LINKING COMPLEX EMERGENCY RESPONSE AND TRANSITION INITIATIVE
CERTI Crisis and Transition Tool Kit

Cross-Cultural Assessment of Trauma-Related Mental Illness
(Phase II).

 

 

 

 

 

 

A Report of Research Conducted by World Vision Uganda

and The Johns Hopkins University

 

Report prepared by:

 

Paul Bolton MB BS MPH MSc

The Johns Hopkins University

 

Lincoln Ndogoni, B.A M.A

World Vision International

 

February 2001

 

 

 

This project was made possible by a cooperative agreement by the CERTI project at the US Agency for International Development, The Johns Hopkins University; and World Vision International. 

 

 

 

 

 

 

 


This project was made possible through Cooperative Agreement Number HRN-A-00-96-9006 between the US Agency for International Development and Tulane University

 

 

 

 

ACKNOWLEDGMENTS

We would like to acknowledge the following organizations and individuals for their contributions to the research: Bill Lyerly of USAID and Tom Ventimiglia, formerly of World Vision, without whose support this project would not have been possible, the staff of the World Vision Uganda and our interviewers and interviewees.  Also Bill Eaton and Bill Weiss of Johns Hopkins University, and Nancy Mock of Tulane University, Richard Mollica of Harvard University and Richard Neugebauer of Columbia University, all of whom gave invaluable advice on the design and execution of this project.

 

 

 

INTRODUCTION

 

This report describes the second field trial of a methodology for assessing mental health problems across cultures.  The first field trial was described in a previous report: ‘Cross-Cultural Assessment of Trauma-Related Mental Illness: A Report of Research Conducted by World Vision Rwanda and The Johns Hopkins University, April 2000', by the same authors.  Both trials represent a collaboration between World Vision (WV) and Johns Hopkins University (JHU) to develop a means by which non-governmental organizations (NGOs) can conduct scientifically valid assessments of the populations they serve.  The initiative is supported and funded by the USAID Africa Bureau Complex Emergency Response and Transition Initiative (CERTI). The objectives of CERTI are to develop a consensus on best health practices during and after a complex humanitarian emergency (CHE), and to strengthen the capacity of organizations providing assistance.  The focus is on Sub-Saharan Africa.

 

The study area for this trial was all of Rakai district and approximately half of the contiguous Masaka district in southwest Uganda.  The districts are largely rural and the major language is Luganda.  Most residents are poor and high death rates from HIV since the 1980s have had continuing adverse economic and social effects.  This region was chosen for this study because it is the local area of operations for World Vision Uganda (WVU).  WVU programs in this area currently focus on economic and education assistance to families and orphans affected by HIV.  World Vision currently does not offer local mental health programs although staff believe the need to be large.  The results of this field trial will serve as a prelude to mental health interventions to be implemented by WVU.

 

The background to the overall project and methodology; its rationale, theoretical basis, and explanations of the technical concepts, are described in detail in the report of the first field trial.  Therefore much of this information has been omitted from this report.  The contents here are limited to a consideration of the methods and results of the Uganda field trial, with a focus on the differences between this trial and the first trial in Rwanda.  The objectives of this trial are the same as those listed in the Rwanda document:

 

 

PROJECT OBJECTIVES

 

            1.  To create an instrument adaptation and validation process useful for NGOs and others to quantitatively assess the mental health burden of trauma at the population level across cultures and situations.

 

            2.  To use this instrument and process to assess part of the mental health burden of trauma for a local civilian population.                  

 

            3.  To use the resulting data to assess the need for interventions, form the baseline for an intervention process, and (at a future date) to help plan and test the impact of such an intervention.

 

 

METHODS                       

 

Overview       

 

As in Rwanda we focused on mental illness, this being the most severe aspect of mental health problems in terms of suffering and effects on function.  In Uganda we planned to assess depression and posttraumatic stress disorder (PTSD) as these are the major illnesses known to result from mental trauma.[1]

 

Our assessment methodology has 6 stages[2]:

 

1. Collecting ethnographic data on local perceptions of mental health.

2. Analyzing these data to determine if western concepts of mental problems (in this case PTSD and depression) are locally appropriate. 

3.  If so, using these data to adapt and translate existing questionnaires which measure these concepts.

4. Testing the validity of these concepts and questionnaires and adjusting them if necessary.

5. Using these questionnaires in a community-based survey of a random sample of the population. 

6. Analyzing the survey data to

(a) assess the prevalence and local characteristics of these mental health problems;

            (b) further test the validity and reliability of the questionnaires.

