The economic burden of noncommunicable diseases and mental health conditions: results for Costa Rica, Jamaica, and Peru/ La carga economica de las enfermedades no transmisibles y la enfermedad mental: resultados para Costa Rica, Jamaica y Peru/ A carga ec (2024)

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A strong interplay exists between population health and economicgrowth (1). First, high-income populations tend to have better healthbecause they have access to more and better nutrition; safe water andsanitation; readily available and quality health care; and psychosocialresources, such as social capital and recreation facilities. Second,healthy populations develop faster economically because healthy workforces tend to be more productive and because healthy children havehigher test scores, better school attendance records, and higher levelsof educational attainment. In addition, healthy populations maintainhigher rates of saving, investment, and physical capital accumulationbecause they expend fewer resources on health care. This process maylead to a virtuous cycle that results in further investment from abroad,increasing workers' access to more-productive machines, technology,and infrastructure. Healthy populations also tend to control theirfertility, allowing them to escape the burden of youth dependency andenjoy a demographic dividend (2). Therefore, understanding patterns inpopulation health is likely to be important, at least in part, forunderstanding patterns in economic growth.

Noncommunicable diseases (NCDs) and mental health conditionsrepresent a huge disease burden and have a substantial impact onindividuals, communities, and societies around the globe. In total,these conditions are responsible for roughly half of healthy life yearslost as measured in disability-adjusted life years (DALYs) and roughlytwo-thirds of deaths worldwide (3, 4). In the Region of the Americas,NCDs are the leading cause of morbidity and mortality and areresponsible for 80% of all deaths (5). Of particular relevance, 35% ofNCD-related deaths occur prematurely (between the ages of 30 and 70),when individuals are in their most economically productive period oflife (5).

As worrying as current rates of NCDs and mental health conditionsare, trends in the relevant risk factors for these conditions indicatethat their global burden is only likely to grow. For example, whilesmoking has declined in some high-income countries, the overall rates ofthe main modifiable risk factors for NCDs and mental healthconditions--such as tobacco use, alcohol use, and obesity--have risenglobally, suggesting that an increase in the rates of chronic conditionsworldwide is likely to follow (6, 7). In addition, more sedentaryoccupations and unhealthy diets are becoming more common.

Demographic trends also point toward an increased future burdenfrom NCDs and mental health conditions. In particular, the dualphenomena of urbanization and rapid population aging have significantimplications. Although urbanization has many benefits in terms ofefficiency and convenience, it can also facilitate dispersion of certainrisk factors for NCDs, such as pollution and second-hand smoking. Giventhat age constitutes the main risk factor for NCDs and mental healthconditions, global population aging is likely to have a major effect onoverall levels of population health. While a thorough discussion of theprevalence of these conditions is beyond the scope of this paper, thesedetails have been provided elsewhere. In addition, more information onNCDs in the Americas and the capacity of countries to respond isavailable in a report that was prepared by the Pan American HealthOrganization (PAHO) (8).

In spite of the high burden of ill health and premature deathcaused by NCDs and mental conditions, the availability of data capturingtheir economic impact is limited (9). This paper focuses on Costa Rica,Jamaica, and Peru (see Table 1 for summary statistics) and is the resultof a collaboration between the Harvard T.H. Chan School of Public Healthand the PAHO Department of Noncommunicable Diseases and Mental Health.That department is responsible for providing technical cooperation inthe Region of the Americas to prevent and control NCDs, as well asmental conditions, and related risk factors in accordance with globaland regional mandates (10).

This paper has two goals. The first is to estimate the economicimpact of NCDs and mental conditions on gross domestic product (GDP).The second is to raise awareness among policymakers and otherdecisionmakers of these conditions' economic costs and theirimplications for national economic progress. Finance ministers andothers in charge of resource allocation are more likely to fund programsand interventions that are evidence based, and persons seeking toinfluence financial decisions (such as by health ministers) can use theresults presented in this paper to identify and promote the adoption ofcost-effective policies, such as the "best buy" NCDinterventions identified by the World Health Organization (WHO) (11,12).

Although Costa Rica, Jamaica, and Peru represent differentgeographic areas in Latin America and the Caribbean and have differentlevels of economic development, they face similar demographicchallenges, including recent steady increases in the proportions oftheir populations aged 60 and above (Figure 1). In 1980, the proportionof the population aged 60 and above was 9.3% for Jamaica, 6.1% for CostaRica, and 5.6% for Peru. This age group now accounts for 13% of theJamaican and Costa Rican populations and 10% of the Peruvian population.According to United Nations Population Division (UNPD) projections(https://esa.un. org/unpd/wpp/Download/Standard/ Population/), by 2050,those aged 60 and above will account for 30% of the population in CostaRica, 28% in Jamaica, and 23% in Peru.

