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Impact of the Health Gym Program on hospital admissions for stroke in the state of Pernambuco, Brazil

Impacto do Programa Academia da Saúde nas internações hospitalares por acidente vascular cerebral no Estado de Pernambuco, Brasil

Impacto del Programa Academia de la Salud en los ingresos hospitalarios por accidente cerebrovascular en el estado de Pernambuco, Brasil

Abstracts

This study aimed to evaluate the impact of the Health Gym Program (HGP) on hospital admissions for stroke in the state of Pernambuco, Brazil. This policy impact evaluation used a quasi-experimental approach consisting of a difference-in-differences estimator, weighted by propensity score matching to deal with potential confounding variables. The study comprised socioeconomic, demographic, and epidemiological data from official Brazilian databases from 2010 to 2019. The treatment group was composed of the 134 municipalities that implemented the HGP since 2011. The 51 municipalities that did not were allocated to the comparison group. The nearest neighbor algorithm (N5) was used to pair treatment and comparison group municipalities and create the weights to evaluate the average treatment effect on the treated (ATT) in the difference-in-differences estimator. In 2010, 2,771 people were hospitalized for stroke (0.51% of all hospitalizations) and in 2019, 11,542 (2%). Municipalities that implemented the HGP had 18.37% fewer hospitalizations than their counterparts in the comparison group. The program’s impact in reducing hospitalization rates was incrementally greater among men (ATT: -0.1932) and those aged 71 to 80 years (ATT: -0.1911). All results were statistically significant at the 5% level. The HGP reduced hospitalization for stroke in several population groups, but primarily in those whose underlying prevalence of stroke is highest, reinforcing the importance of public investments in health promotion policies designed to encourage lifestyle changes.

Keywords:
Program Evaluation; Chronic Disease; Health Promotion; Propensity Score; Stroke


O estudo teve como objetivo avaliar o impacto do Programa Academia da Saúde (PAS) nas internações hospitalares por acidente vascular cerebral (AVC) no Estado de Pernambuco, Brasil. Esta avaliação de impacto das políticas utilizou uma abordagem quase-experimental que consiste em um estimador de diferença-em-diferenças, ponderado pelo pareamento por escore de propensão para lidar com possíveis fatores de confusão. O estudo foi composto por dados socioeconômicos, demográficos e epidemiológicos de bases de dados oficiais brasileiras entre os anos de 2010 e 2019. O grupo de tratamento foi composto pelos 134 municípios que implantaram o PAS a partir de 2011, e os 51 municípios que não implantaram foram alocados no grupo de comparação. O algoritmo do vizinho mais próximo (N5) foi utilizado para emparelhar os municípios tratados e comparar aos municípios do grupo controle, criando os pesos que foram utilizados para avaliar o efeito médio do tratamento sobre o tratado (ATT) no estimador de diferença-em-diferenças. Houve 2.771 internações por AVC em 2010 (0,51% de todas as internações) e 11.542 (2%) em 2019. Os municípios que implementaram o PAS tiveram 18,37% menos internações em comparação com seus homólogos no grupo de comparação. O impacto do programa na redução das taxas de internação foi maior entre os homens (ATT: -0,1932) e naqueles com idade entre 71 e 80 anos (ATT: -0,1911). Todos os resultados foram estatisticamente significativos em um nível de 5%. O PAS reduziu a hospitalização por AVC em vários grupos populacionais, mas principalmente naqueles em que a prevalência subjacente de AVC é mais alta, reforçando a importância dos investimentos públicos em políticas de promoção da saúde destinadas a estimular mudanças nos estilos de vida.

Palavras-chave:
Avaliação de Programas e Projetos de Saúde; Doença Crônica; Promoção da Saúde; Pontuação de Propensão; Acidente Vascular Cerebral


El objetivo de este trabajo es evaluar el impacto del Programa Academia de la Salud (PAS) en los ingresos hospitalarios por accidente cerebrovascular (ACV) en el estado de Pernambuco, Brasil. Esta evaluación del impacto de la política utilizó un enfoque cuasi-experimental que consiste en un estimador de diferencias en diferencias, ponderado por el emparejamiento de puntuación de propensión para hacer frente a posibles factores de confusión. El estudio incluyó datos socioeconómicos, demográficos y epidemiológicos de bases de datos oficiales brasileñas de 2010 a 2019. El grupo de tratamiento se compuso de los 134 municipios que implementaron el PAS a partir de 2011 y los 51 municipios que no lo hicieron se asignaron al grupo de comparación. Se utilizó el algoritmo del vecino más próximo (N5) para emparejar los municipios tratados y los del grupo de comparación y crear las ponderaciones que se emplearon para evaluar el efecto medio del tratamiento sobre los tratados (ATT) en el estimador de diferencias en diferencias. Hubo 2.771 hospitalizaciones por ACV en 2010 (0,51% de todas las hospitalizaciones) y 11.542 (2%) en 2019. Los municipios que aplicaron el PAS tuvieron un 18,37% menos de hospitalizaciones en comparación con sus homólogos del grupo de comparación. El impacto del programa en la reducción de las tasas de hospitalización fue gradualmente mayor entre los hombres (ATT: -0,1932) y entre las personas de 71 a 80 años (ATT: -0,1911). Todos los resultados fueron estadísticamente significativos al nivel del 5%. El PAS redujo la hospitalización por ACV en varios grupos de población, pero principalmente en aquellos en los que la prevalencia subyacente de ACV es mayor, lo que refuerza la importancia de las inversiones públicas en políticas de promoción de la salud diseñadas para impulsar cambios en los estilos de vida.

