Health Systems Strengthening, The Aurum Institute, South Africa.
Yale School of Public Health, United States.
Glob Health Action. 2022 Dec 31;15(1):2008627. doi: 10.1080/16549716.2021.2008627.
The burden and impact of non-communicable diseases (NCDs) are well documented, accounting for 70% of premature deaths globally. In Sub-Saharan Africa, rising NCDs are estimated to account for 27% of mortality by 2020, a 4% increase from 2005. This increase will inevitably lead to a higher demand for NCD treatment services, exerting pressure on limited public financial resources. To get a sense of the resources required to treat NCDs, it is necessary to estimate the costs associated with the diagnosis, treatment and management thereof. Typically, in estimating costs for health services, countries use historical patient level data combined with demographic trend data and non-patient level data to arrive at estimated future costs. This methodology relies heavily on the availability of data from a wide variety of sources stretching beyond the health sector. Low-and-middle-income countries often lack the requisite data and are compelled to use less efficient ways to determine resource allocation. This study explores the use of probability-based cost estimation to estimate the cost of delivering NCD treatment services in South Africa, one such data-poor environment.Probability-based cost estimation, in combination with deterministic cost estimation, is used in arriving at a cost estimate for NCD treatment services at primary healthcare facility level. On its own, deterministic cost estimation can determine total costs, provided all the input variables are known. This is not always possible because of the lack of one or more input variables. In most instances, the lacking input variable is the quantities at which specific conditions will be treated. This problem is addressed by using probability-based cost estimation through which a mean cost is calculated and applied to the target population as a whole, eliminating the need for quantities per condition. Thus, this model contains both deterministic and probabilistic cost estimation elements.
非传染性疾病（NCDs）的负担和影响有充分的记录，占全球过早死亡人数的 70%。在撒哈拉以南非洲，据估计，到 2020 年，不断上升的非传染性疾病将导致死亡率上升 27%，比 2005 年增加 4%。这种增长将不可避免地导致对非传染性疾病治疗服务的更高需求，给有限的公共财政资源带来压力。为了了解治疗非传染性疾病所需的资源，有必要估计与诊断、治疗和管理相关的成本。通常，在估算卫生服务成本时，各国会利用历史上的患者水平数据，结合人口趋势数据和非患者水平数据，得出未来的估计成本。这种方法严重依赖于从卫生部门以外的各种来源获得数据的可用性。中低收入国家通常缺乏必要的数据，不得不采用效率较低的方法来确定资源分配。本研究探讨了在南非这样一个数据匮乏的环境中，利用基于概率的成本估算来估算非传染性疾病治疗服务成本的方法。基于概率的成本估算与确定性成本估算相结合，用于估算初级保健设施一级非传染性疾病治疗服务的成本。仅使用确定性成本估算可以确定总成本，前提是所有输入变量都已知。由于缺乏一个或多个输入变量，这并不总是可行的。在大多数情况下，缺失的输入变量是将治疗特定疾病的数量。通过使用基于概率的成本估算，可以解决这个问题，通过该方法计算出平均成本，并将其应用于整个目标人群，从而无需针对每种情况的数量。因此，该模型包含确定性和概率性成本估算元素。
