The objective of this study was to examine the Minimum Data Set (MDS) and Geriatric Depression Scale (GDS) as measures of depression among nursing home residents.The data for this study were baseline, pre-intervention assessment data from a research study involving nine nursing homes and 704 residents in Massachusetts. Trained research nurses assessed residents using the MDS and the GDS 15-item version. Demographic, psychiatric, and cognitive data were obtained using the MDS. Level of depression was operationalized as: (1) a sum of the MDS Depression items; (2) the MDS Depression Rating Scale; (3) the 15-item GDS; and (4) the five-item GDS. We compared missing data, floor effects, means, internal consistency reliability, scale score correlation, and ability to identify residents with conspicuous depression (chart diagnosis or use of antidepressant) across cognitive impairment strata.The GDS and MDS Depression scales were uncorrelated. Nevertheless, both MDS and GDS measures demonstrated adequate internal consistency reliability. The MDS suggested greater depression among those with cognitive impairment, whereas the GDS suggested a more severe depression among those with better cognitive functioning. The GDS was limited by missing data; the DRS by a larger floor effect. The DRS was more strongly correlated with conspicuous depression, but only among those with cognitive impairment.The MDS Depression items and GDS identify different elements of depression. This may be due to differences in the manifest symptom content and/or the self-report nature of the GDS versus the observer-rated MDS. Our findings suggest that the GDS and the MDS are not interchangeable measures of depression. Depression is common among residents of nursing homes . Of the The Minimum Data Set (MDS), created in response to the US Omnibus Budget Reconciliation Act of 1987, aims to provide a uniform, standardized, and comprehensive assessment of residents in nursing homes . The MDSThis study compares the measurement properties of two measures of geriatric depression: the GDS and the MDS. The GDS is evaluated via a 15 and five item form. The MDS is evaluated by the items located in Section E in total and as a subset included in the Depression Rating Scale.Participants were nursing home residents living in nine Massachusetts nursing facilities participating in a research study between July 1994 and March 1998. Details of the study are described elsewhere [Residents were evaluated with the Minimum Data Set (MDS) Resident Assessment Instrument version 2.0  as well We also examined the depression symptom scales with regard to indicators of clinically recognized depression: chart diagnoses of depression and recent use of antidepressant medication. These data were also obtained by research nurses using structured chart review forms keyed to data elements collected with the MDS. MDS assessors are instructed to note the presence of a disorder related to the resident's current functional, cognitive, and behavioural status, medical treatments, and risk of death . Among tWe stratified the sample into two groups based on the severity of cognitive impairment. The impaired group comprised residents who were comatose (MDS 2.0 item B1 = 1), and/or with a short-term memory problem (B2a = 1) and those who only rarely/never make themselves understood (C4 = 3). All other residents were assigned to the "cognitively intact" group. This decision rule matches a screening rule for the MDS versus GDS depression symptom assessment proposed in the US Centers for Medicare & Medicaid Services' (CMS) working draft of the MDS version 3.0 .We compared sample statistics and psychometric properties for each of the four depression scales across strata defined by cognitive impairment. We evaluated missing data by examining the proportion of residents with complete data on all assessment items, and also by the proportion of residents with complete data on a majority of items in the scale. For all other analyses we substituted missing values with the mean of the resident's non-missing items if a majority of the scale items were not missing. We examined means, standard deviations (SD), proportion of residents scoring at the floor, the internal consistency reliability  and areThe very high frequency of missing data for the GDS encouraged us to examine the frequency of missing data at the item level. We present this information in Table The means for the GDS- and MDS-derived scales were different between the cognitively intact and impaired groups Table . Of noteFigure The GDS-15 had the highest internal consistency reliability coefficient, and the lowest was observed for the DRS. However, this difference does not substantially exceed that expected due to the greater scale length of the GDS-15. Using the Spearman-Brown prophecy formula , the relThe correlation between the GDS- and DRS- derived measures were not statistically different from zero. All of the differences in correlation coefficients are similar among the cognitively impaired and cognitively intact and not statistically different, except for the correlation of the E1SUM and DRS (P < .001).The comparison of mean depression symptom scale scores among residents that received antidepressants and/or had a chart diagnosis of depression, within and across cognitive impairment strata, is reported in Table For the MDS-derived depression scales, the differences in means associated with antidepressant use and depression diagnoses are much more dramatic than for the GDS-derived scales, but only among the cognitively impaired. For both the E1SUM and DRS, the marginal differences for depression diagnoses and antidepressant use describe moderate to large effect sizes. These differences are statistically significant (both P < .001). On the other hand, among those residents not identified as cognitively impaired, the marginal differences associated with antidepressant use and a depression diagnosis were trivial to small and not statistically significant.In this study, we found that MDS- and GDS-derived depression measures were not correlated with one another, but were apparently adequately reliable measures of their intended construct. Thus, we infer that the MDS and the GDS measure different aspects of nursing home residents' depression. Each scale has specific strengths and limitations. The practical utility of the GDS is undermined by a very high frequency of missing data. The proportion of GDS responses missing differs greatly by level of cognitive functioning. The floor effect limits both instruments. While the internal consistency reliability is apparently greater for the GDS, this may simply be due to the greater number of items on the GDS. We observed a weak relationship between GDS-5 scale scores and clinical indicators of depression (diagnoses, antidepressant use), but the strong association between MDS-derived scales and clinical indicators was only seen among cognitively impaired residents.Other investigators have found a low correlation between the DRS and the GDS and other instruments for assessing depression, but these findings vary according to data collection strategies. For example, Anderson et al found a low correlation between the MDS DRS abstracted from residents' charts and symptom data collected with the GDS (r = .13) and the Hamilton Depression Rating Scale  using research interviewers . SimilarIn our study, the assessment methods followed more closely how they were designed to be used. The GDS was used as a direct interview, and the MDS was used as instructed in the MDS manual , and incThe fact that the MDS- and GDS-derived scales do not correlate implies that the two scales evaluate different aspects of a resident's depression. For positive MDS depression symptom ratings, residents must visibly act by making negative statements, be easily angered, and display unrealistic fears to trigger MDS symptoms. The GDS asks residents if they are satisfied with their life, feel helpless or worthless, and are often bored. It is conceivable that GDS-derived measures capture a brooding mental set, reflective of a dysphoric personality trait or adjustment disorder (for example in response to a recent change in living situation) rather than the presence of major depression.RAI include a provision for self-assessment of depression [The lack of correlation between MDS depression measures has implications for proposed revisions to the MDS. Until more is known about the phenomenology and clinical validity of syndromes measured by these and other depression measures used in long-term care settings, adding self-report of symptoms of depression to the MDS is supported by our findings. We note that both CMS's draft revision of the MDS and new pression . HoweverThe Geriatric Depression Scale and Minimum Data Set mood items measure different aspects of the depression syndrome among nursing home residents. The two measures cannot be treated as exchangeable or equivalent, and each has it's own strengths and limitations. The GDS uses self-report, but as a consequence suffers from a high frequency of missing data. The MDS relies upon informant ratings and therefore provides information about most residents. Although the GDS has higher internal consistency reliability than MDS, this is not beyond that expected due to greater scale length. The MDS measures were more strongly related to antidepressant use and record of a diagnosis of depression than were GDS measures. These results highlight the fact that more psychometric research is needed to better understand and improve the measurement of depression among frail nursing home residents.GDS Geriatric Depression ScaleMDS Minimum Data SetDRS Depression Rating ScaleCSDD Cornell Scale for Depression in DementiaHDRS Hamilton Depression Rating Scale.No financial competing interests. TR, JH, GIC, BEF and JNM