Palabras-clave:
Evaluación de Programas y Proyectos de Salud; Enfermedad Crónica; Promoción de la Salud; Puntaje de Propensión; Accidente Cerebrovascular


Introduction

Stroke is one of the most prevalent cerebrovascular diseases and main causes of morbidity worldwide 11. Souza DK, Peixoto SV. Descriptive study on the evolution of hospitalization costs for ambulatory care sensitive conditions in Brazil, 2000-2013. Epidemiol Serv Saúde 2017; 26 Suppl 2:285-94.,22. Damata SR, Formiga LM, Araújo AK, Oliveira EA, Oliveira AK, Formiga RC. Epidemiological profile of elderly afflicted with stroke. Revista Interdisciplinar 2016; 9 Suppl 1:107-17.. In 2017, 150.5 new cases of stroke were registered for each group of 100,000 people around the world. The global prevalence of stroke is higher in men and individuals over 65 years 33. Avan A, Digaleh H, Di Napoli M, Stranges S, Behrouz S, Shojaeianbabaei G, et al. Socioeconomic status and stroke incidence, prevalence, mortality, and worldwide burden: an ecological analysis from the Global Burden of Disease Study 2017. BMC Med 2019; 17:191-221..

In Brazil, the prevalence of stroke per 100,000 inhabitants was 1,008.02 in 2016. This rate was higher in older people (6,816.73) and among men (1,153.94) 44. Departamento de Informática do SUS. Morbidade hospitalar do SUS - por local de residência - Brasil. http://tabnet.datasus.gov.br/cgi/deftohtm.exe?sih/cnv/nruf.def (accessed on 18/Nov/2020).
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. Stroke was also the main cause of hospitalization in the circulatory system diseases subgroup in 2018 44. Departamento de Informática do SUS. Morbidade hospitalar do SUS - por local de residência - Brasil. http://tabnet.datasus.gov.br/cgi/deftohtm.exe?sih/cnv/nruf.def (accessed on 18/Nov/2020).
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,55. Dantas LF, Marchesi JF, Peres IT, Hamacher S, Bozza FA, Quintano Neira RA. Public hospitalizations for stroke in Brazil from 2009 to 2016. PLoS One 2019; 14:e0213837..

Health promotion interventions can reduce the incidence and costs of hospitalization and rehabilitation for stroke 66. Rodrigues MS, Santana LF, Galvão IM. Fatores de risco modificáveis e não modificáveis do AVC isquêmico: uma abordagem descritiva. Rev Med (São Paulo) 2017; 96:187-92.,77. Kyu HH, Bachman VF, Alexander LT, Mumford JE, Afshin A, Estep K, et al. Physical activity and risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events: systematic review and dose-response meta-analysis for the Global Burden of Disease Study 2013. BMJ 2016; 354:364.,88. Kaur P, Kwatra G, Kaur R, Pandian JD. Cost of stroke in low and middle income countries: a systematic review. Int J Stroke 2014; 9:678-82.. Thus, the Brazilian Ministry of Health has invested in public policies to reduce morbidity and, consequently, hospitalizations for chronic diseases (including stroke)99. Silva RN, Guarda FRB, Hallal PC, Martelli PJL. Avaliabilidade do Programa Academia da Saúde no Município do Recife, Pernambuco, Brasil. Cad Saúde Pública 2017; 33:e00159415.,1010. Guarda FRB, Silva RN, Feitosa WMN, Santos Neto PM, Araújo Júnior JLAC. Caracterização das equipes do Programa Academia da Saúde e do seu processo de trabalho. Rev Bras Ativ Fís Saúde 2015; 20:638-49.. The main device used to advance this goal has been the Health Gym Program (HGP), which contributes to health promotion by establishing public spaces, infrastructure, and qualified professionals, designed to promote health and physical activity 99. Silva RN, Guarda FRB, Hallal PC, Martelli PJL. Avaliabilidade do Programa Academia da Saúde no Município do Recife, Pernambuco, Brasil. Cad Saúde Pública 2017; 33:e00159415..

The physical structure of the program follows standards established by the Brazilian Ministry of Health, which finances the construction of public spaces (called HGP hubs) to carry out health promotion activities. The Federal Government finances these hubs and their monthly costs. Each hub of the program is a public leisure space that also has rooms for public use, a walking and running track, and equipment for physical exercises 1111. Ministério da Saúde. Portaria de Consolidação nº 5, de 28 de setembro de 2017. Consolidação das normas sobre as ações e os serviços de saúde do Sistema Único de Saúde. Diário Oficial da União 2012; 29 set..

The program guidelines establish that the HGP activities can be developed by nurses, nutritionists, physical therapists, psychologists, and physical education professionals, among other professionals 1111. Ministério da Saúde. Portaria de Consolidação nº 5, de 28 de setembro de 2017. Consolidação das normas sobre as ações e os serviços de saúde do Sistema Único de Saúde. Diário Oficial da União 2012; 29 set.. The program’s actions include encouraging healthy eating habits, health education activities on many topics, activities focused on changing health risk behaviors (smoking, alcohol use, and stress control), encouraging the autonomous practice of physical activities, and offering exercises guided by physical education professionals (walking, running, gym and dance classes, and resistance training) 1212. Silva AMM, Mambrini JVM, Peixoto SV, Malta DC, Lima-Costa MF. Use of health services by Brazilian older adults with and without functional limitation. Rev Saúde Pública 2017; 51 Suppl 1:5s.,1313. Sá GBAR, Dornelles GC, Cruz KG, Amorim RCA, Andrade SSCA, Oliveira TP, et al. O Programa Academia da Saúde como estratégia de promoção da saúde e modos de vida saudáveis: cenário nacional de implementação. Ciênc Saúde Colet 2016; 21:1849-60..

