Hasil untuk "Demography. Population. Vital events"

Sara Yeatman, Emily Smith-Greenaway

BACKGROUND: Over the past few decades, demographic research on unrealized fertility – the experience of having fewer children than desired – has expanded rapidly, particularly in low-fertility settings. More recently, scholars have shown that unrealized fertility is also widespread in higher-fertility contexts, affecting individuals across diverse sociodemographic conditions. Yet the literature remains fragmented, spanning definitions and measurement approaches, and with limited synthesis across settings. OBJECTIVE: This article reflects on the current state of demographic research on unrealized fertility. Drawing on studies from both the Global North and Global South, we provide a narrative review of how unrealized fertility has been measured, its primary causes, and the persistent gaps in the field. RESULTS: We identify five dominant approaches to measuring unrealized fertility and review their strengths and limitations, as well as the data constraints that hinder cross-contextual comparisons. We then consider the principal causes of unrealized fertility and introduce a preliminary conceptual model for understanding its multilevel drivers. Finally, we highlight key gaps in the literature and propose substantive and methodological directions for future research. CONTRIBUTION: As unrealized fertility is increasingly the focus of public discourse and policy debates, this is a critical moment for demography to deepen and standardize research on the topic. Our review synthesizes the field to date and offers concrete recommendations for advancing demographic research on unrealized fertility.

Paula Mercedes Martes Camargo

El libro MULME: Matrimonio y unión libre en menores de edad en México constituye el primer análisis sistemático en México de un fenómeno que afecta a una de cada cinco mujeres mexicanas. La obra examina el MULME desde tres perspectivas: transmisión intergeneracional, influencia del ambiente familiar y su relación con la maternidad adolescente. Estructurado en cuatro capítulos, el libro utiliza múltiples fuentes de datos para revelar la paradójica persistencia de altas tasas de MULME a lo largo de generaciones, pese a mejoras socioeconómicas. Los hallazgos muestran que el fenómeno se concentra en zonas rurales, estratos bajos y poblaciones indígenas, perpetuado por normas socioculturales y desigualdades de género en una sociedad patriarcal. La reseña destaca la contribución teórica y metodológica del libro a la demografía familiar, y propone futuras líneas de investigación para abordar este fenómeno. Esta obra representa un avance fundamental para comprender un fenómeno largamente invisibilizado en México.

Trevor GrandPre, Ethan Levien, Ariel Amir

Recent methods have been developed to map single-cell lineage statistics to population growth. Because population growth selects for exponentially rare phenotypes, these methods inherently depend on sampling large deviations from finite data, which introduces systematic errors. A comprehensive understanding of these errors in the context of finite data remains elusive. To address this gap, we study the error in growth rate estimates across different models. We show that under the usual bias-variance decomposition, the bias can be decomposed into a finite-time bias and nonlinear averaging bias. We demonstrate that finite-time bias, which dominates at short times, can be mitigated by fitting its monotonic behavior. In contrast, at longer times, nonlinear averaging bias becomes the predominant source of error, leading to a phase transition. This transition can be understood through the Random Energy Model, a mean-field model of disordered systems, where a few lineages dominate the estimator. Applying these methods to experimental data demonstrates that correcting for biases in lineage-based approaches yields consistent results for the long-term growth rate across multiple methods and enables the reverse-engineering of dynamic models. This new framework provides a quantitative understanding of growth rate estimators, clarifies the conditions under which they can be effectively applied to finite data, and introduces model-free approaches for studying the connections between physiology and cell growth.

Mark van der Laan, Susan Gruber

In this technical note we present a targeted maximum likelihood estimator (TMLE) for a previously studied target parameter that aims to transport an average treatment effect (ATE) on a clinical outcome in a source population to what the ATE would have been in another target population. It is assumed that one only observes baseline covariates in the target population, while we assume that one can learn the conditional treatment effect on the outcome of interest in the source population. We also allow that one might observe only a subset of the covariates in the target population while all covariates are measured in the source population. We consider the case that the outcome is a clinical outcome at some future time point that is subject to missingness, or that our outcome of interest is a time to event that is subject to right-censoring. We derive the canonical gradients and present the corresponding TMLEs for these two cases.

