scholarly journals Estimating small area health-related characteristics of populations: a methodological review

2017 ◽  
Vol 12 (1) ◽  
Author(s):  
Azizur Rahman
Keyword(s):  
Small Area ◽  
Health Planning ◽  
Spatial Models ◽  
Statistical Modelling ◽  
Simulation Modelling ◽  
Population Characteristics ◽  
Small Areas ◽  
Individual Level ◽  
Health Related ◽  
Small Area Statistics

Estimation of health-related characteristics at a fine local geographic level is vital for effective health promotion programmes, provision of better health services and population-specific health planning and management. Lack of a micro-dataset readily available for attributes of individuals at small areas negatively impacts the ability of local and national agencies to manage serious health issues and related risks in the community. A solution to this challenge would be to develop a method that simulates reliable small-area statistics. This paper provides a significant appraisal of the methodologies for estimating health-related characteristics of populations at geographical limited areas. Findings reveal that a range of methodologies are in use, which can be classified as three distinct set of approaches: i) indirect standardisation and individual level modelling; ii) multilevel statistical modelling; and iii) micro-simulation modelling. Although each approach has its own strengths and weaknesses, it appears that microsimulation- based spatial models have significant robustness over the other methods and also represent a more precise means of estimating health-related population characteristics over small areas.

The Estimation of Population Microdata by Using Data from Small Area Statistics and Samples of Anonymised Records

1998 ◽  
Vol 30 (5) ◽  
pp. 785-816 ◽  
Author(s):  
P Williamson ◽  
M Birkin ◽  
P H Rees
Keyword(s):  
Small Area ◽  
Census Data ◽  
Population Data ◽  
Combinatorial Optimisation ◽  
Population Characteristics ◽  
Test Statistics ◽  
Enumeration District ◽  
Small Area Statistics ◽  
Using Data ◽  
Combining Information

Census data can be represented both as lists and as tabulations of household/individual attributes. List representation of Census data offers greater flexibility, as the exploration of interrelationships between population characteristics is limited only by the quality and scope of the data collected. Unfortunately, the released lists of household/individual attributes (Samples of Anonymised Records, SARs) are spatially referenced only to areas (single or merged districts) with populations of 120 000 or more, whereas released tabulations are available for units as small as single enumeration districts (Small Area Statistics, SAS). Intuitively, it should be possible to derive list-based estimates of enumeration district populations by combining information contained in the SAR and the SAS. In this paper we explore the range of solutions that could be adapted to this problem which, ultimately, is presented as a complex combinatorial optimisation problem. Various techniques of combinatorial optimisation are tested, and preliminary results from the best performing algorithm are evaluated. Through this process, the lack of suitable test statistics for the comparison of observed and expected tabulations of population data is highlighted.

scholarly journals Forecasting of Lung Cancer Incident Cases at the Small-Area Level in Victoria, Australia

2021 ◽  
Vol 18 (10) ◽  
pp. 5069
Author(s):  
Win Wah ◽  
Rob G. Stirling ◽  
Susannah Ahern ◽  
Arul Earnest
Keyword(s):  
Lung Cancer ◽  
Small Area ◽  
Health Planning ◽  
Temporal Trends ◽  
State Level ◽  
The Elderly ◽  
Ageing Population ◽  
Temporal Models ◽  
Small Area Level ◽  
Incident Cases

Predicting lung cancer cases at the small-area level is helpful to quantify the lung cancer burden for health planning purposes at the local geographic level. Using Victorian Cancer Registry (2001–2018) data, this study aims to forecast lung cancer counts at the local government area (LGA) level over the next ten years (2019–2028) in Victoria, Australia. We used the Age-Period-Cohort approach to estimate the annual age-specific incidence and utilised Bayesian spatio-temporal models that account for non-linear temporal trends and area-level risk factors. Compared to 2001, lung cancer incidence increased by 28.82% from 1353 to 1743 cases for men and 78.79% from 759 to 1357 cases for women in 2018. Lung cancer counts are expected to reach 2515 cases for men and 1909 cases for women in 2028, with a corresponding 44% and 41% increase. The majority of LGAs are projected to have an increasing trend for both men and women by 2028. Unexplained area-level spatial variation substantially reduced after adjusting for the elderly population in the model. Male and female lung cancer cases are projected to rise at the state level and in each LGA in the next ten years. Population growth and an ageing population largely contributed to this rise.