Evidence shows that health promotion programs developed within the scope of primary health care can reduce the incidence of stroke cases 77. Kyu HH, Bachman VF, Alexander LT, Mumford JE, Afshin A, Estep K, et al. Physical activity and risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events: systematic review and dose-response meta-analysis for the Global Burden of Disease Study 2013. BMJ 2016; 354:364.,1414. Ministério da Saúde. Diretrizes de atenção à reabilitação da pessoa com acidente vascular cerebral. https://bvsms.saude.gov.br/bvs/publicacoes/diretrizes_atencao_reabilitacao_acidente_vascular_cerebral.pdf (accessed on 09/Aug/2022).
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,1515. Eckermann S, Willan AR. Active Lives South Australia health economic analysis: an evidence base for the potential of health promotion strategies supporting physical activity guidelines to reduce public health costs while improving wellbeing. Z Gesundh Wiss 2021; 30:1791-807.. Thus, the health promotion actions encouraged by the HGP can reduce the population’s exposure to modifiable risk factors for strokes, such as obesity, dyslipidemia, smoking, hypertension, and physical inactivity 99. Silva RN, Guarda FRB, Hallal PC, Martelli PJL. Avaliabilidade do Programa Academia da Saúde no Município do Recife, Pernambuco, Brasil. Cad Saúde Pública 2017; 33:e00159415.,1010. Guarda FRB, Silva RN, Feitosa WMN, Santos Neto PM, Araújo Júnior JLAC. Caracterização das equipes do Programa Academia da Saúde e do seu processo de trabalho. Rev Bras Ativ Fís Saúde 2015; 20:638-49..

The state of Pernambuco, Brazil, has 267 HGP hubs in 134 municipalities since 2011, representing 9.66% of hubs countrywide, which places it as the second Brazilian state with the most program hubs implemented 1616. Simões EJ, Hallal PC, Siqueira FV, Schmaltz C, Menor D, Malta DC, et al. Effectiveness of a scaled up physical activity intervention in Brazil: a natural experiment. Prev Med 2016; 103S:S66-72..

The adoption of a physically active lifestyle reduces the risk of stroke development 77. Kyu HH, Bachman VF, Alexander LT, Mumford JE, Afshin A, Estep K, et al. Physical activity and risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events: systematic review and dose-response meta-analysis for the Global Burden of Disease Study 2013. BMJ 2016; 354:364. and evidence showed the effectiveness of the HGP in increasing the population’s physical activity level 1717. Departamento de Informática do SUS. Morbidade hospitalar por acidente vascular cerebral, 2019. http://tabnet.datasus.gov.br/cgi/deftohtm.exe?sih/cnv/nruf.def (accessed on 09/Aug/2022).
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and reducing mortality from arterial hypertension in Pernambuco 1818. Rodrigues BLS, Silva RN, Arruda RG, Silva PBC, Silva DKF, Guarda FRB. Impact of the Health Gym Program on mortality from systemic arterial hypertension in Pernambuco state, Brazil. Ciênc Saúde Colet 2021; 26:6199-210.. Still, few studies on the impact of HGP on health indicators exist, and it is still unknown how this program can mitigate hospitalizations for stroke and other chronic diseases.

This study aims to evaluate the impact of the HGP on hospitalization rates for stroke in the state of Pernambuco.

Methods

This study adopts a quasi-experimental approach that combines difference-in-differences (DD) with propensity score matching (PSM), herein referred to as double difference matching (DDM) 1919. Heckman J, Ichimura H, Smith J, Todd P. Characterizing selection bias using experimental data. Econometrica 1998; 66:1017-98.,2020. Blundell R, Dias MC. Evaluation methods for non-experimental data. Fisc Stud 2000; 21:427-68.,2121. Ravallion M. Evaluating anti-poverty programs. In: Evenson RE, Schultz TP, editors. Handbook of development economics. Amsterdam: World Bank; 2005. p. 2-79..

The study used annual panel data from 2007 (four years before the implementation of the HGP) to 2019 (eight years after its implementation) for all 185 municipalities in Pernambuco. The 2,405 observations focus on the population over 40 years of age, and derive from many official databases, including the Brazilian Health Informatics Department (DATASUS), the Brazilian Institute of Geography and Statistics (IBGE), the Information System on Public Budgets for Health (SIOPS), and the Brazilian National Institute for Educational Studies and Research (INEP).

The 185 municipalities in Pernambuco were dichotomized into two groups. The treatment group comprised 134 municipalities that implemented the HGP since 2011; and the comparison group comprised 51 municipalities that never implemented the program over the study period.

The effectiveness of the HGP was measured as the difference in the percentage of hospitalizations for stroke in the period after the implementation of HGP (2011 to 2019) compared to equivalent figures for the pre-treatment period (2007 to 2010) after controlling for covariates.

The main outcome variable used in this study was the natural logarithm of hospitalization for stroke. Potential confounding variables were selected based on the Andersen and Newman healthcare utilization model 2222. Almeida APSC, Nunes BP, Duro SMS, Facchini LA. Socioeconomic determinants of access to health services among older adults: a systematic review. Rev Saúde Pública 2017; 51:50-65.. This conceptual framework is widely used to evaluate health service use in many contexts 2222. Almeida APSC, Nunes BP, Duro SMS, Facchini LA. Socioeconomic determinants of access to health services among older adults: a systematic review. Rev Saúde Pública 2017; 51:50-65., including in Brazil 1212. Silva AMM, Mambrini JVM, Peixoto SV, Malta DC, Lima-Costa MF. Use of health services by Brazilian older adults with and without functional limitation. Rev Saúde Pública 2017; 51 Suppl 1:5s.. This framework privileges three main factors as instrumental in driving health service use: predisposing factors (such as sex and age), enabling factors (such as socioeconomic status), and needs-based factors (such as health status and comorbid conditions). This study considered sex and the proportion of individuals in different age groups as predisposing factors. The number of physicians and gross domestic product (GDP) per capita were used as enabling factors. The number of hospitalizations for hypertension and the number of people with excess weight (overweight and obesity) per each inhabitant in the municipalities were used as needs-based factors.