are fellows of InterRAI, a non-profit group dedicated to improving the health care for persons who are elderly, frail or disabled. InterRAI owns the copyright to the MDS for nursing homes and many other care settings. InterRAI offers free licenses for the use of assessment forms of which it owns the copyright to. Therefore, there are no financial competing interests for members of the writing group. For more information please visit MK and RNJ conceived of the study. MK drafted the manuscript. RNJ performed the analyses and provided critical review of and completed the manuscript. JNM obtained the data, participated in the analysis and provided critical review of the manuscript. JH, MS, GIC and BEF provided critical review of the analyses and manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Over the past decade, the Dutch government has pursued a research-based approach to tackle socioeconomic inequalities in health. We report on the most recent phase in this approach: the development of a strategy to reduce health inequalities in the Netherlands by an independent committee. In addition, we will reflect on the way the report of this committee has influenced health policy and practice.A 6-year research and development program was conducted which covered a number of different policy options and consisted of 12 intervention studies. The study results were discussed with experts and policy makers. A government advisory committee developed a comprehensive strategy that intends to reduce socioeconomic inequalities in disability-free life expectancy by 25% in 2020. The strategy covers 4 different entry-points for reducing socioeconomic inequalities in health, contains 26 specific recommendations, and includes 11 quantitative policy targets. Further research and development efforts are also recommended.Although the Dutch approach has been influenced by similar efforts in other European countries, particularly the United Kingdom and Sweden, it is unique in terms of its emphasis on building a systematic evidence-base for interventions and policies to reduce health inequalities. Both researchers and policy-makers were involved in the process, and there are clear indications that some of the recommendations are being adopted by health policy-makers and health care practice, although more so at the local than at the national level. Before 1980, socioeconomic inequalities in health were a non-issue in public health (research) in the Netherlands. This changed in the early 1980's as a result of the publication of the Black Report in England . GradualSince then, the Dutch Ministry of Health has followed a systematic, research-based approach to tackling socioeconomic inequalities in health. An initial five-year research program mapped the nature and determinants of socioeconomic inequalities in health in the Netherlands . A seconThe report deals with socioeconomic inequalities in health, defined as systematic differences in health status between people with higher and lower socioeconomic status, as indicated by educational level, occupational class, and/or income level. Like other European countries, the Netherlands has substantial inequalities in health between socioeconomic groups. Differences in life expectancy at birth between socioeconomic groups are in the order of 4 years, and differences in healthy life expectancy have recently been calculated to be a staggering 14 years . InequalThe main focus of the program was on developing and evaluating interventions and policies, but a number of other activities  were undertaken as well. Table When the results of the evaluation studies became available, meetings were held in 2000 with scientific experts and representatives from policy makers and from practice in six different areas . During these meetings possible recommendations for new policies and interventions were tested and refined . The inpSubsequently, the committee overseeing the program held a number of plenary meetings to develop a comprehensive strategy to reduce health inequalities. Committee members were appointed by the Minister of Health, and they included former and active politicians of various political backgrounds, as well as a representative of the ministry of health and researchers. A conscious attempt was made to represent the whole (relatively narrow) political spectrum in the Netherlands. Members ranged from left  to right (represented by a former chairman of, and current House of Lords member for, the conservative party, who was later succeeded by another House of Lords member for the same party), and the committee was chaired by a former christian-democrat Minister of Social Affairs. Researchers had an important influence on the whole process: JM was secretary of the committee, and KS acted as co-ordinator of the program, and both were involved in writing draft versions of the final report. The committee reported directly to the Minister of Health.The committee started from the assumption that existing inequalities in health at least partly rank as unjust and that the government is responsible for achieving a reduction of these health differences. This assumption was based on the argument that health should be seen as a condition for the options open to individuals to structure their own life as far as possible according to their own ideas. Those health differences that are the consequence of an unequal distribution of living conditions over which individuals have no control, were thus seen as health inequities, to be tackled by the government. It was argued that this would require a comprehensive strategy, given the persistent and widespread character of socio-economic inequalities in health.The committee wanted its strategy for reducing health inequalities to be based on sound evidence. Ideally, factors targeted by the strategy should be known to contribute to the explanation of health inequalities, and interventions and policies should be known to diminish exposure of lower socioeconomic groups to these factors. While the first requirement could be met relatively easily , the second requirement was more difficult to meet. Although the program produced evidence on effectiveness of interventions and policies and showed some positive results, this left important gaps in the knowledge base, both in terms of coverage of various policy options and in terms of strength of evidence. This problem was also encountered in other countries . The comIn addition, the committee also paid attention to the political feasibility of possible policy recommendations. This aspect was discussed during the plenary meetings, in the light of the  experience of the committee members as well as the outcome of the working conferences that were mentioned before.The committee decided to base its strategy on a number of quantitative targets, because these can aid in plotting a clear policy course and can function as milestones for interim assessments of the strategy. It took the World Health Organization target as its starting point , and refIn order to attain such an ambitious goal, major efforts are required, if only because during the last decades inequalities in health in the Netherlands have increased rather than decreased . AlthougTable In a number of other areas, the committee recommended intensified or new policies. These recommendations were partly based on reported positive results of intervention studies. This applies to the recommendations relating to school health promotion programs, technical and organizational measures to reduce physical workload, reinforcement of primary care in disadvantaged areas by employing practice nurses and peer educators, and local care networks to prevent social problems among chronic psychiatric patients. The results of some of the other intervention studies led to recommendations for further development of those interventions, as in the case of special benefit schemes for families living in poverty and counseling schemes for school absenteeism. Most of the other recommendations, however, are primarily based on an understanding of the factors that have been shown to contribute to health inequalities, and of the best way to deliver interventions targeting these factors.The committee did not attempt to estimate the costs of the recommended interventions and policies.As experience has taught that implementing effective interventions should not be taken for granted, the committee advised that a steering group be formed to drive and control the process of implementing effective interventions. On the one hand, this should function as a highly visible focal point at which the expertise available in the Netherlands is made accessible to all relevant policy areas. On the other hand, the steering group should be able to act on its own initiative to capture and retain attention for socio-economic inequalities in health and to promote the implementation of policy proposals. Given these two functions, the committee advised including experts as well as representatives from the main relevant policy areas in the steering group.Given the fact that research has not yet fully disclosed the origins of socioeconomic inequalities in health, the committee considered continuation of explanatory research to be vital because it may lead to new entry-points for intervention. The same applies to further development of effective interventions and policies. The committee therefore recommended evaluation of all recommended interventions and policies during and after their implementation.The committee published its main report in March 2001 . The repA closing conference took place in October 2001. During that conference, the results of the evaluation studies as well as the proposed policy strategy were presented to a broad public, and reflected upon by, among others, Sir Donald Acheson from the UK. In addition, policy