 

Stages 1-3 refer to the creation of the study instrument.  This is a composite of demographic questions, the standard questionnaires that assess mental illness, and a series of questions on function. Stage 4 refers to preliminary testing of the composite instrument in the study population.  Stages 5-6 refer to the actual survey, once the preliminary testing is completed and if the instrument is validated in step 4.  The survey provides prevalence data on mental illness and functional disability as well as further tests of the instrument’s accuracy.   

 

Ethnographic Mini-Study (stages 1 and 2)

 

The ethnographic study provides preliminary evidence of the local validity of western mental illness concepts.  This is necessary before standard mental health assessment questionnaires based on these concepts are used.  If, for example, the study failed to find evidence of depression, this would suggest either that depression does not occur locally or is not a major problem in the context of the study.  In either case, the use of standard instruments to assess depression would not be indicated.  If, instead, the study finds good evidence that depression occurs then these standard questionnaires can be used.  The usefulness of the ethnographic data does not end there.  The standard questionnaires will require adaptation and translation to make them suitable for local use.  Once this is done they must then be tested to make sure they are accurate.  The ethnographic data is vital to both these needs.

       

 

In Uganda the ethnographic study began with a short training in research and ethnographic methods for the ten World Vision Uganda (WVU) staff who were to be the interviewers.  The first ethnographic method was free listing in which knowledgeable local people generate lists in response to a standardized question.[3] In Uganda this question was :

 

‘What are the main problems that affect the people of this community as a result of HIV?’

 

‘As a result of HIV’ was included because of WVU’s special interest in problems resulting from this epidemic.  Each problem was recorded using the words of the respondent, who was then asked to provide a short description.  For problems that sounded mental or emotional, respondents were also asked whom people consulted about these problems - their role, name and contact information.  After completing this first free list the same respondents were asked to complete three more – on the major tasks and duties a person must perform regularly to care for a)themselves, b)their families and c)their community.  Each respondents described the tasks specific to their sex.  As before, responses were recorded using the language of the informants and a short description of each task was obtained. 

 

 

The data from the first free list were collapsed to provide a composite list of problems in order of the frequency they were mentioned.  This list gave us the local names and short descriptions of the major mental and emotional problems due to HIV (from the community’s perspective) and how highly people prioritized them compared with other problems.  The list also included links to local people who are knowledgeable about these issues.  The data from the free lists on tasks were similarly collapsed into 6 lists of self, family and community –related tasks, separate for each sex. 

 

 

Those identified as knowledgeable about mental health issues in the first free listing were then approached for key informant interviewing, the second ethnographic method.  The purpose was to confirm the local terms and descriptions of mental illness that emerged from the first free lists, and to develop more detailed descriptions of them.  Interviewers began by describing a hypothetical person with many of the mental health symptoms given by free list respondents - only free list symptoms that are part of the DSM criteria for depression and PTSD were used.  Interviewers then asked the informant to name and describe all the problems that these symptoms might represent.  At the second interview with the same informants (the next day) interviewers repeated the same question, to gather any additional thoughts the person may have had since the first interview.

 

The free list and key informant interview data were reviewed for evidence that depression and PTSD occur in this population.  We were primarily interested in how many of the DSM symptoms of these disorders were mentioned as the results of trauma.  Mention of most of the diagnostic symptoms would be independent evidence that these illnesses occur among this population, even if they are not classified into exactly the same syndromes as described in DSM.[4]

 

In Uganda local informants described all the diagnostic symptoms for depression.  We therefore concluded that there was sufficient evidence supporting the existence and importance of depression in this population.  In contrast, none of the characteristic symptoms of PTSD were mentioned in the ethnographic study.  The conclusion was that PTSD did not occur or was not a significant issue (at least in the context of HIV) and plans to study it were dropped.

 

Key informants not only described depression-like symptoms, but also organized them into syndromes similar to depression.  These local syndromes included several symptoms that are not part of the DSM depression criteria (see Results).  The same occurred in Rwanda.  There we conducted a pile sort activity to confirm the link between depression and these local symptoms, to confirm that they were part of the local expression of depression and could be included in the depression questionnaire.  In Uganda we decided that the confirmation of these symptoms was not important to the depression diagnosis and so did not conduct pile sorts.  However, we did include one of the local symptoms in the questionnaire by inserting it at the end of the HSCL, using the same response format (see Appendix A, question B16).  Its connection to the depression symptoms was assessed using data from the validity and survey study data (see Results).  With the elimination of the pile sorts, the completion of the key informant interviews and analysis marked the end of the ethnographic study.  