Even though NCDs and mental health conditions have a significantand growing impact on the health and well-being of populations,policymakers and the public may not be aware of their full consequences.Public spending on large-scale intervention programs aimed at reducingthe risk factors for these diseases (such as obesity) may therefore needto be justified by comparing the expected return on investment fromthese programs with expected returns from other potential uses of publicfunds. This can only be achieved if robust estimates of the economiccosts of NCDs and mental health conditions are available.

Unfortunately, assessing the economic impact of NCDs and mentalhealth conditions is complex. Several approaches to evaluating theeconomic effects of chronic conditions exist, including cost-of-illnessand value-of-a-statistical-life (VSL) methods, which aggregate estimatesfrom individual data. The cost-of-illness method sums up direct medicalcosts, while VSL infers the monetary value of mortality reductions fromwillingness-to-pay studies or wage premia for risky occupations.However, these approaches do not capture the ways in whichsociety's health status affects determinants of economic growth,such as labor markets and capital accumulation.

We expect such macro-level spillover effects to be important--ahypothesis that the literature supports (13). For example, NCDs andmental health conditions increase mortality and reduce productivity,thus reducing labor supply (14). Likewise, health care expendituresincrease in response to chronic conditions, diverting savings away fromproductive investments and thus reducing capital accumulation.

One approach to estimating the impact of these spillover effectsuses cross-country economic growth regressions (15, 16); however,identifying the parameters of interest can be difficult. An alternativeis to build a working model of the economy, which can then be calibratedusing observed data on chronic conditions and other country-specificcharacteristics. We can use such production function approaches tosimulate different scenarios with different prevalence levels of NCDsand mental health conditions. Comparing levels of GDP and of GDP growthin various scenarios provides an economic estimate of the impact ofthese health conditions.

It should be acknowledged that this methodology also haslimitations. For example, we do not consider the behavioral change ofindividuals and firms. One potential alternative is to use a generalequilibrium approach. However, building such a model would be complexand could ultimately require too many restrictive assumptions to betractable.

Despite these limitations, our methodology has two distinctbenefits. First, it is an economically founded approach to estimatingthe cost of chronic conditions that captures the aggregate impact onsociety rather than on individuals. Second, it enables us to describehow the labor market and capital stock--key determinants of economicgrowth--respond to NCDs and mental health conditions and thereforeincorporate adjustment mechanisms. In this paper, we describe how weapply this production function approach to Costa Rica, Jamaica, andPeru.

METHODS

We analyzed the economic burden of NCDs and mental healthconditions using the EPIC-H Plus model. EPIC-H Plus is an updatedversion of two models: 1) the original WHO EPIC model and 2) ourprevious EPIC-H model (17). The original WHO EPIC model estimates theimpact of NCDs and mental health conditions on aggregate output byquantifying reductions in the labor supply due to mortality from chronicconditions. As in the original WHO EPIC model, GDP is modeled as afunction of aggregate labor supply, the aggregate capital stock, andtechnological progress. Health is incorporated into this frameworkbecause chronic conditions, including NCDs and mental health conditions,affect the quantity of labor supplied in the model. A higher prevalenceof NCDs and mental health conditions reduces GDP because the number ofworking-age individuals, and therefore the size of the labor force,decreases.

For accuracy of predictions, modeling and coding adjustments weremade to the original WHO EPIC model to produce an updated model, whichwe refer to as EPIC-H. We subsequently developed and amended this modelto produce the augmented EPIC-H Plus extension, which additionallyincorporates labor supply reductions due to morbidity and the negativeeffects of health expenditures on output, which result from thediversion of productive savings and from reduced capital accumulation.(See Appendix B for a detailed description of data sources for theparameters used in this framework.)

The projections for national income in this framework are based onthe Solow model production function, which is given by

[Y.sub.t] = [A.sub.t][K.sup.[alpha].sub.t][L.sup.1-[alpha].sub.t](1)

where economic output in each year ([Y.sub.t]) is modeled as afunction of technological progress ([A.sub.t]), the capital stock([K.sub.t]), and the stock of labor in the economy ([L.sub.t]). Alpha([alpha]) describes how labor and capital combine to produce output. Theproduction function is calibrated based on data obtained for eachcountry, which include forecasts of population structure and theprevalence of NCDs and mental health conditions. To obtain the aggregatecost of NCDs and mental health conditions, we simulate aggregate incomefor each country over the period of interest in two scenarios: statusquo and counterfactual.

