using statistical software and web technologies in ...marin vlada 1, adriana sarah nica 2 (1)...

Using statistical software and Web Technologies in analyzing information on detection and monitoring of somatic and psycho-

behavioural deficiencies in children and adolescents

Marin Vlada1, Adriana Sarah Nica2

(1) University of Bucharest, Department of Mathematics and Computer Science, Romania, E-Mail: vlada[at]fmi.unibuc.ro

(2) University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania, E-Mail: adisarahnica[at]yahoo.com

Abstract

The paper addresses the project objectives CEEX Research and Development (financed by NASR-National Authority for Scientific Research) DEMODEF (detection and monitoring of somatic and psycho-behavioural deficiencies in children and adolescents) as envisages research and pilot study results will provide a clear, concrete, on physical development and physical and psycho-behavioural shortcomings of youth. Investigations means used to generate project information grouped by several categories of parameters. This information is analyzed using databases and statistical analysis provided by EpiInfo software (EpiInfo is a trademark of the Centres for Disease Control and Prevention-CDC) and SPSS.

Keywords: Statistical Software, Web Technologies, Analyzing Information

1 Introduction and Motivation DEMODEF is a project of Research and Development CEEX financed by NASR (National Authority for Scientific Research of Romania) witch has as main objective “Detection and monitoring of psycho-behavioural deficiencies in children and adolescents (aged 10-18 years) to realize a pilot study to change perceptions and establish accountability for all involved in the complex process of spring growth and development of children and adolescents” Vlada and Nica [1, 8]. Proposed study is a dynamic study over two years. Growth period in which this study shall be made as early detection and swift implementation of remedial treatment there are an optimal time , proposed to obtain notable improvements. This is also why the reason for choosing as age 10-14-18 years, period in witch physical deficiencies appears and aggravates. After the initial investigation there will be proposed corrective programs, witch efficiency could be evidenced in the next investigations.

1.1 Research and investigations The project will investigate, in dynamics, school population in the growth period, period in witch anthropometrical values of the individual changes in close relation with age and found deficiencies. There will be studied significant elements close to growth and locomotor’s development and psycho-behavioral to identify functional disorders and deficiencies. Thus, the locomotors system will track the most important weakness: the spine, chest, hip, knee and foot. From psycho-medical point of view groups will investigate the following parameters: cognitive, emotional, behavioral, pathological and social climate: level of cognitive processes (attention, memory, intelligence) affective disorders (anxiety, depression, emotional liability) personality

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changes, behavioral changes. The data obtained from this study will allow the elaboration of some programs; both with prophylactic and therapeutic character, applicable on a large scale at this group of population, fro the optimization of the health status and to provide a harmonious growth of children and adolescents. The project is realized by six partners (universities, institutes, and research centers) scientific coordinated by Prof. Dr. A. Nica. Novelty of the project is trying to group a series of psycho-behavioral parameters of child development in a related study correlated with the interrelation of these children with main representatives of the family and school (teachers, peers). Data obtained from these assessments are organized in a database that will allow a detailed statistical analysis which will determine the variation of each parameter taken in study and the possible correlation between analyzed deficiencies. Also, from previous research on this subject, trial is conducted on a sample of approximately 1000 subjects. Internally, in 2001 an investigation was done (INMF and CCEFS) on a sample of 600 students (14-18 years), and internationally in 2005 an investigation was done (Zwaannswjk et al.) on a sample of 2449 children and adolescents (14-17 years). More recently, the U.S. has conducted an investigation on a sample of 9878 students, it is incomplete because only uses the parents answers for some statistical data, in SDQ questionnaire [2, 3].

