Gakunga Robai, Korir Anne, Bouttell Janet
Independent Research Scientist, Nairobi, Kenya.
Kenya Medical Research Institute, Centre of Clinical Research, Nairobi, Kenya.
PLoS One. 2025 May 22;20(5):e0324593. doi: 10.1371/journal.pone.0324593. eCollection 2025.
In April 2015, Kenya introduced the National Health Insurance Oncology Benefits Package and its complementary reforms (oncology insurance scheme) to alleviate financial hardship among its members upon a cancer diagnosis. In this study, we hypothesised that the time it took to start treatment would have an impact on health outcomes: the longer patients waited the worse their outcomes would be. We did not have outcomes in the data but we could compute time to treatment initiation (TTI). While assessing the impact of the oncology insurance scheme on TTI, we encountered a substantial sudden increase in average TTI in June 2018 which we needed to explore.
We conducted our analysis using R, a statistical computing software, for interrupted time series analysis (ITSA) on Nairobi Cancer Registry data to assess the impact of the introduction of the oncology insurance scheme on TTI in days among Nairobi County residents diagnosed with cancer. We calculated the monthly median TTI, resulting in 120 data points (one for each of the 120 months of the observation period - January 1st 2010 to December 31st 2019). Since the oncology insurance scheme was available to the entire Kenyan population, a suitable control group was unavailable. To address this, we used auto regressive integrated moving average (ARIMA) modelling to forecast an expected trend, allowing us to estimate both sudden and gradual changes during April 2015 and June 2018 (intervention months).
After cleaning the data, 7584 (35%) cases of the original 21,464 were left for analysis. Females were more than males at 57.8%. Approximately 65% of the cases with known stage at diagnosis were in stages III and IV. No statistically significant impact was associated with the introduction of oncology insurance scheme; an additional 9.06 days (95% CI: -8.7 to 26.8) and a gradual change of 0.88 days per month (95% CI: -0.11 to 1.88). However, a statistically significant sudden increase in monthly median TTI in June 2018 of 34.6 days (95% CI 15.4 to 53.8) and the gradual change of -1.6 days (95% CI -3.5 to 0.4) per month which was not statistically significant, were associated with a healthcare workers' strike. We could not accurately analyse case trends from these data because the registry had not completed collating data for the later years (2015-2019).
These results suggest that the oncology insurance scheme may not have reduced average TTI for the cancer patients as we had hypothesized. However, a healthcare workers' strike (based on corroboration with findings from the 2018 Kenya Household Health Expenditure and Utilization Survey), increased the average TTI among these patients. Data science techniques and ITSAs using cancer registry data is a cost-effective method to answer important population-level research questions in resource-limited settings.
2015年4月，肯尼亚推出了国家健康保险肿瘤福利套餐及其补充改革措施（肿瘤保险计划），以减轻其成员在被诊断患有癌症时的经济困难。在本研究中，我们假设开始治疗的时间会对健康结果产生影响：患者等待的时间越长，结果就会越差。我们的数据中没有结果，但我们可以计算开始治疗的时间（TTI）。在评估肿瘤保险计划对TTI的影响时，我们发现2018年6月平均TTI出现了大幅突然增加，我们需要对此进行探究。
我们使用R（一种统计计算软件）对内罗毕癌症登记数据进行中断时间序列分析（ITSA），以评估肿瘤保险计划的引入对在内罗毕县被诊断患有癌症的居民的TTI（以天为单位）的影响。我们计算了每月的TTI中位数，得出120个数据点（观察期的120个月中每个月一个数据点——2010年1月1日至2019年12月31日）。由于肿瘤保险计划面向全体肯尼亚人口，因此没有合适的对照组。为了解决这个问题，我们使用自回归积分滑动平均（ARIMA）模型来预测预期趋势，使我们能够估计2015年4月至2018年6月（干预月份）期间的突然变化和逐渐变化。
在清理数据后，原始的21464个病例中留下了7584个（35%）用于分析。女性多于男性，占57.8%。诊断时已知分期的病例中约65%处于III期和IV期。肿瘤保险计划的引入没有产生统计学上的显著影响；额外增加了9.06天（95%置信区间：-8.7至26.8），每月逐渐变化0.88天（95%置信区间：-0.11至1.88）。然而，2018年6月每月TTI中位数出现了统计学上显著的突然增加，增加了34.6天（95%置信区间15.4至53.8），每月逐渐变化-1.6天（95%置信区间-3.5至0.4），但这一逐渐变化没有统计学意义，这与医护人员罢工有关。由于登记处尚未完成对后期年份（2015 - 2019年）数据的整理，我们无法从这些数据中准确分析病例趋势。
这些结果表明，肿瘤保险计划可能没有如我们所假设的那样降低癌症患者的平均TTI。然而，医护人员罢工（基于与2018年肯尼亚家庭健康支出与利用调查结果的相互印证）增加了这些患者中的平均TTI。使用癌症登记数据的数据科学技术和ITSA是在资源有限的环境中回答重要的人群层面研究问题的一种经济有效的方法。