Leyi Ouyang

Diplomatic events consistently prompt widespread public discussion and debate. Public sentiment plays a critical role in diplomacy, as a good sentiment provides vital support for policy implementation, helps resolve international issues, and shapes a nation's international image. Traditional methods for gauging public sentiment, such as large-scale surveys or manual content analysis of media, are typically time-consuming, labor-intensive, and lack the capacity for forward-looking analysis. We propose a novel framework that identifies specific modifications for diplomatic event narratives to shift public sentiment from negative to neutral or positive. First, we train a language model to predict public reaction towards diplomatic events. To this end, we construct a dataset comprising descriptions of diplomatic events and their associated public discussions. Second, guided by communication theories and in collaboration with domain experts, we predetermined several textual features for modification, ensuring that any alterations changed the event's narrative framing while preserving its core facts.We develop a counterfactual generation algorithm that employs a large language model to systematically produce modified versions of an original text. The results show that this framework successfully shifted public sentiment to a more favorable state with a 70\% success rate. This framework can therefore serve as a practical tool for diplomats, policymakers, and communication specialists, offering data-driven insights on how to frame diplomatic initiatives or report on events to foster a more desirable public sentiment.

Zoë R Greenwald, Jordan J Feld, Dan Werb et al.

Objective Health administrative data can support population-level research among people who inject drugs (PWID), however these data sources remain underutilized due to the difficulty in identifying drug use in routinely collected data. We validated case-ascertainment algorithms to identify PWID and tested their application in a hepatitis C (HCV) cohort in Ontario, Canada.  Approach We conducted a validation study using reference standard cohorts of PWID recruited via community-based studies and population controls linked to health administrative data in Ontario, Canada (1992-2020). Tested case-ascertainment algorithms included combinations of hospitalizations/emergency department (ED) visits for drug use/poisoning, physician visits for drug use, opioid agonist treatment (OAT), or injecting-related infections. Sensitivity and specificity were estimated for lifetime history and recent injecting (past 1-5 years). We applied a high-performing algorithm among all Ontarians with laboratory-confirmed HCV between 1999-2018, to identify a sub-cohort of PWID with HCV.  Results An algorithm including ≥1 hospitalization/ED visit or ≥1 physician visit for drug use or ≥1 OAT record had high accuracy for identifying IDU history (91.6% sensitivity, 94.2% specificity) and recent IDU (using 3 years lookback: 80.4% sensitivity, 99% specificity). When applied to a provincial cohort of 112,947 Ontarians diagnosed with HCV, this algorithm estimated 46% (N=52,248) had a history of IDU, of whom 52% (N=27,246) had an indication of recent IDU (within the past 3 years).   Conclusion The methods developed in this study can enhance the capacity of population-level health research among people who inject drugs and support applied public health interventions towards hepatitis C elimination.

Jasper Hoeksema, Anastasiia Hraivoronska, Oliver Tse

This chapter focuses on the derivation of a doubly nonlocal Fisher-KPP model, which is a macroscopic nonlocal evolution equation describing population dynamics in the large population limit. The derivation starts from a microscopic individual-based model described as a stochastic process on the space of atomic measures with jump rates that satisfy detailed balance w.r.t. to a reference measure. We make use of the so-called `cosh' generalized gradient structure for the law of the process to pass to the large population limit using evolutionary Gamma-convergence. In addition to characterizing the large population limit as the solution of the nonlocal Fisher-KPP model, our variational approach further provides a generalized gradient flow structure for the limit equation as well as an entropic propagation of chaos result.

Tingsong Xiao, Zelin Xu, Wenchong He et al.

Adverse clinical events related to unsafe care are among the top ten causes of death in the U.S. Accurate modeling and prediction of clinical events from electronic health records (EHRs) play a crucial role in patient safety enhancement. An example is modeling de facto care pathways that characterize common step-by-step plans for treatment or care. However, clinical event data pose several unique challenges, including the irregularity of time intervals between consecutive events, the existence of cycles, periodicity, multi-scale event interactions, and the high computational costs associated with long event sequences. Existing neural temporal point processes (TPPs) methods do not effectively capture the multi-scale nature of event interactions, which is common in many real-world clinical applications. To address these issues, we propose the cross-temporal-scale transformer (XTSFormer), specifically designed for irregularly timed event data. Our model consists of two vital components: a novel Feature-based Cycle-aware Time Positional Encoding (FCPE) that adeptly captures the cyclical nature of time, and a hierarchical multi-scale temporal attention mechanism, where different temporal scales are determined by a bottom-up clustering approach. Extensive experiments on several real-world EHR datasets show that our XTSFormer outperforms multiple baseline methods. The code is available at https://github.com/spatialdatasciencegroup/XTSFormer.