Improving the quality of disaggregated SDG indicators with cluster information for small area estimates

2020 ◽  
Vol 36 (4) ◽  
pp. 955-961
Author(s):  
Rizky Zulkarnain ◽  
Dwi Jayanti ◽  
Tri Listianingrum
Keyword(s):  
Small Area ◽  
Research University ◽  
Direct Method ◽  
Random Effect ◽  
Auxiliary Information ◽  
District Level ◽  
Small Areas ◽  
Associate Dean ◽  
Development Goals

The increasing needs for more disaggregated data motivates National Statistical Offices (NSOs) to develop efficient methods for producing official statistics without compromising on quality. In Indonesia, regional autonomy requires that Sustainable Development Goals (SDGs) indicators are available up to the district level. However, several surveys such as the Indonesian Demographic and Health Survey produce estimates up to the provincial level only. This generates gaps in support for district level policies. Small area estimation (SAE) techniques are often considered as alternatives for overcoming this issue. SAE enables more reliable estimation of the small areas by utilizing auxiliary information from other sources. However, the standard SAE approach has limitations in estimating non-sampled areas. This paper introduces an approach to estimating the non-sampled area random effect by utilizing cluster information. This model is demonstrated via the estimation of contraception prevalence rates at district levels in North Sumatera province. The results showed that small area estimates considering cluster information (SAE-cluster) produce more precise estimates than the direct method. The SAE-cluster approach revises the direct estimates upward or downward. This approach has important implications for improving the quality of disaggregated SDGs indicators without increasing cost. The paper was prepared under the kind mentorship of Professor James J. Cochran, Associate Dean for Research, Prof. of Statistics and Operations Research, University of Alabama.

scholarly journals Geocoding cryptosporidiosis cases in Ireland (2008–2017)—development of a reliable, reproducible, multiphase geocoding methodology

2021 ◽  
Author(s):  
Lisa Domegan ◽  
Patricia Garvey ◽  
Paul McKeown ◽  
Howard Johnson ◽  
Paul Hynds ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Spatial Data ◽  
Application Programming Interface ◽  
Epidemiological Studies ◽  
National Standards ◽  
Small Areas ◽  
Infectious Disease Reporting ◽  
Health Related ◽  
Application Programming ◽  
Fine Resolution

Abstract Background Geocoding (the process of converting a text address into spatial data) quality may affect geospatial epidemiological study findings. No national standards for best geocoding practice exist in Ireland. Irish postcodes (Eircodes) are not routinely recorded for infectious disease notifications and > 35% of dwellings have non-unique addresses. This may result in incomplete geocoding and introduce systematic errors into studies. Aims This study aimed to develop a reliable and reproducible methodology to geocode cryptosporidiosis notifications to fine-resolution spatial units (Census 2016 Small Areas), to enhance data validity and completeness, thus improving geospatial epidemiological studies. Methods A protocol was devised to utilise geocoding tools developed by the Health Service Executive’s Health Intelligence Unit. Geocoding employed finite-string automated and manual matching, undertaken sequentially in three additive phases. The protocol was applied to a cryptosporidiosis notification dataset (2008–2017) from Ireland’s Computerised Infectious Disease Reporting System. Outputs were validated against devised criteria. Results Overall, 92.1% (4266/4633) of cases were successfully geocoded to one Small Area, and 95.5% (n = 4425) to larger spatial units. The proportion of records geocoded increased by 14% using the multiphase approach, with 5% of records re-assigned to a different spatial unit. Conclusions The developed multiphase protocol improved the completeness and validity of geocoding, thus increasing the power of subsequent studies. The authors recommend capturing Eircodes ideally using application programming interface for infectious disease or other health-related datasets, for more efficient and reliable geocoding. Where Eircodes are not recorded/available, for best geocoding practice, we recommend this (or a similar) quality driven protocol.

Ideal for particularly small areas

OPE Journal ◽  
2021 ◽  
Vol 11 (34) ◽  
pp. 30-30
Keyword(s):  
Small Area ◽  
Fill Factor ◽  
Small Areas

Dracula Technologies (Valence, France) has achieved a new fill factor record for an OPV module with fully inkjet-printed layers. These small-area modules target indoor applications

How much is a calorie worth? A study of willingness to pay for calorie labels in restaurant menus

2019 ◽  
pp. 1-24
Author(s):  
SANA RAFIQ
Keyword(s):  
Willingness To Pay ◽  
Well Being ◽  
Economic Benefits ◽  
Individual Level ◽  
Additional Information ◽  
Health Related ◽  
The Individual ◽  
Types Of Information ◽  
Caloric Consumption ◽  
The Impact