2 Create questioners and data entry According to research and plan implementation stages of the project are defined the following models:

• functional model of clinical investigation, anthropometric and psychosocial behavior in children and adolescents (aged 10-18 years)

• sample clinical assessment of functional, psycho-behavioral and anthropometrical deficiencies in children and adolescents (aged 10-18 years)

If psycho-behavioral investigations using reference: Goodman R, Renfrew D, Mullick M (2000) Predicting type of psychiatric disorder from Strengths and difficulties Questionnaire (SDQ) scores in child mental health clinics in London and Dhaka. European Child and Adolescent Psychiatry, 9, 129-134. Algorithm calculates and questionnaire scores for SDQ: Emotional symptoms (emotional disorders); Conduct Problems (conduct problems), hyperactivity scales (hyperactivity), Peer Problems scales (relationship problems), prosaically scales (prosaically behavior), and the total difficulties score (score total difficulties) impact scores (scores of impact - assess the impacts of social malfunctioning and the difficulties suffered by children in general) Goodman, Renfrew, and Mullick [2, 3].

SDQ analyze the student responses, teacher and parent corresponding to the same set of questions. The answers to these questions have led a number of 25 variables, with values taken from them. SDQ predictive algorithm offers the calculation of scores generated using data from student responses, teacher and parent. Difficulties are analyzed following categories: behavioral problems, emotional problems, hyperactivity problems, psychiatric difficulties. 2.1 The utilization of statistical software Epi Info Responses from questionnaires were retrieved via file type questionnaire (view) designed to aid data entry with the program Epi Info. This is a program specific for statistical processing, initially utilized for epidemiology by CDC. The files created with this program are compatible with Microsoft Access, SQL, ODBC databases, HTML. ”With Epi Info™ and a personal computer, epidemiologists and other public health and medical professionals can rapidly develop a questionnaire or form, customize the data entry process, and enter and analyze data. Epidemiologic statistics, tables, graphs, and maps are produced with simple commands such as READ, FREQ,

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LIST, TABLES, GRAPH, and MAP. Epi Map displays geographic maps with data from Epi Info™.” [4, 7].

Epi Info is a software package for processing data organized and systematic form of questionnaire results of studies to be included in communications and reports. Designed primarily for applications in epidemiology, Epi Info can be successfully used in medical and data processing from outside its package of features including data management and statistical programs such as those offered by SAS, SPSS [5,6] In an facilities included one system whose main advantage is that it is permitted free copying and distribution [7]. Details and instructions for use can be found in paper [7]. The main controls parts of the program Epi Info are:

• Make View, which is a text editor used to define the fields used in entering data on one or more pages of a questionnaire (View)

• Enter date, built with polls showing View Make controls the input settings using the codes specified in Make View, has records and search capabilities;

• Analyze Data, which is used to analyze the recorded data files created not only with Epi Info, and with dBase, FoxPro, Excel, etc. These files can contain lists, frequencies, tables, charts, specific epidemiological data;

• Create maps, which is an epidemiological tool used to create maps; • Create Reports, which is used to generate reports.

To create a file-questionnaire will be used Make View. It will use the commands: File → New

→ File name (database name: nume_EPI) Name → Open the view ("Chest1" the name given to the questionnaire). In the left-hand page are three options concerning the management page of the questionnaire (Add Page - add a new page at the end of the existing Insert Page - adding a new page between two existing Delete Page - eliminating the current page) and programming control program that enables the verification of certain operations, thereby avoiding errors that can occur in data input [7].

Placing fields on the current page of the questionnaire, as indicated shown, is made with a click right mouse button from his position in that field is desired appearance (for determining the grid position is useful). As a result, you'll see Field Definition dialog box to be placed in field characteristics: name, type, size, limitations of values, codes, laws, and values.

Figure 1. A file-questionnaire and Field Definition dialog box


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Entering can be done directly from the File menu, ordering Enter date. Other possibilities, after leaving the Make View module of Epi Info main page or choose the Enter Data module directly, or to order Enter Data Programs menu. In this case opens the questionnaire developed, choosing the project and the corresponding views. Initially be placed at least four entries.

Figure 2. A file-questionnaire for Enter data

To perform statistical analysis of primary data using Analyze Data module. In this way using

several commands that you can choose the window controls on the left. Command execution results are shown in the top right (called Output Analysis). In the lower right window (entitled Programme Editor) will show orders / sets of commands that were previously performed, also may introduce new commands in command line mode. We can choose commands are grouped in the left window, in some groups.

