Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Did the Family Health Strategy have an impact on indicators of hospitalizations for stroke and heart failure? Longitudinal study in Brazil: 1998-2013

Did the Family Health Strategy have an impact on indicators of hospitalizations for stroke and... Introduction OPENACCESS The objective was to analyze whether socioeconomic factors related to the context and Citation: Cavalcante DdFB, Brizon VSC, Probst LF, those related to the model of careÐspecifically the coverage of primary care by the Family Meneghim MdC, Pereira AC, Ambrosano GMB Health Strategy (ESF)Ðhad an impact on hospitalizations due to heart failure (HF) and (2018) Did the Family Health Strategy have an impact on indicators of hospitalizations for stroke stroke, in the State of São Paulo/Brazil between 1998 and 2013. and heart failure? Longitudinal study in Brazil: 1998-2013. PLoS ONE 13(6): e0198428. https:// Methods doi.org/10.1371/journal.pone.0198428 A longitudinal ecological study involving 645 municipalities was conducted in the state of Editor: Hajo Zeeb, Leibniz Institute for Prevention Research and Epidemiology BIPS, GERMANY São Paulo/Brazil from 1998 to 2013, using the Hospital Information System (SIH±DataSUS database). The hospitalizations for primary care sensitive conditions: Stroke and heart fail- Received: February 8, 2017 ure (HF) that correspond to the International Classification of Diseases (ICD 10): I50, I63 to Accepted: May 20, 2018 I67; I69, G45 to G46 were analyzed longitudinally during the period indicated regarding the Published: June 26, 2018 percentage of people covered by the Family Health Program (PSF) adjusted for confound- Copyright:© 2018 Cavalcante et al. This is an open ers (population size, gross domestic product -GDP and human development index- HDI). access article distributed under the terms of the Creative Commons Attribution License, which Results permits unrestricted use, distribution, and reproduction in any medium, provided the original There was a significant decrease in the number of hospitalizations for heart failure and author and source are credited. stroke per 10000 (inhabitants) in the period (p<0.0001), with a significant relationship with Data Availability Statement: All relevant data are increased proportion of ESF (p<0.0001), and this remained significant even when possible within the paper and its Supporting Information confounders (population size, GDP and HDI) were included in the model (p<0.0001). files. Funding: The authors received no specific funding Conclusions for this work. GDP per capita was close to or higher than that if many European countries, which shows Competing interests: The authors have declared that no competing interests exist. the relevance of the study. The health care model based on the Family Health Strategy PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 1 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions positively impacted hospitalization indicators for heart failure and stroke, indicating that this model is effective in the prevention of primary care sensitive conditions. Introduction Ambulatory or primary care sensitive conditions (ACSC) represent a set of indicators used internationally to indicate potentially preventable diseases by access to and quality of Primary Health Care. Access to services, continuity of care and the effectiveness of actions in primary care, by avoiding hospitalization and deterioration of the problems, as opposed to high hospi- talization rates, reflect deficiencies in the coverage and performance of primary health care (PHC) [1±3]. The implementation of an indicator for primary care sensitive conditions (PCSCs) may have different purposes, following changes in the health systems used. In the United States, hospitalizations for PCSCs were used to evaluate the performance of health systems, reflecting the accessibility of health services [4]. For example, in the UK, where access to health care is universal, as it is in Brazil, this measurement has become of interest to measure the quality of services offered [5]. In Brazil, with the Health Pact in 2006, health indicators were established for the Unified Health System (SUS) [6] and later the Brazilian List of Hospitalization for Primary Sensitive Conditions was published [7]. Nowadays managers can use this list and take it as reference to the impact that major health disorders could cause in the number of hospitalizations, because previously there were no parameters set for this purpose [6,7]. The Family Health Strategy (ESF) can be considered the government's main efforts to improve primary health care in Brazil. The ESF offers a wide range of primary health care ser- vices provided by a team consisting of one doctor, one nurse, one nursing assistant, and four or more community health workers. Some teams also include an oral health team (dentist and assistant). Each team is assigned to a geographic area that is responsible for registering and monitoring the health status of the population in this area, providing primary care services, and making referrals to other levels of care as needed. Each team is responsible for an average of 3450 and a maximum of 4500 people [8]. In 2013 the population coverage of the ESF and the PACS (Community Health Workers Program) was 53.37% and 64.74%, respectively [9]. Even with governmentÂs efforts with financial incentives to increase the number of ESF teams and ACS, there is still little scientific evidence of the impact of primary care on health indica- tors policies in Brazil. The intervention in primary care sensitive diseases makes it possible to improve the indica- tors of processes (evidence-based decisions, regulatory services, access) and results (user satis- faction, improved indicators, cost reduction) [3]. An effective approach to health care must target a specific level of quality that improves access to health care strategies to strengthen the family and community to cope with and control infectious diseases through self-care and health protection [10,11]. Although a few national studies have indicated a decline in hospitalization rates for heart failure and stroke in both sexes [3,12], cardiovascular diseases are the leading cause of death in the world, and in essence, they lead to a high social and economic burden; where the deaths represent 20% of the cases in high-income countries, 80% of them occur in low- and middle- income countries [13,14]. Thus, due to the worldwide expressiveness of these two indicators (CI and stroke) and with the purpose of contributing to the study of how the cited indicators have performed over time, PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 2 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions in the presence of a universal health program, this study aimed to longitudinally assess the impact of PSF on the indicators of hospitalizations for primary care sensitive conditions: heart failure (HF) and cerebrovascular accident (CVA) in the municipalities of the state of São Paulo/Brazil from 1998 to 2013. Methods The Research Ethics Committee of Piracicaba School of DentistryÐUNICAMP approved the study (Protocol No. 013/2016). This study with a longitudinal and ecological design assessed how the population behaved on a collective, not individual, and longitudinal level [15]. The state of São Paulo had 645 representative units (municipalities), with a population of 43,663,669 inhabitants [16] in 2013. We evaluated the impact of the Family Health Strategy (ESF) on the number of hospitalizations for heart failure (HF) and stroke per 10,000 inhabi- tants from 1998 to 2013. The causes 11 and 12 of constant admissions in the ordinance 221/2008 [7] of the Ministry of Health were considered Primary Care Sensitive Conditions (PCSCs). These causes are iden- tified by International Classification of Diseases (ICD-10) such as heart failure (I50) and cere- brovascular diseases (I63 to I67, I69, G45 and G46) [17]. The dependent variables were the number of hospitalizations for "Heart failure (HF) and stroke" in both sex and all agesÐboth data obtained from the Hospital Information System (SIH). These indicators are obtained by the municipality, and are arranged in aggregate, not individualized form, provided by DATASUS [18]. The SIH provides information on admis- sions to public and private hospitals of the Unified System of Health from Brazil (SUS). The independent variables considered were: period of time (in months) from the first implementa- tion of ESF- (Department of Primary CareÐDAB) [9], the proportion of municipal population covered by the ESF (DAB) [9], period of time (in months) from the