Barriers to Health Care Access for Low Income Families: A Review of Literature

Lazar, Malerie; Davenport, Lisa

doi:10.1080/07370016.2018.1404832

Cited by 235 publications

(159 citation statements)

References 26 publications

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“…However, the clinical significance of these differences is unclear. This greater over-reporting in minority or low-income populations may be due to lower health-literacy, (72) cultural factors, (73,74) or greater barriers to healthcare access in general, thus increasing the number of unscreened women (12,13,(73)(74)(75). The National Breast and Cervical Cancer Early Detection Program (NBCCEDP) was established in the US in 1991 to improve screening rates among low-income and uninsured women (76).…”

Section: Clinical and System-level Impacts Of Using Self-reported Meamentioning

confidence: 99%

Accuracy of Self-report for Cervical and Breast Cancer Screening: A Systematic Review and Meta-analysis

Anderson

Bourne

Peterson

et al. 2019

Preprint

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Background: Guideline-based breast and cervical cancer screenings are fundamental components of high-quality preventive women’s health care services. Accurate measurement of screening rates is vital to ensure all women are adequately screened. Our systematic review and meta-analysis aims to provide an updated synthesis of the evidence on the accuracy of self-reported measures of cervical and breast cancer screening compared to medical records. Methods: To identify studies, we searched MEDLINE®, Cochrane Database of Systematic Reviews, and other sources up to July 2019. Two reviewers sequentially selected studies, abstracted data, and assessed internal validity and strength of the evidence. Adjusted summary numbers for sensitivity and specificity were calculated using a bivariate random-effects meta-analysis. Results: Unscreened women tended to over-report screening among 39 included studies examining the accuracy of self-report for cervical and/or breast cancer screening. The specificity of self-report was 48% (95% CI 41 to 56) for cervical cancer screening and 61% (95% CI 53 to 69) for breast cancer screening while the sensitivity of self-report was much higher at 96% (95% CI 94 to 97) for cervical cancer screening and 96% (95% CI 95 to 98). We have moderate confidence in these findings, as they come from a large number of studies directly assessing the accuracy of self-report compared to medical records and are consistent with findings from a previous meta-analysis. Conclusions: Unscreened women tend to over-report cervical and breast cancer screening, while screened women more accurately report their screening. Future research should focus on assessing the impact of over-reporting on clinical and system-level outcomes.

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Section: Clinical and System-level Impacts Of Using Self-reported Meamentioning

confidence: 99%

Accuracy of Self-report for Cervical and Breast Cancer Screening: A Systematic Review and Meta-analysis

Anderson

Bourne

Peterson

et al. 2019

Preprint

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“…However, as in traditional distance sampling, a better fit to the observed data might potentially be achieved if additional covariates are recorded and their effects on the detection function are modelled. Other non-spatial covariates known to influence malaria reporting include socioeconomic status [8,10,27,28], ethnicity [29,30], linguistic barriers [31], education [16,17,53,54] and the severity of symptoms (e.g. asymptomatic cases [22][23][24][25][26]).…”

Section: Discussionmentioning

confidence: 99%

“…under-reporting [1][2][3][4][5][6]. Such problems of under-reporting are heightened in settings where there are significant barriers to health system access [7][8][9][10], for example due to an unbalanced geographical distribution of clinics [11], difficult travel routes [12,13] or socio-economic barriers to health-seeking [10,14]. Consequently underreporting is particularly likely in rural areas in low-and middle-income countries where all of these variables may combine to limit access to health services [1,2,9,[15][16][17].…”

mentioning

confidence: 99%

“…Factors known to influence reporting include the severity of disease symptoms (e.g. probability of asymptomatic infection [22][23][24][25][26]) and sociodemographic factors that encourage or impede self-reporting, such as poverty and education [8,10,16,27,28], ethnicity [29,30] and language barriers [31]. However, the estimation of reporting completeness at national level can suffer from systematic biases [20].…”

mentioning

confidence: 99%

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Distance sampling for epidemiology: an interactive tool for estimating under-reporting of cases from clinic data

