Towards reliable forecasting of healthcare capacity needs: A scoping review and evidence mapping.

BACKGROUND: The COVID-19 pandemic has highlighted the critical importance of robust healthcare capacity planning and preparedness for emerging crises. However, healthcare systems must also adapt to more gradual temporal changes in disease prevalence and demographic composition over time. To support proactive healthcare planning, statistical capacity forecasting models can provide valuable information to healthcare planners. This systematic literature review and evidence mapping aims to identify and describe studies that have used statistical forecasting models to estimate healthcare capacity needs within hospital settings. METHOD: Studies were identified in the databases MEDLINE and Embase and screened for relevance before items were defined and extracted within the following categories: forecast methodology, measure of capacity, forecast horizon, healthcare setting, target diagnosis, validation methods, and implementation. RESULTS: 84 studies were selected, all focusing on various capacity outcomes, including number of hospital beds/ patients, staffing, and length of stay. The selected studies employed different analytical models grouped in six items; discrete event simulation (N = 13, 15 %), generalized linear models (N = 21, 25 %), rate multiplication (N = 15, 18 %), compartmental models (N = 14, 17 %), time series analysis (N = 22, 26 %), and machine learning not otherwise categorizable (N = 12, 14 %). The review further provides insights into disease areas with infectious diseases (N = 24, 29 %) and cancer (N = 12, 14 %) being predominant, though several studies forecasted healthcare capacity needs in general (N = 24, 29 %). Only about half of the models were validated using either temporal validation (N = 39, 46 %), cross-validation (N = 2, 2 %) or/and geographical validation (N = 4, 5 %). CONCLUSION: The forecasting models' applicability can serve as a resource for healthcare stakeholders involved in designing future healthcare capacity estimation. The lack of routine performance validation of the used algorithms is concerning. There is very little information on implementation and follow-up validation of capacity planning models.

Ensuring access to post-cancer breast reconstructions: COVID-19 lessons from the Dutch Breast Implant Registry.

BACKGROUND: COVID-19 has impacted breast implant surgery for oncological and non-oncological patients worldwide. This population-based study aimed to evaluate the impact of the COVID-19 pandemic on access to reconstructive and cosmetic breast implant surgery in the Netherlands using real-world data to describe trends, and to identify lessons to prevent future capacity problems within (inter)national healthcare. METHODS: This longitudinal study included patients undergoing breast implant surgery from the mandatory nationwide Dutch Breast Implant Registry. For 2020, the first COVID-19 wave, intermediate period, and second wave were defined. We compared data from during the pandemic to a pre-pandemic (2019) reference year, assessing differences in the number of registered breast implants, and patient and surgery-related characteristics. RESULTS: A total of 34133 breast implants (17459 patients) were included. Compared to 2019, fewer implants were registered for post-cancer (n=484; -14.7%), cosmetic (n=480; -3.6%), and gender-affirming indications (n=104; -38.0%) during 2020. Fewer implants were registered in academic (n=196; -22.0%) and regional hospitals (n=1591; -16.5%), but more in private clinics (n=725; +10.1%). After the first wave, up to twice as many implants were registered in private clinics compared to 2019. No differences were found in characteristics of patients undergoing surgery in 2020 versus 2019. CONCLUSION: Hospital-based reconstructive and gender-affirming surgery were heavily impacted during the pandemic, while private-clinic-based cosmetic surgery quickly recovered. These outcomes are useful to fuel discussions about how healthcare could be reorganized in times of capacity problems. We suggest exploring options to deploy private clinics for ambulatory surgery aiming to keep hospital capacity available for acutely ill patients.

Impact of healthcare capacity disparities on the COVID-19 vaccination coverage in the United States: A cross-sectional study.

