| Literature DB >> 32104169 |
Margarida Cristiana Napoleão Rocha1, Mauricio Lisboa Nobre2, Leila Posenato Garcia3.
Abstract
OBJECTIVE: To describe the temporal trends of leprosy indicators among the elderly in Brazil in 2001 - 2018.Entities:
Keywords: Brazil; Elderly; epidemiology; leprosy; time series studies
Year: 2020 PMID: 32104169 PMCID: PMC7025571 DOI: 10.26633/RPSP.2020.12
Source DB: PubMed Journal: Rev Panam Salud Publica ISSN: 1020-4989
Detection rate (per 100 000 inhabitants) of new leprosy cases in the elderly in Brazil, its five regions, and 27 unidades federativas (UF), 2001–2018
Brazil/regions/UF | Rate | Annual change rate (%) | 95%CI | Trend | ||
|---|---|---|---|---|---|---|
2001 | 2018 | |||||
Rondônia | 187.2 | 83.7 | -6.2 | -7.8 | -4.6 | Decreasing |
Acre | 96.1 | 36.6 | -8.7 | -11.8 | -5.5 | Decreasing |
Amazonas | 92.4 | 21.5 | -6.9 | -8.4 | -5.3 | Decreasing |
Roraima | 248.3 | 41.3 | -9.5 | -11.4 | -7.6 | Decreasing |
Pará | 162.3 | 66.8 | -5.0 | -5.5 | -4.4 | Decreasing |
Amapá | 56.0 | 42.5 | -6.4 | -8.7 | -4.1 | Decreasing |
Tocantins | 193.8 | 254.6 | 0.3 | -1.8 | 2.5 | Stationary |
Maranhão | 173.7 | 116.6 | -2.4 | -3.3 | -1.5 | Decreasing |
Piauí | 142.3 | 80.2 | -3.6 | -4.6 | -2.6 | Decreasing |
Ceará | 94.1 | 51.2 | -3.3 | -3.9 | -2.8 | Decreasing |
Rio Grande do Norte | 21.7 | 16.8 | -1.2 | -3.5 | 1.2 | Stationary |
Paraíba | 53.0 | 27.8 | -4.0 | -5.5 | -2.6 | Decreasing |
Pernambuco | 64.4 | 48.2 | -2.8 | -3.7 | -1.8 | Decreasing |
Alagoas | 20.8 | 24.4 | -1.1 | -3.1 | 0.9 | Stationary |
Sergipe | 31.4 | 32.3 | -1.2 | -3.6 | 1.4 | Stationary |
Bahia | 33.5 | 31.7 | -1.3 | -2.7 | 0.2 | Stationary |
Minas Gerais | 34.8 | 10.0 | -8.2 | -9.1 | -7.4 | Decreasing |
Espírito Santo | 97.3 | 21.5 | -9.5 | -11.0 | -8.1 | Decreasing |
Rio de Janeiro | 39.2 | 11.3 | -8.3 | -9.1 | -7.5 | Decreasing |
São Paulo | 19.3 | 5.9 | -7.2 | -7.7 | -6.6 | Decreasing |
Paraná | 45.2 | 11.1 | -8.0 | -9.3 | -6.7 | Decreasing |
Santa Catarina | 8.3 | 3.6 | -6.3 | -9.4 | -3.1 | Decreasing |
Rio Grande do Sul | 4.4 | 2.4 | -6.0 | -7.1 | -4.8 | Decreasing |
Mato Grosso do Sul | 70.3 | 31.2 | -3.8 | -8.3 | 0.8 | Stationary |
Mato Grosso | 257.1 | 272.8 | -0.7 | -3.4 | 2.1 | Stationary |
Goiás | 120.0 | 50.3 | -6.0 | -6.9 | -5.0 | Decreasing |
Federal District | 28.2 | 9.1 | -6.2 | -7.8 | -4.6 | Decreasing |
prepared by the authors based on data from Sinan and Brazilian Institute of Geography and Statistics (IBGE).
