characteristics, and deliver the treatments. A follow-up survey 9-12 months later will record outcomes.

• Study Type: Interventional
• Enrollment (Anticipated): 5019
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Philippines
  • Quezon City, Philippines, 1101
    • Recruiting
    • UPecon Foundation
    • Contact: Joseph J Capuno, PhD; Phone Number: 6329205465; Email: jjcapuno@up.edu.ph
    • Contact: Aleli D Kraft, PhD; Phone Number: 6329205463; Email: adkraft@up.edu.ph

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 40 years to 70 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

Inclusion Criteria:

• Individuals aged 40-70 years old
• Residents of Nueva Ecija province
• Those that have been diagnosed with hypertension but are not currently (past two weeks) taking antihypertensives

Exclusion Criteria:

• Individuals aged below 40 years old or above 70 years old
• Individuals who report they have been diagnosed as having heart disease or diabetes, or who report that they have had a heart attack or a stroke
• Those currently (past 2 weeks) taking medication for hypertension or for diabetes
• Those who have some medical problems that prevents measurement of blood pressure or BMI

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

3

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Information on CVD risk; Respondents will receive information on the predicted probability of having a heart attack or stroke within 10 years. The predictions will be obtained from the Globorisk tool (www.globorisk.org). All information will be provided within a risk perceptions module of the baseline survey. Only this module will differ across the two treatment groups (information and lottery) and the control group. Information obtained from earlier modules will be retrieved automatically and used to make predictions of CVD risk consistent with the risk factor profile of the respondent.	Behavioral: Information on CVD Risk; Respondents will be provided three types of information on CVD risks: a CVD base rate, a personalized CVD risk and an optimal CVD risk. The CVD base rate will be predicted from the respondent's age and sex only. After reporting their own chance of having a heart attack or stroke within ten years, the respondents in the treatment group will be told the risk for someone with the same age, sex, smoking status, body mass index (BMI) and blood pressure as them. Finally, a treatment group respondent will receive information on what the 10-year CVD risk would be for someone of the same age and gender who did not smoke, and had normal blood pressure and BMI.
Experimental: Lottery Incentive; Respondents will be offered a ticket for a lottery with a money prize on condition that they visit a specific public health clinic for a checkup. There will be one prize per barangay giving each respondent a one in ten chance of winning P5000 (US$100). The prize is equivalent to approximately 14 days earnings at the regional minimum wage.	Behavioral: Lottery Incentive; Respondents will simply be told that they can enter a lottery if they go to the specified clinic for a checkup. The health facilities will be told to conduct an assessment deemed appropriate for any particular patient that requests to be issued with a lottery ticket. No instructions will be given that the facilities should follow the PhilPEN protocol. We will evaluate whether they do implement the protocol for patients who qualify (by age if nothing else) for full risk screening.
No Intervention: Control; No intervention will be introduced to the participants in this arm.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Mean 10-year risk of CVD event (heart attack/stroke)	Predicted probability of having a heart attack or stroke within 10 years obtained from office version of Globorisk (www.globorisk.org) based on age, sex, systolic blood pressure, body mass index (BMI) and smoking status recorded in end-point survey. Group mean of predictions will be calculated.	6-9 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Proportion with 10-year CVD risk ≥ 10%	Predicted risk obtained from Globorisk as for primary outcome. If power permits, will also estimate effects on proportion with CVD risk>20% and >30%.	6-9 months
Mean systolic blood pressure (SBP)	Predicted CVD risk is function of blood pressure, BMI and smoking. We will also estimate effects on these risk factors separately. Mean of last two SBP measures on single visit. BP measured using electronic (OMRON) wrap cuff monitor.	6-9 months
Proportion with elevated blood pressure (systolic ≥140)	Predicted CVD risk is function of blood pressure, BMI and smoking. We will also estimate effects on these risk factors separately. Mean of last two SBP measures on single visit. BP measured using electronic (OMRON) wrap cuff monitor.	6-9 months
Mean BMI	Predicted CVD risk is function of blood pressure, BMI and smoking. We will also estimate effects on these risk factors separately. Height and weight measured using standardized instruments.	6-9 months
Proportion overweight/obese (BMI>25)	Predicted CVD risk is function of blood pressure, BMI and smoking. We will also estimate effects on these risk factors separately. Height and weight measured using standardized instruments.	6-9 months
Proportion currently smoking	Predicted CVD risk is function of blood pressure, BMI and smoking. We will also estimate effects on these risk factors separately.	6-9 months
Mean waist circumference	Globorisk predicted 10-year CVD risk is not a function of central adiposity, but this is measured as part of PhilPEN risk assessment. Weight circumference will be measured followed a standardized procedure.	6-9 months
Proportion with waist circumference ≥ 90cm (men) / 80cm (women).	Globorisk predicted 10-year CVD risk is not a function of central adiposity, but this is measured as part of PhilPEN risk assessment. Weight circumference will be measured followed a standardized procedure.	6-9 months
Proportion with undiagnosed hypertension	A measure of diagnosis and medication of hypertension. Numerator = systolic/diastolic BP ≥ 140/90 + not diagnosed with hypertension. Denominator = all respondents.	6-9 months
Proportion taking antihypertensive medication in the last 2 weeks.	A measure of diagnosis and medication of hypertension. Numerator = systolic/diastolic BP ≥ 140/90 + not diagnosed with hypertension. Denominator = all respondents.	6-9 months
Alcohol consumption	A measure of health behavior consistent with those of World Health Organization (WHO) STEPS.	6-9 months
Diet (intake of fruits, vegetables and salty foods)	A measure of health behavior consistent with those of WHO STEPS.	6-9 months
Exercise	A measure of health behavior consistent with those of WHO STEPS.	6-9 months
Knowledge of CVD and diabetes risk factors	Knowledge of CVD and diabetes risk factors assessed using questions adapted from previously fielded instruments.	6-9 months

