Background: Poverty is a major barrier to healthcare access in low-income countries. The degree of equitable access for noncommunicable disease (NCD) patients is not known in rural Haiti. Objectives: We evaluated the poverty distribution among patients receiving care in an NCD clinic in rural Haiti compared with the community and assessed associations of poverty with sex and distance from the health facility. Methods: We performed a cross-sectional study of patients with NCDs attending a public-sector health center in rural Haiti 2013-2016, and compared poverty among patients with poverty among a weighted community sample from the Haiti 2012 Demographic and Health Survey. We adapted the multidimensional poverty index: people deprived ≥44% of indicators are among the poorest billion people worldwide. We assessed hardship financing: borrowing money or selling belongings to pay for healthcare. We examined the association between facility distance and poverty adjusted for age and sex using linear regression. Results: Of 379 adults, 72% were women and the mean age was 52.5 years. 17.7% had hypertension, 19.3% had diabetes, 3.1% had heart failure, and 33.8% had multiple conditions. Among patients with available data, 197/296 (66.6%) experienced hardship financing. The proportions of people who are among the poorest billion people for women and men were similar (23.3% vs. 20.3%, p > 0.05). Fewer of the clinic patients were among the poorest billion people compared with the community (22.4% vs. 63.1%, p < 0.001). Patients who were most poor were more likely to live closer to the clinic (p = 0.002). Conclusion: Among patients with NCD conditions in rural Haiti, poverty and hardship financing are highly prevalent. However, clinic patients were less poor compared with the community population. These data suggest barriers to care access particularly affect the poorest. Socioeconomic data must be collected at health facilities and during community-level surveillance studies to monitor equitable healthcare access. Highlights:• Poverty and hardship financing are highly prevalent among NCD patients in rural Haiti.• Patients attending clinic are less poor than expected from the community. • People travelling farther to clinic are less poor. • Socioeconomic data should be collected to monitor healthcare access equity.
Background. Understanding the cost of delivering breast cancer (BC) care in low-and middle-income countries (LMICs) is critical to guide effective care delivery strategies. This scoping review summarizes the scope of literature on the costs of BC care in LMICs and characterizes the methodological approaches of these economic evaluations. Materials and Methods. A systematic literature search was performed in five databases and gray literature up to March 2020. Studies were screened to identify original articles that included a cost outcome for BC diagnosis or treatment in an LMIC. Two independent reviewers assessed articles for eligibility. Data related to study characteristics and methodology were extracted. Study quality was assessed using the Drummond et al. checklist.Results. Ninety-one articles across 38 countries were included. The majority (73%) of studies were published between 2013 and 2020. Low-income countries (2%) and countries in Sub-Saharan Africa (9%) were grossly underrepresented. The majority of studies (60%) used a health care system perspective. Time horizon was not reported in 30 studies (33%). Of the 33 studies that estimated the cost of multiple steps in the BC care pathway, the majority (73%) were of high quality, but studies varied in their inclusion of nonmedical direct and indirect costs. Conclusion.There has been substantial growth in the number of BC economic evaluations in LMICs in the past decade, but there remain limited data from low-income countries, especially those in Sub-Saharan Africa. BC economic evaluations should be prioritized in these countries. Use of existing frameworks for economic evaluations may help achieve comparable, transparent costing analyses. The Oncologist 2021;26:1-12 Implications for Practice: There has been substantial growth in the number of breast cancer economic evaluations in lowand middle-income countries (LMICs) in the past decade, but there remain limited data from low-income countries. Breast cancer economic evaluations should be prioritized in low-income countries and in Sub-Saharan Africa. Researchers should strive to use and report a costing perspective and time horizon that captures all costs relevant to the study objective, including those such as direct nonmedical and indirect costs. Use of existing frameworks for economic evaluations in LMICs may help achieve comparable, transparent costing analyses in order to guide breast cancer control strategies.
IntroductionWhile travel distance and time are important proxies of physical access to health facilities, obtaining valid measures with an appropriate modelling method remains challenging in many settings. We compared five measures of geographic accessibility in Haiti, producing recommendations that consider available analytic resources and geospatial goals.MethodsEight public hospitals within the ministry of public health and population were included. We estimated distance and time between hospitals and geographic centroids of Haiti’s section communes and population-level accessibility. Geographic feature data were obtained from public administrative databases, academic research databases and government satellites. We used validated geographic information system methods to produce five geographic access measures: (1) Euclidean distance (ED), (2) network distance (ND), (3) network travel time (NTT), (4) AccessMod 5 (AM5) distance (AM5D) and (5) AM5 travel time (AM5TT). Relative ranking of section communes across the measures was assessed using Pearson correlation coefficients, while mean differences were assessed using analysis of variance (ANOVA) and pairwise t-tests.ResultsAll five geographic access measures were highly correlated (range: 0.78–0.99). Of the distance measures, ED values were consistently the shortest, followed by AM5D values, while ND values were the longest. ND values were as high as 2.3 times ED values. NTT models generally produced longer travel time estimates compared with AM5TT models. ED consistently overestimated population coverage within a given threshold compared with ND and AM5D. For example, population-level accessibility within 15 km of the nearest studied hospital in the Center department was estimated at 68% for ED, 50% for AM5D and 34% for ND.ConclusionWhile the access measures were highly correlated, there were significant differences in the absolute measures. Consideration of the benefits and limitations of each geospatial measure together with the intended purpose of the estimates, such as relative proximity of patients or service coverage, are key to guiding appropriate use.
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