implications were discussed. Participants included researchers, policy makers and representatives from practice, not only from the public health and health care field, but also from other policy areas .The official cabinet reaction to the recommendations presented to parliament in November 2001 was positive but further elaboration of the recommendations as well as decision-making was deferred to the next cabinet . A new cThe delay in political decision making does not seem to have hindered the implementation of specific interventions that were evaluated within the programme. So far, at least a few of the interventions that have been proven to be effective have been implemented on a larger scale. These include the integrated programme to prevent school children from starting smoking, and the local care networks for chronic psychiatric patients.While many countries, including the UK, Sweden and Finland have had national research efforts in the field of socioeconomic inequalities in health during the second half of the 1990's, the Dutch program is unique for its emphasis on evaluation of interventions. More generally, the main distinguishing feature of the Dutch approach is its focus on commissioning evaluations of interventions. Although this was done in a systematic way, using an explicit conceptual and methodological framework, the program also had its obvious limitations. It had a modest budget  and funded not more than 12, rather small-scale intervention studies targeting relatively easily modifiable factors. The latter is not only due to the small budget of the program, but also to strict methodological requirements which in practice made it nearly impossible to study the effectiveness of broader policy measures . In hindThe unique elements of the Dutch approach should not distract from the fact that the Dutch experience received important inputs from abroad. Its start is a late response to the British Black Report and is directly related to the efforts of the European Office of the World Health Organization to put health equity on national policy agendas . During The Dutch approach reflects the input of both researchers and policy-makers, although the balance between the two has oscillated over time. The first signals that health inequalities should be addressed came from researchers, but were picked up by policy-makers within the Ministry of Health in the mid-1980's who were then looking for opportunities to strengthen health policy  in the Netherlands. This small group of bureaucrats succeeded in launching and following through the first research program, but left the Ministry or changed posts before the program came to an end. Partly due to continuous personnel changes in the Ministry, the intensity of the exchanges between researchers and policy-makers gradually diminished during the second program. When the final report was published reactions from within the Ministry were rather cool, although the Minister, who had taken a personal interest in the matter, responded very favourably. At this stage, however, it seems that without a continuing "push" from the research-side the bureaucrats could easily loose interest altogether, particularly now that there are rapid changes of cabinet.A major obstacle for a comprehensive package of policy measures seems to be the relatively weak position of the Ministry of Health as compared to other policy areas. It is obvious that a substantial reduction of health inequalities can be achieved only by involving other policy areas next to that of (preventive and curative) health care. This starting point seems to contrast with the ideas of the ministries in other policy areas, that seem to consider this issue as the responsibility of the Ministry of Health in particular. So far, the Ministry of Health does not seem to have a lot of success in convincing other policy areas of the importance of contributing to reducing inequalities in health.The lack of success in mobilising other policy areas at the national level is probably partly related to the fact that the issue of inequalities is perceived as rather abstract by these other areas. This probably requires the issue of inequalities in health to be "re-phrased" for that specific policy area, in terms that fit within their ideas. Housing corporations for example do not consider themselves to be responsible for tackling health inequalities but they do feel responsibility for high quality living conditions, which then might automatically contribute to a better health status of people in lower socio-economic groups. Paradoxically, an approach in which the issue of inequalities in health is cut into small pieces, requires a steering. This forms the background of the plea of the committee for a steering group.Remarkable progress has been made, not only in terms of knowledge production but also in terms of increased confidence among policy-makers and practitioners to take action to reduce inequalities in health. Many health agencies in the Netherlands are working to reduce socioeconomic inequalities in health. This is illustrated by the fact that the 'National Contract on Public Health', concluded in 2001 between many national and local agencies in the field of public health, has selected the reduction of socioeconomic inequalities in health as its first priority. Many local health agencies have already implemented some of the interventions discussed in this paper.


Conventional Comparative Genomic Hybridization (CGH) has been widely used for detecting copy number alterations in cancer and for identifying regions containing candidate tumor responsible genes. Recently, several studies have shown the utility of cDNA microarray CGH for studing gene copy changes in various types of tumors. However, no such studies on T-cell lymphomas have been performed. To date T-cell lymphomas analyzed by the use of chromosome CGH have revealed only slight copy number alterations and not gene amplifications.BCLX, PCNA, FKBP1A, IGFBP2 and cMYC, that are amplified, highly expressed, and also contained in the amplicons on 20p and 2q. The expresion of these genes was analyzed in 39 T-cell lymphomas and 3 other T-cell lines.In the present study, we describe the characterization of three amplicons of the T-cell line huT78 located at 2q34-q37, 8q23-q24 and 20p, where new amplified and overexpressed genes are found. The use of a cDNA microarray containing 7.657 transcripts allowed the identification of certain genes, such as BCLX, PCNA, FKBP1A, IGFBP2 and cMYC). We showed that overexpression of the amplified genes could be attributable to gene dosage. We speculate that deregulation of those genes could be important in the development of T-cell lymphomas and/or in the maintenance of T-cell lines.By the use of conventional CGH and CGH and expression cDNA microarrays we defined three amplicons in the T-cell line huT78 and identified several novel gene amplifications ( ERBB2 (17q12), cMYC (8q24), and CCND1 (11q13) are found in 10–25% of breast tumors. Amplifications are revealed by Comparative Genomic Hybridization (CGH) in small chromosome areas (restricted to 2–10 Mb) where DNA copy number increases from more than 5 to 10-fold. Within these amplicons it is possible to identify critical amplified genes that are also overexpressed: this is the case for cMYC (8q24.12), ERBB2 (17q12-q21), MDM2 (12q14.3-q15) or BCL2 (18q21.3) [Gene amplification plays an important role in the progression and initiation of many solid tumors, as is the case of breast cancer where amplification of the genes ERBB2 7q12, cMYCND1 11q1 are foun cMYC 8q2, and CCN18q21.3)  and in nCCND1 11q are founThe cutaneous T-cell line huT78 was obtained from American Type Culture Collection (Rockville, MD) and cells were grown under recommended culture conditions. High molecular weight DNA was extracted and CGH was performed as described previously ,14..In order to define regions of high-level amplification and to identify amplified and highly expressed genes contained in the amplicons, we performed cDNA microarray experiments on T-cell line huT78 to obtain expression and genomic profiles of the amplicons. We used the CNIO Oncochip (v1.1a) containing 7.657 different sequence-validated I.M.A.G.E cDNA clones  -some of them duplicated to reach a total of 11.718 spots- that represent known genes and expressed sequence tags (ESTs) related to the tumoral process, and tissue specific genes. A complete list can be found at AluI and RsaI digested, labeled with Cy5 (huT78) and Cy3 (control) using BioPrime labeling kit (Life Technologies, Inc., Gaithersburg, MD), and hybridized on microarrays at 50°C for 14–16 h, as previously described [2 values) were calculated and used for the analysis. The CNIO Oncochip contains 7.657 different clones, of which 3.079 are replicated at least twice: thus, we removed and averaged the replicates by using an in-house developed preprocessing tool [Genomic DNA hybridizations on microarrays were performed using DNA extracted from the T-cell line huT78 and from the blood of a control donor used as a reference. Genomic DNAs were escribed . Post-hyin vitro transcription kit (Ambion, Austin, TX) following manufacturer's recommendations. A pool of aRNAs obtained from the Universal Human RNA (Stratagene, La Jolla, CA) was used as a standard reference in all hybridizations. Test or reference amplified RNAs (aRNAs) were labeled with fluorescent Cy5 and Cy3, respectively, as reported [Expression data was also obtained from huT78 cells and from magnetically isolated T lymphocytes obtained from the pooled peripheral blood of 5 anonymous donors that were used as a control. T lymphocytes were isolated by using either magnetic microbeads conjugated to monoclonal mouse anti-human CD3 antibodies purchased from Miltenyi Biotec Inc. (Auburn, CA), or magnetic depletion of non-T-cells with a cocktail of