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Proportion of smokers/ex-smokers who have been advised by a doctor or health worker to quit smoking	For the lottery intervention, we will examine whether attending a health clinic for a checkup increases the probability of receiving medical advice on the adoption of healthier habits, as is prescribed by the PhilPEN protocol.	6-9 months
Proportion of smokers/ex-smokers who have received counselling on smoking cessation	For the lottery intervention, we will examine whether attending a health clinic for a checkup increases the probability of receiving medical advice on the adoption of healthier habits, as is prescribed by the PhilPEN protocol.	6-9 months
Proportion who have been advised by a doctor or other health worker to drink less alcohol (out of all who have ever consumed alcohol)	For the lottery intervention, we will examine whether attending a health clinic for a checkup increases the probability of receiving medical advice on the adoption of healthier habits, as is prescribed by the PhilPEN protocol.	6-9 months
Proportion who have been advised by a doctor or other health worker to eat less salty and/or fatty food	For the lottery intervention, we will examine whether attending a health clinic for a checkup increases the probability of receiving medical advice on the adoption of healthier habits, as is prescribed by the PhilPEN protocol.	6-9 months
Proportion who have been advised by a doctor or other health worker to eat more fruit and vegetables and/or grains and pulses	For the lottery intervention, we will examine whether attending a health clinic for a checkup increases the probability of receiving medical advice on the adoption of healthier habits, as is prescribed by the PhilPEN protocol.	6-9 months
Proportion who have been advised by a doctor or other health worker to be more physically active	For the lottery intervention, we will examine whether attending a health clinic for a checkup increases the probability of receiving medical advice on the adoption of healthier habits, as is prescribed by the PhilPEN protocol.	6-9 months
Proportion of individuals overweight or obese (at baseline) who have been encouraged by a health professional to lose weight	For the lottery intervention, we will examine whether attending a health clinic for a checkup increases the probability of receiving medical advice on the adoption of healthier habits, as is prescribed by the PhilPEN protocol.	6-9 months
Mean perceived 10-year risk of heart attack or stroke for someone of same age and sex as respondent	This outcome will be measured in the baseline survey in response to information provided during the interview	1-4 months
Mean perceived own 10-year risk of heart attack or stroke	This outcome will be measured in the baseline survey in response to information provided during the interview.	1-4 months
Mean perceived own 10-year risk of heart attack or stroke if were to adopt healthy lifestyle	This outcome will be measured in the baseline survey in response to information provided during the interview.	1-4 months
General health measured by SF-36v.1	Measured at baseline, this is an outcome measure not specific to CVD risk.	1-4 months
Labour market employment, hours and earnings	Measured at baseline, this is an outcome measure not specific to CVD risk.	1-4 months
Health care utilization and expenditures	Measured at baseline, this is an outcome measure not specific to CVD risk.	1-4 months