 

Finalizing the English version of the Instrument (Stage 3a)

 

Having completed the depression section of the questionnaire, the function section had to be finalized.  To assess functional disability we created a template in which respondents were asked to select the level of difficulty they experienced in completing specific tasks.  For each task the respondent is asked the level of difficulty they experience in completing that task compared with others of their age and sex.  There are 5 categories to choose from.[5]  The actual tasks inserted into the template vary with each community and are based on the most frequent responses to the three function free lists in the ethnographic study - self, family and community tasks.  Appendix A, Part A shows the template with the tasks for the Uganda study filled in.  Tasks that did not affect other people or were not really tasks were not used.  For example, many respondents in the Rwanda field trial listed prayer as an important task in caring for themselves.  However this was removed because it was not clear that inability to do this would affect others.  Many men listed ‘sending children to school’ as an important task to care for the family.  But their descriptions made it clear that their task in this regard was to earn enough money to pay the fees.  It was removed since it was covered under the ‘earning money’ task.  The most frequently mentioned of the remaining tasks were then inserted into the function questionnaire.[6] 

 

At the end of the questionnaire we included questions on whether the respondent thought they had the local syndrome identified as similar to depression in the ethnographic study, and whether other people had told them that they had it.  We also included questions on the duration of the local syndrome for the validity study (see Validity Study below).  The final part of the questionnaire was demographic questions on sex, age and education.  This completed the English version of the instrument (see Appendix A for the final English version).

 

Translation (Stage 3b)

 

Four translators were hired from the local town of Masaka.  Their qualifications were that they lived in the local area and were fluent in English and Luganda.  Three worked together to translate the English version of the instrument.  This was then back-translated by the fourth translator working in isolation.  All four translators then met together with one of the project directors to reconcile the differences between the translation and back translation.  During this meeting the results of the ethnographic study (the language used by the respondents) were continually consulted.  This was to try to ensure that words chosen for specific symptoms and problems were those known and used by the local population (as opposed to those used by the more educated or in other parts of the country).  On this basis we substituted several words chosen by the translators for words with the same meaning used by the ethnographic study respondents. 

   

 

The resulting Luganda version of the instrument was then reviewed by the group of WVU staff who had worked on the ethnographic study, and later by the 22 interviewers hired for the survey.  This resulted in some simplification in the language used to explain the instrument to the respondents.  Once this was completed the instrument was ready for pilot testing. We also developed and translated a standard consent form, using the same procedure (see Appendix D).

 

Quantitative Study Staff and Procedures

 

Twenty-two interviewers were hired locally.  Qualifications were a high school education, ability to read and write, ability to walk long distances and availability for the entire study period.  The ten WVU staff from the ethnographic study acted as their supervisors.  Their role consisted of:

 

·        assisting interviewers to find houses and identify the correct respondent

·        regularly observe and provide feedback to the interviewer on some interviews

·        re-interview 10% of respondents as a check of test-retest validity and of ‘arm chair’ interviewing

·        check all completed interviews for completeness and clarity

·        revisit all refusals to ensure that these were genuine.

 

Both interviewers and supervisors were given training in interviewing methods and survey procedures, and divided into groups of one supervisor and 2-3 interviewers.

 

Prior to each interview informed consent was obtained.  If, during the interview, it was apparent that the person was severely distressed this was reported to the supervisor who recorded the respondent’s contact information.  These respondents, and those who are diagnosed as depressed according to the interview, will be re-visited by World Vision staff to be assessed for counseling and possible referral. 

     

Instrument Testing (Stage 4)

 

Pilot Study

 

The purpose of the pilot study was to detect any problems with the interview procedure, the consent form and the instrument (including data entry), and to give the supervisors and interviewers field practice before the survey began.  Each was allocated one of the 30 selected study villages[7] in which they interviewed one man and one woman chosen by convenience.  Interviewers, supervisors and the project directors then met and reviewed the experience.  The process went smoothly and few problems emerged.

 

Validity Testing

 

Validity Study

 

Validity refers to how well the instrument measures what it is supposed to measure.  There are various types of validity, but the most important are criterion and construct validity.  These concepts were explained in detail in the Rwanda field trial report.  Briefly, criterion validity refers to the agreement between the questionnaire and an external measure (criterion) of the same construct known to be accurate (referred to as a ‘gold standard’).  This is not strictly possible with this population, since none of the usual ‘gold standards’ are available.

 

 

Instead, we conducted an alternative validity study using an alternative local standard - diagnosis by local people of local illnesses similar to depression.[8]  Supervisors revisited the knowledgeable persons identified in the first free list and asked for the names of people who had the local syndromes most similar to depression.  They also asked for the names of persons who do not have this problem.  Supervisors then assigned these people to interviewers without revealing their reported illness status.