95%CI: 95% Confidence Interval; Statistical test: Prais-Winsten generalized linear regression
FIGURE 1.(a) Detection rate (per 100 000 inhabitants) of new leprosy cases in elderly people; (b) proportion of new leprosy cases in the elderly; (c) proportion of multibacillary new leprosy cases in the elderly; (d) rate of new leprosy cases with grade 2 physical disability at the time of diagnosis (per 1 000 000 inhabitants) in Brazil, 2001-2018
Proportion of new leprosy cases in the elderly in Brazil, its five regions, and 27 unidades federativas (UF), 2001-2018
| Place | Annual change rate (%) | 95%CI | Trend | |
|---|---|---|---|---|
Rondônia | 3.4 | 1.7 | 5.1 | Increasing |
Acre | 2.9 | 0.8 | 5.1 | Increasing |
Amazonas | 3.8 | 2.7 | 4.9 | Increasing |
Roraima | 4.3 | 2.0 | 6.7 | Increasing |
Pará | 4.2 | 3.5 | 5.0 | Increasing |
Amapá | 2.1 | 0.6 | 3.5 | Increasing |
Tocantins | 3.7 | 2.8 | 4.7 | Increasing |
Maranhão | 3.1 | 2.7 | 3.5 | Increasing |
Piauí | 2.9 | 2.4 | 3.3 | Increasing |
Ceará | 2.6 | 2.0 | 3.2 | Increasing |
Rio Grande do Norte | 3.1 | 1.7 | 4.5 | Increasing |
Paraíba | 2.4 | 1.7 | 3.1 | Increasing |
Pernambuco | 2.8 | 2.4 | 3.3 | Increasing |
Alagoas | 3.9 | 2.5 | 5.2 | Increasing |
Sergipe | 4.6 | 3.8 | 5.4 | Increasing |
Bahia | 3.5 | 2.8 | 4.3 | Increasing |
Minas Gerais | 2.8 | 2.2 | 3.3 | Increasing |
Espírito Santo | 2.8 | 1.9 | 3.7 | Increasing |
Rio de Janeiro | 3.0 | 2.2 | 3.7 | Increasing |
São Paulo | 2.7 | 2.3 | 3.1 | Increasing |
Paraná | 2.9 | 2.3 | 3.5 | Increasing |
Santa Catarina | 1.4 | -0.5 | 3.3 | Stationary |
Rio Grande do Sul | 2.8 | 1.9 | 3.6 | Increasing |
Mato Grosso do Sul | 2.4 | 1.7 | 3.2 | Increasing |
Mato Grosso | 3.5 | 3.2 | 3.8 | Increasing |
Goiás | 3.7 | 3.1 | 4.2 | Increasing |
Federal District | 4.5 | 2.8 | 6.2 | Increasing |
prepared by the authors based on data from SINAN and Brazilian Institute of Geography and Statistics (IBGE).
95%CI: 95% Confidence Interval; Statistical test: Prais-Winsten generalized linear regression
Proportion of new multibacillary leprosy cases in the elderly in Brazil, its five regions, and 27 unidades federativas (UF), 2001-2018
| Brazil/regions/UF | Annual change rate (%) | 95CI% | Trend | |
|---|---|---|---|---|
Rondônia | 1.6 | 1.0 | 2.3 | Increasing |
Acre | 1.7 | 0.4 | 2.9 | Increasing |
Amazonas | 1.2 | 0.5 | 1.9 | Increasing |
Roraima | 1.0 | 0.4 | 1.7 | Increasing |
Pará | 1.3 | 1.1 | 1.6 | Increasing |
Amapá | 1.7 | -0.8 | 4.2 | Stationary |
Tocantins | 2.4 | 1.5 | 3.3 | Increasing |
Maranhão | 1.3 | 1.0 | 1.5 | Increasing |
Piauí | 2.5 | 2.0 | 2.9 | Increasing |
Ceará | 0.5 | 0.3 | 0.7 | Increasing |
Rio Grande do Norte | 0.3 | -0.7 | 1.3 | Stationary |
Paraíba | 1.4 | 0.8 | 1.9 | Increasing |
Pernambuco | 1.1 | 0.3 | 1.9 | Increasing |
Alagoas | 1.3 | 0.5 | 2.2 | Increasing |
Sergipe | 1.6 | 1.0 | 2.2 | Increasing |
Bahia | 1.8 | 1.4 | 2.1 | Increasing |
Minas Gerais | -0.1 | -0.8 | 0.7 | Stationary |
Espírito Santo | 1.8 | 1.0 | 2.5 | Increasing |
Rio de Janeiro | 1.1 | 0.7 | 1.4 | Increasing |
São Paulo | 2.2 | 1.9 | 2.6 | Increasing |
Paraná | 1.6 | 1.3 | 1.9 | Increasing |
Santa Catarina | 1.6 | 0.8 | 2.4 | Increasing |
Rio Grande do Sul | 0.4 | -0.1 | 1.0 | Stationary |
Mato Grosso do Sul | 3.0 | 2.3 | 3.8 | Increasing |
Mato Grosso | 3.3 | 2.8 | 3.8 | Increasing |
Goiás | 0.3 | 0.1 | 0.6 | Increasing |
Federal District | 0.8 | -0.6 | 2.1 | Stationary |
prepared by the authors based on data from SINAN and Brazilian Institute of Geography and Statistics (IBGE).