Status quo scenario

GDP gives economic output in each year as forecasted, assuming theprevalence of NCDs and mental health conditions evolves as expected overthe period of interest. We assume that no interventions that wouldreduce the mortality rate of a disease have been implemented.

Counterfactual scenario

This scenario models the complete elimination of the specifieddisease (i.e., the prevalence of NCDs and mental health conditions isset to zero), and this reduction in disease prevalence occurs withoutcost. When considered alongside the status quo scenario, thecounterfactual scenario can be used to calculate the total output lossattributable to NCDs and mental health conditions, and this will be thefocus of this article's analysis.

The model can also be extended to examine a proposed interventionscenario. In such an intervention scenario, GDP is calculated assumingthe elimination of a designated percentage of mortality for thespecified disease. For example, this could be used to evaluate anintervention that reduces the prevalence of NCDs and mental healthconditions by 10%. In this piece, we do not consider an interventionscenario as part of the analysis as we focus on estimating the aggregatecost of NCDs and mental health conditions.

After constructing the GDP projections for these two scenarios, thedifference between GDP values in the counterfactual scenario and in thestatus quo scenario gives the aggregate cost of NCDs and mental healthconditions. The sum of these differences in each year over the period ofinterest gives the total burden. Appendix A has a detailed descriptionof the modeling methodology. Further details of model functionality andderivations are given in Bloom et al. (17, 18).

RESULTS

Tables A2, A3, and A4 (see Appendix C) present baseline-caseestimates of the economic burden of NCDs and mental health conditionsfor Costa Rica, Jamaica, and Peru, during the period of 2015 to 2030.The estimates, which are given in 2015 US$, draw on WHO mortality dataand assume that the same mortality rates observed from 2005 through 2013will hold for 2015-2030. In addition to separate economic burdenestimates for each of four leading noncommunicable diseases (diabetes,cardiovascular disease (CVD), chronic respiratory disease, and cancer)and mental health conditions, estimates of the aggregate cost of allNCDs and mental health conditions are presented in each table. Theseaggregate estimates were obtained by scaling the figure for the fivedomains using the procedure based on disability-adjusted life years(DALYs) that is described by Bloom et al. (17, 18).

The costs associated with NCDs and mental health conditions in thethree countries are substantial

According to the model, all NCDs and mental health conditions willcost Costa Rica, Jamaica, and Peru, respectively, US$ 81.96 billion (US$16 143 per capita), US$ 18.45 billion (US$ 6 306 per capita), and US$477.33 billion (US$ 15 010 per capita), in 2015 US$, from 2015 through2030. Considering these countries' income per capita and the sizeof their economies, these figures represent huge costs. For Costa Rica,Jamaica, and Peru, estimates of the value of lost output are,respectively, 142%, 105%, and 255% of the countries' 2013 GDP.Furthermore, these estimates amount to more than 48 times Peru'stotal health spending in 2013, and more than 18 and 15 times that ofJamaica and Costa Rica, respectively.

Moderate variation exists in the magnitude of the burdens ofdiseases for the three countries

In Costa Rica, respiratory disease alone accounts for 20.1% of thetotal loss, followed by mental health conditions (18.6%), andcardiovascular disease (9.4%); diabetes accounts for only 6%. Peru facesa similar situation: respiratory disease (19.7%), mental healthconditions (20.9%), and cardiovascular disease (8.4%) are the threeleading contributors to lost output, while diabetes accounts for only4.2%. In Jamaica, the magnitude of the burden associated with specificdiseases varies somewhat less than in the other two countries: CVDcontributes 20.8% to the total loss, followed by cancer (13.7%) anddiabetes (13.5%).

The burden of NCDs and mental health conditions in Peru is greaterthan the burden in Costa Rica and Jamaica

Figures 2, 3, and 4 compare the output losses due to NCDs andmental health conditions in Costa Rica, Jamaica, and Peru. We presentthe output losses due to four leading noncommunicable diseases(cardiovascular disease, cancer, chronic respiratory disease, anddiabetes), mental health conditions, and total NCDs. Here, total NCDs(all NCDs plus mental health conditions) include cardiovasculardiseases, cancer, chronic respiratory diseases, cirrhosis, digestivediseases, diabetes, urogenital diseases, blood diseases, endocrinediseases, musculoskeletal disorders and other noncommunicable diseases(including congenital anomalies, skin and subcutaneous diseases, senseorgan diseases, and oral disorders), and mental health conditions.Between 2015 and 2030, Peru will suffer a larger total output loss thaneither Costa Rica or Jamaica (US$ 477.33 billion versus US$ 81.96billion and US$ 18.45 billion, respectively). This higher aggregateoutput loss may be due to Peru's larger population and initiallyhigher level of economic output. Peru has 6 times the population ofCosta Rica and almost 11 times that of Jamaica, with 4 times the GDP ofCosta Rica and almost 10 times that of Jamaica.