 

 

At the end of the instrument were questions asking whether the respondent felt if they had either of the local illnesses.[9]  In the analysis we threw out all responses where the key informant and the respondent disagreed.  This was to try to ensure that we had true cases and true non-cases by reducing the number of inaccurate local diagnoses.  The remaining cases and non-cases of local illnesses formed the external criteria against which the depression instrument was compared - if depression really occurs among the population and can be accurately identified by the instrument there should be a strong correlation between depression diagnosis and the diagnosis by local people of these similar local illnesses.  We used the Phi correlation coefficient because it measures correlations between dichotomous data.[10]

 

Validity Testing in the Main Survey

 

Validity testing was not restricted to the validity study or to the depression section of the instrument.  Validity testing was also done using the data from the main survey and on both the depression and function sections of the instrument.  In some cases supervisors reinterviewed respondents using the instrument, as a check of reliability (see below).  Where there were other adults living in the house and available, they were asked to assess the respondent’s function using the same function questions.  Neither respondent or cohabiting adult witnessed the other interview.  Comparison of the results provided a validity test of the respondents’ responses regarding function.  More details of the validity (and reliability) testing done in the main survey are given in the Analysis and Results sections. 

 

Survey Sampling and Procedure (Stage 5)

 

We used a 30 cluster sampling method.  We first made a list of all the villages in the study area.  This list included estimates of the population of each village based on local government data.  Systematic random sampling was then used to make weighted selections of 30 villages to be included in the survey.  Weighted means that larger villages were more likely to be chosen.  Interviewers and supervisors went to each village and, with local advice, mapped the village.  These maps were not detailed but merely showed sections, based on separation by roads, paths, hills streams, etc, and approximately how many houses were in each section.  These maps were then used to make a random weighted selection (according to number of houses) of a sector in each village.  We needed to have a minimum of 20 houses in each village so, where a chosen sector had less than 20 houses, a contiguous sector was also chosen.

 

 

The 30 villages were allocated among the supervisors and their teams of 2-3 interviewers.  They numbered the houses in the chosen sector(s) of their villages, then used a random number table to select 20 houses.  An interviewer then visited each house and asked an inhabitant to list, in any order, all the adults (persons of at least 18 years of age) living there.  The interviewer would then select the respondent from this list, again using the random number table.  If the respondent was not there an appointment was made to return at a more convenient time, or the interviewer went and found the person if they were not far.  If the respondent refused to be interviewed that interview was marked as a refusal and no-one else from that house was interviewed. Only one person was selected from each house, giving a total sample size of 600.  This number was based on a target of 384 for a simple random sample design, a recommended cluster design effect of 1.3 (Aday, 1989), and an estimated 10% refusal rate and 10% failure rate to find the house or an eligible respondent.[11]  The inflation of the sample size by the design effect is due to the partial loss of randomization inherent in the cluster design: The multistage choice of blocks (or clusters) instead of a single choice of respondents by simple random sampling increases the risk of a non-representative sample being chosen.  Increasing the sample size is an attempt to counteract this.  The sampling method also deviates from pure randomization in the choice of a single respondent from each house, regardless of size.  This gives those living in houses with more adults less chance of being chosen.  However, to conduct true simple random sampling (the most accurate measure) would require a complete list of all the adults living in the study area and their addresses.  This is not available or feasible in this study area, as in many other parts of Africa.

 

Quantitative Analysis (Stage 6)

 

Data from each interview in the validity study and survey was entered into a computer and analyzed using SPSS[12] statistical software.  Analysis consisted of tests of validity and reliability, and assessment of depression and functional disability and the associations between them.

 

Testing Reliability

 

This was done with the main survey data only.  As discussed in the Rwanda field trial report, there are two elements of reliability that should be tested before an instrument can be used: internal consistency reliability and test-retest reliability.  The former is measured by the Cronbach alpha statistic, which is the average correlation between all questions measuring the same thing (such as depression or function) in the same interview.  As a rule of thumb Cronbach’s alphas should be above 0.7 and ideally between 0.8-0.9.[13]  The reliability of each question can be assessed by calculating the alpha with and without it.  Significant increases in alpha without the question would suggest that the question is not measuring the same thing as the other questions, and should be removed.  Studying the effect of each question in this way is called Item Analysis. We measured Cronbach’s alphas separately for the male function, female function and depression questions.

 

Test-retest reliability refers to testing reliability over time.  Supervisors re-interviewed approximately 10% of survey respondents 1-3 days after the initial interview.  We then measured the Pearson correlations between the depression scores and the function scores on the initial and repeat interviews.[14]  Opinions vary as to what is an acceptable score, although correlations above 0.7 are considered desirable (Aday, 1989).