antibodies using the Pan T-cell Isolation Kit (Miltenyi Biotec Inc.). Total RNAs were extracted with Tri Reagent (Molecular Research Center, Cincinnati, OH) following the manufacturer's instructions and amplified using a T7-based method, as previously described ,18. BrieIn order to analyze if the candidate genes contained in the amplicons are altered in primary tumors and other cell lines, we analyzed expression data obtained in a previous study  using tuCGH carried out in huT78 cells revealed three high-level gains at 2q34-q37, 8q23-q24 and 20p, showing copy number gains higher than 5 fold Figure . Low-lev. According to this database, clones were ordered along the chromosomes and their expression and genomic values . Cut-off levels for genomic data were defined at more than 1.7, a significantly high value to assure that the gene is gained at least 4-fold (log2 [genomic data] ≥ 0,8) (data from FISH studies). Within these thirty genes that were gained and overexpressed in the cell line, 5 of them were located in amplicons of chromosome 2 (XRCC5, IGFBP2 and PSMB3) and chromosome 20 (FKBP1A and BCL2L1) revealed by conventional CGH.Only thirty genes that showed high-level genomic gains were also highly expressed in the cutaneous T-cell line huT78 Table . Genes wID1 and BCL2L1 (BCLX) located at 20q11 (Table ID1 and BCLX show amplification (4 and 7 times respectively), only BCLX is highly expressed  and IGFBP2 (2q33-24) around 220 Mb, TUBA1  around 227 Mb and PSMD3 mapped by FISH at 2q35. Amongst these, IGFBP2 is the most highly overexpressed . This gene is upregulated by all-trans-retinoic acid (ATRA), a differentiation inducer capable of causing clinical remission in about 90% of patients with acute promyelocytic leukemia. Other genes located in the amplicon that have not been analyzed in this study could also be important.Amplicon in chromosome 8 is located at 8q23-q24, where the gene FKBP1A and PCNA were overexpressed in 89,7 and 71,8% respectively (35 and 28 out of 39) of the primary tumors and in all the three cell lines analyzed. In addition, BCLX was overexpressed in 64,1% (25/39) of the primary tumors and in Jurkat cell line. Regarding genes in amplicons of chromosome 2 and 8, IGFBP2 was only overexpressed in 15,4% (6/39) of the tumors and cMYC is not highly expressed in any of them. However, both genes were overexpressed in the three cell lines.Expression of the relevant genes found in the amplicons that were gained in this T-cell line were examined in a series of 39 primary T-cell lymphomas and 3 T-cell lines (Jurkat, Molt 16 and Karpas 45) . Three gConventional chromosome CGH of the T-cell line huT78 showed genomic patterns of copy number changes affecting most of the chromosomes. Three highly amplified regions were detected using this technique. Nevertheless, resolution of chromosome CGH is no less than 2 Mb for copy number gains and is a function of both amplicon size and copy number gains .In order to precisely define regions of high-level amplification and to identify amplified and highly expressed genes contained in the amplicons of the T-cell line huT78, we performed cDNA microarray CGH and expression profiling of the cell line. For this purpose, we used the CNIO Oncochip containing 7.657 different sequence-validated cDNA clones that represent known genes and expressed sequence tags (ESTs) related to the tumoral process, and tissue specific genes.Recently, several studies have shown the utility of cDNA microarray CGH for studing gene copy changes in various types of tumors, and the usefulness of defining amplicon boundaries at high resolution (gene level) to assist in locating and identifying candidate oncogenes -11. HoweBy analyzing mRNA levels in parallel, we observed that not all genes that are amplified are also overexpressed. Thus, by analyzing the expression of the genes contained in the amplicons, we selected several genes whose expression seemed to respond to gene copy number gains. We showed that only a few of the genes contained in the amplicon are highly gained and also overexpressed. In this way we could select the candidate genes to study further that could be involved in the tumorogenesis of the T-cell lymphomas.FKBP1A, PCNA and BCLX were overexpressed in 90–65% of the primary tumors. FKBP1A is a receptor for rapamicin as well as for FK506, a powerful immunosuppressant in T-cells, and PCNA is involved in the control of eukaryotic DNA replication and may have a role in DNA repair synthesis. BCLX is involved in both positive and negative regulation of programmed cell death. Thus, these three genes, could be significant in the development of T-cell lymphomas.As stated before , howeverIGFBP2 was only overexpressed in a low proportion of the primary tumors (15%) and cMYC is not highly expressed in any of them. Curiously, both genes were overexpressed in the three cell lines suggesting that they could be of relevance in the maintenance of the cell lines.When the candidate genes located in amplicons of chromosome 2 and 8 were examined, we observed that BCLX, PCNA and FKBP1A. These genes are also overexpressed in 65–90% of a set of 39 T-cell lymphomas and 3 cell lines, while IGFBP2 and cMYC are only overexpressed in T-cell lines. Our data suggest a different role of these genes in such processes. New studies with more clones covering these amplicons can help us to better identify relevant genes in T-cell lymphomas.We have identified 3 amplicons by CGH in the T-cell line huT78. By cDNA microarray CGH. We have narrowed down the regions and selected some amplified and overexpressed genes: BM carried out the genomic and RNA hybridizations of the cell line onto the microarrays, performed FISH experiments, analyzed the results and drafted the manuscript. BM-D participated in the design of the study and in the analysis and discussion of the results. MC performed the amplification of the RNA and hybridization onto the microarray to carry out expression studies of primary tumors. VF performed nucleic acid extractions for hybridizations onto the microarray, grew and label the BAC clones for FISH assays and performed CGH. RD-U carried out statistical analysis of the microarray results. JB conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.This file contains row genomic and expression data for each gene. Each gene is identified by a unique clone identification (Clone ID), the corresponding gene symbol (Gene Symbol) and the Unigene number (Unigene ID). Localization of all clones are also shown (map positions of the cDNA clones were obtained from the EnsEMBL database, as referred in the text).Click here for file


Failure to be comprehensive can distort the results of a systematic review. Conversely, extensive searches may yield unmanageable number of citations of which only few may be relevant. Knowledge of usefulness of each source of information may help to tailor search strategies in systematic reviews.We conducted a systematic review of prevalence/incidence of maternal mortality and morbidities from 1997 to 2002. The search strategy included electronic databases, hand searching, screening of reference lists, congress abstract books, contacting experts active in the field and web sites from less developed countries. We evaluated the effectiveness of each source of data and discuss limitations and implications for future research on this topic.Electronic databases identified 64098 different citations of which 2093 were included. Additionally 487 citations were included from other sources. MEDLINE had the highest yield identifying about 62% of the included citations. BIOSIS was the most precise with 13.2% of screened citations included. Considering electronic citations alone (2093), almost 20% were identified uniquely by MEDLINE (400), 7.4% uniquely by EMBASE (154), and 5.6% uniquely by LILACS (117). About 60% of the electronic citations included were identified by two or more databases.This analysis confirms the need for extending the search to other sources beyond well-known electronic databases in systematic reviews of maternal mortality and morbidity prevalence/incidence. These include regional databases such as LILACS and other topic specific sources such as hand searching of relevant journals not indexed in electronic databases. Guidelines for search strategies for prevalence/incidence studies need to be developed. The importance of comprehensive search strategies to identify 'all relevant articles' when conducting systematic reviews has been long documented . ComprehAt the World Health Organization (WHO), we have conducted a systematic review of prevalence/incidence of maternal mortality and morbidities from 1997 to 2002. The primary objective of this review is to assist in mapping the burden of reproductive ill-health by providing a comprehensive, standardized and reliable tabulation of data on the prevalence/incidence of maternal morbidity and mortality [The methodology of the systematic review and the search strategy have been described elsewhere . In brie® software. Selection of eligible studies involved two stages; the first stage consisted of screening of title and/or abstracts from the citations downloaded. Citations were excluded if any of the following applied: (i) data collection dates were not reported, (ii) data were collected only before 1990, (iii) part of the data were collected before 1980 and disaggregation by year was not possible (in order to exclude data before 1990), (iv) number of study participants was less than 200, (v) the study design was case-control and incidence/prevalence estimates from the defined population cannot be calculated, (vi) the methodology was not described. The second stage consisted of full-text evaluation of those citations that were not excluded in the first step applying the same criteria. For practical reasons, studies identified by other sources