The covariates used in this study were identified based on scientific evidence about the possible confounding effects that they may have on the relationship between risk exposure and the outcome of interest in Brazil. Thus, the epidemiological model that guided the selection of explanatory variables used as reference a set of studies that point to factors associated with hospitalizations for circulatory system and cerebrovascular diseases and stroke in the Brazilian population 2323. Schmidt MH, Selau CM, Soares PS, Franchi EF, Piber VD, Quatrin LB. Acidente vascular cerebral e diferentes limitações: uma análise interdisciplinar. Arq Ciênc Saúde UNIPAR 2019; 23:139-44..

The analysis model used a set of covariates that constitute the municipalities’ socioeconomic, demographic, and health aspects. Socioeconomic variables were annual total health expenditure, GDP per capita, and the proportion of federal social benefits for a highly poor population per 10,000 inhabitants. Demographic variables included the general population by municipality, proportion of residents for each age group (41 to 50, 51 to 60, 61 to 70, 71 to 80, and 81 years and over) per 1,000 inhabitants, and high school pass rate. The variables related to the health aspects were the availability of hospital beds for every 10,000 inhabitants, proportion of people with excess weight per inhabitants, number of physicians, and number of hospitalizations for stroke.

Data analysis

Descriptive statistics (frequencies, means, and standard deviations) were used to characterize and evaluate differences between the socioeconomic, demographic, and epidemiological profile of municipalities in the treatment and comparison groups.

Student’s t-test was used to estimate the differences between the means regarding the hospitalizations of those exposed and not exposed to the HGP, and Fisher’s exact test was used for proportions.

The analytical approach includes a pre-test of the estimation model, the evaluation of the impact of the HGP on the frequency of hospitalizations for stroke using DD and DDM estimators, and post-tests to validate the results.

Pre-tests of the model and empirical strategy

We constructed a graph with the means of the frequency of hospital admissions for stroke (dependent variable) in the pre-treatment period (2007 to 2010) to indirectly test the DD method assumption of a “parallel trend” for the period before the implementation of the HGP 2424. Khandker SR, Koolwal GB, Samad HA. Handbook on impact evaluation: quantitative methods and practices. Washington DC: The World Bank; 2010.. This procedure allows an indirect validation of the sample of counterfactuals selected for the DD model.

The Hausman test verified the hypothesis of endogeneity of the random term 2525. Wooldridge J. Introdução à econometria: uma abordagem moderna. 3rd Ed. São Paulo: Cengage Learning; 2016.. The presence of serial autocorrelation between the regression residuals was also tested using the Wooldridge and Wald tests 2424. Khandker SR, Koolwal GB, Samad HA. Handbook on impact evaluation: quantitative methods and practices. Washington DC: The World Bank; 2010.,2626. Gertler P, Martinez S, Premand P, Rawlings L, Vermeersch C. Impact evaluation in practice. Washington DC: The World Bank; 2010..

Evaluation of the impact of the HGP

Propensity score matching

The implementation of the HGP by municipalities was not random 99. Silva RN, Guarda FRB, Hallal PC, Martelli PJL. Avaliabilidade do Programa Academia da Saúde no Município do Recife, Pernambuco, Brasil. Cad Saúde Pública 2017; 33:e00159415.,1010. Guarda FRB, Silva RN, Feitosa WMN, Santos Neto PM, Araújo Júnior JLAC. Caracterização das equipes do Programa Academia da Saúde e do seu processo de trabalho. Rev Bras Ativ Fís Saúde 2015; 20:638-49.. This lack of randomization in the treatment and comparison groups may result in potential sorting or selection biases 1919. Heckman J, Ichimura H, Smith J, Todd P. Characterizing selection bias using experimental data. Econometrica 1998; 66:1017-98.,2727. Becker SO, Ichino A. Estimation of average treatment effects based on propensity scores. Stata J 2002; 2:358-77.,2828. Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat 2011; 10:150-61.. To reduce the possibility of this statistical problem, the study used the PSM method, which compares the treatment and comparison groups concerning socioeconomic, demographic, and/or health characteristics, and estimates the probability of municipalities joining the HGP based on these profiles, thus creating a counterfactual scenario that enables comparisons between the groups 1919. Heckman J, Ichimura H, Smith J, Todd P. Characterizing selection bias using experimental data. Econometrica 1998; 66:1017-98.,2727. Becker SO, Ichino A. Estimation of average treatment effects based on propensity scores. Stata J 2002; 2:358-77.,2828. Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat 2011; 10:150-61..

The matching procedure uses a balanced score, computed from a logit regression model that used a binary dependent variable of 1 for the municipalities that implemented the HGP; otherwise, it was 0. The propensity score defines the probability of a municipality benefiting from the HGP, given its socioeconomic, demographic, and health-related characteristics 1919. Heckman J, Ichimura H, Smith J, Todd P. Characterizing selection bias using experimental data. Econometrica 1998; 66:1017-98.,2828. Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat 2011; 10:150-61.. The estimated propensity scores were used to compute the weights needed to balance the treatment and comparison groups so that, on average, they become similar to each other 1919. Heckman J, Ichimura H, Smith J, Todd P. Characterizing selection bias using experimental data. Econometrica 1998; 66:1017-98.,2727. Becker SO, Ichino A. Estimation of average treatment effects based on propensity scores. Stata J 2002; 2:358-77.,2828. Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat 2011; 10:150-61..