Justin Finkel, Paul A. O'Gorman

A leading goal for climate science and weather risk management is to accurately model both the physics and statistics of extreme events. These two goals are fundamentally at odds: the higher a computational model's resolution, the more expensive are the ensembles needed to capture accurate statistics in the tail of the distribution. Here, we focus on events that are localized in space and time, such as heavy precipitation events, which can start suddenly and decay rapidly. We advance a method for sampling such events more efficiently than straightforward climate model simulation. Our method combines elements of two recent approaches: adaptive multilevel splitting (AMS), a rare event algorithm that generates rigorous statistics at reduced cost, but that does not work well for sudden, transient extreme events; and "ensemble boosting" which generates physically plausible storylines of these events but not their statistics. We modify AMS by splitting trajectories well in advance of the event's onset following the approach of ensemble boosting, and this is shown to be critical for amplifying and diversifying simulated events in tests with the Lorenz-96 model. Early splitting requires a rejection step that reduces efficiency, but nevertheless we demonstrate improved sampling of extreme local events by a factor of order 10 relative to direct sampling in Lorenz-96. Our work makes progress on the challenge posed by fast dynamical timescales for rare event sampling, and it draws connections with existing methods in reliability engineering which, we believe, can be further exploited for weather risk assessment.

Adalton dos Anjos Fonseca, Denise Moraes Pimenta, Mariana Rodrigues Sebastião de Almeida et al.

Introduction Health inequalities in Brazil have deepened on Covid-19 pandemic, and the most vulnerable people were the more affected. A multidisciplinary team from Cidacs/Fiocruz Bahia developed a Social Disparities Index for Covid-19 (IDS-COVID-19) to support the evaluation of effects of health inequalities on the pandemic in Brazil. Public Involvement and Engagement were the pillars of this research because they allowed us to access first hand experiences about the social context in our country. Objectives This paper aims to describe our Public Involvement and Engagement experience by analysing our challenges, strategies, activities, results, and lessons learned during the construction of IDS-COVID-19. Methods The basis of the IDS-Covid-19 public engagement model was the participation of different social groups through methods and techniques that allow dialogue. Several activities and communication products supported the continuous interactions. Another guideline was the inclusion and the welcoming of participants from the beginning of the project to ensure that the participant's contributions could drive decision-making about the research. Results Participants made several contributions to the research as a new layer of information to the Index, and improvements were made to the interactive panel. They also compromised to support the dissemination and use of the product. Eight representatives of community groups and 29 policymakers participated in our engagement activities during the project. More than 500 people were in our open webinars. In addition, more than 140 news items about IDS-Covid-19 were published in national and international media. Conclusions We highlight as lessons learned the adaptation of some dissemination formats to the public, and the necessity of being flexible and accessible to participants. We strengthened the relationship with relevant stakeholders by exploring individual conversations by phone, WhatsApp, email, and interviews to produce a documentary that registered this whole experience. Cidacs/Fiocruz Bahia has also embedded public engagement and involvement in the study agenda.

Sergi Trias-Llimós, Magali Barbieri, Viviana Egidi et al.

<b>Background</b>: The increasing prevalence of frailty in ageing populations represents a major social and public health challenge which warrants a better understanding of the contribution of frailty to the morbid process. <b>Objective</b>: To examine frailty-related mortality as reported on death certificates in France, Italy, Spain, and the United States in 2017. <b>Methods</b>: We identify frailty at death for the population aged 50 years and over in France, Italy, Spain, and the United States. We estimate the proportions of deaths by sex, age group, and country using specific frailty-related ICD-codes on the death certificate, (1) as the underlying cause of death (UC), (2) elsewhere in Part I (sequence of diseases or conditions or events leading directly to death), and (3) anywhere in Part II (conditions that do not belong in Part I but whose presence contributed to death). <b>Results</b>: The age-standardized proportion of deaths with frailty at ages 50 and over is highest in Italy (25.0Š) followed by France (24.1Š) and Spain (17.3Š), and lowest in the United States (14.0Š). Cross-country differences are smaller when frailty-related codes are either the underlying cause of the death or reported in Part II. Frailty-related mortality increases with age and is higher among females than males. Dementia is the most frequently reported frailty-related code. <b>Conclusions</b>: Notable cross-country differences were found in the prevalence and type of frailty-related symptoms at death, even after adjusting for differential age distributions. <b>Contribution</b>: Strong similarities between countries were found that warrant monitoring frailty at death in low-mortality countries to complement information on frailty prevalence in the living population.