were only entered in Reference Manager® if they were eventually included in the review.We downloaded all citations identified by electronic databases into Reference ManagerWe recorded the source or sources of each citation. For each source, we calculated number of citations identified, included, and number included that were unique to the source. The sensitivity of each source was defined as the number of included citations identified by the source over the total number included. The precision was defined as the number of included citations identified by a source over the number of both included and excluded citations identified by that source. This review had no language restrictions and we recorded the language in which the report was written. Detailed results regarding languages will be reported separately.Two reviewers performed the screening for all citations. In order to estimate the level of disagreement between the two reviewers within 2.5% of the true value, they independently screened title/abstracts for a sample of citations (560). This sample size assumes a 95% confidence interval and that the level of disagreement between the two reviewers will not exceed 10% . The perFor the time period from 1997 to 2002, 64098 different citations from electronic databases were identified of which 2093 were included. Additionally, 487 citations were included from other sources. There were 92 citations for which we could not obtain full text and, therefore, they could not be assessed .Table Overall, in terms of precision, we included 1 in 25 screened citations (4%). The most precise database was BIOSIS where 13.2% of the screened citations were included; IMEMR, LILACS and Popline followed this with 10.1%, 9.4% and 9.1%, respectively  citations were identified by reference lists of articles, expert contacts, congress proceedings, abstract books, hand searching of journals available in libraries that are not indexed in electronic databases, and other emerging databases in developing countries. As expected, there has been a large overlap between databases: 60% were identified by two or more databases and about 44% were identified by MEDLINE and EMBASE together. These two databases also provided the largest number of unique citations and both are considered necessary. PAIS International and Econlit only identified 3 and 1 citations, respectively, that were not identified by any other database and they could probably be disregarded in future reviews.The nature of this systematic review with its focus on settings where burden of disease is highest necessitates extensive searching of developing country sources. However, literature from developing countries is difficult to access and it is not well represented in MEDLINE or other well-known electronic databases ,14. An eThe value of LILACS database to improve the quality of systematic reviews has been previously reported . Our anaA limiting factor for identifying citations is related to late indexing of journals in electronic databases. Search strategies for this review were conducted in early 2003 to identify articles published in 2002 or earlier. While only few articles published in 2002 could be expected not to be in the databases by 2003, some articles published in 1997 were only appearing in the databases as late as 2003. Traditionally, EMBASE has been found to index faster than MEDLINE, thus supporting the argument to search multiple databases . FurtherThis systematic review involved significant financial and human resources over a 3-year period [1. In contrast with RCTs, guidelines for search strategy of systematic reviews of observational studies in general, and incidence/prevalence studies in particular, need to be developed.2. Searching beyond the major electronic databases such as MEDLINE or EMBASE is necessary to identify studies in journals from less developed countries.3. Regional databases indexing citations from local journals not reaching MEDLINE are especially relevant for this type of systematic review. They need to be fully functioning and made worldwide accessible. A network for accessing the full text of these articles would be helpful.The author(s) declare that they have no competing interests.APB, LS, AMG, TA and LH developed the search strategy for the systematic review. APB and LS analysed the data and wrote the manuscript. AMG, TA and LH participated in the interpretation of results and provided key comments on the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:Detailed search strategy for electronic databases used for the systematic review.Click here for file


Sub-national figures providing information about the wealth of the population are useful in defining the spatial distribution of both economic activity and poverty within any given country. Furthermore, since several health indicators such as life expectancy are highly correlated with household welfare, sub-national figures allow for the estimation of the distribution of these health indicators within countries when direct measurement is difficult.We have developed methods that utilize spatially distributed information, including night-time light imagery and population to model the distribution of income per capita, as a proxy for wealth, at the country and sub-national level to support the estimation of the distribution of correlated health indicators.A first set of analysis are performed in order to propose a new global model for the prediction of income per capita at the country level. A second set of analysis is then confirming the possibility to transfer the country level approach to the sub-national level on a country by country basis before underlining the difficulties to create a global or regional models for the extrapolation of sub-national figures when no country data set exists.The methods described provide promising results for the extrapolation of national and sub-national income per capita figures. These results are then discussed in order to evaluate if the proposed methods could not represent an alternative approach for the generation of consistent country specific and/or global poverty maps disaggregated to some sub-national level. Economy, income and poverty do affect and are affected by population's health in many ways.At broad scale, the macro relationship between life expectancy and the gross national product (GNP) is well known and has been presented in different publications .At a smaller scale a very robust relationship exist between an adult individual's income and that individual's health. This has been confirmed in the review done by Benzeaval and Judge  of sixteThe conclusion of another study performed in Tanzania  shows thIn return, high level of poverty also becomes an important factor of vulnerability for the population which becomes more exposed to diseases, especially infectious ones.By identifying the poorest area within a country it becomes possible to plan more effective intervention aimed at improving the health status of the population and therefore potentially reducing their level of poverty. As poverty tends to be clustered in specific places it is important to have access to disaggregated data. In addition to that, aggregated, national-level poverty data tends to mask sub-national variations .The development of variables that can be used as indicators of economic status is not straightforward. Even the measurement of the most basic of economic variables – such as national income levels – is fraught with problems. This reliable measurement of income is particularly problematic for low-income countries, given the lack of well-developed national income accounting methods and the large size of the "informal" sector in these economies. These problems are compounded when information is sought on the spatial and temporal changes in economic activity . Recent However, the use of self-reported income for measurement of economic status is widely regarded to be problematic . In a crFor all these reasons, most of the national household surveys, such as the World Bank's Living Standards Measurement Study (LSMS)  and manyIf sub-national level consumption figures are therefore available for most low income countries, through the use of these tools, the data they are producing is not comparable over countries making it impossible, for the moment, to build a consistent global map of poverty, or food insecurity, disaggregated to some sub-national level. In this context the growing uses of Geographic Information Systems (GIS) as well as the generation of new geocoded data sets might offer new perspectives in order to produce globally consistent poverty maps and help predicting the distribution of welfare at the sub-national level when reliable sub-national data are still not available. As the consumption indicators produced are not comparable over countries this attempt is done using income per capita expecting that this indicator might be comparable.In terms of data, satellite imagery is offering great potential for global data sets depicting weather patterns plus the physical and biological environment. If most of the data sensed concerns bio-physical parameters (e.g. clouds and vegetation...) there is one parameter, sensed by some satellites that can be used in the socio-economic context: night-time light.In their publications on the use of this parameter, Elvidge et al. -12 and ] compare2 and  coMore recently, an attempt to obtain a global map of socio-economic parameters at the sub-national level has been made by Doll et al. . In theiThe methodology presented in this paper follows a different approach, using other parameters in combination with light in order to directly predict GDP per capita and adjust the results to specific conditions. It also demonstrates the role played by other environmental and socio-economic factors on this prediction.The analysis