Differences In Age Structure Of Populations Research Articles

Elucidating Intimate Partner Violence Rate Disparities Between Same- and Opposite-Sex Couples: A Demographic Approach.

Studies on intimate partner violence (IPV) rates typically find higher rates for same-sex couples than opposite-sex couples. Regardless of sexual orientation, the risk for IPV perpetration is concentrated among young adults. Given that the HIV/AIDS epidemic significantly lowered the life expectancy of sexual minority men and that recent social movements have encouraged more youths to "come out," population age differences may contribute to the observed differences in IPV rates between same- and opposite-sex couples. The present study applies direct age standardization and decomposition techniques to data from the National Incident-Based Reporting System and the American Community Survey to examine this possibility across 2,584 same-sex male, 4,029 same-sex female, and 284,614 opposite-sex physical IPV incidents. The results of the direct standardization procedures indicate physical IPV rates for same-sex male and same-sex female households would be 11.8% and 27.3% lower, respectively, if they had the same population age structure as opposite-sex households. The results of the decompositions indicate that differences in population age structure contribute 48% and 44% of the elevation in IPV rates in same-sex male and same-sex female households, respectively, compared to opposite-sex households. These findings demonstrate the promise of demographic methods to explain differences in offending rates between groups. These results suggest the need for prevention and intervention strategies aimed at youths. Future studies should be conducted with data on sexual orientation and gender identity to better elucidate the contribution of population age structure among various sexual and gender minority groups. Research comparing trends among sexual minority populations to the heterosexual population in particular should account for age where possible.

Age at diagnosis for lung, colon, breast and prostate cancers: An international comparative study.

Differences in the average age at cancer diagnosis are observed across countries. We therefore aimed to assess international variation in the median age at diagnosis of common cancers worldwide, after adjusting for differences in population age structure. We used IARC's Cancer Incidence in Five Continents (CI5) Volume XI database, comprising cancer diagnoses between 2008 and 2012 from population-based cancer registries in 65 countries. We calculated crude median ages at diagnosis for lung, colon, breast and prostate cancers in each country, then adjusted for population age differences using indirect standardization. We showed that median ages at diagnosis changed by up to 10 years after standardization, typically increasing in low- and middle-income countries (LMICs) and decreasing in high-income countries (HICs), given relatively younger and older populations, respectively. After standardization, the range of ages at diagnosis was 12 years for lung cancer (median age 61-Bulgaria vs 73-Bahrain), 12 years for colon cancer (60-the Islamic Republic of Iran vs 72-Peru), 10 years for female breast cancer (49-Algeria, the Islamic Republic of Iran, Republic of Korea vs 59-USA and others) and 10 years for prostate cancer (65-USA, Lithuania vs 75-Philippines). Compared to HICs, populations in LMICs were diagnosed with colon cancer at younger ages but with prostate cancer at older ages (both pLMICS-vs-HICs < 0.001). In countries with higher smoking prevalence, lung cancers were diagnosed at younger ages in both women and men (both pcorr < 0.001). Female breast cancer tended to be diagnosed at younger ages in East Asia, the Middle East and Africa. Our findings suggest that the differences in median ages at cancer diagnosis worldwide likely reflect population-level variation in risk factors and cancer control measures, including screening.

Risk factors associated with COVID-19 morbidity and mortality at a national tertiary referral treatment centre in Ghana: a retrospective analysis

Introduction: there is a substantial variation in COVID-19 case fatality rates across different locations, which may be due to differences in population age structure, patient factors, or health system factors. The study evaluated the clinical features and risk of COVID-19 morbidity and mortality among confirmed cases at COVID-19 referral treatment centre.

