A second interesting aspect of the Northern Ireland experience has been the effect of the net migration assumptions. From 1977 onwards, the projections have assumed a higher level of out-migration – and hence population losses - than actually occurred. At least through to 2004, the effect on the disparity between the relevant population projections and the 2006 out-turn has not varied greatly, ranging from an under-estimate of 4-7,000 per annum. Indeed, apart from 1971, this has been the main source of ‗error‘ when the population projections are compared to the 2006 out-turn. This might suggest that, based on past experience, migration assumptions for Northern Ireland should be less ‗pessimistic‘ on future occasions. However, as noted earlier, net migration is subject to an array of influences and can vary sharply from one year to the next. In that context, the (recent) past may not necessarily be a reliable guide to the future. Thus, the lesson to be drawn is perhaps that greater attention needs to be paid to the migration component. This conclusion is reinforced by considering the recent experience with population projections in the Republic of Ireland.
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The literature highlights six cost drivers of drug ex- penditure; population growth, population aging, general inflation, price effects, volume effects and mix of drugs . Whilst Irish drug pricing is substantially higher than our UK counterparts , the ESRI identifies population growth and population aging as the key drivers of future drug costs . People in developed economies are living longer, with life expectancy at their highest level and population projections predicting significant increases in the total number of older people. The proportion of people who are very old is growing fastest and this num- ber is expected to almost double by 2030. According to the World Health Organisation, the present and future generations of older people can also expect to live for considerably longer than their predecessors.
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From the DHSSPSNI statistics there would seem to be a period of expansion in the provision of care packages in the period 1995 to 2003, before stabilising at a fairly constant rate at around 40 packages per 1,000 older population. The rising number of the oldest old population as a proportion of the older population, who could be expected to require more care, is not reflected in a rising number of residential/nursing home care packages since 2002. This could be the result of improving general health of the older population, or the provision of care by other household members. Added to this DHSSPSNI has a policy of promoting independent living especially in the older population. It is also important to look at the future numbers of older people and their potential living arrangements and this can be done by examining the household projections for the older population. The 2008-based household projections are created by applying trends in household formation to the population projections . In the first step, it removes the population in communal establishments by applying the 2001 Census age-sex specific proportion of persons in communal establishments. This assumption is supported by the DHSSPSNI figures on the provision of residential and nursing home care packages.
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The caution of forecasters in projecting mortality improvements can be illustrated for Ireland by assumptions made in population projections in papers read to this Society. True, future population numbers are considerably less sensitive to mortality than migration or fertility assumptions, but, nevertheless, it is as easy to adopt best estimate assumptions in this regard as any other. Each forecaster used a low or even zero rate of improvement and tended to be influenced by relatively very short-term trends. Geary (1935), in forecasting the population of Ireland out to the year 2016 (an 81 year forecast horizon), assumed no change from the mortality rates of Irish Life Table 1, but acknowledged that ―this is an assumption which is fortunately not likely to be realised‖ (p. 28) and observed ―there is little doubt that a figure of 70 [for life expectancy at birth] may be achieved during the next half century‖ (p. 29). Geary (1941) revisited that analysis in the light of the 1936 Census, updating the base mortality assumed to Irish Life Table 2, and in two of the three new projections assumed no mortality improvement. In the third, he projected mortality improvements over the next 30 years, thereafter no improvements. The mortality improvements for both sexes were only made for ages up to 40 years for males and 66 years for females, with improvements assumed to be in line with (the higher) mortality improvements experienced by females over the decade to 1936. Knaggs & Keane (1971), in their population projections over 25 years to the year 1996, assumed that male mortality will improve marginally up to age 15 years, following the trend evident over the period 1961-66, with no improvements at higher ages. For females, mortality rates were assumed to decline in line with the age-specific rate of decline observed over the period 1961-66 up to age 80 years, with no improvements at later ages. Keating (1977), in making population projections out to 1986, assumed no improvements in mortality from the 1971 rates, on the basis that recent short-term trends (5 years or so) both in Ireland and internationally showed little change.