After defining the weights, the blocks of municipalities with similar characteristics were defined and the average treatment effect on the treated (ATT) was estimated using the “nearest neighbor” pairing algorithm (1:5) with replacement, which has the closest propensity score (measured by the absolute difference between the scores). Using this method, each municipality in the treatment group (municipality benefiting from the HGP) was paired with a municipality in the comparison group (municipality not benefiting from the HGP) with the closest propensity score value.

The Stata application “psmatch2” (https://www.stata.com) was used to estimate the propensity score and the ATT. As a robustness test of the PSM, a balancing test was conducted to verify statistical similarities between the matched variables before and after the implementation of the HGP. All statistical tests adopted a 5% significance level.

Difference-in-differences method

The DD estimator is a method used in quasi-experimental approaches to evaluate interventions, using information about the participants (treatment group) and non-participants (comparison group), collected before an intervention/policy was applied and compared to the same information after its application. This procedure allows investigators to build counterfactual scenarios by estimating the difference in the differences in the results observed in a given period 2929. Cavalcante DFB, BrizonVSC, Probst LF, Meneghim MC, Pereira AC, Ambrosano GMB. Did the Family Health Strategy have an impact on indicators of hospitalizations for stroke and heart failure? Longitudinal study in Brazil: 1998-2013. PLoS One 2018; 13:e0198428..

This study used the difference-in-differences method to assess the impact of the HGP, after estimating the propensity scores, since some unobservable characteristics may affect the outcome variable, even with PSM 1919. Heckman J, Ichimura H, Smith J, Todd P. Characterizing selection bias using experimental data. Econometrica 1998; 66:1017-98.,2121. Ravallion M. Evaluating anti-poverty programs. In: Evenson RE, Schultz TP, editors. Handbook of development economics. Amsterdam: World Bank; 2005. p. 2-79..

The DD model in this study considered many implementation periods from 2011 to 2019 since the implementation of the HGP in the municipalities occurred in different years since 2011. Moreover, fixed effects were added to the DD model by municipality and by year.

The impact evaluation (DD and DDM) used a dummy variable (presence of the HGP) that simultaneously shows if the municipality was in the treatment group and in which year the implementation took place. For the comparison group, this variable assumes a value of 0 for the entire study period. For the treatment group, “presence of the HGP” assumed the value 0 for the period before the implementation and value 1 for the year of implementation and subsequent years.

Double difference matching

After performing PSM, the DD model was weighted by the weights derived from PSM. This combined approach yields estimates of the impact of the HGP on hospitalizations for stroke in municipalities matched by common support.

Therefore, the estimator for ATT was the mean difference estimated for the exposed municipalities minus the mean difference of the municipalities in the comparison group matched to the treatment ones.

The calculation of the impact of the HGP on the hospitalizations for stroke by gender and age group was conducted using specific models for each of these populational groups. These models considered the natural logarithms of the hospitalizations. Thus, a model with a different dependent variable for each population stratum was used.

Both DD and DDM estimations were conducted from a cluster-robust variance-covariance matrix of municipalities to correct eventual problems of serial correlation of residuals and heteroscedasticity 3030. Hoechle D. Robust standard errors for panel regressions with cross-sectional dependence. Stata J 2007; 7:281-312.,3131. Cameron CA, Gelbach JB, Miller DL. Bootstrap-based improvements for inference with clustered errors. Rev Econ Stat 2008; 90:414-27..

Post-test of the results

The impact of the HGP on the hospitalizations for stroke was estimated, as a robustness test, using a placebo-dependent variable that from a theoretical point of view is not directly influenced by the effects of the program. Thus, the frequency of hospitalizations for colon cancer was used as the placebo variable.

Results

This section begins with a descriptive summary of the study’s municipality data and proceeds to the pre-tests results, followed by the estimates of the matching by PSM and their balancing test. Then, we present the results of the impact analyses of the HGP on hospitalization for stroke (after controlling for potential selection bias and an array of covariates) using the DD and DDM methods. Finally, we present the robustness of our findings, via the post-test (placebo regression).

In 2010, 540,045 hospitalizations for all causes were recorded in Pernambuco, with 2,771 for stroke (0.51% of all hospitalizations), whereas in 2019, 574,637 hospitalizations were recorded, with 11,542 for stroke (2%).

Table 1 describes that the municipalities in the treatment group had a lower proportion of overweight individuals per inhabitant in 2010 and a higher high school pass rate in 2019 than municipalities in the comparison group, and this difference was statistically significant at a 5% level.

Regarding hospitalizations for stroke, the treated municipalities in 2010 registered more occurrences in general (mean = 14.97; SD = 25.79) and by sex (mean = 7.75; SD = 3.45 among women).

Table 1
Characteristics of the municipalities that implemented and did not implement the Health Gym Program in the state of Pernambuco, Brazil, 2010 and 2019.

Pre-tests of the model

The first pre-test verified the assumption of the parallel trend to the DD model. Figure 1 shows that the lines representing the mean frequency of hospitalizations for stroke for the treatment and control groups have the same curve and follow the same trajectory in the period before the implementation of the HGP (2007 to 2010).