Peru not only has the highest output loss among the countriesstudied at the aggregate level, but also the largest at the per capitalevel (US$ 16 143). Furthermore, Peru's burden of NCDs and mentalhealth conditions is much larger when compared with its baseline GDP. In2015-2030, total losses related to NCDs and mental health conditions forCosta Rica and Jamaica, respectively, are estimated at 142% and 105% ofthe countries' 2013 GDPs, while the corresponding loss for Peruover the same time period is 255% of its 2013 GDP. NCDs and mentalhealth conditions therefore pose a larger burden for Peru's economyin both absolute and relative terms. Among chronic conditions,respiratory diseases and mental health conditions are the leading causesof lost output in Peru.

The lower per capita loss in Jamaica does not necessarily mean thatthe burden of NCDs is small. It is mostly a result of the low GDP percapita in Jamaica at the beginning of the projection period. Inaddition, Jamaica's GDP is expected to grow more slowly than thatof Peru and of Costa Rica (according to economic data from the WorldBank); as a result, the expected per capita loss will be smaller.

We also conducted sensitivity analyses by varying data sources andassumptions (Appendix D). As it is not possible to validate ourestimates directly, it is important to provide evidence that our resultsare robust to a variety of mortality scenarios. From the sensitivityanalysis, we conclude that the results are similar and robust acrossdifferent projection methods and data sources, and that the impact oftreatment cost and morbidity is quite significant.

DISCUSSION

Our study has several implications. The first is that substantialcosts are associated with NCDs and mental health conditions in thesethree countries of Latin America and the Caribbean. Unless theprevalence of chronic conditions can be reduced, the impact on economicgrowth is likely to be substantial, due to consequent reductions ineffective labor supply and capital accumulation. Correspondingly, theestimates imply that cost-effective interventions targeted at reducingthe prevalence of chronic conditions are likely to be cost-beneficialbecause of the substantial economic burden that NCDs and mental healthconditions impose. Furthermore, implementing interventions designed toreduce risk factors for NCDs is likely to lead to a 25% reduction inpremature mortality from NCDs by 2025 (a goal set forth by the WHOGlobal Action Plan for the Prevention and Control of NoncommunicableDiseases 2013-2020 (10)). Finally, these interventions could serve as astrategy to promote economic development, given the expected impact onlabor supply and capital accumulation, and therefore on economicactivity and output.

Caveats

The results we present here are based on a set of assumptions abouthow economies grow and how various inputs, including health, affecteconomic output. We assume that there is no excess labor available toreplace the labor (or rather, effective labor) lost due to NCD-relatedmortality or morbidity. This assumption may be less valid in countriesin which unemployment is high or in which there are large shadoweconomies. However, it is difficult to assess the magnitude of theseeffects on real output (as opposed to measured GDP). These assumptionsshould be borne in mind when interpreting the estimates, and this is animportant topic for future research.

Our results are also based on data that were available andaccessible at the time of writing. We have attempted to assess thesensitivity of these estimates to different information sources andassumptions; however, in pursuing this analysis, we found the dearth ofquality data to be a major impediment to estimating the economic impactof NCDs and mental health conditions. Estimates using alternativemortality sources were found to differ, albeit not substantially in mostcases. More importantly, obtaining comprehensive information on thetreatment costs associated with each disease was difficult. For example,due to a lack of country-specific data, we were forced to rely onseveral different sources to estimate treatment costs for Costa Rica. Bycontrast, the availability of country-specific treatment cost data forJamaica and Peru allowed us to provide estimates for these countriesthat are likely more accurate.

As another example of a data limitation, we determined that weshould use DALY estimates to approximate the morbidity impact ofdifferent conditions. Alternative ways of quantifying this impact relyon survey data and have the merit of providing a direct measure of theeffect of morbidity (e.g., the association between having a conditionand hours worked). However, these alternative methods may require strongassumptions about how costs are measured (e.g., that the relationship iscausal).