Collaborators and Investigators

• Sponsor: UPecon Foundation, Inc.
• Collaborators: University of Lausanne
• Investigators: Principal Investigator: Joseph J Capuno, PhD, UPecon Foundation, Inc.; Principal Investigator: Aleli D Kraft, PhD, UPecon Foundation, Inc.; Principal Investigator: Owen O'Donnell, PhD, University of Lausanne

Publications and helpful links

General Publications

• Attanasio, OP (2009). Expectations and Perceptions in Developing Countries: Their Measurement and Their Use, American Economic Review, 99 (2): 87-92.
• Baltussen, G., Post, G. T., Van Den Assem, M. J., & Wakker, P. P. (2012). Random incentive systems in a dynamic choice experiment. Experimental Economics, 15(3), 418-443.
• Camerer, C. F., Hogarth, R. M., Budescu, D. V., & Eckel, C. (1999). The effects of financial incentives in experiments: A review and capital-labor-production framework. In Elicitation of Preferences (pp. 7-48). Springer Netherlands.
• Chow CK, Teo KK, Rangarajan S, Islam S, Gupta R, Avezum A, Bahonar A, Chifamba J, Dagenais G, Diaz R, Kazmi K, Lanas F, Wei L, Lopez-Jaramillo P, Fanghong L, Ismail NH, Puoane T, Rosengren A, Szuba A, Temizhan A, Wielgosz A, Yusuf R, Yusufali A, McKee M, Liu L, Mony P, Yusuf S; PURE (Prospective Urban Rural Epidemiology) Study investigators. Prevalence, awareness, treatment, and control of hypertension in rural and urban communities in high-, middle-, and low-income countries. JAMA. 2013 Sep 4;310(9):959-68. doi: 10.1001/jama.2013.184182.
• Cooney MT, Dudina AL, Graham IM. Value and limitations of existing scores for the assessment of cardiovascular risk: a review for clinicians. J Am Coll Cardiol. 2009 Sep 29;54(14):1209-27. doi: 10.1016/j.jacc.2009.07.020.
• D'Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008 Feb 12;117(6):743-53. doi: 10.1161/CIRCULATIONAHA.107.699579. Epub 2008 Jan 22.
• Delavande A, Giné X, McKenzie D (2011a). Measuring Subjective Expectations in Developing Countries: A Critical Review and New Evidence. Journal of Development Economics, 94:151-163,
• Delavande A, Giné X, McKenzie D (2011b). Eliciting Probabilistic Expectations with Visual Aids in Developing Countries: How sensitive are answers to variations in elicitation design? Journal of Applied Econometrics, 26(3):479-497.
• Delavande A, Kohler HP. Subjective expectations in the context of HIV/AIDS in Malawi. Demogr Res. 2009 Jun 23;20:817-874. doi: 10.4054/DemRes.2009.20.31.
• Delavande, A. and Kohler, H-P. (2015) HIV/AIDS-related Expectations and Risky Sexual Behaviour in Malawi. Review of Economic Studies, 83: 118-64.
• Department of Health (2012a). Implementing Guidelines on the Implementation of Philippine Package of Essential NCD Interventions (PHIL PEN) on the Integrated Management of Hypertension and Diabetes for Primary Care Facilities, Administrative Order No. 2012-0029. Manila: Department of Health.
• Department of Health (2012b) Operations Manual on the Philippine Package of Essential NCD Interventions (PHIL PEN) on the Integrated Management of Hypertension and Diabetes for Primary Care Facilities. Manila: Department of Health.
• Dimmock SG, Kouwenberg R, Mitchell OS, Peijnenburg K. Estimating ambiguity preferences and perceptions in multiple prior models: Evidence from the field. J Risk Uncertain. 2015 Dec;51(3):219-244. doi: 10.1007/s11166-015-9227-2. Epub 2015 Dec 16.