Figure 3. Excel spreadsheets corresponding SDQ algorithm

Distinguish such work orders data (grouped into "Date"), operating on the variables (obviously

grouped in "Variables"), selection commands (grouped in the "Select / if"), the primary statistical analysis commands (grouped into "Statistics"). Read (import) is the command used to start any work sessions Analysis module. It is used to retrieve data from a file, data will be used for further processing (up to a new Read command). Epi default data format is 2000, but it can be changed so


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that it is possible to take data from other file types (e.g. various versions of Excel, various versions of FoxPro, Paradox or hypertext documents). Epi Info program is accompanied by several "projects" for example and self-learning, of which the simplest is Sample.mdb. For analysis and statistical calculations on data in files using the group "Statistics" which provides commands List, frequencies, means, Graph. Graph command in Group "Statistics", is used to make graphical representations of variables from a data file. The project DEMODEF for SDQ questionnaire was developed three questionnaires-file according to student feedback, teacher, parent respectively (in total for each questionnaire were created 915 records). These information files were used for statistical calculations performed with the software Epi Info, SAS, SPSS and Excel. If SDQ questionnaire was needed to convert database files to Excel. Responses from students, teacher, and parents that was stored in separate databases, namely the corresponding spreadsheet information.

3 The Utilization of SDQ Algorithm

Algorithm was used "Strengths and difficulties Questionnaire" SDQ scores. Analyzed these categories of difficulties:

• behavioural difficulties; • emotional difficulties; • hyperactivity difficulties; • psychiatric difficulties.

Algorithm was used on 101 subjects in England and Bangladesh on 89. Level prediction correlation between SDQ and an independent clinical diagnosis in these cases was significant: Kendall parameter between 0.49 and 0.73 and p <0.0001 probabilities that the prediction on this method to be fair is 81-91%. It is rather false positive than false negative [3].

The algorithm is sufficiently robust to be used in practice to determine the mental health of children. 3.1 SDQ: Generating scores in SAS The scoring algorithm is based on the 25 variables plus impact items for each questionnaire. The algorithm expects to find these variables with specific names: the first letter of each variable name is 'p' for the parent SDQ, 's' for the self-report SDQ and 't' for the teacher SDQ. After this first letter, the variable names are as follows:

consider

Item 1 : considerate

restless

Item 2 : restless

somatic

Item 3 : somatic symptoms

shares

Item 4 : shares readily

tantrum

Item 5 : tempers

loner Item 6 : solitary

obeys Item 7 : obedient

worries

Item 8 : worries

carin Item 9 : helpful if someone hurt


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g

fidgety

Item 10 : fidgety

friend

Item 11 : has good friend

fights Item 12 : fights or bullies

unhappy

Item 13 : unhappy

popular

Item 14 : generally liked

distract

Item 15 : easily distracted

clingy

Item 16 : nervous in new situations

kind Item 17 : kind to younger children

lies Item 18 : lies or cheats

bullied

Item 19 : picked on or bullied

help out

Item 20 : often volunteers

reflect

Item 21 : thinks before acting

steals Item 22 : steals

oldbest

Item 23 : better with adults than with children

afraid

Item 24 : many fears

attends

Item 25 : good attention

ebddiff

Impact question: oveall difficulties in at least one area

distress

Impact question: upset or distressed

imphome

Impact question: interferes with home life

impfrie

Impact question: interferes with friendships

impclas

Impact question: interferes with learning

implies

Impact question: interferes with leisure

For each of these items, if the first response category (not true, no, not at all) has been selected, this is coded as zero, the next response category (somewhat true, yes-minor, just a little) is coded as one and so on. For each informant, the algorithm generates six scores. The first letter of each derived variable is 'p' for parent-based scores, 's' for self-report-based scores and 't' for teacher-based scores. After this first letter, the names of the scores are as follows:


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emotion

emotional symptoms

conduct

conduct problems

hyper

hyperactivity/inattention

peer

peer problems

prosoc

prosocial

ebdtot

total difficulties

impact

impact

Figure 4. Excel spreadsheets corresponding SDQ algorithm

3.2 SDQ: Predictive Algorithm in SAS A computerized algorithm predicts child psychiatric diagnoses from the symptom and impact scores derived from Strengths and Difficulties Questionnaires (SDQs) completed by parents, teachers and young people. The predictive algorithm generates "unlikely", "possible" or "probable" ratings for four broad categories of disorder, namely conduct disorders, emotional disorders, hyperactivity disorders, and any psychiatric disorder. The predictive algorithm is based on up to twelve input variables:

phyper

SDQ hyperactivity score from parent SDQ

thyper

SDQ hyperactivity score from teacher SDQ

shyper

SDQ hyperactivity score from self-report SDQ

pconduct

SDQ conduct problems score from parent SDQ

tconduct

SDQ conduct problems score from teacher SDQ


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sconduct

SDQ conduct problems score from self-report SDQ

pemotion

SDQ emotional symptoms score from parent SDQ

temotion

SDQ emotional symptoms score from teacher SDQ

semotion

SDQ emotional symptoms score from self-report SDQ

pimpact

SDQ impact score from parent SDQ

timpact

SDQ impact score from teacher SDQ

simpact

SDQ impact score from self-report SDQ

"." = value for relevant score missing

The algorithm generates four output variables:

sdqed

prediction of an emotional disorder (0 = unlikely, 1 = possible, 2 = probable)

sdqcd

prediction of a conduct disorder (0 = unlikely, 1 = possible, 2 = probable)

sdqhk

prediction of a hyperactivity disorder (0 = unlikely, 1 = possible, 2 = probable)

anydiag

prediction of any psychiatric disorder (0 = unlikely, 1 = possible, 2 = probable)

Figure 5. Excel spreadsheets corresponding predictive algorithm

4 Results and Analysis Investigations Based on responses from valour stored in databases, according to the algorithm SDQ scores were generated corresponding to the seven parameters investigated: Total difficulties; Emotional symptoms; Conduct problems; Hyperactivity-inattention; Peer problems; Prosocial behaviour; Impact. The results of these calculations are presented in the following tables.


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Table 1. Answers of Children

Total

difficulties Emotional symptoms

Conduct problems

Hyperactivity-

inattention

Peer problems

Prosocial behavior

Impact

Normal 758 795 723 788 761 719 503

To limit 108 55 97 67 121 61 55

Abnormal 49 65 95 60 33 135 357

Table 2. Answers of Parents

Total difficulties

Emotional symptoms

Conduct problems

Hyperactivity-inattention

Peer problems

Prosocial behavior

Impact

Normal 716 680 724 798 656 504 713 To limit 75 79 57 68 82 53 64 Abnormal 124 156 134 49 177 358 138 Children 915 915 915 915 915 915 915

Table 3. Answers of Teachers

Total difficulties

Emotional symptoms

Conduct problems

Hyperactivity-inattention

Peer problems

Prosocial behavior

Impact

Normal 652 784 718 793 704 407 771 To limit 140 70 56 60 95 142 39 Abnormal 123 61 141 62 116 366 105

Total difficulties

Normal; 758; 83%

To limit; 108; 12%

Abnormal; 49; 5%

Normal La limita Anormal

Figure 6. The scores for “Total difficulties”


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Total difficulties

0

100

200

300

400

500

600

700

800

Normal To limit Abnormal

Youngs Parents Teachers

Figure 7. Comparative result Children - Parents- Teachers

4.1 Predictive calculation A computerised algorithm predicts child psychiatric diagnoses from the symptom and impact scores derived from Strengths and Difficulties Questionnaires (SDQs) completed by parents, teachers and young people. The predictive algorithm generates "unlikely", "possible" or "probable" ratings for four broad categories of disorder, namely conduct disorders, emotional disorders, hyperactivity disorders, and any psychiatric disorder.

Based on scores obtained from responses to student, teacher and parent have been implemented in Excel and calculating SDQ predictive algorithm given and calculated values for the four categories of difficulties: behavior difficulties, emotional difficulties, hyperactivity difficulties, psychiatric difficulties.