95%CI: 95% Confidence Interval; Statistical test: Prais-Winsten generalized linear regression
Rate of new cases with grade 2 physical disability at the time of diagnosis (per 1 000 000 inhabitants) in Brazil, its five regions, and 27 unidades federativas (UF), 2001-2018
Brazil/regions/UF | Rate | Annual change rate (%) | 95%CI | Trend | ||
|---|---|---|---|---|---|---|
2001 | 2018 | |||||
Rondônia | 163.9 | 108.6 | -6.7 | -9.8 | -3.5 | Decreasing |
Acre | 128.1 | 116.4 | -2.4 | -5.6 | 0.9 | Stationary |
Amazonas | 220.0 | 32.2 | -6.7 | -8.6 | -4.8 | Decreasing |
Roraima | 451.4 | 29.5 | -9.0 | -13.0 | -4.9 | Decreasing |
Pará | 131.5 | 90.9 | -2.3 | -4.0 | -0.5 | Decreasing |
Amapá | 50.9 | 60.7 | -3.7 | -8.4 | 1.2 | Stationary |
Tocantins | 153.0 | 285.1 | 2.9 | -0.1 | 6.0 | Stationary |
Maranhão | 186.2 | 112.8 | -1.9 | -3.0 | -0.7 | Decreasing |
Piauí | 127.8 | 83.2 | -2.0 | -4.7 | 0.9 | Stationary |
Ceará | 68.7 | 59.0 | -2.1 | -3.9 | -0.3 | Decreasing |
Rio Grande do Norte | 20.1 | 14.7 | -1.7 | -8.3 | 5.5 | Stationary |
Paraíba | 57.9 | 38.6 | -2.2 | -5.4 | 1.2 | Stationary |
Pernambuco | 31.5 | 36.2 | -2.5 | -5.3 | 0.5 | Stationary |
Alagoas | 4.9 | 26.5 | 3.7 | -4.7 | 12.8 | Stationary |
Sergipe | 15.3 | 13.1 | 3.6 | -4.6 | 12.5 | Stationary |
Bahia | 22.3 | 25.0 | -0.1 | -2.3 | 2.3 | Stationary |
Minas Gerais | 62.1 | 17.3 | -8.4 | -10.0 | -6.8 | Decreasing |
Espírito Santo | 123.1 | 13.2 | -10.6 | -13.1 | -8.0 | Decreasing |
Rio de Janeiro | 31.2 | 12.5 | -4.6 | -6.2 | -3.0 | Decreasing |
São Paulo | 22.5 | 11.8 | -5.1 | -6.1 | -4.1 | Decreasing |
Paraná | 73.5 | 13.1 | -8.8 | -11.0 | -6.5 | Decreasing |
Santa Catarina | 9.2 | 5.2 | -5.7 | -9.1 | -2.1 | Stationary |
Rio Grande do Sul | 6.5 | 8.1 | -2.9 | -6.9 | 1.2 | Stationary |
Mato Grosso do Sul | 62.2 | 28.0 | 1.3 | -6.7 | 4.5 | Stationary |
Mato Grosso | 195.2 | 231.2 | 0.6 | -2.0 | 3.2 | Stationary |
Goiás | 79.4 | 44.3 | -5.2 | -6.1 | -4.2 | Decreasing |
Federal District | 52.8 | 9.1 | -9.4 | -11.9 | -6.8 | Decreasing |
prepared by the authors based on data from SINAN and Brazilian Institute of Geography and Statistics (IBGE).
95%CI: 95% Confidence Interval; Statistical test: Prais-Winsten generalized linear regression