• Otgontuya D, Oum S, Buckley BS, Bonita R. Assessment of total cardiovascular risk using WHO/ISH risk prediction charts in three low and middle income countries in Asia. BMC Public Health. 2013 Jun 5;13:539. doi: 10.1186/1471-2458-13-539.
• Ezzati, M, et al. (2004). Comparative quantification of health risks. Global and regional burden of disease attributable to selected major risk factors. Volume 1. Geneva: World Health Organization.
• Food and Nutrition Research Institute-Department of Science and Technology (FNRI-DOST) (2015). Philippine Nutrition Facts and Figures 2013: Clinical and Health Survey. FNRI Bldg., DOST Compound, Bicutan, Taguig City, Metro Manila, Philippines.
• Gale CR, Cooper C, Sayer AA. Framingham cardiovascular disease risk scores and incident frailty: the English longitudinal study of ageing. Age (Dordr). 2014;36(4):9692. doi: 10.1007/s11357-014-9692-6. Epub 2014 Aug 2.
• Gaziano TA, Young CR, Fitzmaurice G, Atwood S, Gaziano JM. Laboratory-based versus non-laboratory-based method for assessment of cardiovascular disease risk: the NHANES I Follow-up Study cohort. Lancet. 2008 Mar 15;371(9616):923-31. doi: 10.1016/S0140-6736(08)60418-3.
• Gray BJ, Bracken RM, Turner D, Morgan K, Mellalieu SD, Thomas M, Williams SP, Williams M, Rice S, Stephens JW; Prosiect Sir Gar Group. Predicted 10-year risk of cardiovascular disease is influenced by the risk equation adopted: a cross-sectional analysis. Br J Gen Pract. 2014 Oct;64(627):e634-40. doi: 10.3399/bjgp14X681805.
• Hajifathalian K, Ueda P, Lu Y, Woodward M, Ahmadvand A, Aguilar-Salinas CA, Azizi F, Cifkova R, Di Cesare M, Eriksen L, Farzadfar F, Ikeda N, Khalili D, Khang YH, Lanska V, Leon-Munoz L, Magliano D, Msyamboza KP, Oh K, Rodriguez-Artalejo F, Rojas-Martinez R, Shaw JE, Stevens GA, Tolstrup J, Zhou B, Salomon JA, Ezzati M, Danaei G. A novel risk score to predict cardiovascular disease risk in national populations (Globorisk): a pooled analysis of prospective cohorts and health examination surveys. Lancet Diabetes Endocrinol. 2015 May;3(5):339-55. doi: 10.1016/S2213-8587(15)00081-9. Epub 2015 Mar 26.
• Harris AJ, Hahn U. Unrealistic optimism about future life events: a cautionary note. Psychol Rev. 2011 Jan;118(1):135-54. doi: 10.1037/a0020997.
• Hemming K and Marsch J (2013). A menu-driven facility for sample-size calculations in cluster randomized controlled trials. The Stata Journal 13(1): 114-135.
• Hill, RV (2009). Using Stated Preferences and Beliefs to Identify the Impact of Risk on Poor Households. Journal of Development Studies, 45(2): 151-171.
• Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica: Journal of the econometric society, 263-291.
• Kariuki JK, Stuart-Shor EM, Leveille SG, Hayman LL. Evaluation of the performance of existing non-laboratory based cardiovascular risk assessment algorithms. BMC Cardiovasc Disord. 2013 Dec 28;13:123. doi: 10.1186/1471-2261-13-123.
• Laibson D (1997). Golden eggs and hyperbolic discounting. Quarterly Journal of Economics 112(2): 443-477.
• Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care. 2003 Mar;26(3):725-31. doi: 10.2337/diacare.26.3.725.
• Littenberg B, MacLean CD. Intra-cluster correlation coefficients in adults with diabetes in primary care practices: the Vermont Diabetes Information System field survey. BMC Med Res Methodol. 2006 May 3;6:20. doi: 10.1186/1471-2288-6-20.