Milana Ljubičić

Adam Gańczorz, Leszek Gąsieniec, Tomasz Jurdziński et al.

The model of population protocols provides a universal platform to study distributed processes driven by pairwise interactions of anonymous agents. While population protocols present an elegant and robust model for randomized distributed computation, their efficiency wanes when tackling issues that require more focused communication or the execution of multiple processes. To address this issue, we propose a new, selective variant of population protocols by introducing a partition of the state space and the corresponding conditional selection of responders. We demonstrate on several examples that the new model offers a natural environment, complete with tools and a high-level description, to facilitate more efficient solutions. In particular, we provide fixed-state stable and efficient solutions to two central problems: leader election and majority computation, both with confirmation. This constitutes a separation result, as achieving stable and efficient majority computation requires $Ω(\log n)$ states in standard population protocols, even when the leader is already determined. Additionally, we explore the computation of the median using the comparison model, where the operational state space of agents is fixed, and the transition function determines the order between (arbitrarily large) hidden keys associated with interacting agents. Our findings reveal that the computation of the median of $n$ numbers requires $Ω(n)$ time. Moreover, we demonstrate that the problem can be solved in $O(n\log n)$ time, both in expectation and with high probability, in standard population protocols. In contrast, we establish that a feasible solution in selective population protocols can be achieved in $O(\log^4 n)$ time.

Yinshan Zhao, Atul Sivaswamy, May K. Lee et al.

Objectives This feasibility study was conducted to inform the design and power evaluation of CODE-MI, a pan-Canadian trial evaluating the impact of using the female-specific 99th-percentile threshold for high-sensitivity cardiac troponin (hs-cTn) on the diagnosis, treatment and outcomes of women presenting to the emergency department with symptoms suggestive for myocardial ischemia. Approach CODE-MI is a multi-center, stepped-wedge cluster randomized trial. The cohort and outcomes will be obtained from routinely collected administrative data. Using linked administrative data from 11 hospitals in Ontario from 2014/10 to 2017/09, this feasibility study obtained the following estimates: number of eligible patients, i.e., women presenting to the emergency department with symptoms suggestive of myocardial ischemia and a 24-hour peak hs-cTn value within the female-specific and overall thresholds (i.e. primary cohort); the rate of the 1-year composite outcome of all-cause mortality, re-admission for non-fatal myocardial infarction, incident heart failure, or emergent/urgent coronary revascularization. Study power was evaluated via simulations. Results Overall, 2,073,849 emergency department visits were assessed. Among women, chest pain (with or without cardiac features) and shortness of breath were the most common complaints associated with a diagnosis of acute coronary syndrome. An estimated 7.7% of women with these complaints are eligible for inclusion in the primary cohort. The rate of the 1-year outcome in the primary cohort varied significantly across hospitals with a median rate of 12.2% (95%CI: 7.9%-17.7%). With 30 hospitals, randomized at 5-month intervals in 5 steps, approximately 19,600 women are expected to be included in CODE-MI, resulting in >82% power to detect a 20% decrease in the odds of the primary outcome at a 0.05 significance level. Conclusion Routinely collected administrative health data serve as a rich and essential resource for conducting pragmatic trials assessing process change, such as CODE-MI. We demonstrated the strength of using linked administrative health data to guide the design of pragmatic clinical trials and accurately evaluate the study power.

Wei Duan, Christoforos N. Hadjicostis, Zhiwu Li

Inspired by privacy problems where the behavior of a system should not be revealed to an external curious observer, we investigate event concealment and concealability enforcement in discrete event systems modeled as non-deterministic finite automata under partial observation. Given a subset of secret events in a given system, concealability holds if the occurrence of all secret events remains hidden to a curious observer (an eavesdropper). A secret event is said to be (at least under some executions) unconcealable (inferable) if its occurrence can be indirectly determined with certainty after a finite number of observations. When concealability of a system does not hold (i.e., one or more secret events are unconcealable), we analyze how a defender, placed at the interface of the system with the eavesdropper, can be used to enforce concealability. The defender takes as input each observed event of the system and outputs a carefully modified event sequence (seen by the eavesdropper) using event deletion, insertion, or replacement. The defender is said to be C-enforceable if, following the occurrence of the secret events and regardless of subsequent activity generated by the system, it can always deploy a strategy to manipulate observations and conceal the events perpetually. We discuss systematic procedures to detect the presence of unconcealable secret events and verify C-Enforceability using techniques from state estimation and event diagnosis. We also propose a polynomial complexity construction for obtaining one necessary and one sufficient condition for C-Enforceability.

