grant

Understanding fertility trends in a cohesive high-fertility population within a low-fertility environment

Organization PENNSYLVANIA STATE UNIVERSITY, THELocation UNIVERSITY PARK, UNITED STATESPosted 15 Sept 2024Deadline 31 Aug 2026
NIHUS FederalResearch GrantFY2025AddressAge DistributionAgricultureAmericanAmishApplication ContextAreaArticulationBirthBirth IntervalsBirth RateBirth SpacingBooksCensusesCessation of lifeCommunitiesCouplesCross Sectional AnalysisCross-Sectional AnalysesCross-Sectional StudiesCross-Sectional SurveyDataData Base ManagementData Base Management SystemsData BasesData SetData SourcesDatabase Management SystemsDatabasesDeathDeath RateDecision MakingDemographic TransitionsDemographyDevelopmentDirectoriesDisease Frequency SurveysDocumentationEconomic DevelopmentEconomical DevelopmentEconomicsEducationEducational aspectsEncapsulatedEnvironmentEquationEthnic OriginEthnicityEventExposure toFecundabilityFecundityFertilityFertility IncentiveGeographyGoalsHealthHealth Care SystemsHealth behaviorHigh Fertility PopulationsHouseholdHousehold HeadsIncentivesIndividualInfrastructureInvestigatorsInvestmentsJobsKnowledgeLife CycleLife Cycle StagesLife TablesLinkLocationMarriageMinorityModernizationNeighborhoodsNorth AmericaNuptialityOccupationalOccupationsOut-MigrationsParturitionPhasePopulationPopulation DatabasePopulation DensityPopulation GrowthProcessProfessional PositionsPublicationsPublished DirectoryRecordsRegistriesReligionResearchResearch PersonnelResearchersScientific PublicationSeriesSex DistributionSiteSocietiesStatistical Data AnalysesStatistical Data AnalysisStatistical Data InterpretationStructureTemporal trendTestingTimeTime trendTrainingTraining ProgramsTrends over timeUnderserved PopulationVariantVariationVital StatisticsWomanWorkaccess to health careaccessibility of health careaccessibility to health careanalyzing longitudinalbear childrenbearing childrencareer preparationcareer readinesschild bearingchildbearingcontextual factorsdata basedata base structuredatabase designdatabase managementdatabase structuredatabase systemsdevelopmentaleconomichealth care accesshealth care availabilityhealth care service accesshealth care service availabilityhealth related behaviorimprovedjob readinesslife courselongitudinal analysislongitudinal databasemethods to study multiple-level influencesmigrationmortality ratemortality ratiomulti-level analysismulti-level modelmultilevel analysismultilevel modelmultilevel modelingpopulation healthpreferencerate of changerelational databaserelational database management systemsreligiousrural arearural health carerural locationrural regionsocialsocial culturesocial factorssocio-culturalsocio-economicsocio-economic developmentsocio-economicallysocioculturalsocioeconomic developmentsocioeconomicallysocioeconomicsstatistical analysissuccesstheoriestrendunder served groupunder served individualunder served peopleunder served populationunderserved groupunderserved individualunderserved peoplework readinessworkforce readinessworkplace readiness
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Full Description

Project Summary
Western populations collectively responded to historically contingent, society-wide developments in the 19th and 20th

centuries by reducing births to record-low levels. However, a minority of Westerners, largely ethnic sectarian religions,

have maintained pre-transition fertility levels despite exposure to contexts that motivate fertility decline. How have these

populations—conspicuous among them being the Amish—maintained high birth rates? Decades of sporadic research

suggest that high birth and low attrition rates explain the unabated 20-25 year Amish population doubling time. Yet, it is

less clear why Amish women maintain high birth rates and, perhaps more revealing, why births vary so widely among

them, with community-level completed birth rates ranging from approximately 4 to 12. Occasional cross-sectional studies

suggest that multiple variables—including household head occupation, religious differences, and individual status—

predict Amish birth rates, but what these results mean, exactly, remains educated guesswork. This project overcomes past

barriers and delivers new knowledge by making two ground-breaking contributions to Amish demography research. First,

this project develops a large population-wide, time-sensitive dataset that will permanently transition Amish demography

research from limited, single-site, cross-sectional analyses to full-scale, continent-wide, time-sensitive analyses. This

transition will substantially advance the scope and interpretability of statistical predictors of births. This longitudinal

database pulls information from Amish-produced population record books that provide extensive documentation of vital

events, sociocultural information, and geocodable addresses at the household level. These geographically referenced data

will then be linked to publically available contextual data. The launch-point for this database is my cross-sectional Amish

population dataset, which incorporates 71 current record books with information about 54,731 unique Amish households

(estimated at about 270,000 individuals), covering approximately 89.6% of all Amish in North America. The proposed

longitudinal database will use all editions of three major directory sets, piloting the process of eventually incorporating all

editions of all Amish directories. Second, this project will validate the data by testing population growth explanations,

beginning with articulation of population structure trends over time. This project will then test the relative influence of

competing fertility theories by disentangling the effects of Amish-internal versus external structural-cultural forces.

Namely, are birth differences more the product of Amish-internal differences in spite of contextual socioeconomic

development or is birth variation more the product of relative isolation from society-wide structures in spite of Amish

internal demarcations? To test these competing theories, I will employ this project’s internally linked, geographically

referenced pilot longitudinal database in time-sensitive analyses. To achieve these project goals, I propose a research and

training program that will enable me to apply conventional and recent advancements in time-sensitive statistical analysis,

fertility theory, and relational database design and management. In so doing, I will quickly achieve researcher

independence in preparation for career-long management, ongoing development, and analysis of a population database

that has sprawling potential to address many demography and population health questions.

Grant Number: 5K99HD111665-02
NIH Institute/Center: NIH

Principal Investigator: Cory Anderson

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