Figure 1
Trend in the mean of hospitalizations for stroke in treated and control municipalities. State of Pernambuco, Brazil, 2007 to 2019.

The Hausman and Wald tests for heteroscedasticity presented statistically significant results at the 1% level (Prob > χ2 < 0.01), showing that the functional form of the estimates (fixed effect models) was adequate for this impact evaluation and that the model is heteroscedastic. The Wooldridge pre-test showed no serial correlation of the regression residuals (Prob > F ≤ 0.01).

Estimates of the matching

The PSM model included the following variables: availability of hospital beds for 10,000 inhabitants, proportion of people with excess weight per inhabitants, number of physicians, number of hospitalizations for hypertension, general population by municipality, high school pass rate, annual total health expenditure, GDP per capita, proportion of federal social benefits for highly poor population per 10,000 inhabitants (Table 2). For this model, the observed ATT shows that the presence of HGP caused an overall reduction of 10.59% in hospitalizations for stroke (ATT = -0.1059; standard error = 0.059) and this result was statistically significant at the 10% level (t-stat = -1.79).

Table 2 presents the results of the balancing test of the matching method, showing that the means of the variables for the treatment and control groups became statically equal after matching. Moreover, all variables presented more than 40% bias reduction, showing that the treatment and control groups are balanced (Rubin statistics R = 0.64, B = 14.8, respectively).

Table 2
Test of difference in means of treated and control groups before and after matching and balance test. Health Gym Program, state of Pernambuco, Brazil, 2007 to 2019.

Regarding the quality of the matching method, Figure 2 shows that the distribution of the observable characteristics of the treatment and control groups before matching was different. The probability distribution in the control group was concentrated in the tail to the left. However, the distribution of these characteristics moved to the center after matching, showing that it reduced the differences in the estimated probability distribution and made the two groups more similar (and different only regarding the presence of the HGP).

Figure 2
Distribution of treatment probability for treated and controls; before and after matching.

Impact assessment of the HGP on hospitalizations for stroke

We tested the impact of the HGP on the frequency of hospitalization for stroke before and after treatment using two regression models: DD and DDM. Table 3 shows the results. Use of the DDM yielded an estimated average treatment effect (ATE) of -0.1837 fewer hospitalizations for stroke for the HGP. This reduction was even greater for men (ATE = -19.32). Regarding the age group, a 19.11% reduction in hospitalizations for stroke was reported for those aged between 71 and 80 years when compared with the same age group in the municipalities that did not implement the HGP.

Table 3
Impact of the Health Gym Program (HGP) on hospital admissions for stroke, and placebo regression coefficients. State of Pernambuco, Brazil, 2007 to 2019.

Post-estimation test (robustness test of the findings)

We used the falsification test to verify if the treatment variable impacted the placebo outcome. Table 3 shows that the presence of HGP does not impact the reduction of hospitalizations for colon cancer (positive coefficient to the ATT, and no statistical significance at 5% level).

Discussion

Although a general downward trend in hospitalizations for stroke has been noted across Brazil, the opposite was reported in Pernambuco from 2010 to 2018 3333. Barreto IJB, Guarda FRB, Silva RN, Farias SJM, Silva AEA, Silva PBC. Gastos com internações hospitalares por doenças relacionadas à inatividade física no Brasil. Lecturas: Educación Física y Deportes 2020; 25:29-43.. The frequency of hospitalizations for stroke as part of hospital admissions for all causes also increased, representing 1.3% of frequency on all hospitalizations in 2018 3333. Barreto IJB, Guarda FRB, Silva RN, Farias SJM, Silva AEA, Silva PBC. Gastos com internações hospitalares por doenças relacionadas à inatividade física no Brasil. Lecturas: Educación Física y Deportes 2020; 25:29-43., whereas in our study stroke represented 2% of hospital admissions on all causes.

In 2010, just before the implementation of the HGP in 2011, the treatment group municipalities had better health-related and demographic indicators, such as a lower proportion of overweight individuals per inhabitant and a higher high school pass rate than the comparison municipalities. Evidence suggests that these characteristics result in higher hospitalization rates for many chronic diseases, including cerebrovascular accidents 3232. Lopes JM, Sanchis GJB, Medeiros JLA, Dantas FG. Hospitalization for ischemic stroke in Brazil: an ecological study on the possible impact of Hiperdia. Rev Bras Epidemiol 2016; 19:122-34.,3434. Pazó RG, Frauches DO, Molina MCB, Cade NV. Modelagem hierárquica de determinantes associados a internações por condições sensíveis à atenção primária no Espírito Santo, Brasil. Cad Saúde Pública 2014; 30:1891-902.,3535. Poorthuis M, Algra AM, Algra A, Kappelle LJ, Klijn C. Female- and male-specific risk factors for stroke: a systematic review and meta-analysis. JAMA Neurol 2017; 74:75-81..

Pazó et al. 3434. Pazó RG, Frauches DO, Molina MCB, Cade NV. Modelagem hierárquica de determinantes associados a internações por condições sensíveis à atenção primária no Espírito Santo, Brasil. Cad Saúde Pública 2014; 30:1891-902. showed that literacy was associated with hospital admissions for primary care sensitive conditions (including stroke) in the state of Espírito Santo, Brazil. Moreover, evidence suggests that the prevalence of stroke in the Brazilian population decreases among more educated people and increases in older and obese people 44. Departamento de Informática do SUS. Morbidade hospitalar do SUS - por local de residência - Brasil. http://tabnet.datasus.gov.br/cgi/deftohtm.exe?sih/cnv/nruf.def (accessed on 18/Nov/2020).
http://tabnet.datasus.gov.br/cgi/deftoht...
,55. Dantas LF, Marchesi JF, Peres IT, Hamacher S, Bozza FA, Quintano Neira RA. Public hospitalizations for stroke in Brazil from 2009 to 2016. PLoS One 2019; 14:e0213837.,2323. Schmidt MH, Selau CM, Soares PS, Franchi EF, Piber VD, Quatrin LB. Acidente vascular cerebral e diferentes limitações: uma análise interdisciplinar. Arq Ciênc Saúde UNIPAR 2019; 23:139-44..