 

Testing Validity in the Main Survey

 

We also did some instrument criterion validity testing in the main survey.  A depression severity scale was created for each respondent by adding the responses of the depression questions.  The mean score for those with no local illness was compared to that of respondents with a single illness and both illnesses.  If the HSCL is valid mean depression scores should increase in the presence of local illnesses.

    

 

Construct validity was also tested in the main survey.  This refers to how well items that are supposed to be associated with each other (for example, various depression questions) are actually associated.  Cronbach’s alpha can be used as a measure of construct validity as can factor analysis.  Factor analysis is similar to Cronbach’s alpha, except that it demonstrates which questions are most highly correlated with each other, rather than the questions as a whole.  This information can be used to understand the different elements of depression that underlie the responses.  Similarity between these constructs and hypothesized constructs support construct validity.  Both the Cronbach’s alpha and the factor analysis were done using the main survey data.  The results of the factor analysis are in Appendix F.

 

 

Criterion validity testing of the function questions was also done in the main survey.  If a cohabiting adult was available when supervisors reinterviewed respondents a second interview was conducted with that person.  The second respondent was questioned about the function of the initial respondent, using the same function questions.  Each was interviewed separately.  Each set of responses was added to create separate function assessment scores.[15]  On this scale 0 represents no difficulty with any task, compared with others of the same age and sex, while 36 represents the maximum level of difficulty for every task.  The function score according to the initial respondent was then compared with the score according to the other adult.  Comparison was by plotting the scores and calculating the line of best fit and its corresponding coefficient of determination.  If agreement is good between respondents and cohabiting adults the coefficient of determination should be high and the slope of the line should be close to 45 degrees with its origin through 0 (ie, a change of one on one scale corresponds to a change of one on the other scale).

 

 

Depression is known to have a significant effect on function.  Therefore testing for an association between depression diagnosis using our depression questionnaire and reduced function using our function questionnaires also provided some criterion validity, both for the depression questions and the function questions.  This is discussed further in the Results section.     

 

Measuring Depression and Functional Disability

 

From the survey data we calculated the prevalence of depression diagnosed according to the DSM algorithm.  We also calculated the prevalence of any ‘health-related’ difficulty with each of the function tasks; separately for men and women and for depressed vs not depressed.  We explored the relationship between depression and function by regression analysis with function assessment score as the dependent variable and depression, local illness, sex, age and education as the independent variables.[16]

 

RESULTS

 

Study Site

 

We studied villages selected from across all of Rakai District and from 8 parishes in Masaka District.  This constitutes the local area of operations for World Vision Uganda.  The total population of this area is 1-2 million.  We selected this site so that World Vision can use the results as an indicator of need and baseline for future interventions.  Both areas have been severely affected by HIV in the past, with high rates of infection and mortality (Nunn et al, 1997; Sewankambo et al, 1994).

 

Results of the Ethnographic Study

 

Free Listing

 

For the first activity (free listing) 10 WVU interviewers spoke with 50 knowledgeable persons in 10 of the 30 villages chosen for the survey.  Interviewers conducted 4 free lists with each respondent on a)problems in the community due to HIV, and important tasks that adults must do regularly to care for b)themselves, c)their families, and d)their communities.  The results of the problem free lists are summarized in Table 1.  From the free lists of important tasks those for which difficulty did not appear to affect others were removed.  From the remainder the nine most frequent responses for men and for women were inserted into the function section of the instrument (see Appendix A).

                                               

Key Informant Interviews

 

The original plan called for free list respondents to identify persons who deal with mental or emotional problems (see Methods).  These persons would then be the key informants for this stage of the ethnographic study.  However, most of the persons so identified were from outside the community (such as social workers and NGO staff) and therefore were not suitable informants.  Therefore, interviewers were advised to reinterview those free list respondents who appeared knowledgeable about mental health issues or to identify new knowledgeable respondents by asking local people for the names of those who dealt with these issues and lived in the community.  In selecting key informants, interviewers were also advised not to interview highly educated persons, to limit the possibility that respondents would express ideas from outside the community that differed from the general community viewpoint.

 

A total of 20 key informants were interviewed – of whom 10 were interviewed twice.  The first interview began with a description of a hypothetical person with many of the DSM depression symptoms that emerged in the free lists.[17]  Key informants were then asked to name and describe what problems this person might have, and to give detailed descriptions of these problems (see Methods).  Respondents described two main syndromes with depression-like features:

 

·        Yo’kwekyawa - translated as hating oneself

·        Okwekubagiza - translated as pitying oneself

 

The reported symptoms of these illnesses are shown in Figure 1.

