尼泊尔 COVID-19 检测设施地理可达性评估(2021 年)

IF 5 Q1 HEALTH CARE SCIENCES & SERVICES The Lancet regional health. Southeast Asia Pub Date : 2024-07-01 DOI:10.1016/j.lansea.2024.100436
Parvathy Krishnan Krishnakumari , Hannah Bakker , Nadia Lahrichi , Fannie L. Côté , Joaquim Gromicho , Arunkumar Govindakarnavar , Priya Jha , Saugat Shrestha , Rashmi Mulmi , Nirajan Bhusal , Deepesh Stapith , Runa Jha , Lilee Shrestha , Reuben Samuel , Dhamari Naidoo , Victor Del Rio Vilas
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引用次数: 0

摘要

背景事实证明,确保公平地获得 SARS-CoV-2 检测服务对于控制 COVID-19 的流行至关重要,尤其是在尼泊尔这样地形复杂的国家。在 2021 年 5 月的第二波疫情中,尼泊尔面临着扩大实验室网络的巨大压力,以确保疫情应对措施的校准。这一扩张导致检测设施的数量从 2021 年 5 月的 69 个实验室增加到 2021 年 11 月的 89 个实验室。我们对 2021 年期间尼泊尔 COVID-19 检测设施的实际使用公平性进行了评估。方法基于最新的公开数据集以及尼泊尔卫生与人口部发布的 2021 年 5 月 1 日至 11 月 15 日 COVID-19 相关日报,我们以 1 平方公里的分辨率测量了尼泊尔 COVID-19 检测的地理可达性差异。此外,我们还提出了一个优化模型,以确定设立检测实验室的最佳地点,从而最大限度地提高检测的可及性,并测试了该模型在尼泊尔的潜在影响。分析发现,尽管尼泊尔加大了工作力度,但在步行和机动车驾驶这两种出行方式下,检测设施的实际可及性仍然很低。与步行模式相比,机动车模式的地理可达性和平等性都更好。如果人人都能使用机动车,那么在同一小时内,任何检测设施(公共和私人)60 分钟内的人口覆盖率将接近行人的三倍:就尼泊尔全国人口而言,一小时内机动车的覆盖率为 61.4%,而行人的覆盖率为 22.2%。除了那些集中在首都加德满都的私人测试中心外,大多数地区的可达性都很低。如果能从尼泊尔现有的所有医疗机构中最优化地选择这 20 家实验室,那么假设使用数学优化方法在原有的 69 家实验室基础上再选择 20 家实验室,就能将 11 月份观察到的 61.4% 的实验室可及性提高到 71.4%。有关 COVID-19 检测设施地理可达性的研究结果将为尼泊尔的健康相关规划提供有价值的信息,尤其是在数据可能有限且决策具有时间敏感性的紧急情况下。未来可能会考虑使用公开数据和数学优化方法。
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Assessment of geographical accessibility to COVID-19 testing facilities in Nepal (2021)

Background

Ensuring equitable physical access to SARS-CoV-2 testing has proven to be crucial for controlling the COVID-19 epidemic, especially in countries like Nepal with its challenging terrain. During the second wave of the pandemic in May 2021, there was immense pressure to expand the laboratory network in Nepal to ensure calibration of epidemic response. The expansion led to an increase in the number of testing facilities from 69 laboratories in May 2021 to 89 laboratories by November 2021. We assessed the equity of physical access to COVID-19 testing facilities in Nepal during 2021. Furthermore, we investigated the potential of mathematical optimisation in improving accessibility to COVID-19 testing facilities.

Methods

Based on up-to-date publicly available data sets and on the COVID-19-related daily reports published by Nepal's Ministry of Health and Population from May 1 to November 15, 2021, we measured the disparities in geographical accessibility to COVID-19 testing across Nepal at a resolution of 1 km2. In addition, we proposed an optimisation model to prescribe the best possible locations to set up testing laboratories maximizing access, and tested its potential impact in Nepal.

Findings

The analysis identified vulnerable districts where, despite ramping up efforts, physical accessibility to testing facilities remains low under two modes of travel—walking and motorized driving. Both geographical accessibility and its equality were better under the motorised mode compared with the walking mode. If motorised transportation were available to everyone, the population coverage within 60 min of any testing facility (public and private) would be close to threefold the coverage for pedestrians within the same hour: 61.4% motorised against 22.2% pedestrian access within the hour, considering the whole population of Nepal. Very low accessibility was found in most areas except those with private test centres concentrated in the capital city of Kathmandu. The hypothetical use of mathematical optimisation to select 20 laboratories to add to the original 69 could have improved access from the observed 61.4% offered by the laboratories operating in November to 71.4%, if those 20 could be chosen optimally from all existing healthcare facilities in Nepal. In mountainous terrain, accessibility is very low and could not be improved, even considering all existing healthcare facilities as potential testing locations.

Interpretation

The findings related to geographical accessibility to COVID-19 testing facilities should provide valuable information for health-related planning in Nepal, especially in emergencies where data might be limited and decisions time-sensitive. The potential use of publicly available data and mathematical optimisation could be considered in the future.

Funding

WHO Special Programme for Research and Training in Tropical Diseases (TDR).

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