performed at both the country and sub-national levels as well as the results obtained are presented in the coming sections.The objective of the country level analysis is to go beyond the observations done so far [-13 and ] and to The relationship between area lit and GDP shown by Elvidge et al. in 1997  has alreStarting from this result, a new set of analysis is performed using the different parameters described in the methods section. For the parameters stored in grids (light and surface area) the GIS tool is used in order to extract the figures based on the country delimitation.The first set of result are reported in Table - Logdp: log of the GDP figure (expressed in ppp)- Logdppc: log of the GDP per capita figures (expressed in ppp)- Lopopun: log of the UN population figures2)- Losurfli: log of the area lit - Losurfco: log of the surface area of the country  the relationship is expected to be linear in logs,b) if there is heteroskedasticity, the log form is one way to remove the problem,c) the parameters can be interpreted as elasticities if both the dependent and the independent variables are in logsThe following observation can then be extracted from Table 1) Three of the light parameters are correlated to each other (lotofre, lonbrpix and losurfli). The forth one (lomeanf), not correlated to the previous ones, seems to contain a different information content connected to light,2) Apart from the mean frequency of light observation all the parameters are more highly correlated with GDP rather than with GDP per capita,3) There is no significant difference in the values obtained for expressing the correlation between the first three light parameters (lotofre, lonbrpix and losurfli) and GDP. Lomeanf shows a lower correlation with GDP,4) Population presents a high correlation with GDP but none with GDP per capita. The same is observed for the surface area of the country,5) The correlation between the first three light parameter mentioned under point 1) and population is good which is not the case with the log of the mean frequency of light observation,6) A high correlation exists between the surface area of the country and the population.The above-mentioned observations seem to indicate that the high correlation coefficient observed between the 3 light parameters (lotofre, lonbrpix and losurfli) and GDP is explained by the strong correlation between these parameters and population and the one between population and GDP. On the contrary the mean frequency of light observation shows a stronger correlation with GDP per capita. By predicting GDP per capita instead of GDP we therefore avoid any circularity in the model.Based on all the observation made it is decided to consider the use of the following variable in order to model GDP per capita at the country level:- the mean frequency of light observation as this variable present the highest correlation with GDP per capita,- the total frequency of light observation as this parameter provides the highest correlation with GDP per capita among the other three light parameters found to be correlated to each other (lotofre, lonbrpix and losurfli). This parameter also contains more variability than the number of cells or the area lit which is an advantage when working with small areas that could be completely highlighted.- the total population and surface area of the country. Even if these parameters do not provide a good correlation with GDP per capita they are a necessary adjustment factor for the light and population variable (density).As the correlation between GDP per capita and the different variables may not be linear the correlation existing between the log of GDP per capita and the square of the log of these variables is analysed. This analysis shows that the square improves the prediction only for the two selected light parameters  which are used as additional variables, the square of the log of population and the surface area of the country being not used.From that point, 63 combination of the 6 variables kept for the analysis are tested in order to find a regression for modelling the log of GDP per capita at the country level. This is firstly done using the full data set, then trying to analyse the role of the climate and the one of the GDP composition by sector. Table The plot of the observed log of GDP per capita versus the predicted figures obtained with this regression as well as the plot presenting the residuals is reported in Figure The application of this model results in a significant over estimation of GDP per capita for 10 countries  and an under estimation for most of the high income countries presenting a GDP per capita figure higher than 12'500 US$. This can be better visualized by transforming the log of GDP per capita into GDP per capita figures for the 171 countries, creating a new graph Figure  similar Figure There are however some countries for which the model gives a good estimate of GDP per capita, such as Qatar (QAT), United Arab Emirates (ARE); Finland (FIN), Sweden (SWE), Monaco (MCO), Canada (CAN), Norway (NOR). Finally, the model gives a clear overestimation of GDP per capita for the United States of America (USA).The following explanation can be given for these countries:- "over lighting" due to an above-average wealth of the country (e.g. for Qatar, the United Arab Emirates and maybe Monaco),- Two explanations are possible regarding the Nordic European countries that appears in this list (Norway, Sweden and Finland) connected to the fact that the night-time light grid that is used in the context of this work is based on data collected during winter: a) specific climatic conditions during winter requiring excessive lighting of the infrastructure which may be at the origin of their location in the graph (the same observation is likely to be done for Canada). b) snow may be at the origin of an over estimation of income in these countries as snow makes lights look bigger and brighter than they appear in the no-snow condition.- The position of the United States in the graph is related to the fact that the square of the log of the total frequency of light observation is the highest observed in the entire sample. Several hypothesis are proposed in order to explain this situation. This includes the fact that light is more easily spread due to the big habitable surface, electricity is cheap and the road network very wide.The Root Mean Square Error (Root MSE) observed when applying this model is of 4000 US$. 27 countries are presenting an error higher than this value. The 5 countries with the highest error are: Luxembourg, Switzerland, Austria, Australia and Germany. The 5 lowest error are observed for: The Federal State of Micronesia, Burkina Faso, Haiti, Mali and Rwanda.Trying to improve the prediction of GDP per capita at the country level by including other variables we can observe that grouping the countries according to their agricultural level is giving better results than grouping them by climatic type.The figures given by the World Bank  or by thIn Figure - below 5 % (38 countries): lopopun, lotofre, losurfun (Adj R-squared: 0.5785)- between 5 and 10 % (28 countries): lopopun, lotofre, losurfun (Adj R-squared: 0.6289)- between 10 and 25 % (55 countries): lopopun lotofre2 losurfun (Adj R-squared: 0.5463)- above 25 % (49 countries): lopopun lotofre losurfun (Adj R-squared: 0.5512)We can observe the same combination of variables (lopopun lotofre losurfun) in 3 of the 4 grouping but the coefficients for each of these variables are significantly different.Figure From the 170 countries used in this analysis, 33 countries are presenting an error higher than the Root MSE (2877 US$). The 5 countries presenting the highest error are: Luxembourg, Australia, Switzerland, New Zealand and Singapore. The 5 lowest error are observed for: Chad, Somalia, Bangladesh, Iran and the Republic of Moldova.The sub-national level analysis has two objectives: to examine the possibility of applying the country level approach to the sub-national level on a country-by-country basis and to explore the possibility of generating global or regional models to be used for countries where sub-national data are either missing or are deemed to be unreliable.This analysis is based on the log of the same parameters successfully used at the country level. The population, total frequency and mean frequency of light observation as well as the surface area figures are extracted from the grids described in the methods section using the boundaries of corresponding sub-national administrative or statistical units.In order to keep the consistency with the values used for country level analysis, the sub-national figures for the population and the total frequency of light observation are adjusted by applying an homogeneous factor and rounding the resulting figures to the closest integer number.The analysis of the correlation factor existing at the sub-national level between each variables and GDP per capita (expressed in log) shows significant heterogeneity from one country to another. For few countries, some variables even appear as being not significant for the prediction of GDP per capita at the sub-national level. The list of variables observed in the country specific regression giving the best prediction vary therefore also from one country to another. In this case we do not look for models that were based on significant variables but take the one that is presenting the highest Adj R-squared value as the number of observation is generally low. The Adj R-squared obtained for these regressions varies from 0.2492 for The Netherlands to 1.00 for Portugal.This analysis indicates that it may be difficult to find a universal global or regional model that could be applied at the sub-national level. It is nevertheless important to underline the fact that at least one of the light variables is