BACKGROUND: To improve healthcare access and mitigate healthcare costs for its population, Nigeria established a National Health Insurance Scheme (NHIS) in 1999. The NHIS remains Nigeria's leading vehicle for achieving universal health coverage; nonetheless, questions remain regarding its quality and effectiveness. Studies on patient satisfaction have served as a useful strategy to further understand the patient experience and the efficacy of health systemsAIM: To synthesise current knowledge on patient satisfaction with the NHISMETHODS: The authors performed a systematic review of primary literature from 1999 to 2020 reporting on NHIS patient satisfaction in eight databases (including PubMed, Embase, and Africa-wide InformationRESULTS: This search returned 764 unique records of which 21 met criteria for full data extraction. The 21 qualifying studies representing 11 of the 36 Nigerian states, were published from 2011 to 2020, and found moderate overall satisfaction with the NHIS (64%). Further, when disaggregated into specific domains, NHIS enrolees were most satisfied with provider attitudes (77%) and healthcare environments (70%), but less satisfied with laboratories (62%), billings (62%), pharmaceutical services (56%), wait times (55%), and referrals (51%). Importantly, time trends indicate satisfaction with the NHIS is increasing - although to differing degrees depending on the domainCONCLUSION: The beneficiaries of the NHIS are moderately satisfied with the scheme. They consider it an improvement from being uninsured, but believe that the scheme can be considerably improved. The authors present two main recommendations: (1) shorter wait times may increase patient satisfaction and can be a central focus in improving the overall scheme, and (2) more research is needed across all 36 states to comprehensively understand patient satisfaction towards NHIS in anticipation of potential scheme expansion

Resumo Objetivou-se analisar a correlação da qualidade dos serviços da Atenção Primária na redução da mortalidade infantil, através do geoprocessamento. Um estudo ecológico, de abordagem transversal, em que foram utilizados dados secundários de todos os 5.565 municípios brasileiros para análise da taxa de mortalidade infantil (TMI) e causa de óbito infantil. Os dados da TMI foram obtidos no Sistema de Informação de Mortalidade. Para a análise espacial, incluímos 5.011 municípios. As análises de clusterização ocorreram no software GEODA e as análises de regressão espacial no ARCGIS 10.5. No Brasil houve uma redução de 45,07% da TMI entre os anos 2000 e 2015. A maior redução ocorreu na região nordeste do país, apesar de ainda ser a região com maior número na TMI. Dos 749 municípios analisados no cluster diferencial para óbito infantil, 153 apresentaram alta TMI. As áreas com maior expansão de alta TMI foram encontradas nas regiões Norte e Nordeste. No Brasil, a TMI mostrou-se inversamente associada à acessibilidade aos serviços de alta complexidade, ao estrato da gestão em saúde e porte populacional, à referência para o parto, à taxa de nascidos vivos, à renda per capita e à taxa de desemprego. Verificou-se uma crescente redução da TMI entre o período de 2000 a 2015.

Abstract This study sought to analyze the correlation of the quality of Primary Health Care services in reducing child mortality, via geoprocessing. It involved an ecological study, with a cross-sectional approach, in which secondary data from all 5,565 Brazilian municipalities were used to analyze the infant mortality rate (IMR) and cause of infant death. The data related to IMR was obtained from the Mortality Information System. For the spatial analysis, 5,011 municipalities were included. The clustering analyses were performed using GEODA software and the spatial regression analyses were performed using ARCGIS 10.5 software. In Brazil, there was a 45.07% reduction in IMR between the years 2000 and 2015. The greatest reduction occurred in the northeastern region of the country, although it is still the region with the highest IMR. Of the 749 municipalities analyzed in the differential cluster for infant death, 153 had high IMR. The areas with the greatest increase in IMR were found in the North and Northeast regions. In Brazil, IMR proved to be inversely associated with the accessibility to high complexity services, health management strata and population size, reference for childbirth, live birth rate, per capita income and unemployment rate. A progressive reduction in IMR was recorded between 2000 and 2015.

Resumo Objetivou-se analisar a correlação da qualidade dos serviços da Atenção Primária na redução da mortalidade infantil, através do geoprocessamento. Um estudo ecológico, de abordagem transversal, em que foram utilizados dados secundários de todos os 5.565 municípios brasileiros para análise da taxa de mortalidade infantil (TMI) e causa de óbito infantil. Os dados da TMI foram obtidos no Sistema de Informação de Mortalidade. Para a análise espacial, incluímos 5.011 municípios. As análises de clusterização ocorreram no software GEODA e as análises de regressão espacial no ARCGIS 10.5. No Brasil houve uma redução de 45,07% da TMI entre os anos 2000 e 2015. A maior redução ocorreu na região nordeste do país, apesar de ainda ser a região com maior número na TMI. Dos 749 municípios analisados no cluster diferencial para óbito infantil, 153 apresentaram alta TMI. As áreas com maior expansão de alta TMI foram encontradas nas regiões Norte e Nordeste. No Brasil, a TMI mostrou-se inversamente associada à acessibilidade aos serviços de alta complexidade, ao estrato da gestão em saúde e porte populacional, à referência para o parto, à taxa de nascidos vivos, à renda per capita e à taxa de desemprego. Verificou-se uma crescente redução da TMI entre o período de 2000 a 2015.