COVID-19 and all-cause mortality in South Africa – the hidden deaths in the first four waves

Accurate statistics are essential for policy guidance and decisions. However, the reported number of cases and COVID-19 deaths are known to be biased due to under-ascertainment of SARS-CoV-2 and incomplete reporting of deaths. Making use of death data from the National Population Register has made it possible to track in near-real time the number of excess deaths experienced in South Africa. These data reveal considerable provincial differences in the impact of COVID-19, likely associated with differences in population age structure and density, patterns of social mixing, and differences in the prevalence of known comorbidities such as diabetes, hypertension, and obesity. As the waves unfolded, levels of natural immunity together with vaccination began to reduce levels of mortality. Mortality rates during the second (Beta) wave were much higher than mortality in the third (Delta) wave, which were higher than in either the first or the fourth (Omicron) waves. However, the cumulative death toll during the second (Beta) wave was of a similar order of magnitude as that during the third (Delta) wave due to the longer duration of the Delta wave. Near-real time monitoring of all-cause deaths should be refined to provide more granular level information to enable district-level policy support. In the meanwhile, there is an urgent need to re-engineer the civil registration and vital statistics system to enable more timely access to cause of death information for public health actions. Significance: This study highlights that in South Africa there were about three times the number of excess deaths from natural causes during 2020 and 2021 than reported COVID-19 deaths. Although the cause of death remains unknown, the strong temporal correlation between excess deaths and reported COVID-19 deaths within each province indicates that the majority of excess deaths were associated with COVID-19. Many countries have found it difficult to estimate excess deaths, or to identify and report COVID-19 deaths accurately, demonstrating the value of near-real time monitoring of mortality through the use and demographic analysis of data obtained from the country’s National Population Register. Open data set: https://www.samrc.ac.za/sites/default/files/files/2022-02-09/Estimated%20deaths%20for%20SA%2007%20Feb%202022%20with%20adj2.xlsx

1363Just a flu? Comparing COVID-19 and influenza mortality

Abstract Background Benchmarks are needed for assessing the severity of the COVID-19 pandemic. However, comparisons can be misleading unless marked differences in age-specific mortality and differences in population age structure are considered. Methods Using COVID-19 death rates for New York City as at 2 June 2020, we used indirect age standardization to estimate standardized mortality ratios (SMR) for the first winter waves of the 1918 and 2009 influenza pandemics and the severe 2017-2018 influenza season in the United States (US). Data were obtained from published statistics. Results After adjusting for age, New York City’s death rate during the 1918 winter influenza pandemic wave was 6.7 times higher overall compared with the first wave of COVID-19 in 2020. New York City's first wave COVID-19 death rate was an estimated 59 times higher than that of the 2009 US influenza pandemic, and 14 times higher than that of the severe 2017-2018 influenza season. In &amp;lt; 45 year-olds, the 1918 influenza death rate was 42 times higher than COVID-19 in 2020. In ≥ 65 year-olds, compared with the 2009 pandemic, the COVID-19 death rate was 320 times higher, while in children it was one half. Conclusions The 1918 pandemic was more deadly than COVID-19, which was, in turn, far more deadly than both the 2009 influenza pandemic and severe seasonal influenza. Age-specific mortality differences should be considered in decisions on COVID-19 vaccination strategies. Key messages Fundamental epidemiological methods remain valuable for modern epidemic risk assessment. COVID-19 is not just a ‘flu’.

Infection fatality rate of COVID-19 inferred from seroprevalence data.

ObjectiveTo estimate the infection fatality rate of coronavirus disease 2019 (COVID-19) from seroprevalence data.MethodsI searched PubMed and preprint servers for COVID-19 seroprevalence studies with a sample size ≥ 500 as of 9 September 2020. I also retrieved additional results of national studies from preliminary press releases and reports. I assessed the studies for design features and seroprevalence estimates. I estimated the infection fatality rate for each study by dividing the cumulative number of COVID-19 deaths by the number of people estimated to be infected in each region. I corrected for the number of immunoglobin (Ig) types tested (IgG, IgM, IgA).FindingsI included 61 studies (74 estimates) and eight preliminary national estimates. Seroprevalence estimates ranged from 0.02% to 53.40%. Infection fatality rates ranged from 0.00% to 1.63%, corrected values from 0.00% to 1.54%. Across 51 locations, the median COVID-19 infection fatality rate was 0.27% (corrected 0.23%): the rate was 0.09% in locations with COVID-19 population mortality rates less than the global average (< 118 deaths/million), 0.20% in locations with 118–500 COVID-19 deaths/million people and 0.57% in locations with > 500 COVID-19 deaths/million people. In people younger than 70 years, infection fatality rates ranged from 0.00% to 0.31% with crude and corrected medians of 0.05%.ConclusionThe infection fatality rate of COVID-19 can vary substantially across different locations and this may reflect differences in population age structure and case-mix of infected and deceased patients and other factors. The inferred infection fatality rates tended to be much lower than estimates made earlier in the pandemic.

