Introduction: Collective evidence on risk factors for dental caries remains elusive in low- and middle-income countries (LMICs). The objective was to conduct a systematic review and meta-analysis on risk factors for dental caries in deciduous or permanent teeth in LMICs. Methods: Studies were identified electronically through databases, including Cochrane Oral Health Group Trials Register, Cochrane Central Register of Controlled Trials, PubMed/MEDLINE, and CINAHL, using “prevalence, dental caries, child, family, socioeconomic, and LMIC” as the keywords. A total of 11 studies fit the inclusion criteria. Quality assessment of the included studies was performed using the Newcastle-Ottawa Scale (NOS). The MedCalc software and Review Manager 5.4.1 were used. Results: From 11,115 participants, 38.7% (95% CI: 28.4–49.5%) had caries and 49.68% were female. Among those with caries, 69.74% consumed sugary drinks/sweets (95% CI: 47.84–87.73%) and 56.87% (95% CI: 35.39–77.08%) had good brushing habits. Sugary drinks had a two times higher likelihood of leading to caries (OR: 2.04, p < 0.001). Good oral hygiene reduced the risk of caries by 35% (OR: 0.65, p < 0.001). Concerning maternal education, only secondary education reduced the likelihood of caries (OR: 0.96), but primary education incurred 25% higher risks (OR: 1.25, p = 0.03). A 65% reduction was computed when caregivers helped children with tooth brushing (OR: 0.35, p = 0.04). Most families had a low socioeconomic status (SES) (35.9%, 95% CI: 16.73–57.79), which increased the odds of caries by 52% (OR: 1.52, p < 0.001); a high SES had a 3% higher chance of caries. In the entire sample, 44.44% (95% CI: 27.73–61.82%) of individuals had access to dental services or had visited a dental service provider. Conclusion: Our findings demonstrate that high sugar consumption, low maternal education, and low and high socioeconomic status (SES) increased the risk of dental caries in LMICs. Good brushing habits, higher maternal education, help with tooth brushing, and middle SES provided protection against caries across LMIC children. Limiting sugars, improving oral health education, incorporating national fluoride exposure programs, and accounting for sociodemographic limitations are essential for reducing the prevalence of dental caries in these settings.

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This paper is concerned with interpretation of natural language place descriptions, as they are a rich source of geographic information. A place description is interpreted by matching geographic entities occurring in the text against the OpenStreetMap (OSM) database. This paper is mainly concerned with interpretation of paraphrased places, i.e., entities for which no name is given and which may only by described. Our objective is to determine suitable entity types that allow querying the OpenStreetMap database for the respective place. For example, if we wish to identify a place to eat, we have to check for entities of an a-priori unknown type (cafe, restaurant, etc.). Challenges arise from the open-endedness of language, its ambiguity, and context-sensitivity as well as from mismatches between human conceptualization of place and database ontologies. The contributions of this paper are, first, to present a hard problem that is key to geo-information retrieval beyond named entities. Second, we propose context-sensitive methods for identifying place types based on semantic word similarity. We evaluate the methods on text extracted from Wikipedia and travel blogs, revealing their contribution to advancing automated interpretation of place descriptions to paraphrased places.

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This doctoral thesis aims to design a geographic information system based on locations to make the information about the human location interpretable for computers. This research employs natural language place descriptions as its primary research input and focuses on five research components as main goals towards understanding place descriptions: incorporating context-sensitive information, spatial information extraction, and modeling, reasoning, place geo-referencing, and querying.

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