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Background: The vast majority of maternal deaths in low-and middle-income countries are preventable. Delay in obtaining access to appropriate health care is a fairly common problem which can be improved. The objective of this study was to explore the association between delay in providing obstetric health care and severe maternal morbidity/death.Methods: This was a multicentre cross-sectional study, involving 27 referral obstetric facilities in all Brazilian regions between 2009 and 2010. All women admitted to the hospital with a pregnancy-related cause were screened, searching for potentially life-threatening conditions (PLTC), maternal death (MD) and maternal near-miss (MNM) cases, according to the WHO criteria. Data on delays were collected by medical chart review and interview with the medical staff. The prevalence of the three different types of delays was estimated according to the level of care and outcome of the complication. For factors associated with any delay, the PR and 95%CI controlled for cluster design were estimated.Results: A total of 82,144 live births were screened, with 9,555 PLTC, MNM or MD cases prospectively identified. Overall, any type of delay was observed in 53.8% of cases; delay related to user factors was observed in 10.2%, 34.6% of delays were related to health service accessibility and 25.7% were related to quality of medical care. The occurrence of any delay was associated with increasing severity of maternal outcome: 52% in PLTC, 68.4% in MNM and 84.1% in MD.Conclusions: Although this was not a population-based study and the results could not be generalized, there was a very clear and significant association between frequency of delay and severity of outcome, suggesting that timely and proper management are related to survival. © 2014 Pacagnella et al.; licensee BioMed Central Ltd.
This study is part of the Brazilian Network for Surveillance of Severe Maternal Morbidity study, which is a cross-sectional multicentre study of 27 obstetric referral maternity hospitals in all geographical regions of Brazil, in a mix of health facilities (public and private health facilities, university and non-university hospitals) that provide specialized obstetric care and perform a minimum number of 1,000 deliveries per year. To be part of this study, the facility was required to have broadband internet connection, data on the prevalence of some obstetric interventions during delivery based on scientific evidence and the availability of written protocols of service procedures. The main goals of this network were to establish the prevalence of maternal near miss events among women admitted to these hospitals and prospectively evaluate the use of the new criteria for near miss events established by the World Health Organization in 2009 [14]. Methodological details related to the research protocol and its implementation are published elsewhere [15,16]. We estimated that a sample of 390 cases of maternal near miss (MNM) would be sufficient to show a difference of 10% in a near miss incidence between adolescents and adults (α = 0.05, β = 0.2, the ratio between adolescents and adults was 4:1; the incidence of near miss between adolescents was 8.5/1000 live births and among non-adolescents it was 7.5/1000 live births). This was done because the aim of the original research was to also evaluate the occurrence of maternal morbidity specifically for adolescent mothers. Based on the prevalence of this condition in previous studies [17], a total of 75,000 births would have been monitored, therefore determining the number of health facilities taking part in the study. In each hospital, a local research team (with the local medical investigator plus a medical or nursing coordinator) performed a prospective surveillance on severe maternal morbidity, daily reviewing all women admitted to hospital, considering the inclusion criteria: the presence of at least one WHO potentially life-threatening condition (PLTC) (Table 1 – [14]). Data was collected between July 2009 and June 2010. During daily visits to the maternity wards, medical charts were selected for further data retrieval which was performed after hospital discharge, transfer to another hospital or death. If there was any missing information or doubt, the attending medical team was also contacted for necessary clarifications. Potentially life-threatening maternal conditions [14] Data was collected using an 80 item pre-coded form including data on patient demographic and economic characteristics, obstetric history, antenatal care status, previous morbid conditions, occurrence of life-threatening conditions and first complication in the chain of events leading to these conditions, duration of hospitalization, criteria for classification of severe maternal morbidity [14] maternal and neonatal outcomes, as well as information on the delay in providing care. Information on ethnicity/skin colour was also obtained from clinical records using the provider assignment. For data management we used an open-access, web-based database solution (OpenClinica®, Akaza Research, LLC, 2009, Waltham, MA, USA, https://community.openclinica.com/). This data management system is compliant