N. Marquez, A. Littman, V. Rossi et al.

Background The increased risk of COVID-19 infection among incarcerated individuals due to environmental hazards is well known and recent studies have highlighted the higher rates of infection and mortality prisoners in the United States face due to COVID-19. However, the impact of COVID-19 on all-cause mortality rates in incarcerated populations has not been studied. Methods Using data reported by the Florida Department of Corrections on prison populations and mortality events we conducted a retrospective cohort study of all individuals incarcerated in Florida state prisons between 2015 and 2020. We calculated excess deaths by estimating age-specific expected deaths from mortality trends in 2015 through 2019 and taking the difference between observed and expected deaths during the pandemic period. We calculated life table measures using standard demographic techniques and assessed significant yearly changes using bootstrapping. Findings The Florida Department of Corrections reported 510 total deaths from March 1, 2020 to December 31, 2020 among the state prison population. This was 42% higher (rate ratio 1.42, 95% CI 1.15 to 1.89) than the expected number of deaths in light of mortality rates for previous years. Reported COVID-19 deaths in a month were positively correlated with estimated excess deaths (80.4%, p <.01). Using age-specific mortality estimates, we found that life expectancy at age 20 declined by 4 years (95% CI 2.06-6.57) between 2019 and 2020 for the Florida prison population. Interpretation The Florida prison population saw a significant increase in all-cause mortality during the COVID-19 pandemic period, leading to a decrease in life expectancy of more than four years. Life years lost by the Florida prison population were likely far greater than those lost by the general United States population, as reported by other studies. This difference in years lost highlights the need for increased interventions to protect vulnerable incarcerated populations during pandemics. Funding Vital Projects Fund, Arnold Ventures, US Centers for Disease Control

A. Hinga, V. Marsh, A. Nyaguara et al.

Verbal autopsy is a pragmatic approach for generating cause-of-death data in contexts without well-functioning civil registration and vital statistics systems. It has primarily been conducted in health and demographic surveillance systems (HDSS) in Africa and Asia. Although significant resources have been invested to develop the technical aspects of verbal autopsy, ethical issues have received little attention. We explored the benefits and burdens of verbal autopsy in HDSS settings and identified potential strategies to respond to the ethical issues identified. This research was based on a case study approach centred on two contrasting HDSS in Kenya and followed the Mapping-Framing-Shaping Framework for empirical bioethics research. Data were collected through individual interviews, focus group discussions, document reviews and non-participant observations. 115 participants were involved, including 86 community members (HDSS residents and community representatives), and 29 research staff (HDSS managers, researchers, census field workers and verbal autopsy interviewers). The use of verbal autopsy data for research and public health was described as the most common potential benefit of verbal autopsy in HDSS. Community members mentioned the potential uses of verbal autopsy data in addressing immediate public health problems for the local population while research staff emphasized the benefits of verbal autopsy to research and the wider public. The most prominent burden associated with the verbal autopsy was emotional distress for verbal autopsy interviewers and respondents. Moral events linked to the interview, such as being unsure of the right thing to do (moral uncertainty) or knowing the right thing to do and being constrained from acting (moral constraint), emerged as key causes of emotional distress for verbal autopsy interviewers. The collection of cause-of-death data through verbal autopsy in HDSS settings presents important ethical and emotional challenges for verbal autopsy interviewers and respondents. These challenges include emotional distress for respondents and moral distress for interviewers. This empirical ethics study provides detailed accounts of the distress caused by verbal autopsy and highlights ethical tensions between potential population benefits and risks to individuals. It includes recommendations for policy and practice to address emotional and moral distress in verbal autopsy.

Richard E. Lucas

Subjective well-being (SWB) is an overall evaluation of the quality of a person’slife from his or her own perspective. One common method of assessing thisconstruct requires respondents to think about their life as a whole and to providea “global” evaluation that summarizes across life domains or affective experiencesover extended periods of time. The validity of these global measures has beenchallenged, however; and experiential measures, which ask respondents to reporton their momentary evaluative experiences many times over a constrained timeperiod, have been suggested as a more valid alternative. This paper addresses theempirical evidence for one important challenge to global measures: the possibilitythat temporarily salient information overwhelmingly influences global judgments,reducing their reliability and validity. This paper critiques prior evidence for thischallenge and presents new concerns about the assumed validity of the proposedalternative: experiential measures.