AbstractWe asked individuals about their willingness to pay (WTP) either: (1) for a mandate requiring restaurants to post calorie information on their menus; or (2) to avoid such a mandate. On average, more people were in in favor of the mandate and were willing to pay four times more than those who were against it, thereby leading to a Kaldor–Hicks improvement from this policy. To ensure robustness, we tested the impact of providing three types of information during individuals’ WTP determinations: (1) visual examples of the proposed calorie labels; (2) data on their effectiveness at the individual level; and (3) data on their wider social and economic benefits. For those in favor, providing a simple visual of the label had no impact on WTP. Data on the individual effectiveness of the labels increased the WTP, while evidence on broader obesity reduction and economic benefits reduced it. For opponents, WTP did not change with provision of additional information except when provided with information on social and economic benefits. Under this condition, the opponents increased their WTP 12-fold to avoid a mandate of this policy. Finally, we measured individual well-being under this policy and found directionally similar results, confirming a net improvement in aggregate welfare. Our results suggest that messaging that focuses on private benefits (providing calorie information so that individuals can effectively choose to reduce excessive caloric consumption) rather than wider public benefits (reduction in overall health-related costs and obesity) is more likely to be effective.

scholarly journals A multilevel mixed effects analysis of informal carers health in Australia: the role of community participation, social support and trust at small area level

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Itismita Mohanty ◽  
Theo Niyonsenga ◽  
Tom Cochrane ◽  
Debra Rickwood
Keyword(s):  
Health Outcomes ◽  
Community Participation ◽  
General Health ◽  
Small Area ◽  
Positive Influence ◽  
Mixed Effects ◽  
Level Variation ◽  
Individual Level ◽  
Small Area Level ◽  
Informal Carers

Abstract Background Informal carers suffer from worse health outcomes than non-carers due to their caregiving role. Yet, in a society carers health is as important as that of their care recipients. This study investigated the self-assessed mental and general health outcomes of informal carers in Australia. It evaluated the influence of carers’ personal social capital- a logically linked sequence of their social behaviour such as community participation, social support and trust in others- on their health outcomes. The study estimated the magnitude of small area level variation at Statistical Area Level 1 (SA1) along with individual level variation in carers’ health outcomes. Methods The study used a multilevel mixed effects cross-sectional design using data from the Household Income and Labour Dynamics of Australia survey, wave 14. It included Australians aged 15 years and older that were surveyed in the year 2014. The sample consisted of 12,767 individuals and 5004 SA1s. The outcome measures included- mental health, general health and physical functioning, domains of the Short Form 36 Questionnaire, a widely used multi-dimensional measure of health-related quality of life. Results Informal carers suffered from poor mental (Beta = − 0.587, p = 0.003) and general health (Beta = − 0.670, p = 0.001) outcomes compared to non-carers in Australia. These health outcomes exhibited significant variation acrossSA1s in Australia, with 12–13% variation in general and mental health. However, within small local areas, differences at the individual level, accounted for most of the variation in outcomes. Moreover, levels of community participation, personal social connection and trust, as perceived by individuals in the communities, had a positive influence on both mental and general health of carers and non-carers, and were more beneficial for carers compared to non-carers. Conclusion It seems that the positive influence of social capital for carers helps them in coping with the negative impact of their caregiving duty on health outcomes. Findings suggested that some targeted community support programs for carers to build on their personal social cohesion and trust in their community could help in improving their poor health profiles. Moreover, improved informal carers’ health may help the health system in better managing their resources.

INFANT AND CHILD MORTALITY IN THREE CULTURALLY CONTRASTING STATES OF INDIA

2001 ◽  
Vol 33 (4) ◽  
pp. 603-622 ◽  
Author(s):  
PAULA GRIFFITHS ◽  
ANDREW HINDE ◽  
ZOË MATTHEWS
Keyword(s):  
Tamil Nadu ◽  
Child Survival ◽  
Family Health ◽  
Uttar Pradesh ◽  
Statistical Modelling ◽  
Cross Sectional ◽  
Individual Level ◽  
Survival Status ◽  
The Individual ◽  
The Impact

Using cross-sectional, individual-level survey data from Maharashtra, Tamil Nadu and Uttar Pradesh collected under the Indian National Family Health Survey programme of 1992–93, statistical modelling was used to analyse the impact of a range of variables on the survival status of children during their first 2 years of life. Attention was focused on the potential impact of the mother’s autonomy. The strongest predictors of mortality were demographic and biological factors, breast-feeding behaviour, and use and knowledge of health services. Variables that can be interpreted as being related to maternal autonomy, such as the presence of a mother-in-law in the household, did not have a significant direct effect on child survival at the individual level, and their indirect effects were very limited.

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