Abstract This study sought to analyze the correlation of the quality of Primary Health Care services in reducing child mortality, via geoprocessing. It involved an ecological study, with a cross-sectional approach, in which secondary data from all 5,565 Brazilian municipalities were used to analyze the infant mortality rate (IMR) and cause of infant death. The data related to IMR was obtained from the Mortality Information System. For the spatial analysis, 5,011 municipalities were included. The clustering analyses were performed using GEODA software and the spatial regression analyses were performed using ARCGIS 10.5 software. In Brazil, there was a 45.07% reduction in IMR between the years 2000 and 2015. The greatest reduction occurred in the northeastern region of the country, although it is still the region with the highest IMR. Of the 749 municipalities analyzed in the differential cluster for infant death, 153 had high IMR. The areas with the greatest increase in IMR were found in the North and Northeast regions. In Brazil, IMR proved to be inversely associated with the accessibility to high complexity services, health management strata and population size, reference for childbirth, live birth rate, per capita income and unemployment rate. A progressive reduction in IMR was recorded between 2000 and 2015.

Resumo O objetivo deste artigo é analisar o uso da inteligência artificial espacial no contexto da imunização contra COVID-19 para a seleção adequada dos recursos necessários. Trata-se de estudo ecológico de caráter transversal baseado em uma abordagem espaço-temporal utilizando dados secundários, em Unidades Básicas de Saúde do Brasil. Foram adotados quatro passos analíticos para atribuir um volume de população por unidade básica, aplicando algoritmos de inteligência artificial a imagens de satélite. Em paralelo, as condições de acesso à internet móvel e o mapeamento de tendências espaço-temporais de casos graves de COVID-19 foram utilizados para caracterizar cada município do país. Cerca de 18% da população idosa brasileira está a mais de 4 quilômetros de distância de uma sala de vacina. No total, 4.790 municípios apresentaram tendência de agudização de casos de Síndrome Respiratória Aguda Grave. As regiões Norte e Nordeste apresentaram o maior número de Unidades Básicas de Saúde com mais de 5 quilômetros de distância de antenas de celular. O Plano nacional de vacinação requer o uso de estratégias inovadoras para contornar os desafios do país. O uso de metodologias baseadas em inteligência artificial espacial pode contribuir para melhoria do planejamento das ações de resposta à COVID-19.

Abstract This article explores the use of spatial artificial intelligence to estimate the resources needed to implement Brazil’s COVID-19 immu nization campaign. Using secondary data, we conducted a cross-sectional ecological study adop ting a time-series design. The unit of analysis was Brazil’s primary care centers (PCCs). A four-step analysis was performed to estimate the popula tion in PCC catchment areas using artificial in telligence algorithms and satellite imagery. We also assessed internet access in each PCC and con ducted a space-time cluster analysis of trends in cases of SARS linked to COVID-19 at municipal level. Around 18% of Brazil’s elderly population live more than 4 kilometer from a vaccination point. A total of 4,790 municipalities showed an upward trend in SARS cases. The number of PCCs located more than 5 kilometer from cell towers was largest in the North and Northeast regions. Innovative stra tegies are needed to address the challenges posed by the implementation of the country’s National COVID-19 Vaccination Plan. The use of spatial artificial intelligence-based methodologies can help improve the country’s COVID-19 response.

Resumo O objetivo deste artigo é analisar o uso da inteligência artificial espacial no contexto da imunização contra COVID-19 para a seleção adequada dos recursos necessários. Trata-se de estudo ecológico de caráter transversal baseado em uma abordagem espaço-temporal utilizando dados secundários, em Unidades Básicas de Saúde do Brasil. Foram adotados quatro passos analíticos para atribuir um volume de população por unidade básica, aplicando algoritmos de inteligência artificial a imagens de satélite. Em paralelo, as condições de acesso à internet móvel e o mapeamento de tendências espaço-temporais de casos graves de COVID-19 foram utilizados para caracterizar cada município do país. Cerca de 18% da população idosa brasileira está a mais de 4 quilômetros de distância de uma sala de vacina. No total, 4.790 municípios apresentaram tendência de agudização de casos de Síndrome Respiratória Aguda Grave. As regiões Norte e Nordeste apresentaram o maior número de Unidades Básicas de Saúde com mais de 5 quilômetros de distância de antenas de celular. O Plano nacional de vacinação requer o uso de estratégias inovadoras para contornar os desafios do país. O uso de metodologias baseadas em inteligência artificial espacial pode contribuir para melhoria do planejamento das ações de resposta à COVID-19.