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Having commented on the important fall in fertility documented in the paper, one must also call attention to the exceptional position of Ireland in regard to fertility. In this regard I feel that tables such as Tables 6 and 10 and Appendix 1 are somewhat misleading, since they compare Ireland in 1976 with Europe in 1971. The rest of the world has not stood still as Irish fertility declined over the years 1971-76. In round terms, there has been a 25 per cent reduction in fertility in Europe since 1971, significantly higher than that which occurred in Ireland. Thus the Irish level of fertility is far more exceptional than would be understood from a study of Table 6 or Appendix 1. In order to set the record straight on this point. I include with this note a table setting out the fertility rate, the birth and death rates in most European countries in 1975. In Ireland the total fertility rate is 3.5. The next highest is Spain at 2.7. Apart from Italy at 2.1, this rate is below 2 in all othei EEC countries, Scandanavia, Austria, and Switzerland. Turning to birth and death rates, which are sensitive to population age structures, we see that whereas Ireland now has an excess of births over deaths equal to over 1 per cent, both East and West Germany, Austria, Belgium, and Luxembourg now record more deaths than births. This will soon be the case in Britain, Sweden, and before too long, in France and even Italy. I feel, therefore, that the fertility rates used in tonight's paper are perhaps likely to be proved too conservative by events. This was the case with the rates used in the NESC paper. It seems improbable that the total fertility rate that will prevail in Ireland in 1986, as shown in Table 12, which implies a Net Reproduction Rate of about 1.4, should be so much higher than that found almost anywhere in Europe today, where the NRR is already below unity.
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T2DM incidence from cohort studies in the Pacific Islands is limited to Samoa  and American Samoa . Incidence of T2DM-related morbidity was measured in Nauru and showed a decline from 26.2 per 1000 person- years over 1975/76–1982 to 22.5 per 1000 person-years over 1982–87 . In Samoa, the 4-year T2DM incidence over 1991–1995 was reported as 1% (29–43 years) and 4% (44–60 years) in each sex, equivalent to annual incidences of 2.5 per 1000 person-years (29–43 years) and 10 per 1000 person-years (44–60 years) based on the mean for the four- year period. When age-adjusted to the 1991 Samoa popula- tion census , this is equivalent to an annual T2DM incidence of 5.3 per 1,000 person-years for each sex. In American Samoa for the same age group, the average an- nual T2DM incidence (age-adjusted to 1990 American Samoa census)  was 28.7 per 1000 person-years (men) and 21.7 per 1000 person-years (women) over 1990–1994. In Fiji no prospective cohort study of T2DM has been con- ducted, but prevalence surveys have been performed over the last three decades. Analysis of T2DM prevalence trends based on six population surveys in Fiji indicates an increase from 7.7 to 15.6% between 1980 and 2011, with projection to 2020 estimated to be 19.3% . Fiji’s population, ap- proximately 828,000 at the most recent 2007 census, com- prises 57% Melanesian (i-Taukei) and 38% Indian. The remaining 5% is heterogeneous and is composed of Asian, European, and other Pacific island populations .
Development Schemes, such as Mahaveli Ganga, Gin Ganga, Nilwala Ganga, will definitely promote new urban centers. "Another area which is generative of much development and urban growth is the area around Trincomalee where a free trade zone has been mooted" (De Alwis, 1973). However, the uncertainties of administrative decisions hinder the preparation of meaningful estimates of urbanization in the future. Therefore, a rough estimate was prepared assuming that the creation of new urban areas during the projection period will be the same as the proportionate increase of the new towns and the population therein during
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Mathare was selected purposively due to the fact that being a slum area; poverty rates are very high. In addition the slum settlement accommodates a large unemployed population, and more so the youth. Sampling was through multistage cluster sampling due to the fact that there is no available sampling frame for urban unemployed youth. At the first stage of sampling, the three administrative areas of Mathare: Mathare 4A, Mathare North, and Mathare Area 4 formed the clusters. Two clusters (Mathare 4A, Mathare North) were considered for this study since poverty levels were very high. At the second stage of sampling, a sample of four zones (from the six and three zones of Mathare 4A and Mathare North respectively) was randomly selected from each cluster. The last step of this procedure involved selecting the ultimate units- the respondents who were then surveyed. For each zones, 12 youth were interviewed, thus N = 48.
time between diagnosis and death or censoring, as well as a categorical variable of deprivation status at diagnosis based on postcode areas split into five categories. Follow- up information was available until 31st of December 2016, and survival times were censored at 12 years post diag- nosis. There were in total 303,792 individuals included in the analyses. We used 8 different models or approaches to estimate the LEL for this group of patients, as listed below, to illustrate various modelling choices. If not otherwise specified all estimations were based on flexible parametric models for excess mortality, with 5 degrees of freedom for the baseline cumulative hazard and 3 degrees of freedom for time-varying effects. The effect of age and calen- dar year were modelled continuously using splines with 4 degrees of freedom and interactions between age and year were included using splines with 2 degrees of free- dom. All effects were allowed to be time-varying, i.e. we relaxed the proportional excess hazard assumption, which is important since the proportional hazards assumption is often violated in population based cancer studies. For the expected mortality and survival we used a population lifetable specific to England stratified by age, sex and cal- endar year. Extrapolations of excess mortality was based on the model parameters, and future expected mortality rates were assumed the same as in 2013 (the last year of available data in the population lifetable).
