경로당 노인의 건강상태와 건강관리서비스 이용 관련요인 분석 (Health Status and Use of Health Care Services of the Elderly Utilizing Senior citizen Centers)
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- 농촌의학ㆍ지역보건
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- 제27권1호
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- pp.99-113
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- 2002
보건소 인력이 노인의 건강을 유지, 증진시키기 위한 노인건강관리 프로그램을 개발하는데 필요한 기초자료로 활용하기 위해 경로당 이용 노인의 건강상태와 보건소에서 제공하고 있는 노인건강관리 서비스 이용실태를 파악한 조사연구로 S시의 C구에 거주하는 65세 남 녀 노인중 경로당을 이용하고 있는 남자노인 66명과 여자노인 139명 총 205명을 대상으로 하였다. 경로당 이용 노인의 일반적 특성, 신체적 건강상태, 사회적 건강상태, 노인건강관리 서비스 이용실태는 연구자가 제작한 질문지를 이용하였고, 수단적 일상생활 기능은 Lawton이 개발한 도구를 우리 나라 실정에 적합하게 수정보완하여 6개 문항으로 된 도구로 측정하였다. 정신적 건강상태는 Folstein(1975)이 개발한 것을 우리나라 실정에 맞게 수정한 Mini Mental State Examlnation-Korea(MMSE-K) 도구를 사용하였으며, 정서적 건강상태는 Radloff가 개발한 Center for Epidemiologic Studies-Depression Scale(CES-D)도구를 이용하여 측정하였다. 자료는 SPSS/WIN을 이용하여 남 녀 노인의 일반적 특성, 건강상태, 노인건강 관리서비스 이용실태에 대한 실수와 백분율을 구하고, 각 변수간의 차이에 대한 유의성 검정은 t-test, 카이자승법 및 ANOVA로, 노인겅강관리 서비스 이용관련요인은 카이자승검정 방법을 분석하였으며 연구결과는 다음과 같다. 1. 경로당 이용 노인중 남자노인 40.9%와 여자노인 17.3%만이 자신의 건강상태에 대해 건강하다고 생각하고 있는 것으로 나타났다. 흡연비율은 남자노인 46.9%, 여자노인 18.5%였으며, 음주는 남자노인의 57.6%가, 여자노인 16.5%만이 음주하는 것으로 나타났다. 남자노인 13.3%, 여자노인 14.4%가 수면이 불충분하다고 응답하였고, 운동을 규칙적으로 하는 노인은 남자가 47%, 여자 25.9%으로 나타났다. 남자노인 42.4%, 여자노인 43.9%가 지난 1년동안 건강검진을 받지 않았으며, 아침이닦기와 저녁 이닦기 등 구강보건은 94.6%, 83.4%의 노인이 생활속에서 실천하고 있었다. 2. 경로당 이용 노인의 일상생활기능(IADL)은 0-18점에서 평균 7.4점이였으며, 남자노인은 일상생활용품이나 약사러가기, 버스와 전철 혼자타기와 관련된 일상생활기능이 여자노인보다 유의하게 높았다. 정신적인 면에서 우울한 편에 속하는 남자노인은 7.6%, 여자노인은 21.6%로 나타났으며, 인지적인 측면에서는 남자노인의 48.5%, 여자노인의 28.8%가 치매의심군에 속하는 것으로 나타났다. 사회적인 측면에서는 남자노인의 57.6%, 여자노인의 62.6%에서 친밀한 사람이 없었으며, 친밀한 관계를 유지하고 있는 노인의 경우, 남자노인은 가장 친밀한 사람을 친구로 응답한 경우가 52.5%였고 여자노인은 자식이 53.8%로 나타났다. 3. 건강상태에 관련된 요인들 중 연령이 높아질수록 치매율이 유의하게 높았고(p=0.000), 치과방문회수가 유의하게 높았다(p=0.000). 4. 앞으로 더 강화해야 할 노인건강관리서비스 요구도와 관련된 요인들 중 교육 정도가 낮은 노인, 사별한 노인일수록 무료순회진료 및 진료서비스 요구도가 유의하게 높았고,운동을 안하는 노인, 수면만족도가 높은 노인, 구강보건수행 정도가 높은 노인, 사회적 친밀도가 높은 노인일수록 건강검진 서비스 요구도 및 노인건강증진운동 서비스 요구도가 유의하게 높았다. 또한 주관적 건강인식이 건강하지 않다고 응답한 노인은 건강하다고 응답한 노인에 비해, 흡연을 안하는 노인, 음주를 안하는 노인일수록 노인건강증진운동 서비스애 대한 요구도가 유의하게 높았다. 결론적으로 경로당 이용노인을 대상으로 한 건강관리서비스 제공은 노인의 주관적 건강인식, 배우자 유무, 가족동거유형, 용돈과 같은 사회 심리 경제적인 요인과 흡연, 음주 등의 신체적 건강상태를 고려할 필요가 있으며, 노인들의 건강행동을 실천하게 하는 프로그램을 시행함과 동시에 사회 심리 경제적인 문제해결이 병행되어야 할 것이다. 보건소의 노인건강관리서비스는 이러한 특징과 차이를 기초로 수행되어야 하나 향후 반복적인 연구를 통하여 노인에 대한 건강관리 서비스가 개발되어져야 할 것이다.
연구배경: COPD 환자에서 최대산소섭취량의 측정은 호흡재활치료에서 운동 강도의 결정과 치료 반응을 평가 하는데 사용된다. 하지만 운동부하 심폐기능 검사는 공간및 비용 등의 문제로 우리나라에서는 보편화되어 있지 않다. 한편 6분 보행검사는 간단하게 운동능력을 평가할 수 있는 방법으로 신뢰도가 높고 운동능력의 변화를 비교적잘 반영한다. 본 연구에서는 중등도 이상의 COPD 환자에서
The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.