with Good Clinical Practice (GCP) and regulatory guidelines, allowing differentiated user roles and privileges, password and electronic signatures, SSL encryption and de-identification of Protected Health Information (PHI). The study was funded by the Department of Science and Technology from the Brazilian Ministry of Health and CNPq which played no other role in the development, data collection, analysis or interpretation of the results from this study. The study was approved by the National Council on Ethics in Human Research, by the local Institutional Review Boards of the coordinating centre (IRB from the School of Medical Sciences, University of Campinas – Approval letter CEP 097/2009) and also by local IRB of all the participating centres: Maternidade Cidade Nova Dona Nazarina Daou (Manaus, AM), Maternidade Climério de Oliveira (Salvador, BA), Hospital Geral de Fortaleza (Fortaleza, CE), Hospital Geral Dr. César Cals (Fortaleza, CE), Maternidade Escola Assis Chateaubriand (Fortaleza, CE), Hospital Materno Infantil de Goiania (Goiania, GO), Hospital Universitário da Universidade Federal do Maranhao (Sao Luis, MA), Maternidade Odete Valadares (Belo Horizonte, MG), Instituto de Saúde Elıdio de Almeida (Campina Grande, PB), Hospital Universitário Lauro Wanderley da Universidade Federal da Paraíba (Joao Pessoa, PB), Centro Integrado de Saúde Amaury de Medeiros (Recife, PE), Instituto de Medicina Integral Prof. Fernando Figueira (Recife, PE), Hospital das Clınicas da Universidade Federal de Pernambuco (Recife, PE), Hospital das Clınicas da Universidade Federal do Paraná (Curitiba, PR), Hospital Maternidade Fernando Magalhaes (Rio de Janeiro, RJ), Instituto Fernandes Figueira (Rio de Janeiro, RJ), Hospital das Clinicas da Universidade Federal do Rio Grande do Sul (Porto Alegre, RS), Faculdade de Medicina de Botucatu da Universidade Estadual Paulista (Botucatu, SP), Hospital da Mulher da Universidade Estadual de Campinas (Campinas, SP), Hospital e Maternidade Celso Pierro da Pontifícia Universidade Católica (Campinas, SP), Hospital Israelita Albert Einstein (São Paulo, SP), Faculdade de Medicina de Jundiaí (Jundiaí, SP), Hospital das Clınicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (Ribeirão Preto, SP), Santa Casa de Limeira (Limeira, SP), Santa Casa de São Carlos (São Carlos, SP), Casa Maternal Leonor Mendes de Barros (São Paulo, SP), Hospital São Paulo da Universidade Federal de São Paulo (São Paulo, SP). Approval included access to medical records of all women and their children. Women enrolled in the study were identified by the occurrence of any of the conditions listed in the WHO criteria – Table 1 as a potential life-threatening condition (PLTC). After hospital discharge and based on progression of the patient, each case was classified as a PLTC case, as a maternal near-miss (MNM) event or maternal death (MD), according to outcome [14]. By definition, a woman with a PLTC has a condition that could potentially lead to death due to haemorrhage, hypertension or other clinical and obstetrical complications or any indicators of severity. A MNM case was defined as “a woman who nearly died but survived a complication occurring during pregnancy, childbirth, or within 42 days of termination of pregnancy” [14]. This definition of maternal near miss includes clinical and laboratory evidence of organ dysfunction or failure, as well as any procedure for management that could be proxy of organ failure, such as intubation and ventilation unrelated to anaesthesia, any dose of continuous vasoactive drugs (dopamine, epinephrine or norepinephrine) required or hysterectomy for bleeding control. In all cases, we searched for data on quality of care indicators with possible shortcomings and delays that could cause or contribute to the occurrence of PLTC, MNM or MD. Since we were unable to identify the “real” delay in time from the onset of complication to outcome, we used the operational definition of “delay” as any shortcoming and failure at all levels of obstetric care that could led to a real delay in time. These shortcomings/delays were classified as: a) sub-standard care/delay related to user factors – which refers to economic and educational status, a woman’s autonomy, illness-related behaviour, knowledge and attitudes about use of the health system and includes delay in identifying the condition, seeking medical care and refusing to accept treatment offered; b) sub-standard care/delay related to service accessibility – distribution of services, distance, transportation and general costs which included cases with difficulties in obtaining medical supplies or equipment which may lead to substandard care; and c) sub-standard care/delay related to quality of medical care – scope of medical services, management and support systems included delays in determining the appropriate diagnosis and providing appropriate patient treatment. The local research investigator and coordinator were instructed to pursue evidence of delay regarding users, health service and medical care. First, medical records were scrutinized by the local researchers for data on patient decision to seek care, time from the onset of the problem to arrival, the woman’s pilgrimage, timely diagnosis, medication and