present in all the regression which is not the case for the population or the surface area of the sub-national units.For 3 countries  the high correlation existing between GDP and population seems to indicate that these sub-national figures have been generated using a linear model based on population only. This finding demonstrates that not all reported sub-national data are reliable and emphasizes the need for independent methods for generating sub-national estimates. The sub-national level data for these 3 countries are therefore taken out from the sample for all the analysis reported in this publication.Figure Table Among these 97 units, 31 are containing at least one city with a population larger than 1,000,000 inhabitants . Their presence in the list can be explained by the high concentration of buildings which represents vertical highlighted structures for which the satellite sensor is not able to capture the total intensity of the light being produced. This also confirms that in some countries the capital cities are more highly lit than their in country counterparts. Five other units contains oil and/or gas production infrastructure (2 in Indonesia, 2 in the USA and 1 in the Russian Federation). The separation of the lights produced by gas flares from the city lights could be the explanation for the under prediction of income per capita in these units. The difficulty in these cases is to know if the income produced by this activity remains within the concerned unit or goes directly to the government or even outside the country. This observation is to be extended to the offshore infrastructures that should also be considered in the model.Even if no particular characteristics are identified for the remaining 61 units, these results illustrate the possibility to transfer the approach developed at the country level to the sub-national level on a country-by-country basis. This analysis also demonstrate the need to have income figures for some sub-national units in order to find the regression that provides the extrapolated figures for the remaining ones. This concerns units containing big cities (more than 1,000,000 inhabitants) and units containing oil and/or gas production. Additional analysis have to be performed in order to make sure that these are the only cases or if other specificity also have to be taken into account.An out of sample analysis should also be performed in order to determine what is the smallest sample  necessary to obtain a regression providing prediction of acceptable quality.As the availability of disaggregated income data is very poor, and despite the observation made in the previous section regarding the heterogeneity of the country specific sub-national model, tests and analysis are done in order to see if it is possible to generate a model that would allow the generation of sub-national income per capita series for countries where no sub-national figures are available. Four approaches are used:- application of the country level model described in Table 2- generation of a model based on all the sub-national data (with and without country dummy)- generation of a model grouping the countries by climatic types- generation of a model grouping the countries by agricultural level using the GDP composition by sectorThe best results being obtained with the last approach, only this one is described in details in this section. For the other approaches only the major findings are reported.Applying the country level regression to the sub-national data set and presenting the results on a summary graph  gives an acceptable prediction for sub-national units presenting a GDP per capita figure bellow 5,000 US$. Above this limit the application of this approach under estimate GDP per capita. When we generate country specific graphs we can observe that the important variety of income per capita figures at disposal in the sample is in fact at the origin of the observation done earlier on the summary graph and not the result of a good prediction for each country considered in the sample. This emphasizes the need to make country specific graph for analysing the results.This analysis also demonstrates that applying the country level regression to the sub-national level may introduce an important quantitative error. In this regards, the attempt done by Doll et al. in their publication  should oWhen using the entire sub-national sample for generating one unique regression, the combination of variables giving the best results (Adj R-squared = 0.7692) is the same set found for the country level model  and is presenting an important advantage due to the existing correlation with the country level model we can not consider that the results obtained are of sufficient quality for applying it to other countries where we would not have any sub-national figures.Grouping the countries part of the sub-national data set by climatic types improves the prediction of GDP per capita for Bangladesh, France, India, The Netherlands, The Russian Federation, Sweden and Spain. This improvement mainly concerns sub-national units presenting an observed GDP per capita higher than 10,000 US$ and is not related to a particular climatic type.When the sub-national sample is grouped according to the percentage of GDP associated with agriculture in each country, using the same cut off point than for the country level analysis Figure , the fol- Below 5 % of GDP due to agriculture: Austria, Belgium, Finland, France, Germany, Portugal, Slovenia, Spain, Sweden, the Netherlands and USA,- Between 5 and 10 %: Argentina, Brazil, Greece, Mexico, Russian Federation and South Africa,- Between 10 and 25 %: China, Indonesia, and Thailand,- More than 25 %: Bangladesh, India and Mozambique.The regression found for each group (based on significant variables except for the country specific constant and the country dummy) are presenting an Adj R-squared varying from 0.5731, for the countries presenting a percentage of GDP due to agriculture between 5 and 10 %, to 0.8013 for the countries with a percentage higher than 25 %. Like for the grouping by climatic type, the list of variables found is different for each group. Figure Using this approach, 108 sub-national units are presenting a prediction error bigger than the Root MSE, which represent a mean percentage of units by country of 21 %. The countries for which this grouping induces a significant improvement of the prediction, compare to the models used previously, are: Bangladesh, India, Belgium, Spain, France, the Netherlands, Portugal, Slovenia, Sweden, USA, Finland and the Russian Federation which represents 5 more countries than when using the grouping by climatic types. We can also observe that this improvement does mainly concerns countries were agriculture represent less than 5 % of the country GDP. Only exceptions: Bangladesh, India and the Russian Federation.Even so, 3 of the countries for which the unique regression with dummy variable was not capturing the variability of GDP per capita before (Mexico, Argentina and Greece) are still presenting the same type of results.Due to the small number of observation for each grouping it is not possible to identify if a correlation exists between the residuals observed at the country level and the country specific sub-national constant making it difficult to generate a regional model for these groupings using the same approach than the one described previously Figure .This study demonstrates that night-time lights data are useful in generating estimates of both national and sub-national GDP per capita figures. Because night-time lights are produced with zero reliance on national reporting data, they provide an independent measure of economic activity.The country level results reported here confirm the conclusion given by Doll et al.  regardinBy introducing parameters other than area lit in the regression we have demonstrated the possibility of independent estimation of GDP per capita at the country level with a high level of confidence. This offers an interesting possibility for completing country level data sets for which data are missing or are in error. Special attention should nevertheless be paid to small densely populated territories with high level of lighting (e.g. Singapore and Monaco). In these areas, the models tend to underestimate GDP per capita as light produced by vertical infrastructures like high rise buildings fails to expand the area of lighting. This effect may be reduced if brightness information on the lights is available.The graph reported in Figure The result demonstrate that the approach developed for the country level can also be apply at the sub-national level but only through the generation of country specific models. Figure In this regard the possibility to generate country specific model for the extrapolation of sub-national income per capita figure is offering an interesting solution for countries where sub-national data on welfare are not available or where the application of other methods  would be difficult.Nevertheless, the fact that income per capita figures are necessary for some sub-national units in order to generate the country specific model represent a limitation to the application of this approach. If the analysis done in the context of this work already gives an indication of the type of units for which it would be necessary to have a good estimation of the income per capita figures additional work would be necessary in order to confirm this list, maybe add other type of units and also have a better indication regarding the minimum number of units for which input data are needed to insure a good prediction. In addition to that, further analysis are also needed in order to define the level of desegregation to which it would be possible to go based on the developed approach. It will finally be necessary to consider