Background: The impact of the COVID-19 vaccination campaign in the US has been hampered by a substantial geographical heterogeneity of the vaccination coverage. Several studies have proposed vaccination hesitancy as a key driver of the vaccination uptake disparities. However, the impact of other important structural determinants such as local disparities in healthcare capacity is virtually unknown. Methods: In this cross-sectional study, we conducted causal inference and geospatial analyses to assess the impact of healthcare capacity on the vaccination coverage disparity in the US. We evaluated the causal relationship between the healthcare system capacity of 2417 US counties and their COVID-19 vaccination rate. We also conducted geospatial analyses using spatial scan statistics to identify areas with low vaccination rates. Findings: We found a causal effect of the constraints in the healthcare capacity of a county and its low-vaccination uptake. Counties with higher constraints in their healthcare capacity were more probable to have COVID-19 vaccination rates ≤50, with 35% higher constraints in low-vaccinated areas (vaccination rates ≤ 50) compared to high-vaccinated areas (vaccination rates > 50). We also found that COVID-19 vaccination in the US exhibits a distinct spatial structure with defined "vaccination coldspots". Interpretation: We found that the healthcare capacity of a county is an important determinant of low vaccine uptake. Our study highlights that even in high-income nations, internal disparities in healthcare capacity play an important role in the health outcomes of the nation. Therefore, strengthening the funding and infrastructure of the healthcare system, particularly in rural underserved areas, should be intensified to help vulnerable communities. Funding: None.

Significant burden of post-COVID exertional dyspnoea in a South-Italy region: knowledge of risk factors might prevent further critical overload on the healthcare system.

Background: Exertional dyspnoea in post-COVID syndrome is a debilitating manifestation, requiring appropriate comprehensive management. However, limited-resources healthcare systems might be unable to expand their healthcare-providing capacity and are expected to be overwhelmed by increasing healthcare demand. Furthermore, since post-COVID exertional dyspnoea is regarded to represent an umbrella term, encompassing several clinical conditions, stratification of patients with post-COVID exertional dyspnoea, depending on risk factors and underlying aetiologies might provide useful for healthcare optimization and potentially help relieve healthcare service from overload. Hence, we aimed to investigate the frequency, functional characterization, and predictors of post-COVID exertional dyspnoea in a large cohort of post-COVID patients in Apulia, Italy, at 3-month post-acute SARS-CoV-2 infection. Methods: A cohort of laboratory-confirmed 318 patients, both domiciliary or hospitalized, was evaluated in a post-COVID Unit outpatient setting. Post-COVID exertional dyspnoea and other post-COVID syndrome manifestations were collected by medical history. Functional characterization of post-COVID exertional dyspnoea was performed through a 6-min walking test (6-mwt). The association of post-COVID exertional dyspnoea with possible risk factors was investigated through univariate and multivariate logistic regression analysis. Results: At medical evaluation, post-COVID exertional dyspnoea was reported by as many as 190/318 patients (59.7%), showing relatively high prevalence also in domiciliary-course patients. However, functional characterization disclosed a 6-mwt-based desaturation walking drop in only 24.1% of instrumental post-COVID exertional dyspnoea patients. Multivariate analysis identified five independent predictors significantly contributing to PCED, namely post-COVID-fatigue, pre-existing respiratory co-morbidities, non-asthmatic allergy history, age, and acute-phase-dyspnoea. Sex-restricted multivariate analysis identified a differential risk pattern for males (pre-existing respiratory co-morbidities, age, acute-phase-dyspnoea) and females (post-COVID-fatigue and acute-phase-dyspnoea). Conclusion: Our findings revealed that post-COVID exertional dyspnoea is characterized by relevant clinical burden, with potential further strain on healthcare systems, already weakened by pandemic waves. Sex-based subgroup analysis reveals sex-specific dyspnoea-underlying risk profiles and pathogenic mechanisms. Knowledge of sex-specific risk-determining factors might help optimize personalized care management and healthcare resources.

Global COVID-19 case fatality rates influenced by inequalities in human development and vaccination rates.