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The population projection for the high (HPG) and low (LPG) growth scenario and for the Nairobi Centric as well as the Multi-Centric distribution is shown in Table 10. By 2030, the population in NMR is likely to range between 11.1 and 15.8 million which by 2050 is expected to increase to 15.4 to 34.1 million. Without development controls in Nairobi, it is likely that upto 60% of NMR population could end up living in Nairobi City (NC). On the other hand, with strategic development controls in Nairobi, nearly 55% of the future population could be accommodated in the Other Metropolitan Region (OMR).
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The changes in the ensemble mean SWH (three members for each RCP scenario) for the future relative to the histori- cal period are presented in Fig. 4. The ensemble mean an- nual SWH in Fig. 4b and c shows a reduction of 5–10 % off the Atlantic coastline under the RCP4.5 and RCP8.5 sce- narios, with very little variation between the individual en- semble members (not shown). For DJF, as can be seen in Fig. 4e and f, the mean SWH decreases by over 5 % under RCP4.5 and by up to 10 % for RCP8.5 off the west coast. In JJA, there are mean decreases of up to 15 % off the south coast under RCP8.5 (Fig. 4l). The reduction in mean SWH for RCP4.5 in JJA is lower (Fig. 4k), at just over 5 % off the south coast. Nevertheless, this reduction is still significant us- ing the inter-model standard deviation test. The projections in MAM and SON show smaller, less robust decreases of be- tween 1–4 % in SWH under both scenarios (Fig. 4h–i and 4n–o, respectively). Off the west coast, the decrease in SWH under the RCP4.5 scenario for these seasons was not signifi- cant. These projected changes in SWH may consequently re- duce the amount of wave power available for exploitation for any future deployments of WECs. However, as was shown in Gallagher et al. (2016a), Ireland possesses a large and ener- getic wave energy resource, particularly off the west coast, and this reduction is expected to have a minimal impact on
For immigrants simulation starts at the date of immigration, and for newborns it starts at the date of birth. MicCore offers the possibility that transition rates for immigrants differ from those applied to native members of the population. Simulation steps are discrete, from one event to the next. Time-to-event is measured in continuous time. The same approach is used in the LifePaths model. The approach is known as discrete-event simulation (DES or DEVS), which is the common method in the field of system theory (Zinn 2010; Zinn et al. 2010). The DEVS method is consistent with the theory of competing risks. To determine the transition that occurs and the waiting time to that transition, latent waiting times are computed for all possible transitions and the transition with the lowest waiting time is selected. Ages at transition are stored in milliseconds (a feature of the Java-based modeling and simulation framework JAMES II used in MicMac, see below). Calendar dates of transitions are derived by combining age and simulated dates of birth. If MicCore is used for projection, the states individuals occupy at given calendar dates are obtained from the individual life histories. Aggregation of individual data at a given calendar date gives population size and composition at that date. The microsimulation results are very similar to the cohort- component projections if the number of simulated life histories is sufficiently large (Zinn 2010; Willekens 2011b).
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This paper uses demographic techniques to estimate denominators for all the 199 countries. The age wise proportions have been computed from the population estimates of the world population prospects, 2017 revision. 2-5 In this paper, we have tried to estimate the denominators for all these groups for the purpose of monitoring, which may be used at sub-national levels, when no population proportions are available, with some assumptions. All the computations have been carried out in Microsoft Excel 2013. The methodology of estimation of population proportions of different groups is mentioned in the following table.