last implementation of the ESF (DAB) [9], population (IBGE) [15], GDP (Gross Domestic Product) [19] and mHDI- Municipal Human Development Index (SEADE Foundation-SP) [19]. Data analysis Initially descriptive statistics of the variables were calculated. Later regression models were adjusted to assess the association of ESF and ACS coverage ratio (all ACS irrespective of the care model) with the number of admissions for HF and stroke. As these data consisted of repeated measurements, the presence of correlations among observations of the same munici- palities was assumed. Thus, the data were analyzed using Generalized Linear Mixed Models for Non-Gaussian Longitudinal Data using GLIMMIX of the SAS [20]. The models were ini- tially estimated considering the number of hospitalizations for HF and stroke as dependent variables and the proportion of ESF and ACS (adjusted by year) as predictors. The next step was to test the possible confounding variables (population, GDP and mHDI) in the model. Results Table 1 shows the data of descriptive statistics related to the number of hospitalizations for heart failure (HF) and stroke (per 10,000 inhabitants.) in the state of São Paulo between 1998 and 2013. HF has decreased over the years from a median value of 26.9/10,000 inhabitants in 1998 to 11.7/10,000 inhabitants in 2013. The stroke values showed fluctuations over time, pre- senting median values of 6.2/10,000 inhabitants in 1998, 10.3/ 10,000 inhabitants in 2004 and 6.5/10,000 inhabitants in 2013. Table 2 shows the median value of the proportion of the ESF and PACS between 1998 and 2013 in the state of São Paulo. Proportion of ESF remained with a median of 0% between 1998 PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 3 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions Table 1. Median (minimum and maximum) number of hospitalizations for heart failure and strokes for 10,000 inhabitants in the cities of São Paulo due time. Year Heart Failure Stroke 1998 26.9 (0.05±210.7) 6.2 (0.04±312.7) 1999 27.8 (0.2±178.0) 8.9 (0.07±382.4) 2000 24.9 (0.6±231.0) 9.3 (0.06±409.0) 2001 23.5 (0.6±257.6) 10.0 (0.1±295.1) 2002 22.6 (0.1±209.0) 9.7 (0.2±236.4) 2003 21.6 (0.6±148.4) 9.8 (0.1±262.4) 2004 20.9 (0.7±205.8) 10.3 (0.1±177.7) 2005 19.3 (0.6±205.8) 8.6 (0.03±174.1) 2006 18.4 (0.2±156.3) 9.0 (0.09±185.8) 2007 17.4(0.09±145.1) 8.0 (0.04±218.7) 2008 14.5 (0.5±207.0) 5.9 (0.04±218.7) 2009 15.5 (0.3±329.9) 7.0 (0.2±165.1) 2010 15.1 (0.3±269.3) 7.5 (0.06±126.4) 2011 14.7 (0.3±267.2) 8.1 (0.2±141.1) 2012 12.9 (0.6±279.2) 7.9 (0.09±113.2) 2013 11.7 (0.5±259.5) 6.5 (0.1±63.7) https://doi.org/10.1371/journal.pone.0198428.t001 and 2000, and increased from 2001 to 2013, ranging from 4.9 to 40.2% coverage. Moreover, the population coverage from 25 thousand to over 31 thousand could be seen. GDP ranged from R$5494.38 to R$18620.84, while the HDI ranged from 0.788 to 0.688 from 1998 to 1999; and (due to index metric questions) remained at this level until 2009, and increased to 0.729 in 2012±2013. Tables 3 and 4 show the results of estimated regression models. There was a significant decrease in the number of hospitalizations for HF (p< 0.0001). A significant relationship was also observed between the number of hospitalizations for HF and stroke per 10,000 inhabitants Table 2. Median (minimum and maximum) of the proportion of the Family Health Strategy (ESF), Health Program of Community Agents (PACS), population, Gross Domestic Product (GDP) and the Human Development Index (HDI) in the cities of São Paulo according to the time. Year ESF% PACS% Population GDP HDI 1998 0 (0±77.6) 0 (0±80.1) 25068.0 (1961.0±9887614.0) - 0.788 (0.5±0.8) 1999 0 (0±100.0) 0 (0±100.0) 25068.0 (1961.0±9887614.0) 5494.9 (1868.2±56985.2) 0.688 (0.5±0.8) 2000 0 (0±100.0) 5.3 (0±100.0) 26085.0 (1905.0±9968485.0) 5770.5 (2049.7±93963.7) 0.688 (0.5±0.8) 2001 4.9 (0±100.0) 11.6 (0±100.0) 27891.0 (1836.0±10499133.0) 6610.3 (2235.1±88847.4) 0.688 (0.5±0.8) 2002 10.3 (0±100.0) 20.2 (0±100.0) 27891.0 (1836.0±10499133.0) 7673.4 (2466.2±107008.0) 0.688 (0.5±0.8) 2003 13.8 (0±100.0) 24.7 (0±100.0) 28174.0 (1795.0±10600060.0) 8566.4 (2705.7±109963.1) 0.688 (0.5±0.8) 2004 19.0 (0±100.0) 28.8 (0±100.0) 28726.0 (1749.0±10677019.0) 8934.5 (2763.8±101877.1) 0.688 (0.5±0.8) 2005 21.7 (0±100.0) 31.2 (0±100.0) 28726.0 (1749.0±10677019.0) 9561.8 (3135.0±102099.9) 0.688 (0.5±0.8) 2006 25.0 (0±100.0) 34.8 (0±100.0) 30159.0 (1599.0±10927985.0) 10719.9 (3343.4±138980.8) 0.688 (0.5±0.8) 2007 27.4 (0±100.0) 36.9 (0±100.0) 30384.5 (1546.0±11016703.0) 12043.3 (4282.2±211883.8) 0.688 (0.5±0.8) 2008 32.2 (0±100.0) 48.3 (0±100.0) 30384.5 (1546.0±11016703.0) 12357.9 (4736.3±171506.5) 0.688 (0.5±0.8) 2009 33.0 (0±100.0) 46.0 (0±100.0) 29817.5 (1663.0±11168194.0) 14224.4 (5672.5±163436.4) 0.689 (0.5±0.8) 2010 38.1 (0±100.0) 47.3 (0±100.0) 30066.0 (1643.0±11245983.0) 16588.6 (6285.1±241014.6) 0.729 (0.6±0.9) 2011 38.5 (0±100.0) 50.4 (0±100.0) 30290.0 (1627.0±11312351.0) 17752.6 (6743.5±287501.3) 0.729 (0.6±0.9) 2012 40.3 (0±100.0) 56.3 (0±100.0) 30603.0 (1612.0±11379114.0) 18620.8 (7232.6±283589.5) 0.729 (0.6±0.9) 2013 40.2 (0±100.0) 52.6 (0±100.0) 31063.5 (2856.0±11446275.0) 18620.8 (7232.6±283589.5) 0.729 (0.6±0.9) https://doi.org/10.1371/journal.pone.0198428.t002 PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 4 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions Table 3. Generalized linear mixedmodels of hospitalizations for heart failure per 10,000 inhabitants. Model 1 (Unadjusted) Model 2 ( adjusted) Variable Estimate Standard Error p-value Estimate Standard Error p-value Year -0.04598 0.001696 <0.0001 -0.04894 0.002503 <0.0001 ESF proportion -0.00156 0.000542 0.0041 -0.00181 0.000570 0.0015 ACS proportion -0.00084 0.000519 0.1055 -0.00070 0.000545 0.2004 Note: ESF: Family Health Strategy, ACS: Health of Community Agents. adjusted for Population, GDP (Gross Domestic Product) and HDI value (Human Development Index). https://doi.org/10.1371/journal.pone.0198428.t003 with the increase in ESF proportion (p <0.01) (model 1) and this relationship remained signif- icant when possible confounders (population, GDP and HDI) were included in the model (p <0.001) (model 2). Discussion The analysis indicated that there was significant decrease in the number of hospitalizations for HF in the state of São Paulo in the period 1998±2013 (p <0.0001). The number of hospitaliza- tions for heart failure and strokes was associated with the increase in the Family Health Strat- egy proportion (p <0.01) and this finding remained even with inclusion of potential confounding covariates in the model (population, GDP and HDI). However, the coefficients were low, since the magnitude of the effects was small. Nevertheless, the data suggested effec- tiveness of the primary care approach in prevention of PCSCs. Brazilian National Primary Care Policy includes several associated factors that may have contributed simultaneously to the decrease in hospitalizations such as the longitudinal patient care approach through the offer of multidisciplinary teams, free therapeutic support and pre- ventive policies and treatment protocols [21±24]. All these factors are also mentioned in the Strategic Action Plan for Confronting Noncommunicable Chronic Diseases (CNCD) from 2011 to 2022 [25]. In the period of this study several health policies were in force or were implemented, which may have been responsible for the reduction in indicators during this time. Evaluating ESF in Brazil, with a longitudinal approach in 30% of the municipalities in Bra- zil, showed that population coverage was directly associated with the reduction in hospitaliza- tions and mortality rates due to cerebrovascular disease, and its effect increased within the implementation period [26]. Another study found recurrence of cardiovascular disease in pri- mary health care models with and without ESF, both models of state care in Brazil. The model with ESF was associated with lower risk of death from all causes, and there was a 16.4% reduc- tion in the absolute risk of death from cardiovascular disease for ESF [27]. This last previously Table 4. Generalized linear mixedmodels of hospitalizations for stroke per 10,000 inhabitants. Model 1 (Unadjusted) Model 2 ( adjusted) Variable Estimate Standard Error p-value Estimate Standard Error p-value Year -0.01475 0.00246 <0.0001 -0.02614 0.003554 <0.0001 ESF proportion -0.00413 0.000762 <0.0001 -0.00407 0.00079 <0.0001 ACS proportion -0.00139 0.00072 0.0521 -0.000744 0.000746 0.3185 Note: ESF: Family Health Strategy, ACS: Health of Community Agents. adjusted for Population, GDP (Gross Domestic Product) and HDI value (Human Development Index). https://doi.org/10.1371/journal.pone.0198428.t004 PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 5 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions published study corroborated the findings of a cohort study conducted in Paris, which exam- ined the increased incidence of cardiovascular diseases as factors related to the social determi- nants and decrease in services [28]. In addition to access to and quality of ESF care, preventive and health promotion initiatives, such as the implementation of Centers to Support Family Health (NASF) occurred simulta- neously in several municipalities. These centers offered differentiated collective action services, with a multidisciplinary team approach including incentives to reduce smoking, alcoholism and encouraging healthy practicesÐfor example the abandonment of inactivity [29]. The lack of access to medicines in the public sector is one of the major barriers to fighting the chronic diseases, and management models should intensify efforts to adopt public policies that guarantee dispensed medication [30]. Thus, in Brazil, the use of therapeutic protocols by the National Medication Policy [31] (1998) and the National Policy on Pharmaceutical Care [32] (2004) has ensured that many essential medicines are available free of charge, or at a reduced cost [33], thereby enhancing the care and strengthening the assistance. Another protective factor related to the reduction in hospitalizations was the implementa- tion of an immunization policy against influenza since 1999. This was important because patients infected with influenza and respiratory infections showed a hemodynamic instability, and immunization against influenza was a protective factor in preventing morbidity and mor- tality from cardiovascular disease [34]. Since 1980, tobacco control that includes a set of actions to reduce the prevalence of smok- ing has been articulated by the Brazilian Ministry of Health. However, only in 2003 Brazil signed the Framework Convention on Tobacco Control (FCTC) with the effective implemen- tation of actions between 2005 and 2006. It was the most relevant event that resulted in the implementation of National Policy to Control Tobacco Use with actions focused on the reduced demand of tobacco, with an important market regulation, protection measures such as banning smoking in collective environments and promoting cessation of tobacco use [35,36]. This approach corroborated the findings of a previous study that showed a strong association of chronic conditions with major cardiovascular risk factors for smoking [37]. Another transversal policy was the development and implementation of the Strategic Action Plan for Confronting Chronic Noncommunicable Diseases (2011±2022), with nine national targets related to morbidity and mortality issues and four risk factors: smoking, inade- quate diet, physical inactivity, and excessive use of alcohol; and four groups of lesions: cardio- vascular, cancers, diabetes and chronic respiratory diseases [38,39]. Analyzing the ranking of the major causes for Disability-Adjusted Life Year (DALY) in Bra- zil and macro-regions (data not shown), it was shown that for Brazil, as a whole, diabetes mel- litus (5.1%), ischemic heart disease (5.0%) and strokeÐfirst occurrence (4.6%), totalizing 14.7% of the total DALYs was characteristic of an epidemiological pattern of developed coun- tries [40]. Moreover, in the WHO survey conducted in 23 countries, including Brazil, losses of $ 84 billion due to coronary heart disease, stroke and diabetes were estimated from 2006 to 2015 [41]. Cardiovascular diseases, despite their decline, have been and continue to be the main cause of death in Brazil. The decline in cardiovascular disease was greater for cerebrovascular dis- eases (34%) and heart disease (44%). Mortality from ischemic heart disease decreased by 26% [29]. As differential, this study presented a longitudinal analysis, as there are few in the literature, especially using mixed models to verify the impact of the health care policy over time. It is worth noting that the ESF has been present since 1994, but the state of São Paulo was the last state to implement the strategy, although it is the state with the highest HDI and largest popu- lation (over 40 million). Despite the StrategyÂs own funding, with financial incentives for PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 6 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions incorporating family health teams, oral health teams, NASF teams and other initiatives, there was still a delay in implementing this strategy, which made it difficult to change the health care policy, as demonstrated by the slow growth in population coverage from 1998 to 2013 (which made the results more interesting), far below the percentage of other regions of Brazil. Never- theless, the statistical model was able to identify changes in the epidemiological profile and its relationship with the population covered by the ESF. We emphasize this was an ecological study and limitations were expected. The first is the possibility of ecological fallacy, in which ecological associations do not always reflect individual associations. It was not possible to determine whether Individuals with the outcomes were under ESF coverage, because the level of aggregate was the municipality [26]. Furthermore, the ESF was influenced by political changes. Changes of healthcare managers, since they have their political ideologies and personal conceptions of public management, and these did not always coincide with the current Brazilian health policy. Nevertheless, two English studies have confirmed that the provision of care in Primary Care had a direct impact on mortality rates and recommended that improvement in the health of the population required a reduction in health inequalities; and it was treated as a political priority with effec- tive territorial monitoring for reduction of hospitalizations and consequently of mortality [42,43]. Finally, we could assume that the result for the state of São Paulo could be inferred for all other states in Brazil, considering that the beginning and later development of this strategy in São Paulo was very precarious and presented numerous technical difficulties, mainly because the State had an organized primary health care network in the 1990s. Conclusion We concluded that the health care model based on the Family Health Strategy has positively impacted the hospitalization indicators for heart failure and stroke, indicating that this model was effective in preventing Ambulatory or primary care sensitive conditions (PCSCs). Supporting information S1 File. Study data. (XLS) Author Contributions Conceptualization: Denise de Fa Âtima Barros Cavalcante.  à Data curation: Valeria Silva Candido Brizon, Livia Fernandes Probst. Formal analysis: Gla Âucia Maria Bovi Ambrosano.   à Investigation: Denise de Fatima Barros Cavalcante, Valeria Silva Candido Brizon, Livia Fer- nandes Probst.   Methodology: Denise de Fatima Barros Cavalcante, Livia Fernandes Probst, Glaucia Maria Bovi Ambrosano. Supervision: Antonio Carlos Pereira. Validation: Marcelo de Castro Meneghim, Antonio Carlos Pereira, Glaucia Maria Bovi Ambrosano. Visualization: Antonio Carlos Pereira. PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 7 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions Writing ± original draft: Denise de Fa Âtima Barros Cavalcante. Writing ± review & editing: Livia Fernandes Probst, Marcelo de Castro Meneghim, Antonio Carlos Pereira, Gla Âucia Maria Bovi Ambrosano. References 1. Freund T, Heller G, Szecsenyi J. Hospitalisations for ambulatory care sensitive conditions in Germany. Z Evid Fortbilt Qual Gesundhwes, 108 (5±6) 251±7, 2014. 2. Gibson OR, Segal L, McDermott RA. A systematic review of evidence on the association between hos- pitalization for chronic disease related ambulatory care sensitive conditions and primary health care resourcing. BMC Health Services Research. 2013; 13:336. https://doi.org/10.1186/1472-6963-13-336 PMID: 23972001 3. Batista SRR, Jardim PCBV, Sousa ALL, Salgado CM. Hospitalizac Ëões por condic Ëões cardiovasculares sensõ Âveisà atenc Ëão prima  ria em municõ Âpios goianos. Rev Saude Publica, 46(1), 34±42. Epub January 06, 2012. PMID: 22218758 4. Caminal J, Starfield B, Sanchez E, Casanova C, Morales M. The role of primary care in preventing ambulatory care sensitive conditions. European Journal of Public Health. 14(3), 2004. 5. Purdy S, Grifin T, Salisbury C, Sharp D. Ambulatory care sensitive conditions: terminology and disease coding need to be more specific toa id policy makers and clinicians. Public Health. 