Aim: COVID-19 has exerted distress on virtually every aspect of human life with disproportionate mortality burdens on older individuals and those with underlying medical conditions. Variations in COVID-19 incidence and case fatality rates (CFRs) across countries have incited a growing research interest regarding the effect of social factors on COVID-19 case-loads and fatality rates. We investigated the effect of population median age, inequalities in human development, healthcare capacity, and pandemic mitigation indicators on country-specific COVID-19 CFRs across countries and regions. Subject and methods: Using population secondary data from multiple sources, we conducted a cross-sectional study and used regional analysis to compare regional differences in COVID-19 CFRs as influenced by the selected indicators. Results: The analysis revealed wide variations in COVID-19 CFRs and the selected indicators across countries and regions. Mean CFR was highest for South America at 1.973% (± 0.742) and lowest for Oceania at 0.264% (± 0.107), while the Africa sub-region recorded the lowest scores for pandemic preparedness, vaccination rate, and other indicators. Population Median Age [0.073 (0.033 0.113)], Vaccination Rate [-3.3389 (-5.570.033 -1.208)], and Inequality-Adjusted Human Development Index (IHDI) [-0.014 (-0.023 -0.004)] emerged as statistically significant predictors of COVID-19 CFR, with directions indicating increasing Population Median Age, higher inequalities in human development and low vaccination rate are predictive of higher fatalities from COVID-19. Conclusion: Regional differences in COVID-19 CFR may be influenced by underlying differences in sociodemographic and pandemic mitigation indicators. Populations with wide social inequalities, increased population Median Age and low vaccination rates are more likely to suffer higher fatalities from COVID-19.

Changes in Health Care Access during the COVID-19 Pandemic: Estimates of National Japanese Data, June 2020-October 2021.

The COVID-19 pandemic has disrupted health care access around the world, both for inpatients and outpatients. We applied a quasi-Poisson regression to national, monthly data on the number of outpatients, number of inpatients, length of average hospital stay, and the number of new hospitalizations from March 2015 to October 2021 to assess how these outcomes changed between June 2020 to October 2021. The number of outpatient visits were lower-than-predicted during the early phases of the pandemic but normalized by the fall of 2021. The number of inpatients and new hospitalizations were lower-than-predicted throughout the pandemic, and deficits in reporting continued to be observed in late 2021. The length of hospital stays was within the predicted range for all beds, but when stratified by bed type, was higher than predicted for psychiatric beds, lower-than-predicted for tuberculosis beds, and showed variable changes in long-term care insurance beds. Health care access in Japan was impacted by the COVID-19 pandemic.

Associations Between Obesity, Physical Inactivity, Healthcare Capacity, and the Built Environment: Geographic Information System Analysis.

Background: Obesity is one of the major critical health conditions affecting many people across the world. One of the major causes of obesity is identified to be sedentary lifestyles and physical inactivity, which may be associated with environmental factors. Objective: The study analyzes variations in obesity and physical inactivity in the State of South Carolina, US, and their association with healthcare capacity and the built environment. Methods: Data were obtained from different secondary sources and surveys, 2012, and then linked on the county-level using ArcGIS. Global Moran's I was used to examine the spatial distribution at the state level, and Anselin's local Moran's I was used to detect any significant clusters at the county level. Ordinary least squares regression models were calculated for obesity and physical inactivity separately. Results: More than 70% of SC counties had high levels of obesity and physical inactivity. Spatial analysis showed statistical clusters of high obesity, high physical inactivity, and low access to exercise opportunities in rural areas compared to urban areas. Conversely, clusters of high density of health-care facilities appeared in urban areas. Through the regression models, the density of primary care physicians (p = 0.025) and access to exercise opportunities (p = 0.075) were negatively associated with obesity, while the low perception of own health (p = 0.001) and obesity rate (0.011) were positively associated with physical inactivity. Conclusion: GIS was useful to illustrate and identify significant geographic variations and high clusters of obesity and physical inactivity in rural areas, compared with high clusters of access to exercise opportunities and health-care facilities in urban areas. The international health community is encouraged to utilize spatial information systems to examine variations and recommend evidence-based recommendations to redistribute equitable public health efforts. The development of strategies and initiatives toward reducing variation in health and sustainable development is key to promote the population wellbeing.