 

Table 1: Results of Free Lists on major problems resulting from HIV*

 

Problem

Ranking

# of responses that include this problem

Many orphans

1

24

Lack of child counseling/parental guidance/care

1

24

Lack of food

3

22

Difficulty educating orphans

4

21

Lack of affordable medications for HIV and its effects

4

21

Poverty

6

19

Community development retarded

7

14

Poor accommodation

7

14

Insufficient care for orphans

9

13

Loss of hope

10

10

Worry and self pity

10

10

Deaths of businessmen resulting in no jobs

12

9

Reduced productivity due to physical weakness

13

8

Rape, prostitution, defilement

13

8

Broken homes

13

8

Poor nutrition due to no-one left to cook

16

7

Drunkenness as a means of escape

16

7

Stigmatization and social isolation/hiding

16

7

Hatred of self, life and God and desire for vengeance on the world by those with HIV

16

7

Fear of marriage due to fear of catching HIV

20

6

The living have to care for more dependents

20

6

Poor roads or land untended due to lack of people to maintain them

20

6

Reduced income

23

5

Grief and loss for the dead

23

5

Drug abuse resulting in crime

23

5

Many widowers and widows

26

4

Madness

26

4

Disputes in the homes (couples blame each other for getting HIV)

26

4

Suicide

29

3

Many elderly and other dependents are left without assistance

29

3

* problems stated by less than three respondents are not included.

 

 

Between them Yo’kwekyawa and Okwekubagiza include all the symptom criteria for depression except problems sleeping and difficulty thinking or concentrating - both were mentioned by key informants but not as part of either of these syndromes.  The mention of all of the 9 DSM-IV depression symptom criteria supports criterion validity of the depression concept among this population.  Among the symptoms shared by both syndromes, suicidal thoughts and feeling no interest in things appear to be predominantly features of Yo’kwekyawa whereas sadness is more a feature of Okwekubagiza, based on the relative frequency of responses.  Other shared symptoms - crying easily, feeling lonely, worry too much, feeling worthless, and irritability - appear to be equally a part of both disorders.  Therefore, we could not identify a single disorder which is similar to depression.  Rather, both Yo’kwekyawa and Okwekubagiza contain elements of depression with Yo’kwekyawa apparently more similar to severe depression and Okwekubagiza to milder illness.  

 

 

Figure 1: Local Depression-like syndromes and their symptoms.*

 

Yo’kwekyawa

            Symptoms:      

                          feeling lonely

                                no interest in things

                                worrying too much about things

                                feeling hopeless about the future

                                hating the world†

                                thought of killing self

                                irritability

                                bad, criminal or reckless behavior†

                                feeling sad

                                feeling worthless

                                not responding when greeted/withdrawn

                                crying easily

                                poor appetite

                                feelings of severe suffering and pain

           

Okwekubagiza

            Symptoms:

                          feeling sad

                                feeling lonely

                                worrying too much about things

                                feeling worthless

                                low energy.  Feeling slowed down

                                crying easily

                                feeling fidgety

                                feeling no interest in things

                                feeling everything is an effort

                                irritability

                                unappreciative of assistance†

                                thoughts of killing self


 

* includes only symptoms included by two or more respondents.  Symptoms are listed in decreasing order of frequency mentioned.

Local symptoms that are not part of the DSM depression criteria.

 

Both local syndromes included symptoms that are not part of the DSM depression criteria (see figure 1).  Of these, reckless behavior was of particular interest, since those who mentioned it described an attitude of not caring about one’s health or well-being.  Such an attitude could have repercussions for other aspects of mental and physical health, particularly behavior-related illnesses like AIDS.  To explore whether this symptoms was really associated with depression we added a question at the end of the HSCL which asked about feelings of not caring about one’s health (see Appendix A, question B16).  With the addition of this question the instrument was completed and ready for translation and subsequent validity and reliability testing. 

 

Validity and reliability of survey instruments

 

Validity Study

 

Supervisors first spoke with knowledgeable people in the community and asked them to identify persons who they felt had Yo’kwekyawa and/or Okwekubagiza and those who they felt had neither syndrome.  They then assigned these persons to their interviewers without revealing their reported illness status (see Methods).  A total of 116 local persons were interviewed in this way; 61 of whom were reported to have Yo’kwekyawa and/or Okwekubagiza and 55 who were reported to have neither syndrome.