• Nordet P, Mendis S, Duenas A, de la Noval R, Armas N, de la Noval IL, Pupo H. Total cardiovascular risk assessment and management using two prediction tools, with and without blood cholesterol. MEDICC Rev. 2013 Oct;15(4):36-40. doi: 10.37757/MR2013V15.N4.9.
• Pandya A, Weinstein MC, Gaziano TA. A comparative assessment of non-laboratory-based versus commonly used laboratory-based cardiovascular disease risk scores in the NHANES III population. PLoS One. 2011;6(5):e20416. doi: 10.1371/journal.pone.0020416. Epub 2011 May 31.
• Parker DR, Evangelou E, Eaton CB. Intraclass correlation coefficients for cluster randomized trials in primary care: the cholesterol education and research trial (CEART). Contemp Clin Trials. 2005 Apr;26(2):260-7. doi: 10.1016/j.cct.2005.01.002.
• Paula Á.,Gil S, and Todd PE (2014). How beliefs about HIV status affect risky behaviors: evidence from Malawi. Journal of Applied Econometrics, 29: 944-964.
• Persson M, Carlberg B, Weinehall L, Nilsson L, Stegmayr B, Lindholm LH. Risk stratification by guidelines compared with risk assessment by risk equations applied to a MONICA sample. J Hypertens. 2003 Jun;21(6):1089-95. doi: 10.1097/00004872-200306000-00008.
• Prelec, D. (1998). The probability weighting function. Econometrica, 497-527.
• Radcliffe, N. M., & Klein, W. M. P. (2002). Dispositional, unrealistic, and comparative optimism: Differential relations with knowledge and processing of risk information and beliefs about personal risk. Personality and Social Psychology Bulletin, 28, 836-846.
• Rosenstock IM. Why people use health services. Milbank Mem Fund Q. 1966 Jul;44(3):Suppl:94-127. No abstract available.
• Roth GA, Huffman MD, Moran AE, Feigin V, Mensah GA, Naghavi M, Murray CJ. Global and regional patterns in cardiovascular mortality from 1990 to 2013. Circulation. 2015 Oct 27;132(17):1667-78. doi: 10.1161/CIRCULATIONAHA.114.008720.
• Selvarajah S, Kaur G, Haniff J, Cheong KC, Hiong TG, van der Graaf Y, Bots ML. Comparison of the Framingham Risk Score, SCORE and WHO/ISH cardiovascular risk prediction models in an Asian population. Int J Cardiol. 2014 Sep;176(1):211-8. doi: 10.1016/j.ijcard.2014.07.066. Epub 2014 Jul 15.
• Shah P, Harris AJ, Bird G, Catmur C, Hahn U. A pessimistic view of optimistic belief updating. Cogn Psychol. 2016 Nov;90:71-127. doi: 10.1016/j.cogpsych.2016.05.004. Epub 2016 Aug 16.
• Sharot T, Garrett N. Forming Beliefs: Why Valence Matters. Trends Cogn Sci. 2016 Jan;20(1):25-33. doi: 10.1016/j.tics.2015.11.002. Epub 2015 Dec 15.
• Singh J, Liddy C, Hogg W, Taljaard M. Intracluster correlation coefficients for sample size calculations related to cardiovascular disease prevention and management in primary care practices. BMC Res Notes. 2015 Mar 20;8:89. doi: 10.1186/s13104-015-1042-y.
• Tulloch-Reid MK, Younger NO, Ferguson TS, Francis DK, Abdulkadri AO, Gordon-Strachan GM, McFarlane SR, Cunningham-Myrie CA, Wilks RJ, Anderson SG. Excess Cardiovascular Risk Burden in Jamaican Women Does Not Influence Predicted 10-Year CVD Risk Profiles of Jamaica Adults: An Analysis of the 2007/08 Jamaica Health and Lifestyle Survey. PLoS One. 2013 Jun 21;8(6):e66625. doi: 10.1371/journal.pone.0066625. Print 2013.
• Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty, 5(4), 297-323.