et al. 2020

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Background: Distance sampling methods are widely used in ecology to estimate and map the abundance of animal and plant populations from spatial survey data. The key underlying concept in distance sampling is the detection function, the probability of detecting the occurrence of an event as a function of its distance from the observer, as well as other covariates that may influence detection. In epidemiology, the burden and distribution of infectious disease is often inferred from cases that are reported at clinics and hospitals. In areas with few public health facilities and low accessibility, the probability of detecting a case is also a function of the distance between an infected person and the "observer" (e.g. a health centre). While the problem of distance-related under-reporting is acknowledged in public health; there are few quantitative methods for assessing and correcting for this bias when mapping disease incidence. Here, we develop a modified version of distance sampling for prediction of infectious disease incidence by relaxing some of the framework's fundamental assumptions. We illustrate the utility of this approach using as our example malaria distribution in rural Burkina Faso, where there is a large population at risk but relatively low accessibility of health facilities. Results: The modified distance-sampling framework was used to predict the probability of reporting malaria infection at 8 rural clinics, based on road-travel distances from villages. The rate at which reporting probability dropped with distance varied between clinics, depending on road and clinic positions. The probability of case detection was estimated as 0.3-1 in the immediate vicinity of the clinic, dropping to 0.1-0.6 at a travel distance of 10 km, and effectively zero at distances > 30-40 km. Conclusions: To enhance the method's strategic impact, we provide an interactive mapping tool (as a self-contained R Shiny app) that can be used by non-specialists to interrogate model outputs and visualize how the overall probability of under-reporting and the catchment area of each clinic is influenced by changing the number and spatial allocation of health centres.

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“…Therefore this explains the increase in participation among those who were peasants and this is due to need basis. Moreover, lack of education and distrust of health care providers also limit the health seeking behaviour of low income earners (Lazar & Davenport, 2018).…”

Section: Discussionmentioning

confidence: 99%

Factors associated with Assisted Partner Notification Services in Seme and Kisumu west sub counties, Kenya.

Samson¹,

Magu²,

Otieno³

2020

IJRP

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Background: Assisted partner notification service (aPNS) is a public health strategy which entails a health worker interviewing persons identified with an STI (index cases) about their sexual partner(s) and/or contacts and then providing the index case with some level of assistance notifying their partner(s) and assuring their testing. Assisted partner notification services was implemented, as a public health intervention to identify individuals' new HIV infections and facilitate linkage to care. Methods: Participants aged 18 years and above were selected and enrolled in the study through simple random sampling. Participants provided written informed consent to participate in the study. Logistic regression (bivariate and multivariate) analysis was performed on data to determine factors associated with participation in aPNS. Backward stepwise elimination was used to select variables to be included in the multivariate level to estimate the odds ratios (OR). Results: We enrolled 423 participants of which 62.5 % were aged 18-25 and women (n=228, 54.5%) and men (n=190, 45.5%). Overall participation in aPNS was 69.1%. The predictors of participation in aPNS were occupation and residence. The odds of participating in aPNS was higher for those who practiced peasantry (aOR=2.04; 95% CI, 1.08-3.85, p=0.029). However, those in the urban were less likely to participate in aPNS compared to those in the rural setting notification (aOR=0.57; 95% CI, 0.33-0.98, p=0.042) and those employed were less likely to participate in aPNS compared to those unemployed (aOR=0.56; 95% CI, 0.33-0.95, p=0.032). Conclusion: There is need to target interventions targeting assisted partner notification in the urban settings and among those in the workforce.

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Barriers to Health Care Access for Low Income Families: A Review of Literature

Cited by 235 publications

References 26 publications

Accuracy of Self-report for Cervical and Breast Cancer Screening: A Systematic Review and Meta-analysis

Accuracy of Self-report for Cervical and Breast Cancer Screening: A Systematic Review and Meta-analysis

Distance sampling for epidemiology: an interactive tool for estimating under-reporting of cases from clinic data

Factors associated with Assisted Partner Notification Services in Seme and Kisumu west sub counties, Kenya.

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