Formulas to calculate these values are: • behaviour difficulties =IF(OR(AND(F4=2,E4=2),AND(F4=2,E4=1)),2,IF(OR(AND(F4=0,E4=1),AN

D(F4=1,E4=0),AND(F4=1,E4=1),AND(F4=2,E4=2)),1,IF(OR(C4>=0,D4>=0,E4>=0),0,3)))

• emotional difficulties =IF(OR(AND(elevi!AT4>=6,elevi!BF4>=2),AND(profesori!AI4>=4,prof

esori!AU4>=2),AND(parinti!AI4>=5,parinti!AU4>=2)),2,IF(OR(parinti!AI4>=4,profesori!AI4>=3,elevi!AT4>=5),1,IF(OR(parinti!AI4>=0,profesori!AI4>=0,elevi!AT4>=0),0,3)))

• hyperactivity difficulties =IF(OR(AND(parinti!AH4>=5,parinti!AU4>=1),AND(profesori!AH4>=5,

profesori!AU4>=1),AND(elevi!AS4>=6,elevi!BF4>=1)),1,IF(K4>=1,2,IF(OR(AND(K4=1,H4=2),AND(K4=1,G4=2)),1,IF(OR(parinti!AH4>=0,profesori!AH4>=0,elevi!AS4>=0),0,3))))

• psychiatric difficulties =IF(OR(I4>=2,,H4>=2,G4>=2),2,IF(OR(I4>=1,H4>=1,G4>=1),1,IF(OR(I

4>=0,H4>=0,G4>=0),0,3)))


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Comparation of results

0

100

200

300

400

500

600

700

800

900

Hyp. D. Conduct D. Emot. D. Psy. D.

Unlikely Possible Probable

Figure 8. Comparation of results

Behavior difficulties

Unlikely

67%

Possible

25%

Probable

8%


Figure 9. Behaviour difficulties

Emotional disorder

Unlikely

83%

Possible

17%

Probable

0%


Figure 10. Emotional difficulties


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Hyperactivity disordery

Unlikely

92%

Possible

8%

Probable

0%


Figure 11. Hyperactivity difficulties

Psychiatric desorder

Unlikely

55%

Possible

37%

Probable

8%


Figure 12. Psychiatric difficulties

5 Conclusions The proposed issue is interesting to better understand the changes that individuals must assume the lifestyle and type of food to eliminate / improve somatic deficiencies. Research also needs knowledge of health information necessary for parents of their children. There is evidence the number of affected subjects, the degree of damage and no better identification of high risk population in this respect. The results of this first detection and monitoring in children and adolescents in Bucharest would be a prerequisite for socio-economic policies, education and health to promote and encourage healthy and harmonious development of future adults. Also the results of this research can be the starting point for similar investigations across the country.


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6 References [1] DEMODEF Project: http://demodef.googlepages.com, access July 2010. [2] Goodman R., Renfrew D., Mullick M. (2000): Predicting type of psychiatric disorder from Strengths and

Difficulties Questionnaire (SDQ) scores in child mental health clinics in London and Dhaka. European Child and Adolescent Psychiatry, 9, 129-134.

[3] SDQ (Strengths and Difficulties Questionnaire): http://www.sdqinfo.org, access July 2010. [4] Epi Info – CDC, www.cdc.gov/epiinfo, access July 2010. [5] SAS - Predictive analytics software, http://www.sas.com, access July 2010. [6] SPSS - Statistical Package for the Social Sciences, http://www.spss.com, access July 2010. [7] Tiberiu Spircu (2006): Medical Informatics and Biostatistics, Publishing House "Carol Davila",

Bucharest. [8] M. Vlada and A. Sarah Nica (2007): Analysis and results of project DEMODEF CNIV-2007, National [9] Virtual Learning Conference, Educational Software, 5th Edition, 27-29 October 2007, pp. 149-156, ISSS

1842-4708 (in Romanian), Publishing House of the University of Bucharest.

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