 

After eliminating interviews for which the respondent and key informant disagreed on the presence of Yo’kwekyawa and/or Okwekubagiza we were left with 67 interviews.  We then compared diagnosis of depression using the questionnaire with diagnosis of Yo’kwekyawa, and diagnosis of depression with diagnosis of Okwekubagiza.  The results of these comparisons are given in Figures 2 and 3.  Phi correlation between Yo’kwekyawa and depression was 0.57 (p=0.000) and between Okwekubagiza and depression was 0.56 (p=0.000) which is similar for both local disorders, substantial and highly significant.  The pattern of disagreement was also of interest: all persons said to have Yo’kwekyawa were also diagnosed as depressed (ie, the specificity of Yo’kwekyawa for depression was 100%), whereas 23 of the 26 persons with depression had Okwekubagiza (ie, the sensitivity of Okwekubagiza for depression was 88%).  The high specificity and sensitivity for depression of Yo’kwekyawa and Okwekubagiza respectively supports the impression from the qualitative data that these syndromes represent the more and less severe ends of the depression spectrum. 

 

Figure 2: Comparison of diagnosis of depression by questionnaire with diagnosis of Yo’kwekyawa by local people.

 

 

 

 

 

 

 

Depression

Present

Absent

Yo’kwekyawa

Present

11

0

11

Absent

14

40

54

25

40

65*

* Two of the interviews were missing data on Yo’kwekyawa

 

 

Figure 3: Comparison of diagnosis of depression by questionnaire with diagnosis of Okwekubagiza by local people.

 

 

 

 

 

 

 

Depression

Present

Absent

Okwekubagiza

Present

23

13

36

Absent

28

31

26

41

67

 

 

A second test of criterion validity was to explore the relationships between Yo’kwekyawa and Okwekubagiza and depression symptom severity.  We created a depression score which consisted of simple addition of the scores on all of the depression questions (including the local symptom of not caring about one’s health), then compared the mean depression scores of persons without Yo’kwekyawa or Okwekubagiza with those with either syndrome and those with both.  A boxplot of the results is shown in figure 4.  This shows increased severity of depression symptoms in the presence of a local illness, and even further increase when both illnesses are present.  This supports a direct connection between these local illnesses and depression. 

 

Although figure 4 shows a comparison between no local illnesses, either illness and both illnesses, there were no cases in which a respondent was said to have Yo’kwekyawa but not Okwekubagiza.  therefore category 1.00 of ‘illness’ in figure 4, represents Okwekubagiza only, and category 2.00 represents Yo’kwekyawa and Okwekubagiza together.  That all cases of Yo’kwekyawa are accompanied by Okwekubagiza supports the impression from the ethnographic data that both represent depression in its more severe and milder forms respectively.

 

 

Figure 4: Boxplot of depression severity (Descore2) by local illness category*

* .00 = said to have neither Yo’kwekyawa or Okwekubagiza

 1.00 = said to have one of these illnesses         

 2.00 = said to have both illnesses.

 

Validity and Reliability Results from the Main Survey

 

Tables 2-4 show Cronbach’s alpha coefficients and item analyses for the questions on depression and male and female function.  These demonstrate good internal reliability for all three groups of questions.  The correlations of the local depression symptom added to the instrument (not caring about one’s health) are high and comparable to those of the DSM symptoms.  This suggests that it is part of (or at least strongly associated with) the local expression of depression.    

 

Test-Retest Reliability

 

Seventy-two interviews (12% of total) were chosen at random and were repeated by the supervisors 1-3 days after the initial interviews.  Pearson correlations were 0.681 (p=.000) for the depression scale based on the intial and repeat interviews and 0.833 (p=0.000) for the corresponding functional disability scales.  This corresponds to acceptable reliability for the depression scale and good reliability for the functional disability scale.

 

Table 2: Cronbach’s alpha coefficient and item analysis for survey questions on Depression (N=587).

 

Cronbach Alpha if item deleted

Lack of energy   

.826

Blaming self for things     

.840

Crying easily

.827

Poor appetite 

.832

Poor sleeping    

.827

Lack of hope

.826

Feeling sad    

.821

Feeling lonely 

.825

Thoughts of suicide

.838

Worrying too much  

.825

Loss of interest in things

.825

Everything takes too much effort   

.820

Feeling worthless

.824

Loss of interest in sex/sexual pleasure

.835

Don’t care about health/wellbeing

.837

Cronbach’s alpha for all questions

.838

 

Table 3: Cronbach’s alpha coefficient and item analysis for survey questions on male function (N=222).                                                                        

Cronbach alpha if item deleted

Personal Hygiene

.879

Farming

.861

Heading the home

.876

Manual labor

.867

Plan for the family

.867

Participate in community development activities

.863

Attend meetings

.871

Participate in burial ceremonies

.875

Socialize

.891

Cronbach’s alpha for all questions

.886

 

Table 4: Cronbach’s alpha coefficient and item analysis for survey questions on female function (N=362).