123 (2):169±73, 2009. https://doi.org/10.1016/j.puhe.2008.11.001 PMID: 19144363 6. Brasil, Ministe  rio da Sau  de, Portaria nÊ 399/GM de 22 de fevereiro de 2006, Pacto pela Sau  de, Brasõ Âlia, DF. 7. Brasil, Ministe  rio da Sau  de, Portaria SAS/MS nÊ 221, de 17 de abril de 2008, Lista Brasileira de Interna- c Ëões por Condic Ëões Sensõ Âveisà Atenc Ëão Prima  ria. 8. Macinko J, Guanais FC, Evaluation of the impact of the Family Health Program on infant mortality in Brazil, 1990±2002. Journal of Epidemiology and Community Health. 2006; 60 (1): 13±19. https://doi. org/10.1136/jech.2005.038323 PMID: 16361449 9. Brasil, Ministe  rio da Sau  de: Dados de cobertura do Programa Sau  de da Famõ Âlia http://dab.saude.gov. br/portaldab/historico_cobertura_sf.php, acessado em fevereiro 2016. 10. Schmittdiel JA, Shortell SM, Rundall TG, Selby JV. Effect of primary helth care orientation on chronic care management. Annals of family medicine. 4 (2), mar/apr 2006. 11. Rosano A, Loha CA, Falvo R, Van der Zee J, Ricciardi W, Guasticchi G et al. The relationship between avoidable hospitalization and acessibility to primary care: a systematic review. European Journal of Public Health. 2012; 23 (3):356±360. https://doi.org/10.1093/eurpub/cks053 PMID: 22645236 12. Boing AF, Vicenzi RB, Magajewski F, Boing AC, Moretti-Pires RO, Peres KG et al. Reduc Ëão das inter- nac Ëões por condic Ëões sensõ Âveisà atenc Ëão prima  ria no Brasil entre 1998±2009. Rev Saude Publica, 46 (2), 359±366. Epub February 14, 2012. PMID: 22331182 13. Gaziano TA. Reducing the growing burden of cardiovascular disease in the developing world. Health Aff. 26:13±24, 2007; 14. WHO. Preventing chronic diseases a vital investment. WHO, 2008. 15. Bonita R, Beaglehole R, Kjellstrom T. Basic epidemiology. 2nd ed. Genebra: World Health Organiza- tion; 2006. 16. Instituto Brasileiro de Geografia e Estatõ Âstica (IBGE), Disponõ Âvel: http://www.ibge.gov.br/home/ 17. Organizac Ëão Mundial de Sau  de. Classificac Ëão Estatõ Âstica Internacional de Doenc Ë as e Problemas Rela- cionadosà Sau  de±CID-10. Disponõ Âvel em: www.datasus.gov.br/cid10/v2008/cid10.htm 18. Brasil, Ministe  rio da Sau  de. Departamento de Informa  tica do SUS (DATASUS)- Disponõ Âvel em: http:// datasus.saude.gov.br/ 19. Fundac Ëão Sistema Estadual de Ana  lise de Dados (SEADE)- Disponõ Âvel em: http://www.seade.go.,br/ 20. SAS, SAS Software, Version 9,1, Cary, North Carolina: SAS Institute Inc, 1999. 21. Alfradique ME, Bonolo PF, Dourado I, Lima-Costa MF, Macinko J, Mendonc Ë a CS et al. Internac Ëões por condic Ëões sensõ Âveisà atenc Ëão prima  ria: a construc Ëão da lista brasileira como ferramenta para medir o desempenho do sistema de sau  de (Projeto ICSAPÐBrasil). Cad Saude Publica, 2009; 25 (6): 1337± 1349. PMID: 19503964 22. Niti M, Ng T, Avoidable hospitalisation rates in Singapore, 1991±1998: assessing trends and inequities of quality in primary care, Journal of Epidemiology and Community Health. 2003; 57 (1): 17±22. https:// doi.org/10.1136/jech.57.1.17 PMID: 12490643 PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 8 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions 23. Pappas G, Hadden WC, Kozak LJ, Fisher GF. Potentially avoidable hospitalizations: inequalities in rates between US socioeconomic groups. American Journal of Public Health, 1997; 87(5):811±816. PMID: 9184511 24. Macinko J, Oliveira VB, Turci MA, Guanais FC, Bonolo PF, Lima-Costa MF. The Influence of Primary Care and Hospital Supply on Ambulatory Care±Sensitive Hospitalizations Among Adults in Brazil, 1999±2007, American Journal of Public Health. 2011; 101 (10): 1963±1970. https://doi.org/10.2105/ AJPH.2010.198887 PMID: 21330584 25. Brasil, Ministe  rio da Sau  de. Plano de Ac Ëões Estrate  gicas para o Enfrentamento das Doenc Ë as Cro à nicas não transmiss õveis (DCNT) de 2011 a 2022. Brasõ Âlia-DF, 2011. 26. Rasella D, Harhay MO, Pamponet ML, Aquino R, Barreto ML. Impact of primary health care on mortality from heart and cerebrovascular diseases in Brazil: a nationwide analysis of longitudinal data. The BMJ. 349:g4014, 2014; https://doi.org/10.1136/bmj.g4014 PMID: 24994807 27. Cabral NL, Franco S, Longo A, Moro C, Buss TA, Collares D et al. The Brazilian Family Health Program and secondary stroke and myocardial infarction prevention: a 6-year cohort study. Am J Public Health. 102:e90±5, 2012. https://doi.org/10.2105/AJPH.2012.301024 PMID: 23078478 28. Chaix B, Kestens Y, Bean K, Leal C, Karusisi N, Meghiref K et al. Cohort Profile: Residential and non- residential environments, individual activity spaces and cardiovascular risk factors and diseases- The RECORD cohort study. Int J Epidemiol. 41:1283±1292,2012. https://doi.org/10.1093/ije/dyr107 PMID: 29. Shmidt MI, Duncan BB, Azevedo e Silva G, Menezes AM, Monteiro AM, Barreto, et al. Chronic non- communicable diseases in Brazil: burden and current challenges, Lancet; 377(9781): 1949±61, 2011. https://doi.org/10.1016/S0140-6736(11)60135-9 PMID: 21561658 30. Hogerzeil HV, Mirza Z. The world medicines situation 2011: access to essential medicines as part of the right to health. Geneva: World Health Organization; 2011 31. Brasil, Ministe  rio da Sau  de, Portaria GM no 3,916, de 30 de outubro de 1998, Aprova a Polõ Âtica Nacio- nal de Medicamentos, Dia  rio Oficial da União 1998; 10 nov. 32. Conselho Nacional de Sau  de, Resoluc Ëão no 338, de 6 de maio de 2004. Aprova a Polõ Âtica Nacional de Assistência Farmacêutica, Dia  rio Oficial da União 2004; 20 mai. 33. Paniz VMV, Cechin ICCF, Fassa AG, Piccini RX, Tomasi E, Thume  E et al. Acesso a medicamentos para tratamento de condic Ëões agudas prescritos a adultos nas regiões Sul e Nordeste do Brasil. Cad Saude Publica. 2016; 32 (4): e00009915. https://doi.org/10.1590/0102-311X00009915 PMID: 34. Lentsck MH, Latorre MRDO, Mathias TAF. Tendência das internac Ëões por doenc Ë as cardiovasculares sensõ Âveisà atenc Ëão prima  ria. Rev Bras Epidemiol. 2015; 18 (2): 372±384. https://doi.org/10.1590/ 1980-5497201500020007 PMID: 26083509 35. Brasil, Ministe  rio da Sau  de. Polõ Âtica Nacional de controle do tabaco: relato  rio de gestão e progresso 2011±2012 /Instituto Nacional de Ca à ncer Jose  Alencar Gomes da Silva. Comissão Nacional para Implementac Ëão da Convenc Ëão-Quadro para controle do Tabaco (CONICQ).±Rio de Janeiro: INCA, 36. Portes LH, Machado CV. Convenc Ëão-Quadro para o Controle do Tabaco: adesão e implantac Ëão na Ame  rica Latina. Revista Panamericana de Salud Publica, 38(5), 370±379, (2015). PMID: 26837522 37. Galil AGS, Cupertino AP, Banhato EFC, Campos TS, Colugnati FAB, Richter KP et al. Factors associ- ated with tobacco use among patients with multiple chronic conditions. International Journal of Cardiol- ogy. 221 1004:1007, 2016. 38. Malta DC, Silva JB Jr. O Plano de Ac Ëões Estrate  gicas para o Enfrentamento das Doenc Ë as Cro à nicas Não Transmissõ Âveis no Brasil e a definic Ëão das metas globais para o enfrentamento dessas doenc Ë as ate  2025: uma revisão. Epidemiologia e Servic Ë os de Sau  de, 22(1), 151±164. (2013). 39. Malta DC, Silva JB Jr. Plano de Ac Ëões Estrate  gicas para o Enfrentamento das Doenc Ë as Cro à nicas Não Transmiss õveis no Brasil apo  s três anos de implantac Ëão, 2011±2013. Epidemiologia e Servic Ë os de Sau  de, 23(3), 389±395,(2014). 40. Schramm JMA, Andrade JM, Leite IC, Valente JG, Gadelha AMJ, Portela MCet al. Transic Ëão epidemio- lo  gica e o estudo de carga de doenc Ë a no Brasil. Cienc Saude Coletiva, 2004, vol. 9, n.4, pp, 897±908 41. Abegunde DO, Mathers CD, Adam T, Ortegon M, Strong K. The burden and costs of chronic diseases in low-income and middleincome countries, Lancet; 370: 1929±38, 2007. https://doi.org/10.1016/ S0140-6736(07)61696-1 PMID: 18063029 42. Asaria P, Fortunato L, Fecht D, Tzoulaki I, Abellan JJ, Hambly P et al. Trends and inequalities in cardio- vascular disease mortality across 7932 English electoral wards, 1982±2006. Bayesian spatial analysis. Int J Epidemiol; 41: 1737±1749, 2012. https://doi.org/10.1093/ije/dys151 PMID: 23129720 PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 9 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions 43. Hone T, Rasella D, Barreto M, Atun R, Majeed A, Millett C. Large reductions in amenable mortality associated with Brazil's Primary Care expansion and strong health governance. Health Affairs 36, no.1 (2017):149±158. https://doi.org/10.1377/hlthaff.2016.0966 PMID: 28069858 PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 10 / 10 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png PLoS ONE Pubmed Central

Did the Family Health Strategy have an impact on indicators of hospitalizations for stroke and heart failure? Longitudinal study in Brazil: 1998-2013

Loading next page...