Optimising health system capacity: A case study of community care staff's role transition in response to the coronavirus pandemic.

The coronavirus disease (COVID-19) increased the demand for critical care spaces and the task for individual countries was to optimise the capacity of their health systems. Correlating governance and health system capacity to respond to global crises has subsequently garnered the pace in reviewing normalised forms of identifying health priorities. Aligning global health security and universal health security enhances the capacity and resilience of a health system. However, weak methods of governance hinder the alignment necessary for controlling infection spread and coping with the increase in demand for hospital critical care. A range of qualitative studies has explored staff experiences of providing care in hospitals amidst the COVID-19 pandemic. Nonetheless, limited understanding of the influence of governance on health and social care staff experiences in response to the COVID-19 pandemic exists. This case study aimed to explore the influence of health system governance on community care staff experiences of role transition in response to the COVID-19 pandemic in England. We used criterion sampling to include community care staff initially recruited to deliver a community integrated model of dementia care at two facilities repurposed in March 2020 to optimise hospital critical care space. Six community care staff participated in the narrative correspondence inquiry. A lack of control over resources, limitations in collective action in decision making and lack of a voice underpinned staff experiences of role transition in contexts of current crisis preparedness, transition shock and moral dilemmas. Health system governance influenced the disposition of community care staff's role transition in response to the COVID-19 pandemic. Staff's mere coping clouds the glass of wider issues in health system governance and capacity. The normative dominance that the control over resources and centrally determined health system priorities ordain require reviewing to enable optimal health and social care cross systems' capacity and resilience.

Analysis of Obligatory Involvement of Medical Students in Pandemic Response in the Czech Republic: Competencies, Experiences, and Legal Implications.

Objectives: Medical students in the Czech Republic were mandated by the law to take part in the COVID-19 pandemic response in order to expand healthcare capacity. Our study aimed to analyze student's competencies defined in the legislation and compare them with competencies assigned to them in clinical settings during their deployment. Methods: Online survey with statistical analysis of collected data. Results: The survey was completed by 997 respondents. A major convergence between the system of credentials defined in the legal framework and the competencies that students performed were identified. Conclusion: Medical students represented a valuable resource for addressing shortages of qualified healthcare staff in critical situation. However, the system of competencies and credentials must be aligned with the educational framework to clearly define acquisition of competencies during the course of medical studies and the legal framework regulating students' deployment must ensure consistency of actual and formal competencies in order to guarantee high standards of care and safety of the patients.

Mathematical models for assessing vaccination scenarios in several provinces in Indonesia.

To mitigate casualties from the COVID-19 outbreak, this study aims at assessing the optimal vaccination scenarios, considering several existing healthcare conditions and assumptions, by developing SIQRD (Susceptible-Infected-Quarantine-Recovery-Death) models for Jakarta, West Java, and Banten, in Indonesia. The models include an age-structured dynamic transmission model that naturally allows for different treatments among different age groups of the population. The simulation results show that the timing and period of the vaccination should be well planned and prioritizing particular age groups will give a significant impact on the total number of casualties.

Identification of Vulnerable Populations and Areas at Higher Risk of COVID-19-Related Mortality during the Early Stage of the Epidemic in the United States.