Abstract This article explores the use of spatial artificial intelligence to estimate the resources needed to implement Brazil’s COVID-19 immu nization campaign. Using secondary data, we conducted a cross-sectional ecological study adop ting a time-series design. The unit of analysis was Brazil’s primary care centers (PCCs). A four-step analysis was performed to estimate the popula tion in PCC catchment areas using artificial in telligence algorithms and satellite imagery. We also assessed internet access in each PCC and con ducted a space-time cluster analysis of trends in cases of SARS linked to COVID-19 at municipal level. Around 18% of Brazil’s elderly population live more than 4 kilometer from a vaccination point. A total of 4,790 municipalities showed an upward trend in SARS cases. The number of PCCs located more than 5 kilometer from cell towers was largest in the North and Northeast regions. Innovative stra tegies are needed to address the challenges posed by the implementation of the country’s National COVID-19 Vaccination Plan. The use of spatial artificial intelligence-based methodologies can help improve the country’s COVID-19 response.

Objective: To conduct a geospatial analysis of suicide deaths among young people in the state of Paraná, southern Brazil, and evaluate their association with socioeconomic and spatial determinants. Methods: Data were obtained from the Mortality Information System and the Brazilian Institute of Geography and Statistics. Data on suicide mortality rates (SMR) were extracted for three age groups (15-19, 20-24, and 25-29 years) from two 5-year periods (1998-2002 and 2008-2012). Geospatial data were analyzed through exploratory spatial data analysis. We applied Bayesian networks algorithms to explore the network structure of the socioeconomic predictors of SMR. Results: We observed spatial dependency in SMR in both periods, revealing geospatial clusters of high SMR. Our results show that socioeconomic deprivation at the municipality level was an important determinant of suicide in the youth population in Paraná, and significantly influenced the formation of high-risk SMR clusters. Conclusion: While youth suicide is multifactorial, there are predictable geospatial and sociodemographic factors associated with high SMR among municipalities in Paraná. Suicide among youth aged 15-29 occurs in geographic clusters which are associated with socioeconomic deprivation. Rural settings with poor infrastructure and development also correlate with increased SMR clusters.

ABSTRACT OBJECTIVE To evaluate the association among characteristics of primary health care center (PHCC) with hospitalizations for primary care sensitive conditions (PCSC) in Brazil. METHOD In this study, a cross-sectional ecological study was performed. This study analyzed the 27 capitals of Brazil’s federative units. Data were aggregated from the following open access databases: National Program for Access and Quality Improvement in Primary Care, the Hospital Information System of Brazilian Unified Health System and Annual Population Census conducted by the Brazilian Institute of Geography and Statistics. Associations were estimated among characteristics of primary care with the number of three PCSC as the leading causes of hospitalization in children under-5 population in Brazil: asthma, diarrhea, and pneumonia. RESULTS In general, PHCC showed limited structural adequacy (37.3%) for pediatric care in Brazil. The capitals in South and Southeast regions had the best structure whereas the North and Northeast had the worst. Fewer PCSC hospitalizations were significantly associated with PHCC which presented appropriate equipment (RR: 0.98; 95%CI: 0.97–0.99), structural conditions (RR: 0.98; 95%CI: 0.97–0.99), and signage/identification of professionals and facilities (RR: 0.98; 95%CI: 0.97–0.99). Higher PCSC hospitalizations were significantly associated with PHCC with more physicians (RR: 1.23, 95%CI: 1.02–1.48), it forms (RR: 1.01, 95%CI: 1.01–1.02), and more medications (RR: 1.02, 95%CI: 1.01–1.03) CONCLUSION Infrastructural adequacy of PHCC was associated with less PCSC hospitalizations, while availability medical professional and medications were associated with higher PCSC hospitalizations.

ABSTRACT OBJECTIVE To evaluate the association among characteristics of primary health care center (PHCC) with hospitalizations for primary care sensitive conditions (PCSC) in Brazil. METHOD In this study, a cross-sectional ecological study was performed. This study analyzed the 27 capitals of Brazil’s federative units. Data were aggregated from the following open access databases: National Program for Access and Quality Improvement in Primary Care, the Hospital Information System of Brazilian Unified Health System and Annual Population Census conducted by the Brazilian Institute of Geography and Statistics. Associations were estimated among characteristics of primary care with the number of three PCSC as the leading causes of hospitalization in children under-5 population in Brazil: asthma, diarrhea, and pneumonia. RESULTS In general, PHCC showed limited structural adequacy (37.3%) for pediatric care in Brazil. The capitals in South and Southeast regions had the best structure whereas the North and Northeast had the worst. Fewer PCSC hospitalizations were significantly associated with PHCC which presented appropriate equipment (RR: 0.98; 95%CI: 0.97–0.99), structural conditions (RR: 0.98; 95%CI: 0.97–0.99), and signage/identification of professionals and facilities (RR: 0.98; 95%CI: 0.97–0.99). Higher PCSC hospitalizations were significantly associated with PHCC with more physicians (RR: 1.23, 95%CI: 1.02–1.48), it forms (RR: 1.01, 95%CI: 1.01–1.02), and more medications (RR: 1.02, 95%CI: 1.01–1.03) CONCLUSION Infrastructural adequacy of PHCC was associated with less PCSC hospitalizations, while availability medical professional and medications were associated with higher PCSC hospitalizations.