 
/lp/pubmed-central/did-the-family-health-strategy-have-an-impact-on-indicators-of-J8CDkR7VPg

References (80)

Publisher
Pubmed Central
Copyright
© 2018 Cavalcante et al
ISSN
1932-6203
eISSN
1932-6203
DOI
10.1371/journal.pone.0198428
Publisher site
See Article on Publisher Site

Abstract

Introduction OPENACCESS The objective was to analyze whether socioeconomic factors related to the context and Citation: Cavalcante DdFB, Brizon VSC, Probst LF, those related to the model of careÐspecifically the coverage of primary care by the Family Meneghim MdC, Pereira AC, Ambrosano GMB Health Strategy (ESF)Ðhad an impact on hospitalizations due to heart failure (HF) and (2018) Did the Family Health Strategy have an impact on indicators of hospitalizations for stroke stroke, in the State of São Paulo/Brazil between 1998 and 2013. and heart failure? Longitudinal study in Brazil: 1998-2013. PLoS ONE 13(6): e0198428. https:// Methods doi.org/10.1371/journal.pone.0198428 A longitudinal ecological study involving 645 municipalities was conducted in the state of Editor: Hajo Zeeb, Leibniz Institute for Prevention Research and Epidemiology BIPS, GERMANY São Paulo/Brazil from 1998 to 2013, using the Hospital Information System (SIH±DataSUS database). The hospitalizations for primary care sensitive conditions: Stroke and heart fail- Received: February 8, 2017 ure (HF) that correspond to the International Classification of Diseases (ICD 10): I50, I63 to Accepted: May 20, 2018 I67; I69, G45 to G46 were analyzed longitudinally during the period indicated regarding the Published: June 26, 2018 percentage of people covered by the Family Health Program (PSF) adjusted for confound- Copyright:© 2018 Cavalcante et al. This is an open ers (population size, gross domestic product -GDP and human development index- HDI). access article distributed under the terms of the Creative Commons Attribution License, which Results permits unrestricted use, distribution, and reproduction in any medium, provided the original There was a significant decrease in the number of hospitalizations for heart failure and author and source are credited. stroke per 10000 (inhabitants) in the period (p<0.0001), with a significant relationship with Data Availability Statement: All relevant data are increased proportion of ESF (p<0.0001), and this remained significant even when possible within the paper and its Supporting Information confounders (population size, GDP and HDI) were included in the model (p<0.0001). files. Funding: The authors received no specific funding Conclusions for this work. GDP per capita was close to or higher than that if many European countries, which shows Competing interests: The authors have declared that no competing interests exist. the relevance of the study. The health care model based on the Family Health Strategy PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 1 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions positively impacted hospitalization indicators for heart failure and stroke, indicating that this model is effective in the prevention of primary care sensitive conditions. Introduction Ambulatory or primary care sensitive conditions (ACSC) represent a set of indicators used internationally to indicate potentially preventable diseases by access to and quality of Primary Health Care. Access to services, continuity of care and the effectiveness of actions in primary care, by avoiding hospitalization and deterioration of the problems, as opposed to high hospi- talization rates, reflect deficiencies in the coverage and performance of primary health care (PHC) [1±3]. The implementation of an indicator for primary care sensitive conditions (PCSCs) may have different purposes, following changes in the health systems used. In the United States, hospitalizations for PCSCs were used to evaluate the performance of health systems, reflecting the accessibility of health services [4]. For example, in the UK, where access to health care is universal, as it is in Brazil, this measurement has become of interest to measure the quality of services offered [5]. In Brazil, with the Health Pact in 2006, health indicators were established for the Unified Health System (SUS) [6] and later the Brazilian List of Hospitalization for Primary Sensitive Conditions was published [7]. Nowadays managers can use this list and take it as reference to the impact that major health disorders could cause in the number of hospitalizations, because previously there were no parameters set for this purpose [6,7]. The Family Health Strategy (ESF) can be considered the government's main efforts to improve primary health care in Brazil. The ESF offers a wide range of primary health care ser- vices provided by a team consisting of one doctor, one nurse, one nursing assistant, and four or more community health workers. Some teams also include an oral health team (dentist and assistant). Each team is assigned to a geographic area that is responsible for registering and monitoring the health status of the population in this area, providing primary care services, and making referrals to other levels of care as needed. Each team is responsible for an average of 3450 and a maximum of 4500 people [8]. In 2013 the population coverage of the ESF and the PACS (Community Health Workers Program) was 53.37% and 64.74%, respectively [9]. Even with governmentÂs efforts with financial incentives to increase the number of ESF teams and ACS, there is still little scientific evidence of the impact of primary care on health indica- tors policies in Brazil. The intervention in primary care sensitive diseases makes it possible to improve the indica- tors of processes (evidence-based decisions, regulatory services, access) and results (user satis- faction, improved indicators, cost reduction) [3]. An effective approach to health care must target a specific level of quality that improves access to health care strategies to strengthen the family and community to cope with and control infectious diseases through self-care and health protection [10,11]. Although a few national studies have indicated a decline in hospitalization rates for heart failure and stroke in both sexes [3,12], cardiovascular diseases are the leading cause of death in the world, and in essence, they lead to a high social and economic burden; where the deaths represent 20% of the cases in high-income countries, 80% of them occur in low- and middle- income countries [13,14]. Thus, due to the worldwide expressiveness of these two indicators (CI and stroke) and with the purpose of contributing to the study of how the cited indicators have performed over time, PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 2 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions in the presence of a universal health program, this study aimed to longitudinally assess the impact of PSF on the indicators of hospitalizations for primary care sensitive conditions: heart failure (HF) and cerebrovascular accident (CVA) in the municipalities of the state of São Paulo/Brazil from 1998 to 2013. Methods The Research Ethics Committee of Piracicaba School of DentistryÐUNICAMP approved the study (Protocol No. 013/2016). This study with a longitudinal and ecological design assessed how the population behaved on a collective, not individual, and longitudinal level [15]. The state of São Paulo had 645 representative units (municipalities), with a population of 43,663,669 inhabitants [16] in 2013. We evaluated the impact of the Family Health Strategy (ESF) on the number of hospitalizations for heart failure (HF) and stroke per 10,000 inhabi- tants from 1998 to 2013. The causes 11 and 12 of constant admissions in the ordinance 221/2008 [7] of the Ministry of Health were considered Primary Care Sensitive Conditions (PCSCs). These causes are iden- tified by International Classification of Diseases (ICD-10) such as heart failure (I50) and cere- brovascular diseases (I63 to I67, I69, G45 and G46) [17]. The dependent variables were the number of hospitalizations for "Heart failure (HF) and stroke" in both sex and all agesÐboth data obtained from the Hospital Information System (SIH). These indicators are obtained by the municipality, and are arranged in aggregate, not individualized form, provided by DATASUS [18]. The SIH provides information on admis- sions to public and private hospitals of the Unified System of Health from Brazil (SUS). The independent variables considered were: period of time (in months) from the first implementa- tion of ESF- (Department of Primary CareÐDAB) [9], the proportion of municipal population covered by the ESF (DAB) [9], period of time (in