COVID-19 in unequally ageing European regions.

The map presented in this brief note summarizes regional differences in population age structures between the NUTS-3 regions of Europe in the context of unequal age- and sex-specific death risks associated with the spread of the COVID-19 pandemic. Since older people are exposed to much higher death risks, older populations are expected to face much more difficult challenges coping with the pandemic. The urban/rural dimension turns out to be very important as the remote rural areas are also the oldest. In the map NUTS-3 regions of Europe are colored according to the deviation from European pooled estimate of the proportion of population at risk of death due to COVID-19. We assume that 5/6 of the populations get infected and experience age-specific infection-fatality ratios (IFRs) modelled by the Imperial College COVID-19 Response Team. We adjust IFRs by sex ratios of age-specific case-fatality ratios observed in the European countries that are included in the COVerAGE-DB. Thus, we effectively introduce a summary measure of population age structures focused on the most vulnerable to the pandemic. Such an estimate for the total European population is 1%. The map reflects the unequal population age structures rather than the precise figures on COVID-19 fatality. It is a case-if scenario that highlights the possible effect of the population age structures, a demographic perspective. This analysis clearly shows the contribution of regional differences in population age structures to the magnitude of the pandemic - other things equal, we expect to see a four-fold variation in average regional infection-fatality ratios across Europe due only to differences in the population structures.

Breast cancer age at diagnosis patterns in four Latin American Populations: A comparison with North American countries

BackgroundIn the Latin America countries (LAC), one in five breast cancer (BC) cases occur in women younger than 45 years, almost twice the frequency seen in developed countries. Most BC cases in younger women are premenopausal and are generally more difficult to detect at early stages and to treat than postmenopausal cancers. We employ data from four high quality population-based registries located in LAC and assess the extent to which the higher frequency of BC occurring in younger women is due to a younger population structure, compared to that of developed countries. Next, we analyze secular and generational trends of incidence rates in search for additional explanations. MethodsUsing data from the International Agency for Research on cancer, between 1988 and 2007, the age distribution of BC incident cases for registries located in Brazil, Colombia, Costa Rica, Ecuador is compared to that of USA and Canadian registries, both before and after removing differences in population age structure. An age-period-cohort modelling of incidence rates is also conducted in all compared registries to identify secular and generational effects. ResultsBC incident cases in the LAC registries present, on average, at an earlier age than in the USA and Canadian registries and for 2003–2007, between 20 and 27% of cases occur in women aged 20–44. About two thirds of the difference in age distribution between LAC and USA registries is attributable to the younger age distribution in the LAC base populations. The USA registries show the highest age-specific BC incidence rates of all compared aggregated registries, at all ages. However, in all the LAC registries incidence rates are rapidly increasing, fueled by a strong birth cohort effect. This cohort effect may be explained by important reduction in fertility rates occurring during the second half of the 20th century, but also by a greater exposure to other risk factors for BC related to the adoption of life styles more prevalent in developed countries. ConclusionThe younger age at presentation of BC incident cases seen in the analyzed LAC registries, and possibly in many Latin American countries, is not only attributable to their relatively young population age structure but also to the low incidence rates in older women. As more recently born cohorts, with greater exposure to risk factors for postmenopausal BC, reach older age, incidence rates will be more similar to the rates seen in the USA and Canadian registries. There is a need for additional research to identify determinants of the higher BC rate among younger women in these countries.

RELATIVE EFFECTIVENESS IN BREAST CANCER TREATMENT: A HEALTH PRODUCTION APPROACH.

