Estimates in small geographic areas: a necessary step towards reducing health inequalitiesThis supplement of Revista Brasileira de Epidemiologia (RBE), containing articles on unpublished results, explores the analytical potential of the Health Information Systems (SIS, acronym in Portuguese for “Sistemas de Informação em Saúde”) and national surveys in small geographic areas. This thematic issue aims to broaden the knowledge of the Brazilian scientific community about estimates in small areas and to support public policies that promote equity. To this end, international and national collaborations were articulated in an attempt to advance an innovative proposal as a health management tool and support for public policies.
Brazil is a country full of social inequalities thar are reflected in the differences in distribution of income, unequal access to health services, housing, employment and education1. Economic and social deprivation impacts health and causes situations such as inadequate access to food, hunger, child malnutrition, infectious diseases, decreased life expectancy and increased mortality from preventable and treatable diseases, including diarrhea, respiratory infections, HIV/AIDS, tuberculosis and malaria1–3.
Current evidence show that these inequalities are substantial in different population groups in most behaviors and health-related outcomes4,5. In this context, monitoring health indicators of different populations and features that amplify social differences and inequities is the first step in the planning of actions and programs that can reduce the occurrence and severity of diseases, since the objective is to identify features that can explain health outcomes, for example, the environment, urbanization, the labor market, type of leisure activities, education, and health institutions6,7.
The SIS and population surveys are important tools to assess and monitor the dynamics, conditions and risk factors of diseases. Geographic Information Systems (GIS) have been the most used for analyzing health data in a specialized way and according to the principles of epidemiology (person, place, time, disease). Thus, geoepidemiology or spatial epidemiology analyzes the geographical variations of diseases or other health-related outcomes taking into account demographic, environmental, behavioral, socioeconomic, genetic and infectious risk factors8.
Examining health indicators from estimates for small areas can lead to important results that are often masked by national or state average statistics. The results at this level of disaggregation can help managers plan and allocate resources, as well as identify possible solutions to such problems9,10.
In this sense, estimating health indicators in small areas is an advance for Brazilian public health, considering that this method is little used in the country with the purpose of guiding public management, and could be implemented using data from surveillance systems already in operation, such as the Mortality Information System (SIM), the Surveillance System for Risk and Protection Factors for Chronic Diseases by Telephone Survey (Vigitel), the Surveillance System for Violence and Accidents (VIVA), among others. The use of secondary previously consolidated information systems offers advantages such as wide population coverage and the possibility of including different geographical areas. Furthermore, the low cost of data collection is noteworthy, since the information is made available free of charges by the organizations that collect and organize it, which allows many studies to be carried out.
Brazil produces abundant research data in the health field with national coverage, accounting for large regions, federated units, metropolitan regions and capitals. However, there is lack of information about disaggregated levels, due to the high cost of these surveys.
This RBE supplement presents methodological advances that allow analyses in small areas. The SIS and its corrections were widely used in this supplement, supporting local managers and researchers in understanding the importance of these adjustments. The SIM was used with corrections for underreporting, redistribution of Garbage Codes and ill-defined causes, application of Baysean methods, among others11,12.
In addition, this issue is an innovation as it uses data from epidemiological surveys by conglomerates from the census sector, opening up new methodological perspectives for measuring intra-urban inequalities. Direct and indirect estimation methods for small areas were used to determine the prevalence of risk factors in smaller units, that is, a census sector or a set of census sectors.
The RBE issue is expected to produce rich knowledge for scientific growth, and the findings should contribute to back health managers and the society in guiding public policies, as well as inspiring a methodological progress in estimates for small geographic areas.
-
Financial support: Secretariat of Health Surveillance, Ministry of Health. TED 148-2018, project “Inequalities of indicators of chronic non-communicable diseases, violence and their risk factors in small geographic areas”; National Council for Scientific and Technological Development (CNPq); and Minas Gerais State Research Support Foundation (FAPEMIG) (PPM-00428-17 and RED-00081-16).
REFERENCES
-
1 Barreto ML. Health inequalities: a global perspective. Ciên Saúde Coletiva 2017; 22(7): 2079-108. https://doi.org/10.1590/1413-81232017227.02742017
» https://doi.org/10.1590/1413-81232017227.02742017 - 2 Food and Agriculture Organization. El estado de la inseguridad alimentaria en el mundo 2014. Roma: Food and Agriculture Organization; 2014.
- 3 World Health Organization. World Health Statistics 2015. Genebra: World Health Organization; 2015.
-
4 Mackenbach JP. The persistence of health inequalities in modern welfare states: The explanation of a paradox. Soc Sci Med 2012; 75(4): 761-9. https://doi.org/10.1016/j.socscimed.2012.02.031
» https://doi.org/10.1016/j.socscimed.2012.02.031 -
5 Pampel FC, Krueger PM, Denney JT. Socioeconomic disparities in health behaviors. Annu Rev Sociol 2010; 36: 349-70. https://doi.org/10.1146/annurev.soc.012809.102529
» https://doi.org/10.1146/annurev.soc.012809.102529 -
6 Oliveira DC, Barreira AS, Trunk MT, Guzmán AF. Efecto de las desigualdades socioeconómicas en la mortalidad de la ciudad de fortaleza, ceará. Brasil durante el año 2007. Rev Esp Salud Publica [Internet] 2010 [accessed on Dec. 10, 2020]; 84(4): 441-50. Available from: http://scielo.isciii.es/scielo.php?script=sci_arttext&pid=S1135-57272010000400009
» http://scielo.isciii.es/scielo.php?script=sci_arttext&pid=S1135-57272010000400009 -
7 Diez-Roux AV. Investigating neighborhood and area effects on health. Am J Public Health 2001; 91(11): 1783-9. https://doi.org/10.2105/ajph.91.11.1783
» https://doi.org/10.2105/ajph.91.11.1783 - 8 Gordis L. Epidemiologia. 5ᵃ ed. Filadélfia: Revinter; 2017.
-
9 Dwyer-Lindgren L, Bertozzi-Villa A, Stubbs RW, Morozoff AH, Shirude S, Unützer J, et al. Trends and Patterns of Geographic Variation in Mortality from Substance Use Disorders and Intentional Injuries Among US Counties, 1980-2014. JAMA 2018; 319(10): 1013-23. https://doi.org/10.1001/jama.2018.0900
» https://doi.org/10.1001/jama.2018.0900 -
10 El Bcheraoui C, Mokdad AH, Dwyer-Lindgren L, Bertozzi-Villa A, Stubbs RW, Morozoff C, et al. Trends and Patterns of Differences in Infectious Disease Mortality Among US Counties, 1980-2014. JAMA 2018; 319(12): 1248-60. https://doi.org/10.1001/jama.2018.2089
» https://doi.org/10.1001/jama.2018.2089 -
11 Teixeira R, Ishitani LH, Marinho F, Pinto Junior EPP, Katikireddi SV, Malta DC. Proposta metodológica para redistribuição de óbitos por causas garbage nas estimativas de mortalidade para Doenças Crônicas Não Transmissíveis. Rev Bras Epidemiol 2020. (no prelo.) https://doi.org/10.1590/1980-549720210004.supl.1
» https://doi.org/10.1590/1980-549720210004.supl.1 -
12 Assunção RM, Barreto SM, Guerra HL, Sakurai E. Mapas de taxas epidemiológicas: uma abordagem Bayesiana. Cad Saúde Pública 1998; 14(4): 713-23. https://doi.org/10.1590/S0102-311X1998000400013
» https://doi.org/10.1590/S0102-311X1998000400013
Publication Dates
-
Publication in this collection
16 Apr 2021 -
Date of issue
2021