blood products provided for the medical condition informed, patient referral, improper management and refusal by the patient or family member to accept treatment. Since all facilities had written protocols according to Ministry of Health recommendations, the study coordinators considered that management was improper when there was an evident discrepancy between the protocol and patient management. Furthermore, the local research coordinator was encouraged to retrieve information from the medical staff who was asked to help identify gaps in information when completing the research form to find more evidence on the sequence of care offered to each woman. Neither the women nor family members were interviewed. Local researchers were requested to seek more information, detail and documented delays, whenever there were reports of the occurrence of delay in medical records or when there was a positive impression by the staff responsible. When any delay related to health service and medical care could be identified, the level of care (primary, secondary or tertiary) was further specified. All cases were reviewed by the coordinating centre. To minimize data collection bias and to ensure the high quality of information, standard procedures were adopted for all cases. These procedures included preparatory meetings, site visits, close monitoring of data collection and data entry, concurrent query management, inconsistency checks, double data collection for selected medical charts, and the use of a detailed manual of operation. During site visits, implementation of the study was assessed and randomly selected medical records were checked against data already included in the database [15]. Implementation of auditing to record/monitor access and changes in data aligned with a set of validation/cross-checking rules was part of online data management. Checking rules related to delays were considered as follows: • When there was “absent antenatal care,” the researchers considered delay related to health service accessibility. Although in Brazil there are generally good antenatal healthcare services available, there is a major difference between services available in diverse regions of the country, due to institutional problems, i.e. insufficient number of health professionals and the low quality of health services. Thus, it is assumed that inadequate antenatal care is an institutional problem more than an individual decision. In addition, if the number of antenatal visits was below the minimum for that specific gestational age, as recommended by the Brazilian Ministry of Health, antenatal care was considered “inadequate”. • When “direct inter-hospital transfer” was checked in the form, researchers considered delay related to health service accessibility. In Brazil, the transfer of a pregnant woman to a tertiary referral hospital is theoretically mediated by the assistance of a public call centre regulating service availability, checking for the number of existing beds in these units daily and deciding where to transfer each specific patient, according to geographical location and resources. • In cases of severe preeclampsia/eclampsia, researchers looked for magnesium sulphate administration as a management criterion. For all patients who did not receive magnesium sulphate, local researchers were asked to identify the criterion used to classify preeclampsia as severe and the possibility of delay related to quality of medical care. • When “discharge required by the patient” or “evasion” was identified, the delay was considered due to “refused treatment” and was related to user factors. • We considered that when a legal base to support safe abortion is lacking, women usually waited longer to seek medical care after they suffered an abortion. Therefore, this was classified as related to user factors. • Finally, all data entered into the form (open field) was individually evaluated by researchers at the coordinating centre. With the explanations provided, it was often possible to understand and set specific delays in care. Using this comprehensive package of data quality procedures, reliable and high quality information was obtained. Data was analysed by the principal investigators who were not involved in data collection using EpiInfo® and SPSS® software. Initially, the occurrence of all types of delays was described according to the level of care and maternal outcome. On bivariate analysis, χ2 or Fisher’s exact tests were used to compare groups controlled by the cluster design in the analysis. Missing data was excluded and the total number available for each analysis was shown in tables. To assess the role of selected socio-demographic, antenatal and obstetric variables as predictors of delays, the prevalence ratios (PR) and their respective 95% CI were estimated, also adjusted for cluster design effect. To identify factors independently associated with the occurrence of any delay, multivariate analysis was conducted (Poisson multiple regression model) using pseudo-maximum-likelihood estimates with stepwise backward elimination procedure, removing from the model variables with the largest p-value until no variable with p-value > 0.05 remained.
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