We characterized vulnerable populations located in areas at higher risk of COVID-19-related mortality and low critical healthcare capacity during the early stage of the epidemic in the United States. We analyze data obtained from a Johns Hopkins University COVID-19 database to assess the county-level spatial variation of COVID-19-related mortality risk during the early stage of the epidemic in relation to health determinants and health infrastructure. Overall, we identified highly populated and polluted areas, regional air hub areas, race minorities (non-white population), and Hispanic or Latino population with an increased risk of COVID-19-related death during the first phase of the epidemic. The 10 highest COVID-19 mortality risk areas in highly populated counties had on average a lower proportion of white population (48.0%) and higher proportions of black population (18.7%) and other races (33.3%) compared to the national averages of 83.0%, 9.1%, and 7.9%, respectively. The Hispanic and Latino population proportion was higher in these 10 counties (29.3%, compared to the national average of 9.3%). Counties with major air hubs had a 31% increase in mortality risk compared to counties with no airport connectivity. Sixty-eight percent of the counties with high COVID-19-related mortality risk also had lower critical care capacity than the national average. The disparity in health and environmental risk factors might have exacerbated the COVID-19-related mortality risk in vulnerable groups during the early stage of the epidemic.

Factors influencing the COVID-19 daily deaths' peak across European countries.

OBJECTIVES: The purpose of this study was to determine predictors of the height of coronavirus disease 2019 (COVID-19) daily deaths' peak and time to the peak, to explain their variability across European countries. STUDY DESIGN: For 34 European countries, publicly available data were collected on daily numbers of COVID-19 deaths, population size, healthcare capacity, government restrictions and their timing, tourism and change in mobility during the pandemic. METHODS: Univariate and multivariate generalised linear models using different selection algorithms (forward, backward, stepwise and genetic algorithm) were analysed with height of COVID-19 daily deaths' peak and time to the peak as dependent variables. RESULTS: The proportion of the population living in urban areas, mobility at the day of first reported death and number of infections when borders were closed were assessed as significant predictors of the height of COVID-19 daily deaths' peak. Testing the model with a variety of selection algorithms provided consistent results. Total hospital bed capacity, population size, the number of foreign travellers and the day of border closure were found to be significant predictors of time to COVID-19 daily deaths' peak. CONCLUSIONS: Our analysis demonstrated that countries with higher proportions of the population living in urban areas, countries with lower reduction in mobility at the beginning of the pandemic and countries having more infected people when closing borders experienced a higher peak of COVID-19 deaths. Greater bed capacity, bigger population size and later border closure could result in delaying time to reach the deaths' peak, whereas a high number of foreign travellers could accelerate it.

Impact of the subcutaneous formulations of trastuzumab and rituximab on efficiency and resource optimization in Spanish hospitals: H-Excelencia study.

BACKGROUND: Subcutaneous (SC) versus intravenous (IV) administration is advantageous in terms of patient convenience and hospital efficiency. This study aimed to compare the effect of optimizing the processes involved in SC versus IV administration of rituximab and trastuzumab on hospital capacity and service quality. METHODS: This cross-sectional resource utilization study interviewed oncologists, hematologists, nurses, and pharmacists from 10 hospitals in Spain to estimate changes in processes associated with conversion from IV to SC rituximab and trastuzumab, based on clinical experience and healthcare use from administrative databases. RESULTS: Efficient use of SC formulations increased the monthly capacity for parenteral administration by 3.35% (potentially increasable by 5.75% with maximum possible conversion according to the product label). The weekly capacity for hospital pharmacy treatment preparation increased by 7.13% due to conversion to SC formulation and by 9.33% due to transferring SC preparation to the cancer treatment unit (potentially increasable by 12.16 and 14.10%, respectively). Monthly hospital time decreased by 33% with trastuzumab and 47% with rituximab. In a hypothetical hospital, in which all processes for efficient use of SC rituximab and/or trastuzumab were implemented and all eligible patients received SC formulations, the estimated monthly capacity for preparation and administration increased by 23.1% and estimated hospital times were reduced by 60-66%. CONCLUSIONS: Conversion of trastuzumab and rituximab to SC administration could improve the efficiency of hospitals and optimize internal resource management processes, potentially increasing care capacity and improving the quality of care by reducing time spent by patients at hospitals.