including the amount of light due to gas or oil production infrastructure in the model if the income they are producing stays within the sub-national units where they are located.Despite the caveats, the results obtained clearly demonstrate an interesting potential for making independent estimates of GDP per capita at the sub-national level especially for low income countries where the prediction obtained are of good quality and the need for them definitively the most important. Improving the data sets required to operationalize this approach may be far easier than improving the national reporting of sub-national economic data.It would nevertheless be important to compare the results obtained by this approach with other methods also producing sub national estimated using consumption indicators in the context of poverty mapping exercises [ and 17]17]]. In Trying to generate a consistent global or regional poverty map desegregated to some sub-national level, the grouping of the sample at disposal by climate or percentage of GPD due to agricultural level is definitively improving the prediction but does not provide us with a model generating consistent estimates for all the countries. Between these two grouping the preference again goes to the second one which improves the estimation for the middle and high income countries. The result obtained when applying this model to South Africa as well as the distribution of the prediction error expressed in percentages are reported in Figure In this regards, the analysis performed is not offering us the possibility to generate consistent global map showing the sub-national distribution of income per capita figures.These type of exercises are sorely needed to improve our knowledge regarding the health and well being of people in the poorest areas of the world. In this context night-time lights remain a useful data set for the evaluation of the impact of international efforts to improve the economic and therefore health conditions of these populations. It would therefore be important to pursue the type of work described in this paper and to see if the results obtained could not be improved.For both the country and the sub-national level model, the combination of the two groupings used in the context of this present work could for example improve the proposed models but this would have to be confirmed through additional analysis.The more recent global night-time light mosaic that NOAA has compiled is another element which might improve the results presented in the context of this work. This new global mosaic has the advantage of covering a longer period of observation (full years instead of the six month composite used in this study) which would improve the homogeneity of the distribution of the number of observation per pixel and also reduce the effect of the snow observed on images collected during winter. These new products were processed with major improvements in the exclusion of all but the highest quality segments of the individual orbits. They also include one information that was missing in the data set used for the context of this work: the digital number brightness of the lights. It has been found that adding in brightness of the lighting greatly improves the relationship to variables such as electric power consumption and GDP. Even if some difficulties to make products with brightness values remains, due to saturation in urban centers and the lack of on-board calibration, this new generation of grids could allow a significant improvement of the models described in the present paper. Another advantage of this data set is that the NGDC is producing a full global composite for each year from 1992 through 2004 allowing therefore for trend analysis.Before being able to test such new source of spatially distributed information it would be important to correctly address the question of the lack of documentation regarding the methods used for generating sub-national income estimates reported by individual countries in case this indicator would provide better results than consumption when trying to generate a consistent global poverty map. Inclusion of erroneous data may lead to misleading interpretations .Such discrepancies also underline the value of high quality geospatial data for use in making independent estimates of economic activity. This for example includes standardization of the basic vector GIS layers  in order to insure a proper use of the information collected at the sub national level or stored in different raster layers (night-time light and population for the present work). Among the different initiatives that are trying to answer the need for standardization in this area we can mention the Second Administrative Level Boundaries data set project (SALB). The first objective of this project is to create a redistributable Second Administrative Level Boundaries global data set (SALB) representative of January 2000 to be used with the GIS technology. The information finally collected has extended the period of representativity of the database for finally covering the period 1990-present. A process has also been put in place in order to insure the updates of the database in the future. You can find all the relevant information about this project as well as the data already available on the project web site [Finally, the shifts observed between all these layers of geographical distributed information is emphasizing the need for defining a "master" that could be used as a ground reference when generating or working with them. If the precision of its georeferencing is confirmed, the global mosaic of satellite images that are now publicly available (e.g the Landsat mosaic) could for example constitute this master.In order to perform the analysis at the country and at the sub-national level it was necessary to compile existing data sets on income as well as geographically distributed parameters that would be used to model these figures using a GIS. These concerns:- night-time light imagery- population- international and sub-national boundaries- surface area- climateThe data sets used for the context of this paper are presented now. In order to insure the consistency of the analysis, all the data sets compiled or created were adjusted to 1995, which is the year of representativity of the only night-time light grid available at the beginning of this research.We utilized GDP per capita data that have been collected at both the country and sub-national level. For the country level, GDP in International Dollars (GDP I$) for 171 countries have been calculated for the year 1996 from GDP figures expressed in local currency unit using price level data  and wereFor the sub-national level, GDP figures representative of the first or second administrative or statistical level have been collected for 26 countries (653 units) from the 5 continents Table . These fThe night-time light grid used has been provided by NOAA's National Geophysical Data Center (NGDC). This grid data set is the result of a 6-month 1 km resolution composite based on images collected between October 1994 and March 1995 by the U.S. Air Force Defence Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) . Only th- the number of cells highlighted at night- the area lit (surface area being highlighted at night) which is giving an indication of the extension of the highlighted surfaces,- the total frequency of light observation (obtained by adding the percents frequency of light detection observed in each cell on a given surface) which is giving an indication of the total intensity of the highlighting- finally, the mean frequency of light observation, in the highlighted areas, which gives us an indication of the dispersion of this intensity. For example: one pixel with a 100 % of light observation or 100 pixels with 1 % of light observation are giving the same value for the total frequency of light observation but a totally different figure for the mean frequency of light observation and area lit.These information being stored in a grid it is possible to extract them at the country or sub-national level for the analysis.Regarding population, the country level data that have been used are the UN population figures for the year 1995 . For theClimate is known to have an important influence on many elements on the earth surface, including human behaviour and well being , and alsFive major climatic types are recognized in this system, each type being designated by a capital letter (each of them being divided into sub types):A – Tropical moist climates: all months have an average temperature above 18 degrees celsius.B – Dry climates: with deficient precipitation during most of the yearC – Moist mid-latitude climates with mild wintersD – Moist mid-latitude climates with cold wintersE – Polar climates: with extremely cold winters and summersThe delimitation of the units of analysis , corresponding to the GDP figures collected for the context of this study, have also been prepared in a format that could be used in the GIS tool. In order to insure the consistency from one country to another the delimitation of the international borders has been adjusted to 1995.Finally, the surface area of the units of analysis has also been included in the data set.Two types of software have been used in analysing national and sub-national income: a Geographic Information System (GIS) and a statistical package. In order to take advantage of the specificity of the two modes of representation observed in the compiled data set (vector and raster) we have worked with the ArcView GIS software (version 3.2) for the vector part complemented by the Spatial Analyst extension (version 1.1) for the raster part. As no statistical analysis could be done directly in the GIS software the statistical support was provided by the STATA software (Version 5).