Junchuan Fan, Kathleen Stewart

With the rapid advancement of information and communication technologies, many researchers have adopted alternative data sources from private data vendors to study human movement dynamics in response to large-scale natural or societal events. Big geosocial data such as georeferenced tweets are publicly available and dynamically evolving as real-world events are happening, making it more likely to capture the real-time sentiments and responses of populations. However, precisely-geolocated geosocial data is scarce and biased toward urban population centers. In this research, we developed a big geosocial data analytical framework for extracting human movement dynamics in response to large-scale events from publicly available georeferenced tweets. The framework includes a two-stage data collection module that collects data in a more targeted fashion in order to mitigate the data scarcity issue of georeferenced tweets; in addition, a variable bandwidth kernel density estimation(VB-KDE) approach was adopted to fuse georeference information at different spatial scales, further augmenting the signals of human movement dynamics contained in georeferenced tweets. To correct for the sampling bias of georeferenced tweets, we adjusted the number of tweets for different spatial units (e.g., county, state) by population. To demonstrate the performance of the proposed analytic framework, we chose an astronomical event that occurred nationwide across the United States, i.e., the 2017 Great American Eclipse, as an example event and studied the human movement dynamics in response to this event. However, this analytic framework can easily be applied to other types of large-scale events such as hurricanes or earthquakes.

K. Kyei, R. S. Ramuya

Relevance of the research: Th e study of fertility and mortality is of great importance because it shows the dynamics of the population and the need for eff ective planning measures required to be put in place to avert catastrophe. It is therefore important for South Africa also to check whether its fertility is heading toward the same trajectory seen in these developed countries. Purpose of the article: This study seeks to determine the level and trend of the fertility in South Africa using four provinces, Mpumalanga, Kwazulu-Natal, North West and Limpopo, for the years 2011, 2012, 2013 and 2014, as case studies. Th us, the study aims to determine: (a) Whether the fertility rate was changing in the provinces during the years 2011, 2012, 2013 and 2014. (b) What is the trend? And; (c) Whether the fertility is falling below replacement level or not. Scientifi c novelty of the article: Th e study shows that fertility in South Africa has indeed declined with some provinces having the level of fertility below replacement level. Th e decline seen in this study is mainly due to the fertility of the black majority population. Th e high population growth rate was mainly due to the fertility of the blacks, therefore, when recent fertility levels in the country are low, the implication is that the fertility of the blacks has gone down, and in fact this is what this study is showing, and advises that unless measures are put in place, the fertility of the blacks or fertility in South Africa will go down below replacement level. Fertility levels have declined worldwide, including South Africa. But the extent to which South Africa’s fertility has declined is not very clear because data from two previous censuses on fertility and mortality were very poor. South Africa (SA) had reliable data on the White popula-tion of the country, but with the new dispensation since 1994, data collection, especially from the black population that forms majority, has not been easy. Th e quality of data from the black population that accounts for over 80 percent of the population has not been good and reliable, probably because of the low level of education of this segment. Fertility and mortality data from the two previous censuses, 2001 and 2011, were not reliable when compared with data from vital statistics and national population register. Th us, estimates made from these censuses’ data on these events are questionable. South Africa, however, has data from the General Household Surveys (GHS), and this study uses data from these surveys to attempt to fi nd the level and trend of fertility. Th e GHS is an annual household survey which measures the living circumstances of South African households. Demographic and statistical methods are used to calculate measures, like the UN age ratio scores, to assess the quality of the data from four provinces, namely, North West, Kwazulu-Natal, Mpumalanga and Limpopo provinces, for the period 2011 to 2014. Th e four provinces are selected as a case study to compare rural and urban fertility characters. Th e results show that the quality of the survey data is fairly good and reliable with KwazuluNatal province having the most reliable data. Th e fertility levels were lowest in the more urbanized and educated provinces of Kwazulu-Natal and North West, and highest in the less educated and rural provinces of Limpopo and Mpumalanga. Th e study further shows that fertility in all the provinces has been declining since 2012, with the gross reproduction rate falling below replacement level; with implication that the fertility of the black population is declining fast. Th e decline in the fertility level could be attributed to quite a number of factors including education, urbanization, and improvement in primary health care. Because of the sharp decline in the fertility level in the recent years, the study calls for proper policy intervention to avoid population “extinction”.