months) from the last implementation of the ESF (DAB) [9], population (IBGE) [15], GDP (Gross Domestic Product) [19] and mHDI- Municipal Human Development Index (SEADE Foundation-SP) [19]. Data analysis Initially descriptive statistics of the variables were calculated. Later regression models were adjusted to assess the association of ESF and ACS coverage ratio (all ACS irrespective of the care model) with the number of admissions for HF and stroke. As these data consisted of repeated measurements, the presence of correlations among observations of the same munici- palities was assumed. Thus, the data were analyzed using Generalized Linear Mixed Models for Non-Gaussian Longitudinal Data using GLIMMIX of the SAS [20]. The models were ini- tially estimated considering the number of hospitalizations for HF and stroke as dependent variables and the proportion of ESF and ACS (adjusted by year) as predictors. The next step was to test the possible confounding variables (population, GDP and mHDI) in the model. Results Table 1 shows the data of descriptive statistics related to the number of hospitalizations for heart failure (HF) and stroke (per 10,000 inhabitants.) in the state of São Paulo between 1998 and 2013. HF has decreased over the years from a median value of 26.9/10,000 inhabitants in 1998 to 11.7/10,000 inhabitants in 2013. The stroke values showed fluctuations over time, pre- senting median values of 6.2/10,000 inhabitants in 1998, 10.3/ 10,000 inhabitants in 2004 and 6.5/10,000 inhabitants in 2013. Table 2 shows the median value of the proportion of the ESF and PACS between 1998 and 2013 in the state of São Paulo. Proportion of ESF remained with a median of 0% between 1998 PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 3 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions Table 1. Median (minimum and maximum) number of hospitalizations for heart failure and strokes for 10,000 inhabitants in the cities of São Paulo due time. Year Heart Failure Stroke 1998 26.9 (0.05±210.7) 6.2 (0.04±312.7) 1999 27.8 (0.2±178.0) 8.9 (0.07±382.4) 2000 24.9 (0.6±231.0) 9.3 (0.06±409.0) 2001 23.5 (0.6±257.6) 10.0 (0.1±295.1) 2002 22.6 (0.1±209.0) 9.7 (0.2±236.4) 2003 21.6 (0.6±148.4) 9.8 (0.1±262.4) 2004 20.9 (0.7±205.8) 10.3 (0.1±177.7) 2005 19.3 (0.6±205.8) 8.6 (0.03±174.1) 2006 18.4 (0.2±156.3) 9.0 (0.09±185.8) 2007 17.4(0.09±145.1) 8.0 (0.04±218.7) 2008 14.5 (0.5±207.0) 5.9 (0.04±218.7) 2009 15.5 (0.3±329.9) 7.0 (0.2±165.1) 2010 15.1 (0.3±269.3) 7.5 (0.06±126.4) 2011 14.7 (0.3±267.2) 8.1 (0.2±141.1) 2012 12.9 (0.6±279.2) 7.9 (0.09±113.2) 2013 11.7 (0.5±259.5) 6.5 (0.1±63.7) https://doi.org/10.1371/journal.pone.0198428.t001 and 2000, and increased from 2001 to 2013, ranging from 4.9 to 40.2% coverage. Moreover, the population coverage from 25 thousand to over 31 thousand could be seen. GDP ranged from R$5494.38 to R$18620.84, while the HDI ranged from 0.788 to 0.688 from 1998 to 1999; and (due to index metric questions) remained at this level until 2009, and increased to 0.729 in 2012±2013. Tables 3 and 4 show the results of estimated regression models. There was a significant decrease in the number of hospitalizations for HF (p< 0.0001). A significant relationship was also observed between the number of hospitalizations for HF and stroke per 10,000 inhabitants Table 2. Median (minimum and maximum) of the proportion of the Family Health Strategy (ESF), Health Program of Community Agents (PACS), population, Gross Domestic Product (GDP) and the Human Development Index (HDI) in the cities of São Paulo according to the time. Year ESF% PACS% Population GDP HDI 1998 0 (0±77.6) 0 (0±80.1) 25068.0 (1961.0±9887614.0) - 0.788 (0.5±0.8) 1999 0 (0±100.0) 0 (0±100.0) 25068.0 (1961.0±9887614.0) 5494.9 (1868.2±56985.2) 0.688 (0.5±0.8) 2000 0 (0±100.0) 5.3 (0±100.0) 26085.0 (1905.0±9968485.0) 5770.5 (2049.7±93963.7) 0.688 (0.5±0.8) 2001 4.9 (0±100.0) 11.6 (0±100.0) 27891.0 (1836.0±10499133.0) 6610.3 (2235.1±88847.4) 0.688 (0.5±0.8) 2002 10.3 (0±100.0) 20.2 (0±100.0) 27891.0 (1836.0±10499133.0) 7673.4 (2466.2±107008.0) 0.688 (0.5±0.8) 2003 13.8 (0±100.0) 24.7 (0±100.0) 28174.0 (1795.0±10600060.0) 8566.4 (2705.7±109963.1) 0.688 (0.5±0.8) 2004 19.0 (0±100.0) 28.8 (0±100.0) 28726.0 (1749.0±10677019.0) 8934.5 (2763.8±101877.1) 0.688 (0.5±0.8) 2005 21.7 (0±100.0) 31.2 (0±100.0) 28726.0 (1749.0±10677019.0) 9561.8 (3135.0±102099.9) 0.688 (0.5±0.8) 2006 25.0 (0±100.0) 34.8 (0±100.0) 30159.0 (1599.0±10927985.0) 10719.9 (3343.4±138980.8) 0.688 (0.5±0.8) 2007 27.4 (0±100.0) 36.9 (0±100.0) 30384.5 (1546.0±11016703.0) 12043.3 (4282.2±211883.8) 0.688 (0.5±0.8) 2008 32.2 (0±100.0) 48.3 (0±100.0) 30384.5 (1546.0±11016703.0) 12357.9 (4736.3±171506.5) 0.688 (0.5±0.8) 2009 33.0 (0±100.0) 46.0 (0±100.0) 29817.5 (1663.0±11168194.0) 14224.4 (5672.5±163436.4) 0.689 (0.5±0.8) 2010 38.1 (0±100.0) 47.3 (0±100.0) 30066.0 (1643.0±11245983.0) 16588.6 (6285.1±241014.6) 0.729 (0.6±0.9) 2011 38.5 (0±100.0) 50.4 (0±100.0) 30290.0 (1627.0±11312351.0) 17752.6 (6743.5±287501.3) 0.729 (0.6±0.9) 2012 40.3 (0±100.0) 56.3 (0±100.0) 30603.0 (1612.0±11379114.0) 18620.8 (7232.6±283589.5) 0.729 (0.6±0.9) 2013 40.2 (0±100.0) 52.6 (0±100.0) 31063.5 (2856.0±11446275.0) 18620.8 (7232.6±283589.5) 0.729 (0.6±0.9) https://doi.org/10.1371/journal.pone.0198428.t002 PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 4 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions Table 3. Generalized linear mixedmodels of hospitalizations for heart failure per 10,000 inhabitants. Model 1 (Unadjusted) Model 2 ( adjusted) Variable Estimate Standard Error p-value Estimate Standard Error p-value Year -0.04598 0.001696 <0.0001 -0.04894 0.002503 <0.0001 ESF proportion -0.00156 0.000542 0.0041 -0.00181 0.000570 0.0015 ACS proportion -0.00084 0.000519 0.1055 -0.00070 0.000545 0.2004 Note: ESF: Family Health Strategy, ACS: Health of Community Agents. adjusted for Population, GDP (Gross Domestic Product) and HDI value (Human Development Index). https://doi.org/10.1371/journal.pone.0198428.t003 with the increase in ESF proportion (p <0.01) (model 1) and this relationship remained signif- icant when possible confounders (population, GDP and HDI) were included in the model (p <0.001) (model 2). Discussion The analysis indicated that there was significant decrease in the number of hospitalizations for HF in the state of São Paulo in the period 1998±2013 (p <0.0001). The number of hospitaliza- tions for heart failure and strokes was associated with the increase in the Family Health Strat- egy proportion (p <0.01) and this finding remained even with inclusion of potential confounding covariates in the model (population, GDP and HDI). However, the coefficients were low, since the magnitude of the effects was small. Nevertheless, the data suggested effec- tiveness of the primary care approach in prevention of PCSCs. Brazilian National Primary Care Policy includes several associated factors that may have contributed simultaneously to the decrease in hospitalizations such as the longitudinal patient care approach through the offer of multidisciplinary teams, free therapeutic support and pre- ventive policies and treatment protocols [21±24]. All these factors are also mentioned in the Strategic Action Plan for Confronting Noncommunicable Chronic Diseases (CNCD) from 2011 to 2022 [25]. In the period of this study several health policies were in force or were implemented, which may have been responsible for the reduction in indicators during this time. Evaluating ESF in Brazil, with a longitudinal approach in 30% of the municipalities in Bra- zil, showed that population coverage was directly associated with the reduction in hospitaliza- tions and mortality rates due to cerebrovascular disease, and its effect increased within the implementation period [26]. Another study found recurrence of cardiovascular disease in pri- mary health care models with and without ESF, both models of state care in Brazil. The model with ESF was associated with lower risk of death from all causes, and there was a 16.4% reduc- tion in the absolute risk of death from cardiovascular disease for ESF [27]. This last previously Table 4. Generalized linear mixedmodels of hospitalizations for stroke per 10,000 inhabitants. Model 1 (Unadjusted) Model 2 ( adjusted) Variable Estimate Standard Error p-value Estimate Standard Error p-value Year -0.01475 0.00246 <0.0001 -0.02614 0.003554 <0.0001 ESF proportion -0.00413 0.000762 <0.0001 -0.00407 0.00079 <0.0001 ACS proportion -0.00139 0.00072 0.0521 -0.000744 0.000746 0.3185 Note: ESF: Family Health Strategy, ACS: Health of Community Agents. adjusted for Population, GDP (Gross Domestic Product) and HDI value (Human