出版社：Indian Association of Preventive and Social Medicine Uttar Pradesh and Uttarakhand Chapter
摘要：Background: Hot-spot detection of Maternal Mortality Ratio (MMR) can assist in identifying the exact geographic location of regions that need urgent attention. Aims &Objectives: To detect hot-spots of MMR at district level in the selected nine states of India and the observed pattern was further correlated with hot-spots of certain known risk factors of MMR in the same region. Material &Methods: Data on MMR was obtained from Annual Health Survey 2012-13. Moran’s I was computed for MMR to quantify spatial autocorrelation. The hot-spot analysis of MMR and its potential risk factors were performed using Getis-Ord Gi* statistic, a measure of local indicators of spatial autocorrelation (LISA). The spatial analysis was based on queen’s contiguity weight matrix and analyses were done using ArcGIS 10.3. Results: The Moran’s I value of MMR was found to be 0.69 indicating a positive spatial autocorrelation. Districts with MMR hot-spotting was largely observed in Uttar Pradesh and Madhya Pradesh, followed by Assam, Bihar and Jharkhand. The hot-spot analysis unveiled an inverse relation of MMR with female literacy rate, mothers who received any antenatal check-up (%), mothers who utilized Janani Suraksha Yojana (%), safe delivery (%) and urbanization (%). Marriages among females below 18 years (%), total fertility rate and women with unmet need for spacing (%) had a direct relation with MMR. Conclusion: Information on hot-spots as depicted in this study can help locate the regions vulnerable to MMR and the potential risk factors, which in turn could aid in implementing targeted intervention programs.
其他摘要：Background: Hot-spot detection of Maternal Mortality Ratio (MMR) can assist in identifying the exact geographic location of regions that need urgent attention. Aims &Objectives: To detect hot-spots of MMR at district level in the selected nine states of India and the observed pattern was further correlated with hot-spots of certain known risk factors of MMR in the same region. Material &Methods: Data on MMR was obtained from Annual Health Survey 2012-13. Moran’s I was computed for MMR to quantify spatial autocorrelation. The hot-spot analysis of MMR and its potential risk factors were performed using Getis-Ord Gi* statistic, a measure of local indicators of spatial autocorrelation (LISA). The spatial analysis was based on queen’s contiguity weight matrix and analyses were done using ArcGIS 10.3. Results: The Moran’s I value of MMR was found to be 0.69 indicating a positive spatial autocorrelation. Districts with MMR hot-spotting was largely observed in Uttar Pradesh and Madhya Pradesh, followed by Assam, Bihar and Jharkhand. The hot-spot analysis unveiled an inverse relation of MMR with female literacy rate, mothers who received any antenatal check-up (%), mothers who utilized Janani Suraksha Yojana (%), safe delivery (%) and urbanization (%). Marriages among females below 18 years (%), total fertility rate and women with unmet need for spacing (%) had a direct relation with MMR. Conclusion: Information on hot-spots as depicted in this study can help locate the regions vulnerable to MMR and the potential risk factors, which in turn could aid in implementing targeted intervention programs.
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