Coronavirus Disease Model to Inform Transmission-Reducing Measures and Health System Preparedness, Australia.

The ability of health systems to cope with coronavirus disease (COVID-19) cases is of major concern. In preparation, we used clinical pathway models to estimate healthcare requirements for COVID-19 patients in the context of broader public health measures in Australia. An age- and risk-stratified transmission model of COVID-19 demonstrated that an unmitigated epidemic would dramatically exceed the capacity of the health system of Australia over a prolonged period. Case isolation and contact quarantine alone are insufficient to constrain healthcare needs within feasible levels of expansion of health sector capacity. Overlaid social restrictions must be applied over the course of the epidemic to ensure systems do not become overwhelmed and essential health sector functions, including care of COVID-19 patients, can be maintained. Attention to the full pathway of clinical care is needed, along with ongoing strengthening of capacity.

Spatiotemporal transmission dynamics of the COVID-19 pandemic and its impact on critical healthcare capacity.

The role of geospatial disparities in the dynamics of the COVID-19 pandemic is poorly understood. We developed a spatially-explicit mathematical model to simulate transmission dynamics of COVID-19 disease infection in relation with the uneven distribution of the healthcare capacity in Ohio, U.S. The results showed substantial spatial variation in the spread of the disease, with localized areas showing marked differences in disease attack rates. Higher COVID-19 attack rates experienced in some highly connected and urbanized areas (274 cases per 100,000 people) could substantially impact the critical health care response of these areas regardless of their potentially high healthcare capacity compared to more rural and less connected counterparts (85 cases per 100,000). Accounting for the spatially uneven disease diffusion linked to the geographical distribution of the critical care resources is essential in designing effective prevention and control programmes aimed at reducing the impact of COVID-19 pandemic.

Identification of Vulnerable Populations and Areas at Higher Risk of COVID-19 Related Mortality in the U.S.

Background: The role of health-related disparities including sociodemographic, environmental, and critical care capacity in the COVID-19 pandemic are poorly understood. In the present study, we characterized vulnerable populations located in areas at higher risk of COVID-19 related mortality and low critical healthcare capacity in the U.S. Methods: Using Bayesian multilevel analysis and small area disease risk mapping, we assessed the spatial variation of COVID-19 related mortality risk for the U.S. in relation with healthcare disparities including race, ethnicity, poverty, air quality, and critical healthcare capacity. Results: Overall, highly populated, regional air hub areas, and minorities had an increased risk of COVID-19 related mortality. We found that with an increase of only 1 ug/m3 in long term PM2.5 exposure, the COVID-19 mortality rate increased by 13%. Counties with major air hubs had 18% increase in COVID-19 related death compared to counties with no airport connectivity. Sixty-eight percent of the counties with high COVID-19 related mortality risk were also counties with lower critical care capacity than national average. These counties were primary located at the North- and South-Eastern regions of the country. Conclusion: The existing disparity in health and environmental risk factors that exacerbate the COVID-19 related mortality, along with the regional healthcare capacity, determine the vulnerability of populations to COVID-19 related mortality. The results from this study can be used to guide the development of strategies for the identification and targeting preventive strategies in vulnerable populations with a higher proportion of minority groups living in areas with poor air quality and low healthcare capacity.

Healthcare Capacity, Health Expenditure, and Civil Society as Predictors of COVID-19 Case Fatalities: A Global Analysis.