Development Index). https://doi.org/10.1371/journal.pone.0198428.t004 PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 5 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions published study corroborated the findings of a cohort study conducted in Paris, which exam- ined the increased incidence of cardiovascular diseases as factors related to the social determi- nants and decrease in services [28]. In addition to access to and quality of ESF care, preventive and health promotion initiatives, such as the implementation of Centers to Support Family Health (NASF) occurred simulta- neously in several municipalities. These centers offered differentiated collective action services, with a multidisciplinary team approach including incentives to reduce smoking, alcoholism and encouraging healthy practicesÐfor example the abandonment of inactivity [29]. The lack of access to medicines in the public sector is one of the major barriers to fighting the chronic diseases, and management models should intensify efforts to adopt public policies that guarantee dispensed medication [30]. Thus, in Brazil, the use of therapeutic protocols by the National Medication Policy [31] (1998) and the National Policy on Pharmaceutical Care [32] (2004) has ensured that many essential medicines are available free of charge, or at a reduced cost [33], thereby enhancing the care and strengthening the assistance. Another protective factor related to the reduction in hospitalizations was the implementa- tion of an immunization policy against influenza since 1999. This was important because patients infected with influenza and respiratory infections showed a hemodynamic instability, and immunization against influenza was a protective factor in preventing morbidity and mor- tality from cardiovascular disease [34]. Since 1980, tobacco control that includes a set of actions to reduce the prevalence of smok- ing has been articulated by the Brazilian Ministry of Health. However, only in 2003 Brazil signed the Framework Convention on Tobacco Control (FCTC) with the effective implemen- tation of actions between 2005 and 2006. It was the most relevant event that resulted in the implementation of National Policy to Control Tobacco Use with actions focused on the reduced demand of tobacco, with an important market regulation, protection measures such as banning smoking in collective environments and promoting cessation of tobacco use [35,36]. This approach corroborated the findings of a previous study that showed a strong association of chronic conditions with major cardiovascular risk factors for smoking [37]. Another transversal policy was the development and implementation of the Strategic Action Plan for Confronting Chronic Noncommunicable Diseases (2011±2022), with nine national targets related to morbidity and mortality issues and four risk factors: smoking, inade- quate diet, physical inactivity, and excessive use of alcohol; and four groups of lesions: cardio- vascular, cancers, diabetes and chronic respiratory diseases [38,39]. Analyzing the ranking of the major causes for Disability-Adjusted Life Year (DALY) in Bra- zil and macro-regions (data not shown), it was shown that for Brazil, as a whole, diabetes mel- litus (5.1%), ischemic heart disease (5.0%) and strokeÐfirst occurrence (4.6%), totalizing 14.7% of the total DALYs was characteristic of an epidemiological pattern of developed coun- tries [40]. Moreover, in the WHO survey conducted in 23 countries, including Brazil, losses of $ 84 billion due to coronary heart disease, stroke and diabetes were estimated from 2006 to 2015 [41]. Cardiovascular diseases, despite their decline, have been and continue to be the main cause of death in Brazil. The decline in cardiovascular disease was greater for cerebrovascular dis- eases (34%) and heart disease (44%). Mortality from ischemic heart disease decreased by 26% [29]. As differential, this study presented a longitudinal analysis, as there are few in the literature, especially using mixed models to verify the impact of the health care policy over time. It is worth noting that the ESF has been present since 1994, but the state of São Paulo was the last state to implement the strategy, although it is the state with the highest HDI and largest popu- lation (over 40 million). Despite the StrategyÂs own funding, with financial incentives for PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 6 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions incorporating family health teams, oral health teams, NASF teams and other initiatives, there was still a delay in implementing this strategy, which made it difficult to change the health care policy, as demonstrated by the slow growth in population coverage from 1998 to 2013 (which made the results more interesting), far below the percentage of other regions of Brazil. Never- theless, the statistical model was able to identify changes in the epidemiological profile and its relationship with the population covered by the ESF. We emphasize this was an ecological study and limitations were expected. The first is the possibility of ecological fallacy, in which ecological associations do not always reflect individual associations. It was not possible to determine whether Individuals with the outcomes were under ESF coverage, because the level of aggregate was the municipality [26]. Furthermore, the ESF was influenced by political changes. Changes of healthcare managers, since they have their political ideologies and personal conceptions of public management, and these did not always coincide with the current Brazilian health policy. Nevertheless, two English studies have confirmed that the provision of care in Primary Care had a direct impact on mortality rates and recommended that improvement in the health of the population required a reduction in health inequalities; and it was treated as a political priority with effec- tive territorial monitoring for reduction of hospitalizations and consequently of mortality [42,43]. Finally, we could assume that the result for the state of São Paulo could be inferred for all other states in Brazil, considering that the beginning and later development of this strategy in São Paulo was very precarious and presented numerous technical difficulties, mainly because the State had an organized primary health care network in the 1990s. Conclusion We concluded that the health care model based on the Family Health Strategy has positively impacted the hospitalization indicators for heart failure and stroke, indicating that this model was effective in preventing Ambulatory or primary care sensitive conditions (PCSCs). Supporting information S1 File. Study data. (XLS) Author Contributions Conceptualization: Denise de Fa Âtima Barros Cavalcante.  à Data curation: Valeria Silva Candido Brizon, Livia Fernandes Probst. Formal analysis: Gla Âucia Maria Bovi Ambrosano.   à Investigation: Denise de Fatima Barros Cavalcante, Valeria Silva Candido Brizon, Livia Fer- nandes Probst.   Methodology: Denise de Fatima Barros Cavalcante, Livia Fernandes Probst, Glaucia Maria Bovi Ambrosano. Supervision: Antonio Carlos Pereira. Validation: Marcelo de Castro Meneghim, Antonio Carlos Pereira, Glaucia Maria Bovi Ambrosano. Visualization: Antonio Carlos Pereira. PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 7 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions Writing ± original draft: Denise de Fa Âtima Barros Cavalcante. Writing ± review & editing: Livia Fernandes Probst, Marcelo de Castro Meneghim, Antonio Carlos Pereira, Gla Âucia Maria Bovi Ambrosano. References 1. Freund T, Heller G, Szecsenyi J. Hospitalisations for ambulatory care sensitive conditions in Germany. Z Evid Fortbilt Qual Gesundhwes, 108 (5±6) 251±7, 2014. 2. Gibson OR, Segal L, McDermott RA. A systematic review of evidence on the association between hos- pitalization for chronic disease related ambulatory care sensitive conditions and primary health care resourcing. BMC Health Services Research. 2013; 13:336. https://doi.org/10.1186/1472-6963-13-336 PMID: 23972001 3. Batista SRR, Jardim PCBV, Sousa ALL, Salgado CM. Hospitalizac Ëões por condic Ëões cardiovasculares sensõ Âveisà atenc Ëão prima  ria em municõ Âpios goianos. Rev Saude Publica, 46(1), 34±42. Epub January 06, 2012. PMID: 22218758 4. Caminal J, Starfield B, Sanchez E, Casanova C, Morales M. The role of primary care in preventing ambulatory care sensitive conditions. European Journal of Public Health. 14(3), 2004. 5. Purdy S, Grifin T, Salisbury C, Sharp D. Ambulatory care sensitive conditions: terminology and disease coding need to be more specific toa id policy makers and clinicians. Public Health. 