• Ueda P, Woodward M, Lu Y, Hajifathalian K, Al-Wotayan R, Aguilar-Salinas CA, Ahmadvand A, Azizi F, Bentham J, Cifkova R, Di Cesare M, Eriksen L, Farzadfar F, Ferguson TS, Ikeda N, Khalili D, Khang YH, Lanska V, Leon-Munoz L, Magliano DJ, Margozzini P, Msyamboza KP, Mutungi G, Oh K, Oum S, Rodriguez-Artalejo F, Rojas-Martinez R, Valdivia G, Wilks R, Shaw JE, Stevens GA, Tolstrup JS, Zhou B, Salomon JA, Ezzati M, Danaei G. Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries: a pooled analysis of prospective cohorts and health surveys. Lancet Diabetes Endocrinol. 2017 Mar;5(3):196-213. doi: 10.1016/S2213-8587(17)30015-3. Epub 2017 Jan 24.
• van Wilgenburg, K, Baillon, A, O'Donnell, O and Quimbo, S (2017). Do (non-standard) risk and time preferences explain health insurance enrollment? mimeo, Erasmus School of Economics, Erasmus University Rotterdam
• World Health Organization (2007) Prevention of Cardiovascular Disease: Guidelines forAssessment and Management of Cardiovascular risk. Geneva: WHO Press
• World Health Organization (2010). Package of Essential Noncommunicable Disease Interventions for Primary Care in Low Resource Settings. Geneva: WHO
• World Health Organization (2012) Noncommunicable diseases in the Western Pacific Region: a profile. Manila: WHO
• World Health Organization (2016). HEARTS: Technical package for cardiovascular disease management in primary care. Geneva: World Health Organization.
• Yikona JI, Wallis EJ, Ramsay LE, Jackson PR. Coronary and cardiovascular risk estimation in uncomplicated mild hypertension. A comparison of risk assessment methods. J Hypertens. 2002 Nov;20(11):2173-82. doi: 10.1097/00004872-200211000-00016.
• Yusuf S, Rangarajan S, Teo K, Islam S, Li W, Liu L, Bo J, Lou Q, Lu F, Liu T, Yu L, Zhang S, Mony P, Swaminathan S, Mohan V, Gupta R, Kumar R, Vijayakumar K, Lear S, Anand S, Wielgosz A, Diaz R, Avezum A, Lopez-Jaramillo P, Lanas F, Yusoff K, Ismail N, Iqbal R, Rahman O, Rosengren A, Yusufali A, Kelishadi R, Kruger A, Puoane T, Szuba A, Chifamba J, Oguz A, McQueen M, McKee M, Dagenais G; PURE Investigators. Cardiovascular risk and events in 17 low-, middle-, and high-income countries. N Engl J Med. 2014 Aug 28;371(9):818-27. doi: 10.1056/NEJMoa1311890.
• Attanasio OP, Meghir C, Vera-Hernández, AM. (2005). Elicitation, validation, and use of probability distributions of future income in developing countries. University College; London: 2005. [unpublished working paper]
• Capuno J, Kraft A, O'Donnell O. Effectiveness of clinic-based cardiovascular disease prevention: A randomized encouragement design experiment in the Philippines. Soc Sci Med. 2021 Aug;283:114194. doi: 10.1016/j.socscimed.2021.114194. Epub 2021 Jul 3.

Study record dates

Study Major Dates

• Study Start (Actual): January 20, 2018
• Primary Completion (Actual): May 31, 2018
• Study Completion (Anticipated): December 31, 2021

Study Registration Dates

• First Submitted: April 18, 2018
• First Submitted That Met QC Criteria: April 18, 2018
• First Posted (Actual): May 1, 2018

Study Record Updates

• Last Update Posted (Actual): April 17, 2019
• Last Update Submitted That Met QC Criteria: April 15, 2019
• Last Verified: April 1, 2019

More Information

Terms related to this study

• Keywords: Risk perceptions and attitudes, time preference, information
• Additional Relevant MeSH Terms: Cardiovascular Diseases
• Other Study ID Numbers: UPecon r4d

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: Undecided

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No