Alfian Sofa D, Abdulah Rizky, Hak Eelko
Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Jl. Raya Jatinangor, KM 21, Jatinangor, Sumedang, Indonesia.
Drug Utilization and Pharmacoepidemiology Research Group, Centre of Excellence for Pharmaceutical Care Innovation, Universitas Padjadjaran, Jatinangor, Indonesia.
BMC Public Health. 2025 May 24;25(1):1915. doi: 10.1186/s12889-025-23109-0.
Childhood vaccination is a fundamental public health intervention, playing an essential role in improving health outcomes and preventing serious infections. Despite proven benefits of vaccination programs, its coverage in Indonesia remains inadequate over the years. Therefore, this study aims to develop a prediction rule using intrapersonal, interpersonal, organizational, community, and policy-related factors to distinguish between Indonesian children < 2 years at high and low risk of incomplete vaccination.
The prediction rule was developed using cross-sectional data from the 2017 Indonesia Demographic Health Survey. Data on vaccination status was obtained from a vaccination card, which was filled out by health care providers during vaccination. Multivariable logistic regression was applied to develop a prognostic score based on the regression coefficients of associated parental intrapersonal, interpersonal, organizational, community, and policy-related factors. Discrimination of the model was assessed with Receiver Operating Characteristic (ROC) curve.
The sample population in this study comprised 3,790 respondents, and 2,414 (63·7%) were incompletely vaccinated. Several factors such as a mother at young age, absence of a mobile telephone, limited antenatal care attendance, absence of postnatal checks within two months after birth, had not received tetanus vaccination during pregnancy, and low socio-economic status were independently associated with incomplete vaccination. The area under curve (AUC) of the model was 0·67, which showed moderate discrimination, but was acceptable. Using a cut-off score of > 20 points, only half of the parents with a high probability of incompletely vaccinated children are selected with a sensitivity of 60% and specificity of 64%, and only 41% of parents with incompletely vaccinated children are missed.
This novel, easy-to-use prediction rule could be a useful tool to complement current strategies and further encourage tailored vaccine uptake interventions, particularly to parents with a high chance of incompletely vaccinated children in Indonesia.
儿童疫苗接种是一项基本的公共卫生干预措施，在改善健康状况和预防严重感染方面发挥着至关重要的作用。尽管疫苗接种计划已被证明有益，但多年来印度尼西亚的疫苗接种覆盖率仍然不足。因此，本研究旨在利用个人、人际、组织、社区和政策相关因素制定一个预测规则，以区分印度尼西亚2岁以下儿童完全接种疫苗的高风险和低风险。
使用2017年印度尼西亚人口与健康调查的横断面数据制定预测规则。疫苗接种状况的数据来自疫苗接种卡，由医疗保健提供者在接种疫苗时填写。应用多变量逻辑回归，根据相关的父母个人、人际、组织、社区和政策相关因素的回归系数制定一个预后评分。用受试者工作特征（ROC）曲线评估模型的辨别力。
本研究的样本人群包括3790名受访者，其中2414名（63.7%）接种不完全。母亲年龄小、没有手机、产前检查次数有限、出生后两个月内没有产后检查、孕期未接种破伤风疫苗以及社会经济地位低等几个因素与接种不完全独立相关。该模型的曲线下面积（AUC）为0.67，显示出中等辨别力，但可以接受。使用>20分的临界值，只有一半接种不完全儿童可能性高的父母被选中，灵敏度为60%，特异性为64%，只有41%接种不完全儿童的父母被遗漏。
这个新颖、易于使用的预测规则可能是一个有用的工具，可以补充当前的策略，并进一步鼓励有针对性的疫苗接种干预措施，特别是针对印度尼西亚接种不完全儿童可能性高的父母。