Regarding the profile of hospital admissions by sex, this study found higher rates of hospitalization for stroke among women, thus corroborating the study by Gomez et al. 3636. Gomez PVL, Balk RS, Castro AAM, Lara S, Graup S. Perfil de pacientes com acidente vascular cerebral atendidos por um programa de extensão universitário na atenção básica. Fisioter Bras 2018; 19:464-71..

The first pre-test of the model verified the feasibility of using the DD estimator to assess the impact of the HGP on the frequency of hospitalizations for stroke. This test verified the assumption of the DD method, which requires that the trajectory of the dependent variable for the treatment and control groups must be parallel in the period before the implementation of a policy 3737. Malani A, Reif J. Interpreting pre-trends as anticipation: impact on estimated treatment effects from tort reform. J Public Econ 2015; 124:1-17.,3838. Foguel M. Modelo de resultados potenciais. In: Menezes-Filho NA, Pinto CCX, editors. Avaliação econômica de projetos sociais. São Paulo: Fundação Itaú Social; 2017. p. 39-54.,3939. Collischon M. Methods to estimate causal effects: an overview on IV, DiD, and RDD and a guide on how to apply them in practice. SocArXiv 2021; 18 mar. https://osf.io/preprints/socarxiv/usvta/.
https://osf.io/preprints/socarxiv/usvta/...
. The result of this test allows us to infer that unobservable characteristics of the municipalities that implemented and did not implement the HGP had a similar impact in these two groups before the program’s implementation. Thus, the difference in the number of hospitalizations for stroke between the exposed and the unexposed municipalities reflects only the average effect of the program after its implementation 3838. Foguel M. Modelo de resultados potenciais. In: Menezes-Filho NA, Pinto CCX, editors. Avaliação econômica de projetos sociais. São Paulo: Fundação Itaú Social; 2017. p. 39-54.,3939. Collischon M. Methods to estimate causal effects: an overview on IV, DiD, and RDD and a guide on how to apply them in practice. SocArXiv 2021; 18 mar. https://osf.io/preprints/socarxiv/usvta/.
https://osf.io/preprints/socarxiv/usvta/...
.

Some studies already showed the variables constituting the estimation models (PSM, DD, and DDM) as associated with hospital admissions for stroke 3232. Lopes JM, Sanchis GJB, Medeiros JLA, Dantas FG. Hospitalization for ischemic stroke in Brazil: an ecological study on the possible impact of Hiperdia. Rev Bras Epidemiol 2016; 19:122-34.,3434. Pazó RG, Frauches DO, Molina MCB, Cade NV. Modelagem hierárquica de determinantes associados a internações por condições sensíveis à atenção primária no Espírito Santo, Brasil. Cad Saúde Pública 2014; 30:1891-902.,4040. Machado CSR, Lima ACC. Distribuição espacial do SUS e determinantes das despesas. Revista Econômica do Nordeste 2021; 52:121-45. reinforcing the theoretical basis for the selection of the model’s components 4040. Machado CSR, Lima ACC. Distribuição espacial do SUS e determinantes das despesas. Revista Econômica do Nordeste 2021; 52:121-45.,4141. Botelho TS, Machado Neto CD, Araújo FLC, Assis SC. Epidemiologia do acidente vascular cerebral no Brasil. Temas em Saúde 2016; 16:361-77. and corroborating the results that suggest the influence of the availability of hospital beds, excess weight, number of physicians, and number of hospitalizations for hypertension, schooling level, income, and poverty level in municipalities on hospitalizations for stroke 3232. Lopes JM, Sanchis GJB, Medeiros JLA, Dantas FG. Hospitalization for ischemic stroke in Brazil: an ecological study on the possible impact of Hiperdia. Rev Bras Epidemiol 2016; 19:122-34.,3434. Pazó RG, Frauches DO, Molina MCB, Cade NV. Modelagem hierárquica de determinantes associados a internações por condições sensíveis à atenção primária no Espírito Santo, Brasil. Cad Saúde Pública 2014; 30:1891-902.,4040. Machado CSR, Lima ACC. Distribuição espacial do SUS e determinantes das despesas. Revista Econômica do Nordeste 2021; 52:121-45..

Although PSM could show that the HGP was associated with a reduction in hospitalization rates for stroke, we only used this methodology to balance the treatment and comparison groups so that they would be comparable to minimize potential selection bias and the absence of common support 1919. Heckman J, Ichimura H, Smith J, Todd P. Characterizing selection bias using experimental data. Econometrica 1998; 66:1017-98.,2020. Blundell R, Dias MC. Evaluation methods for non-experimental data. Fisc Stud 2000; 21:427-68.,2121. Ravallion M. Evaluating anti-poverty programs. In: Evenson RE, Schultz TP, editors. Handbook of development economics. Amsterdam: World Bank; 2005. p. 2-79.. Moreover, the use of PSM as one of the parameters of the DD model increases the robustness and reliability of our findings 4242. Bertrand M, Duflo E, Mullainatha S. How much should we trust differences in differences estimates? Q J Econ 2004; 119:249-75.,4343. Angrist J, Pischke JS. Mostly harmless econometrics: an empiricist’s companion. Princeton: Princeton University Press; 2008. and has been used in other studies evaluating the impact of public policies 2121. Ravallion M. Evaluating anti-poverty programs. In: Evenson RE, Schultz TP, editors. Handbook of development economics. Amsterdam: World Bank; 2005. p. 2-79..