Background: The rapid growth in cases of COVID-19 has challenged national healthcare capacity, testing systems at an advanced ICU, and public health infrastructure level. This global study evaluates the association between multi-factorial healthcare capacity and case fatality of COVID-19 patients by adjusting for demographic, health expenditure, population density, and prior burden of non-communicable disease. It also explores the impact of government relationships with civil society as a predictor of infection and mortality rates. Methods: Data were extracted from the Johns Hopkins University database, World Bank records and the National Civic Space Ratings 2020 database. This study used data from 86 countries which had at least 1,000 confirmed cases on 30th April 2020. Negative binomial regression model was used to assess the association between case fatality (a ratio of total number of confirmed deaths to total number of confirmed cases) and healthcare capacity index adjusting for other covariates. Findings: Regression analysis shows that greater healthcare capacity was related to lesser case-fatality [incidence rate ratio (IRR) 0.5811; 95% confidence interval (CI) 0.4727-0.7184; p < 0.001] with every additional unit increase in the healthcare capacity index associated with a 42% decrease in the case fatality. Health expenditure and civil society variables did not reach statistical significance but were positively associated with case fatalities. Interpretation: Based on preliminary data, this research suggests that building effective multidimensional healthcare capacity is the most promising means to mitigate future case fatalities. The data also suggests that government's ability to implement public health measures to a degree determines mortality outcomes.

Managing Coronavirus Disease 2019 Spread With Voluntary Public Health Measures: Sweden as a Case Study for Pandemic Control.

BACKGROUND: The coronavirus disease 19 (COVID-19) pandemic has spread globally, causing extensive illness and mortality. In advance of effective antiviral therapies, countries have applied different public health strategies to control spread and manage healthcare need. Sweden has taken a unique approach of not implementing strict closures, instead urging personal responsibility. We analyze the results of this and other potential strategies for pandemic control in Sweden. METHODS: We implemented individual-based modeling of COVID-19 spread in Sweden using population, employment, and household data. Epidemiological parameters for COVID-19 were validated on a limited date range; where substantial uncertainties remained, multiple parameters were tested. The effects of different public health strategies were tested over a 160-day period, analyzed for their effects on intensive care unit (ICU) demand and death rate, and compared with Swedish data for April 2020. RESULTS: Swedish mortality rates are intermediate between rates for European countries that quickly imposed stringent public health controls and those for countries that acted later. Models most closely reproducing reported mortality data suggest that large portions of the population voluntarily self-isolate. Swedish ICU use rates remained lower than predicted, but a large fraction of deaths occurred in non-ICU patients. This suggests that patient prognosis was considered in ICU admission, reducing healthcare load at a cost of decreased survival in patients not admitted. CONCLUSIONS: The Swedish COVID-19 strategy has thus far yielded a striking result: mild mandates overlaid with voluntary measures can achieve results highly similar to late-onset stringent mandates. However, this policy causes more healthcare demand and more deaths than early stringent control and depends on continued public will.

Fistula after attended delivery and the challenge of obstetric care capacity in the eastern Democratic Republic of Congo.

OBJECTIVE: To analyze the history of women with fistula in the eastern Democratic Republic of Congo (DRC) to understand the determinants of fistula development. METHODS: In a retrospective observational study, data were analyzed from a survey of all women who underwent surgical fistula repair at HEAL Africa Hospital, Goma, between April 1, 2009, and March 1, 2012. Characteristics and obstetric histories were obtained by self-report. RESULTS: The mean age of the 202 participants at treatment was 30.7 years (range 5-69). The mean duration of fistula was 45.6 months (range 0-600). In total, 171 (91.4%) fistulas were caused by obstructed labor, and 147 (86.5%) were vesicovaginal. Most women (129/175 [73.8%]) reported having received care during early labor under the supervision of a nurse or doctor in a healthcare facility. Among 176 women for whom delivery data were available, 102 (57.9%) delivered at a hospital, 42 (23.8%) at a health center, and 32 (18.2%) at home. Only 46 (26.3%) of 175 women were transferred to a higher level of care during labor. CONCLUSIONS: In the eastern DRC, efforts to enable transport to a healthcare facility and to encourage attended births must be accompanied by improvements in the capacity of existing facilities and in the training of staff to enable the timely diagnosis of labor abnormalities and appropriate intervention.