Pharmaceuticals' relative effectiveness has come to the fore in the policy arena, reflecting the need to understand how relative efficacy (what can work) translates into added benefit in routine clinical use (what does work). European payers and licensing authorities assess value for money and post-launch benefit-risk profiles, and efforts to standardize assessments of relative effectiveness across the European Union (EU) are under way. However, the ways that relative effectiveness differs across EU healthcare settings are poorly understood. To understand which factors influence differences in relative effectiveness, we developed an analytical framework that treats the healthcare system as a health production function. Using evidence on breast cancer from England, Spain, and Sweden as a case study, we investigated the reasons why the relative effectiveness of a new drug might vary across healthcare systems. Evidence was identified from a literature review and national clinical guidance. The review included thirteen international studies and thirty country-specific studies. Cross-country differences in population age structure, deprivation, and educational attainment were consistently associated with variation in outcomes. Screening intensity appeared to drive differences in survival, although the impact on mortality was unclear. The way efficacy translates into relative effectiveness across health systems is likely to be influenced by a range of complex and interrelated factors. These factors could inform government and payer policy decisions on ways to optimize relative effectiveness, and help increase understanding of the potential transferability of data on relative effectiveness from one health system to another.

Variation in Age and Size in Fennoscandian Three-Spined Sticklebacks (Gasterosteus aculeatus)

Average age and maximum life span of breeding adult three-spined sticklebacks (Gasterosteus aculeatus) were determined in eight Fennoscandian localities with the aid of skeletochronology. The average age varied from 1.8 to 3.6 years, and maximum life span from three to six years depending on the locality. On average, fish from marine populations were significantly older than those from freshwater populations, but variation within habitat types was large. We also found significant differences in mean body size among different habitat types and populations, but only the population differences remained significant after accounting for variation due to age effects. These results show that generation length and longevity in three-spined sticklebacks can vary significantly from one locality to another, and that population differences in mean body size cannot be explained as a simple consequence of differences in population age structure. We also describe a nanistic population from northern Finland exhibiting long life span and small body size.

Local population differences and the needs of people with cognitive impairment

Introduction. Variations in local population age structure have attracted less attention than national population ageing. As moderate and severe cognitive impairment is a major cause of need for long-term care, population-based estimates of the numbers and characteristics of this group were calculated, to explore the effects of local differences. Method. The UK Office of Population Census and Surveys (OPCS) study of disability in adults (N > 14000) was reanalysed. A group with moderate or severe cognitive impairments was identified and age-specific estimators of sociodemographic characteristics, household types, disabilities and service use were combined with population estimates for district health authorities in England and Wales. Results. The proportion of the 65 plus population who are 85 plus varies from 8% to 15% across districts, equivalent to national population projections for 1986 and 2031 respectively. The estimated prevalence of the study group varies from 53 to 70 per 1000 population aged 65 plus, with 34–48% of cases aged 85 plus. Curiously, the proportion with severe disabilities varies little across districts. If national norms applied, local rates of institutionalization would vary from 18 to 27 per 1000 aged 65 plus. Conclusion. Local differences in population age structure are large compared to national changes over decades. Local differences have substantial effects on overall prevalence and on the proportion of the cognitively impaired who would be institutionalized if national patterns applied. Service design should be influenced by these complex variations, with estimates modified by local surveys. © 1997 John Wiley & Sons, Ltd.

Effects of the choice of age-adjustment method on maps of death rates.

Maps of morbidity or mortality rates, whether considered individually or as a layer in a geographic information system application, invite multiple comparisons of area rates. However, comparisons of rates across different populations require standardization of the age-specific rates to account for differences in population age structures. The indirect standardization method, or equivalently the standardized mortality ratio (SMR), has been recommended for small areas where age-specific rates can be quite variable. Although theoretically equivalent to directly adjusted rates under the assumption of independent age and area effects, indirect summary measures are not comparable across areas when this assumption is violated. We tested the validity of this assumption for the 10 most common causes of death in the United States during 1980-84 and examined the geographic clustering apparent when categorized death rates, adjusted by different methods, are presented as thematic maps. Although overall agreement between the methods was good (rank correlation coefficient > 82 per cent for each cause), when the adjusted rates were classified into quintiles 18 per cent of the states fell into different categories depending on the method of adjustment. Using an internal standard for the indirect method reduced this discrepancy to 4.9 per cent. However, both traditional chi-square tests and a generalized logistic spline model identified significant interactions between age and area for each cause of death, a violation of the assumption required for equivalence of the methods. Potential variation in geographic inferences is illustrated by maps of direct and indirect rates and an empirical Bayes posterior mean, which is a function of these traditionally adjusted rates. Based on these results, we recommend the direct age-adjustment method for rate maps.