123 (2):169±73, 2009. https://doi.org/10.1016/j.puhe.2008.11.001 PMID: 19144363 6. Brasil, Ministe  rio da Sau  de, Portaria nÊ 399/GM de 22 de fevereiro de 2006, Pacto pela Sau  de, Brasõ Âlia, DF. 7. Brasil, Ministe  rio da Sau  de, Portaria SAS/MS nÊ 221, de 17 de abril de 2008, Lista Brasileira de Interna- c Ëões por Condic Ëões Sensõ Âveisà Atenc Ëão Prima  ria. 8. Macinko J, Guanais FC, Evaluation of the impact of the Family Health Program on infant mortality in Brazil, 1990±2002. Journal of Epidemiology and Community Health. 2006; 60 (1): 13±19. https://doi. org/10.1136/jech.2005.038323 PMID: 16361449 9. Brasil, Ministe  rio da Sau  de: Dados de cobertura do Programa Sau  de da Famõ Âlia http://dab.saude.gov. br/portaldab/historico_cobertura_sf.php, acessado em fevereiro 2016. 10. Schmittdiel JA, Shortell SM, Rundall TG, Selby JV. Effect of primary helth care orientation on chronic care management. Annals of family medicine. 4 (2), mar/apr 2006. 11. Rosano A, Loha CA, Falvo R, Van der Zee J, Ricciardi W, Guasticchi G et al. The relationship between avoidable hospitalization and acessibility to primary care: a systematic review. European Journal of Public Health. 2012; 23 (3):356±360. https://doi.org/10.1093/eurpub/cks053 PMID: 22645236 12. Boing AF, Vicenzi RB, Magajewski F, Boing AC, Moretti-Pires RO, Peres KG et al. Reduc Ëão das inter- nac Ëões por condic Ëões sensõ Âveisà atenc Ëão prima  ria no Brasil entre 1998±2009. Rev Saude Publica, 46 (2), 359±366. Epub February 14, 2012. PMID: 22331182 13. Gaziano TA. Reducing the growing burden of cardiovascular disease in the developing world. Health Aff. 26:13±24, 2007; 14. WHO. Preventing chronic diseases a vital investment. WHO, 2008. 15. Bonita R, Beaglehole R, Kjellstrom T. Basic epidemiology. 2nd ed. Genebra: World Health Organiza- tion; 2006. 16. Instituto Brasileiro de Geografia e Estatõ Âstica (IBGE), Disponõ Âvel: http://www.ibge.gov.br/home/ 17. Organizac Ëão Mundial de Sau  de. Classificac Ëão Estatõ Âstica Internacional de Doenc Ë as e Problemas Rela- cionadosà Sau  de±CID-10. Disponõ Âvel em: www.datasus.gov.br/cid10/v2008/cid10.htm 18. Brasil, Ministe  rio da Sau  de. Departamento de Informa  tica do SUS (DATASUS)- Disponõ Âvel em: http:// datasus.saude.gov.br/ 19. Fundac Ëão Sistema Estadual de Ana  lise de Dados (SEADE)- Disponõ Âvel em: http://www.seade.go.,br/ 20. SAS, SAS Software, Version 9,1, Cary, North Carolina: SAS Institute Inc, 1999. 21. Alfradique ME, Bonolo PF, Dourado I, Lima-Costa MF, Macinko J, Mendonc Ë a CS et al. Internac Ëões por condic Ëões sensõ Âveisà atenc Ëão prima  ria: a construc Ëão da lista brasileira como ferramenta para medir o desempenho do sistema de sau  de (Projeto ICSAPÐBrasil). Cad Saude Publica, 2009; 25 (6): 1337± 1349. PMID: 19503964 22. Niti M, Ng T, Avoidable hospitalisation rates in Singapore, 1991±1998: assessing trends and inequities of quality in primary care, Journal of Epidemiology and Community Health. 2003; 57 (1): 17±22. https:// doi.org/10.1136/jech.57.1.17 PMID: 12490643 PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 8 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions 23. Pappas G, Hadden WC, Kozak LJ, Fisher GF. Potentially avoidable hospitalizations: inequalities in rates between US socioeconomic groups. American Journal of Public Health, 1997; 87(5):811±816. PMID: 9184511 24. Macinko J, Oliveira VB, Turci MA, Guanais FC, Bonolo PF, Lima-Costa MF. The Influence of Primary Care and Hospital Supply on Ambulatory Care±Sensitive Hospitalizations Among Adults in Brazil, 1999±2007, American Journal of Public Health. 2011; 101 (10): 1963±1970. https://doi.org/10.2105/ AJPH.2010.198887 PMID: 21330584 25. Brasil, Ministe  rio da Sau  de. Plano de Ac Ëões Estrate  gicas para o Enfrentamento das Doenc Ë as Cro à nicas não transmiss õveis (DCNT) de 2011 a 2022. Brasõ Âlia-DF, 2011. 26. Rasella D, Harhay MO, Pamponet ML, Aquino R, Barreto ML. Impact of primary health care on mortality from heart and cerebrovascular diseases in Brazil: a nationwide analysis of longitudinal data. The BMJ. 349:g4014, 2014; https://doi.org/10.1136/bmj.g4014 PMID: 24994807 27. Cabral NL, Franco S, Longo A, Moro C, Buss TA, Collares D et al. The Brazilian Family Health Program and secondary stroke and myocardial infarction prevention: a 6-year cohort study. Am J Public Health. 102:e90±5, 2012. https://doi.org/10.2105/AJPH.2012.301024 PMID: 23078478 28. Chaix B, Kestens Y, Bean K, Leal C, Karusisi N, Meghiref K et al. Cohort Profile: Residential and non- residential environments, individual activity spaces and cardiovascular risk factors and diseases- The RECORD cohort study. Int J Epidemiol. 41:1283±1292,2012. https://doi.org/10.1093/ije/dyr107 PMID: 29. Shmidt MI, Duncan BB, Azevedo e Silva G, Menezes AM, Monteiro AM, Barreto, et al. Chronic non- communicable diseases in Brazil: burden and current challenges, Lancet; 377(9781): 1949±61, 2011. https://doi.org/10.1016/S0140-6736(11)60135-9 PMID: 21561658 30. Hogerzeil HV, Mirza Z. The world medicines situation 2011: access to essential medicines as part of the right to health. Geneva: World Health Organization; 2011 31. Brasil, Ministe  rio da Sau  de, Portaria GM no 3,916, de 30 de outubro de 1998, Aprova a Polõ Âtica Nacio- nal de Medicamentos, Dia  rio Oficial da União 1998; 10 nov. 32. Conselho Nacional de Sau  de, Resoluc Ëão no 338, de 6 de maio de 2004. Aprova a Polõ Âtica Nacional de Assistência Farmacêutica, Dia  rio Oficial da União 2004; 20 mai. 33. Paniz VMV, Cechin ICCF, Fassa AG, Piccini RX, Tomasi E, Thume  E et al. Acesso a medicamentos para tratamento de condic Ëões agudas prescritos a adultos nas regiões Sul e Nordeste do Brasil. Cad Saude Publica. 2016; 32 (4): e00009915. https://doi.org/10.1590/0102-311X00009915 PMID: 34. Lentsck MH, Latorre MRDO, Mathias TAF. Tendência das internac Ëões por doenc Ë as cardiovasculares sensõ Âveisà atenc Ëão prima  ria. Rev Bras Epidemiol. 2015; 18 (2): 372±384. https://doi.org/10.1590/ 1980-5497201500020007 PMID: 26083509 35. Brasil, Ministe  rio da Sau  de. Polõ Âtica Nacional de controle do tabaco: relato  rio de gestão e progresso 2011±2012 /Instituto Nacional de Ca à ncer Jose  Alencar Gomes da Silva. Comissão Nacional para Implementac Ëão da Convenc Ëão-Quadro para controle do Tabaco (CONICQ).±Rio de Janeiro: INCA, 36. Portes LH, Machado CV. Convenc Ëão-Quadro para o Controle do Tabaco: adesão e implantac Ëão na Ame  rica Latina. Revista Panamericana de Salud Publica, 38(5), 370±379, (2015). PMID: 26837522 37. Galil AGS, Cupertino AP, Banhato EFC, Campos TS, Colugnati FAB, Richter KP et al. Factors associ- ated with tobacco use among patients with multiple chronic conditions. International Journal of Cardiol- ogy. 221 1004:1007, 2016. 38. Malta DC, Silva JB Jr. O Plano de Ac Ëões Estrate  gicas para o Enfrentamento das Doenc Ë as Cro à nicas Não Transmissõ Âveis no Brasil e a definic Ëão das metas globais para o enfrentamento dessas doenc Ë as ate  2025: uma revisão. Epidemiologia e Servic Ë os de Sau  de, 22(1), 151±164. (2013). 39. Malta DC, Silva JB Jr. Plano de Ac Ëões Estrate  gicas para o Enfrentamento das Doenc Ë as Cro à nicas Não Transmiss õveis no Brasil apo  s três anos de implantac Ëão, 2011±2013. Epidemiologia e Servic Ë os de Sau  de, 23(3), 389±395,(2014). 40. Schramm JMA, Andrade JM, Leite IC, Valente JG, Gadelha AMJ, Portela MCet al. Transic Ëão epidemio- lo  gica e o estudo de carga de doenc Ë a no Brasil. Cienc Saude Coletiva, 2004, vol. 9, n.4, pp, 897±908 41. Abegunde DO, Mathers CD, Adam T, Ortegon M, Strong K. The burden and costs of chronic diseases in low-income and middleincome countries, Lancet; 370: 1929±38, 2007. https://doi.org/10.1016/ S0140-6736(07)61696-1 PMID: 18063029 42. Asaria P, Fortunato L, Fecht D, Tzoulaki I, Abellan JJ, Hambly P et al. Trends and inequalities in cardio- vascular disease mortality across 7932 English electoral wards, 1982±2006. Bayesian spatial analysis. Int J Epidemiol; 41: 1737±1749, 2012. https://doi.org/10.1093/ije/dys151 PMID: 23129720 PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 9 / 10 Longitudinal study on the impact of hospital admissions for primary care sensitive conditions 43. Hone T, Rasella D, Barreto M, Atun R, Majeed A, Millett C. Large reductions in amenable mortality associated with Brazil's Primary Care expansion and strong health governance. Health Affairs 36, no.1 (2017):149±158. https://doi.org/10.1377/hlthaff.2016.0966 PMID: 28069858 PLOS ONE | https://doi.org/10.1371/journal.pone.0198428 June 26, 2018 10 / 10

Journal

PLoS ONEPubmed Central

Published: Jun 26, 2018

There are no references for this article.