Moving forward, we recommend that evaluations of the impact of NCDsand mental health conditions begin by encouraging the collection ofcomprehensive data to better measure the pathways linking NCDs andmental health conditions to economic outcomes. For example, expendituresurveys based on nationally representative samples of patients in eachcountry could help to determine the actual costs associated with eachdisease of interest. Then, these estimates would not have to be inferredeither indirectly from other sources or from cost data in othercountries, as is currently necessary. Finally, although we focus onprojecting future scenarios in this paper, it would be interesting toevaluate the historical impact of NCDs on economic growth in a differentanalysis.

https://doi.org/10.26633/RPSP.2018.18

Acknowledgments. We are grateful to the staff members from theDepartment of Noncommunicable Diseases and Mental Health of the PanAmerican Health Organization (Anselm Hennis, Rosa Sandoval, BrindisOchoa, Ramon Martinez, Delia Itziar Belausteguigoitia, and CarlosSantos-Burgoa) for conducting a series of workshops in March 2015,August 2015, and May 2016. In addition, we would like to thank DanielCadarette for outstanding editorial assistance, as well as the journalreviewers and editors of this paper for their helpful comments.

Funding. We gratefully acknowledge funding from the Pan AmericanHealth Organization for this project.

Conflicts of Interest. None declared.

Disclaimer. Authors hold sole responsibility for the viewsexpressed in the manuscript, which may not necessarily reflect theopinion or policy of the RPSP/ PAJPH or PAHO.

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Manuscript received on 3 April 2017. Accepted for publication on 23August 2017.

David E. Bloom, [1] Simiao Chen, [1] and Mark E. McGovern [2]

[1] Department of Global Health and Population, Harvard T.H. ChanSchool of Public Health, Boston, Massachusetts, United States ofAmerica. Send correspondence to David E. Bloom at[emailprotected]

[2] CHaRMS--Centre for Health Research at Queen's ManagementSchool, Queen's University Belfast, Belfast, Antrim, NorthernIreland.

Caption: FIGURE 1. Percent of total population aged 60+ in CostaRica, Jamaica, Peru, Latin America and the Caribbean, and the world, in1980, 2015, and 2050

TABLE 1. Summary statistics for Costa Rica, Jamaica, and Peru CostaStatistic Rica Jamaica PeruPopulation (millions, 2014) 4.8 2.7 312014 gross domestic product 29.4 11.2 127.7(billions, 2005 constant US$)2014 gross domestic product 6 188 4 112 4 124per capita (2005 constant US$)Savings rate (%) (a) 17 15 21Life expectancy (years, 2013) 79.2 73.4 74.3Percentage of persons 60+ (2015) 12.8 12.8 10.0Source: Data from the World Bank (http://data.worldbank.org/).(a) The savings rate is the average rate between 2011 and 2014.FIGURE 2. Estimates of lost gross domestic product (GDP) output due tofour leading noncommunicable diseases (NCDs), mental health conditions,and all NCDs and mental health conditions in Costa Rica, Jamaica, andPeru, 2015-2030 Costa Rica Jamaica PeruDiabetes 4.88 2.48 19.81Cardiovasculardisease 7.69 3.83 39.90Respiratorydisease 16.44 1.03 93.91Cancer 6.48 2.52 30.78Mental healthconditions 15.26 2.76 99.52All NCDsand mentalhealthconditions 81.96 18.45 477.33Source: Prepared by the authors based on the results of the study.Note: Table made from bar graph.FIGURE 3. Estimates of lost gross domestic product (GDP) per capitaoutput due to four leading noncommunicable diseases (NCDs), mentalhealth conditions, and all NCDs and mental health conditions in CostaRica, Jamaica, and Peru, 2015-2030 Costa Rica Jamaica PeruDiabetes 961 848 623Cardiovasculardisease 1 514 1 310 1 255Respiratorydisease 3 238 352 2 950Cancer 1 275 862 968Mental healthconditions 3 005 944 3 129All NCDsand mentalhealthconditions 16 143 6 306 15 010Source: Prepared by the authors based on the results of the study.Note: Table made from bar graph.FIGURE 4. Estimates of lost gross domestic product (GDP) output due tononcommunicable diseases and mental health conditions for 2015-2030 asa percentage of 2013 GDP (in constant 2015 US$)PercentageCosta Rica 142%Jamaica 105%Peru 155%Source: Prepared by the authors based on the results of the study.Note: Table made from bar graph.

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The economic burden of noncommunicable diseases and mental health conditions: results for Costa Rica, Jamaica, and Peru/ La carga economica de las enfermedades no transmisibles y la enfermedad mental: resultados para Costa Rica, Jamaica y Peru/ A carga ec (2024)

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