The presence of variables with non-significant coefficients in the PSM model (such as in DD and DDM models) does not suggest that they should be removed from the estimation models, as a variable can only be excluded from the model if theoretical evidence shows that it is not related to the outcome variable 4444. Stuart EA. Matching methods for causal inference: a review and a look forward. Stat Sci 2010; 25:1-21.,4545. Caliendo M, Kopeinig S. Some practical guidance for the implementation of propensity score matching. J Econ Surv 2008; 22:31-72..

The impact of the HGP on the frequency of hospitalizations for stroke was statistically significant for the two regression models tested, showing that municipalities that implemented the program had 18.37% fewer hospitalizations when compared with municipalities that did not implement it. Although DD and DDM methods are consistent for evaluating the impact of public policies, the use of DDM with robust standard errors creates eventual problems of heteroscedasticity, avoiding overestimating the significance of the regression coefficients 4242. Bertrand M, Duflo E, Mullainatha S. How much should we trust differences in differences estimates? Q J Econ 2004; 119:249-75.,4343. Angrist J, Pischke JS. Mostly harmless econometrics: an empiricist’s companion. Princeton: Princeton University Press; 2008., making them more robust to assess the impact of HGP on stroke hospitalizations.

This result may be associated with the potential increase in physical activity level and greater participation of the population in health promotion activities offered by the HGP 99. Silva RN, Guarda FRB, Hallal PC, Martelli PJL. Avaliabilidade do Programa Academia da Saúde no Município do Recife, Pernambuco, Brasil. Cad Saúde Pública 2017; 33:e00159415.,1717. Departamento de Informática do SUS. Morbidade hospitalar por acidente vascular cerebral, 2019. http://tabnet.datasus.gov.br/cgi/deftohtm.exe?sih/cnv/nruf.def (accessed on 09/Aug/2022).
http://tabnet.datasus.gov.br/cgi/deftoht...
.

The impacts of the HGP on hospitalization for stroke occurred among both men and women, and in older people, which is consistent with studies that showed that these population strata were frequent participants in the HGP in Pernambuco 1717. Departamento de Informática do SUS. Morbidade hospitalar por acidente vascular cerebral, 2019. http://tabnet.datasus.gov.br/cgi/deftohtm.exe?sih/cnv/nruf.def (accessed on 09/Aug/2022).
http://tabnet.datasus.gov.br/cgi/deftoht...
. Notably, the program reduced the frequency of hospitalizations for the age group from 71 to 80 years, thus showing the potential for this program to reduce hospitalizations for individuals with stroke prevalence 4646. Santana NM, Figueiredo FWS, Lucena DMM, Soares FM, Adami F, Cardoso LCP, et al. The burden of stroke in Brazil in 2016: an analysis of the Global Burden of Disease study findings. BMC Res Notes 2018; 11:735..

The HGP does not impact the frequency of hospitalizations for colon cancer in the placebo regression, showing that the treatment group is directing the results 4747. Athey S, Imbens GW. The state of applied econometrics: causality and policy evaluation. J Econ Perspect 2017; 31:3-32., which reinforces the robustness of the results and allows us to infer that the estimators used in this study were adequate to evaluate the impact of the HGP on hospitalizations for stroke.

Although our study contributes to the literature in using robust methodologies to evaluate the effect of the HGP on hospitalization for stroke, it also has many limitations. First, results derived from one state in Brazil may lack generalizability, not just to Brazil but to other jurisdictions. However, although caution is important when making inferences from this study, our results have good internal validity regarding the measurement of the impact of the HGP in this state. Second, our study used municipal-level data to evaluate the impact of the HGP rather than individual-level data, as they were not available. However, the use of aggregated data did not diminish the robustness of assessments derived when compared to other studies that adopt similar methods and used individual-level data 2727. Becker SO, Ichino A. Estimation of average treatment effects based on propensity scores. Stata J 2002; 2:358-77.,2828. Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat 2011; 10:150-61.,4242. Bertrand M, Duflo E, Mullainatha S. How much should we trust differences in differences estimates? Q J Econ 2004; 119:249-75.,4343. Angrist J, Pischke JS. Mostly harmless econometrics: an empiricist’s companion. Princeton: Princeton University Press; 2008..

Conclusion

This study showed that the HGP in the state of Pernambuco reduced the frequency of hospitalizations for stroke in general and especially among men and those over 71 years of age, therefore showing that the program was effective particularly among the groups in which the prevalence of stroke is highest.

The HGP directly reduced hospitalizations for stroke, lowered the demand for hospital beds (and other health and social care resources), and has the potential to reduce human suffering due to cerebrovascular accidents. Also, the HGP may indirectly reduce social security expenses (temporary leave, retirement, and pensions) regarding the consequences of stroke for the patient and unpaid caregivers that need to take time away from other productive activities. The study findings highlight the effectiveness of the HGP and encourages strategic investments in similar health and social care programs.

Acknowledgments

This study was supported by the Pernambuco State Foundation for the Support of Science and Technology (FACEPE), and the Brazilian National Research Council (CNPq).

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Publication Dates

  • Publication in this collection
    10 Feb 2023
  • Date of issue
    2023

History

  • Received
    24 Jan 2022
  • Reviewed
    17 Nov 2022
  • Accepted
    12 Dec 2022
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