The article presents the results of analysis of waiting period of consultation of specialists in Moscow polyclinics exemplified by the ambulatory center of polyclinic No 201 of Moscow health department. The data of comparative analysis of indicators of polyclinics of different administrative okrugs of Moscow was also involved. The material was collected using the unified medical informative analytical system of polyclinic No 201 and included data concerning waiting periods of consultation of specialists with permitted self-appointment--surgeon, urologist, therapist, obstetrician-gynecologist and otorinolaringologist. The results of study demonstrated that the indicators of waiting period of consultation of specialists are highest in the Southern administrative okrug as compared with other okrugs. However, in one of amnulatory associations of this okrug (polyclinic Na 201) waiting period of consultation of obstetrician-gynecologist, ophthalmologist, urologist and surgeon was reliably lower in comparison with corresponding okrug mean indicators. This occurrence is related to high support of association with these categories of specialists. The longest waiting period both in okrug and association was established for otorinolaringologisit (4.4 and 6.3 days correspondingly). This is related to low support of population with these specialists. The presented analysis of waiting period of consultation of specialists of polyclinic section is a foundation for adequate decisior making in health management targeted to increasing of accessibility of medical care to population.
Spatial Analysis and Regional Economics Laboratory, Université du Québec, Institut national de la recherche scientifique, Urbanisation, Culture et Société, 385 rue Sherbrooke est, Montréal (Québec), H2X 1E3, Canada. firstname.lastname@example.org
Over the past two decades, geographical accessibility of urban resources for population living in residential areas has received an increased focus in urban health studies. Operationalising and computing geographical accessibility measures depend on a set of four parameters, namely definition of residential areas, a method of aggregation, a measure of accessibility, and a type of distance. Yet, the choice of these parameters may potentially generate different results leading to significant measurement errors. The aim of this paper is to compare discrepancies in results for geographical accessibility of selected health care services for residential areas (i.e. census tracts) computed using different distance types and aggregation methods.
First, the comparison of distance types demonstrates that Cartesian distances (Euclidean and Manhattan distances) are strongly correlated with more accurate network distances (shortest network and shortest network time distances) across the metropolitan area (Pearson correlation greater than 0.95). However, important local variations in correlation between Cartesian and network distances were observed notably in suburban areas where Cartesian distances were less precise.Second, the choice of the aggregation method is also important: in comparison to the most accurate aggregation method (population-weighted mean of the accessibility measure for census blocks within census tracts), accessibility measures computed from census tract centroids, though not inaccurate, yield important measurement errors for 5% to 10% of census tracts.
Although errors associated to the choice of distance types and aggregation method are only important for about 10% of census tracts located mainly in suburban areas, we should not avoid using the best estimation method possible for evaluating geographical accessibility. This is especially so if these measures are to be included as a dimension of the built environment in studies investigating residential area effects on health. If these measures are not sufficiently precise, this could lead to errors or lack of precision in the estimation of residential area effects on health.
Cites: Am J Prev Med. 2004 Oct;27(3):211-715450633
Cites: Int J Health Geogr. 2007;6:417295912
Cites: Int J Epidemiol. 2007 Apr;36(2):348-5517182634
Cites: Am J Prev Med. 2007 May;32(5):375-8217478262
The Canadian Cardiovascular Society is the national professional society for cardiovascular specialists and researchers in Canada. In the spring of 2004, the Canadian Cardiovascular Society Council formed an Access to Care Working Group in an effort to use the best science and information to establish reasonable triage categories and safe wait times for access to common cardiovascular services and procedures. The Working Group has elected to publish a series of commentaries to initiate a structured national discussion on this very important issue. Access to treatment with implantable cardioverter defibrillators is the subject of the present commentary. The prevalence pool of potentially eligible patients is discussed, along with access barriers, regional disparities and waiting times. A maximum recommended waiting time is proposed and the framework for a solution-oriented approach is presented.
We examined the prevalence of substance use disorders among homeless and vulnerably housed persons in three Canadian cities and its association with unmet health care needs and access to addiction treatment using baseline data from the Health and Housing in Transition Study.
In 2009, 1191 homeless and vulnerably housed persons were recruited in Vancouver, Toronto, and Ottawa, Canada. Interviewer administered questionnaires collected data on socio-demographics, housing history, chronic health conditions, mental health diagnoses, problematic drug use (DAST-10=6), problematic alcohol use (AUDIT=20), unmet physical and mental health care needs, addiction treatment in the past 12 months. Three multiple logistic regression models were fit to examine the independent association of substance use with unmet physical health care need, unmet mental health care need, and addiction treatment.
Substance use was highly prevalent, with over half (53%) screening positive for the DAST-10 and 38% screening positive for the AUDIT. Problematic drug use was 29%, problematic alcohol use was lower at 16% and 7% had both problematic drug and alcohol use. In multiple regression models for unmet need, we found that problematic drug use was independently associated with unmet physical (adjusted odds ratio [AOR] 1.95; 95% confidence interval [CI] 1.43-2.64) and unmet mental (AOR 3.06; 95% CI 2.17-4.30) health care needs. Problematic alcohol use was not associated with unmet health care needs. Among those with problematic substance use, problematic drug use was associated with a greater likelihood of accessing addiction treatment compared to those with problematic alcohol use alone (AOR 2.32; 95% CI 1.18-4.54).
Problematic drug use among homeless and vulnerably housed individuals was associated with having unmet health care needs and accessing addiction treatment. Strategies to provide comprehensive health services including addiction treatment should be developed and integrated within community supported models of care.
Cites: J Gen Intern Med. 2009 Jul;24(7):841-719415393
To discover whether the period lifetable provides more valid estimates of length of wait in prospect than are obtained using the lengths either of (current) waits captured at the time of the mid-period census or of the (prior) waits of those extracted over a specified period. We determined whether there was a surplus (or a deficiency) of extractions within the cross-classification of cohort and waiting time category which straddled each census. We used census-, event- and lifetable-based methods to produce three period-specific estimates of the percentage of waits of 0-2 months, and we determined whether length of wait grew shorter (or longer) from one period to the next. We used Lambda B to indicate the extent to which we were able to predict the direction of change in length of wait once we knew the direction of change in size of list. We found a direct correlation between change in length of wait and change in size of list, as expected under the stock-flow model, when length of wait was estimated using the lifetable for the period (L(B) = 58.33, 95% confidence interval [CI] = 29-88), but we obtained a null correlation when we used census-based estimates (L(B) = 6.45) and we obtained an inverse correlation when we used event-based estimates (L(B) = 57.14, 95% CI = 31-83). The period lifetable appears to provide more valid estimates of length of wait and should therefore be substituted for census- and event-based methods of estimation, wherever possible.