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The sex ratio for the total population was 102 at both censuses. An acceptable pattern is tnat sex ratios, except at advanced ages, should vary around the sex ratio for the total population. Generally, sex ratios are over hundred at the younger age groups due to excess of males at Dirtn and decline slowly as age advances. The decline is due to differential mortality in favour of females. From tne results in TaDle I (Appendix A), sex ratios vary between 80 at age 25-29 to the highest value of 132 for age group 65-69. The values at the age groups 5-9, 10-14 and 35-39 through 65-69 are unacceptable a priori, because theoretically, any age group should be less than the age group preceding it due to losses effected by mortality. Erratic oehaviour of sex ratios is sometimes a characteristic of a population affected by sex differential
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Although our inability to consider the health and social care systems separately has its disadvantages, there are also advantages. As our analysis below involves projections over a period of almost thirty years, there is obviously a huge margin of error attaching to the projections. By looking at categories of workers in more aggregated forms, we are hopefully reducing the margin of error and are certainly steering away from creating the false impression that we can project smaller categories of workers with any degree of reliability. It is this consideration that leads us away from considering nurses/midwives and care assistants separately, even though we do have this data. Over time duties that are performed by nurses could be transferred to care assistants, whereby there would be a shift between the two groups in terms of numbers needed. By aggregating the two groups and providing long-run projections for them as a single group, such switching is removed as a concern from the analysis.
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The table shows that average parity per women increases as the age of women increases, except at the end of the reproductive age group. This indicates that reporting of children ever borne by women is plausible. This may be due to the high literacy rate of 71 per cent of the total female population. However, irregularity is found at the age group 50 + , possibly indicating memory lapse of the older women. Logically, the parity of women at the end of the reproductive age (4.5) should not be less than the parity of women in age groups 40-44 and 45-49 (5.2 and 5.4 respectively), under conditions of constant or declining fertility. In addition, completed fertility (4.5) is lower than the total fertility rate (4.7) obtained from the ASFR in Table 3.6. All of these observations call for some adjustment of the reported data.
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Figure 2.3a presents the time series of annual mean tem- perature anomalies, relative to the 1981–2000 mean, for the five greenhouse gas scenarios for 2041–2060. These are calculated using values averaged over all grid points covering Ireland. The dashed lines are lines of regression, calculated by the least-squares algorithm to show the linear trend over the two decades. There is a broad range of values for temperature changes projected across the five emission groups. Group B1 shows the least warming (its mean anomaly is 0.66°C) and the greatest interannual variability. Its large spread, seen through its annual mean anomalies ranging from −0.4°C to greater than 1.4°C, is most likely from its being composed of a single-member ensemble. The remain- ing four emission scenarios show greater warming than B1, with A2, A1B and RCP45 having large areas of overlap, and similar 20-year means (1.1°C, 1.2°C and 1.24°C, respectively). RCP85 shows the greatest warming, with a trend line always exceeding the other groups (its mean anomaly is 1.56°C). As expected, there is a general upward trend in temperature, which is more pronounced for the high-emission scenarios. Figure 2.1. Annual 2-m temperature for the period 1981–2000. (a) Observations; (b) CLM-ERA 4 km data; (c) CLM-ERA minus observations (bias).
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Well, maybe. But the 4 most-recent pentadal TFR’s shown in Figure 3 suggest otherwise. Yes, these TFR’s are decreasing, but they are decreasing at a decreasing rate, one which would make our Reference population projection too low, not too high. Moreover, our reference population projection may also be low because we do not consider the effect thereon of the cessation of China’s one-child policy, which existed from 1980 to 2015 . Accordingly, our viewpoint is that if the low TFR’s described above by BLS occur autonomously, great! But, if they do not, then humanity should engineer itself to help safeguard Earth’s climate.
Although no long-term projections routinely take carrying capacity into account, some researchers (Cohen 1998, NRC 2000) have argued that it may be worth considering limiting factors when projecting population, especially over long time horizons, in particular locations where resources are especially limiting and potential for trade is low, or when the intention is to warn against an undesirable outcome, to describe a path to a desirable state, or to analyze the consequences of some hypothetical relationship between demographic factors and constraints. Lutz et al. (1996) examined the potential demographic consequences of an assumed carrying capacity of 2.5 billion for Sub-Saharan Africa as an illustration of how such an exercise might be carried out. They demonstrated that if a catastrophe due to war, famine, disease, or some other means resulted in a sudden 20% increase in mortality and left fertility unchanged at a high level, the population of the region would regain its 20% loss within 10-15 years. However, the demographic recovery depends on the age and sex structure of the mortality reduction as well as on assumptions regarding fertility, so that incorporating carrying capacities into population projections requires more detailed accounting of the effects of a catastrophe than is commonly assumed.
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