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  1. Abstract Background

    Factors influencing the health of populations are subjects of interdisciplinary study. However, datasets relevant to public health often lack interdisciplinary breath. It is difficult to combine data on health outcomes with datasets on potentially important contextual factors, like political violence or development, due to incompatible levels of geographic support; differing data formats and structures; differences in sampling procedures and wording; and the stability of temporal trends. We present a computational package to combine spatially misaligned datasets, and provide an illustrative analysis of multi-dimensional factors in health outcomes.

    Methods

    We rely on a new software toolkit, Sub-National Geospatial Data Archive (SUNGEO), to combine data across disciplinary domains and demonstrate a use case on vaccine hesitancy in Low and Middle-Income Countries (LMICs). We use data from the World Bank’s High Frequency Phone Surveys (HFPS) from Kenya, Indonesia, and Malawi. We curate and combine these surveys with data on political violence, elections, economic development, and other contextual factors, using SUNGEO. We then develop a stochastic model to analyze the integrated data and evaluate 1) the stability of vaccination preferences in all three countries over time, and 2) the association between local contextual factors and vaccination preferences.

    Results

    In all three countries, vaccine-acceptance is more persistent than vaccine-hesitancy from round to round: the long-run probability of staying vaccine-acceptant (hesitant) was 0.96 (0.65) in Indonesia, 0.89 (0.21) in Kenya, and 0.76 (0.40) in Malawi. However, vaccine acceptance was significantly less durable in areas exposed to political violence, with percentage point differences (ppd) in vaccine acceptance of -10 (Indonesia), -5 (Kenya), and -64 (Malawi). In Indonesia and Kenya, although not Malawi, vaccine acceptance was also significantly less durable in locations without competitive elections (-19 and -6 ppd, respectively) and in locations with more limited transportation infrastructure (-11 and -8 ppd).

    Conclusion

    With SUNGEO, researchers can combine spatially misaligned and incompatible datasets. As an illustrative example, we find that vaccination hesitancy is correlated with political violence, electoral uncompetitiveness and limited access to public goods, consistent with past results that vaccination hesitancy is associated with government distrust.

     
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    Free, publicly-accessible full text available December 1, 2024
  2. The success of deep learning neural network models often relies on the accessibility of a large number of labelled training data. In many health care settings, however, only a small number of accurately labelled data are available while unlabelled data are abundant. Further, input variables such as clinical events in the medical settings are usually of longitudinal nature, which poses additional challenges. In this paper, we propose a semi‐supervised joint learning method for classifying longitudinal clinical events. Specifically, our model consists of a sequence generative model and a label prediction model, and the two parts are learned end to end using both labelled and unlabelled data in a joint manner to obtain better prediction performance. Using five mortality‐related classification tasks on the Medical Information Mart for Intensive Care (MIMIC) III database, we demonstrate that the proposed method outperforms the purely supervised method that uses labelled data only and existing two‐step semi‐supervised methods.

     
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