Department of Geography, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
Independent Researcher, Lucknow, Uttar Pradesh, India.
BMC Pregnancy Childbirth. 2024 Oct 14;24(1):670. doi: 10.1186/s12884-024-06850-z.
Understanding the geographic variation of unintended pregnancy is crucial for informing tailored policies and programs to improve maternal and child health outcomes. Although spatial analyses of unintended pregnancy have been conducted in several developing countries, such research is lacking in India. This study addresses this gap by investigating the geographic distribution and determinants of unintended pregnancy in India.
We analysed data from the National Family Health Survey-5 encompassing 232,920 pregnancies occurring between 2014 and 2021 in India. We conducted a spatial analysis to investigate the distribution of unintended pregnancies at both state and district levels using choropleth maps. To assess spatial autocorrelation, Global Moran's I statistic was employed. Cluster and outlier analysis techniques were then utilized to identify significant clusters of unintended pregnancies across India. Furthermore, we employed Spatial Lag Model (SLM) and Spatial Error Model (SEM) to investigate the factors influencing the occurrence of unintended pregnancies within districts.
The national rate of unintended pregnancy in India is approximately 9.1%, but this rate varies significantly between different states and districts of India. The rate exceeded 10% in the states situated in the northern plain such as Haryana, Delhi, Uttar Pradesh, Bihar, and West Bengal, as well as in the Himalayan states of Himachal Pradesh, Uttarakhand, Sikkim, and Arunachal Pradesh. Moreover, within these states, numerous districts reported rates exceeding 15%. The results of Global Moran's I indicated a statistically significant geographical clustering of unintended pregnancy rates at the district level, with a coefficient of 0.47 (p < 0.01). Cluster and outlier analysis further identified three major high-high clusters, predominantly located in the districts of Arunachal Pradesh, northern West Bengal, Bihar, western Uttar Pradesh, Haryana, Delhi, alongside a few smaller clusters in Odisha, Madhya Pradesh, Uttarakhand, and Himachal Pradesh. This geographic clustering of unintended pregnancy may be attributed to factors such as unmet needs for family planning, preferences for smaller family sizes, or the desire for male children. Results from the SEM underscored that parity and use of modern contraceptive were statistically significant predictors of unintended pregnancy at the district level.
Our analysis of comprehensive, nationally representative data from NFHS-5 in India reveals significant geographical disparities in unintended pregnancies, evident at both state and district levels. These findings underscore the critical importance of targeted policy interventions, particularly in geographical hotspots, to effectively reduce unintended pregnancy rates and can contribute significantly to improving reproductive health outcomes across the country.
了解意外怀孕的地域差异对于制定有针对性的政策和方案以改善母婴健康结果至关重要。尽管已经在几个发展中国家进行了意外怀孕的空间分析，但印度缺乏此类研究。本研究通过调查印度意外怀孕的地理分布和决定因素来填补这一空白。
我们分析了涵盖 2014 年至 2021 年期间发生的 232,920 例妊娠的国家家庭健康调查-5 数据。我们使用面域图进行空间分析，以调查州和地区层面上意外怀孕的分布情况。为了评估空间自相关，我们采用了全局 Moran's I 统计量。然后，我们利用聚类和异常值分析技术来识别印度各地显著的意外怀孕聚类。此外，我们还使用空间滞后模型 (SLM) 和空间误差模型 (SEM) 来调查影响区内意外怀孕发生的因素。
印度全国意外怀孕率约为 9.1%，但在印度不同的州和地区之间存在显著差异。在位于北部平原的州，如哈里亚纳邦、德里、北方邦、比哈尔邦和西孟加拉邦，以及喜马拉雅山脉的喜马偕尔邦、北阿坎德邦、锡金邦和阿鲁纳恰尔邦，意外怀孕率超过 10%。此外，在这些州中，许多地区的报告率超过 15%。全局 Moran's I 的结果表明，在地区层面上，意外怀孕率存在具有统计学意义的地理聚类，系数为 0.47（p<0.01）。聚类和异常值分析进一步确定了三个主要的高-高聚类，主要位于阿鲁纳恰尔邦、北西孟加拉邦、比哈尔邦、北阿坎德邦、北方邦、哈里亚纳邦，以及奥里萨邦、中央邦、北阿坎德邦和喜马偕尔邦的一些较小的聚类。这种意外怀孕的地理聚类可能归因于计划生育未满足的需求、对较小家庭规模的偏好或对男孩的渴望等因素。SEM 的结果强调，地区层面上，生育次数和现代避孕措施的使用是意外怀孕的统计学显著预测因素。
我们对印度 NFHS-5 的全面、全国代表性数据进行的分析揭示了意外怀孕在州和地区层面上存在显著的地域差异。这些发现突出表明，需要有针对性的政策干预措施，特别是在地理热点地区，以有效降低意外怀孕率，并为改善全国的生殖健康结果做出重大贡献。