 

Cronbach alpha if item deleted

Personal Hygiene

.878

Caring for children

.880

Cooking

.861

Washing clothes and utensils

.862

Cleaning house and surrounds

.860

Growing food

.867

Participate in community development activities

.860

Attend meetings

.874

Console and assist the bereaved

.868

Cronbach’s alpha for all questions

.881

 

Main Survey findings

 

Number Interviewed    

 

We conducted a community-based survey in Rakai and part of Masaka Districts.  Interviewers contacted 600 households in 30 villages and identified an eligible adult in all cases except one.  587 adults were interviewed and there were 12 refusals - Response rate = 98%.

 

Demographics

 

                                                                                      Table 5: Demographics of Study Sample                                                                                  

N (% of total)

Mean Age (95%CI*)     

Mean Education (95%CI*)

Male

223 (38%)

40.2 (37.6-42.8)

6.2(5.7-6.7)

Female

364 (62%)

38.7 (36.9-40.5)

5.0 (4.6-5.4)

Total

587

39.3 (37.8-40.7)

5.5 (5.2-5.8)

                          *95% Confidence Interval

 

There are significantly fewer men than women in the sample.  We found similar proportions of adult males: females in other population studies conducted in this area (Paxton et al, 1998; Kamali et al, 1999) but no explanation.  Possibly the effect is due to HIV.  Comparison of figures 5 and 6 (which show the age distribution of both sexes) suggest a gap in the number of males 35-65 years of age.  This gap corresponds with the impact of HIV in other African populations (UNAIDS, 2000) and reflects the age cohort most at risk from HIV since the epidemic began in the 1980s.  There is a small but significant difference in education levels by sex, perhaps reflecting a continuing bias against educating females.

 

Figure 5: Age distribution of males in sample (years)

 

Figure 6: Age distribution of females in sample (years)

 

 

 

 

Prevalence of Depression

 

Table 6 shows the prevalence of depression, diagnosed according to the algorithm in Appendix E.  The table also shows the mean depression scores, created by adding all the depression question responses.   

 

Men and women show similar high rates of depression diagnosis, while women show a higher (but not statistically significant) depression score.  We also looked at the prevalence of thoughts of suicide.  Twenty-two of the 587 respondents (3.7%) answered that they had been troubled ‘quite a bit’ or ‘extremely’ during the last week alone by thoughts of ending their life.  Of these, five were men (2.2% of male respondents) and 17 were women (4.7%) of female respondents. 

 

Table 6: Depression prevalence and depression scores for main survey sample.

 

Prevalence of Depression (95%CI)

Mean depression score (95%CI)

Men

25.1% (19.4-30.8)

11.6 (10.4-12.8)

Women

23.9% (19.5-28.3)

13.5 (12.7-14.4)

Total

24.4% (20.9-27.9)

12.8 (12.1-13.5)

 

Function

 

Demographics of functional impairment

 

We created a functional impairment scale as in the validity study - by simple addition of responses to the function questions (see Methods).  The results for the sample population are shown in table 7.  Men and women showed similar levels of dysfunction overall, as well as highly significant increases in functional impairment associated with depression diagnosis.

 

 

Table 7: Distributions of Functional Impairment Scores for Men and Women.

 

N

Minimum

Maximum

Mean (95%CI)

Mean for those not depressed (95%CI)

Mean for those depressed (95%CI)

Men

223

0.0

34.0

3.96 (3.06-4.86)

1.91(1.33-2.48)

10.07 (7.47-12.67)

Women

364

0.0

36.0

4.47 (3.74-5.21)

2.97 (2.33-3.61)

9.25 (7.23-11.27)

Total

587

0.0

36.0

4.28 (3.71-4.84)

2.57 (2.12-3.03)

9.57 (7.99-11.15)

                                   

Validity of functional impairment assessment

 

During the main survey thirty-seven reinterviews were conducted in which a cohabiting adult was asked to assess the function of the respondent.  Scatter plots comparing the function scores based on the respondent’s own responses (‘funscore) and that of the other person (‘othefunc’) and the corresponding lines of best fit are in Appendix G for all 37 such interviews, and separately for those diagnosed as depressed and non-depressed.  The lines and their formulae suggest good agreement between functional self assessment and assessment by others - all lines show an approximately 1:1 increase (ie, 45 degree slope) with a point of origin close to zero.  The coefficient of determination is high - >0.8 for each line.  This means that the relationship between the scores explains >80% of the variation in the plotted values.  Originally we suspected that respondents with depression might be more pessimistic than others and therefore exaggerate their disability and its association with depression. However, these results suggest a greater (or at least equal) agreement between assessment by the depressed and those around them. 

 


Relationship between functional disability, depression and related variables        

           

Table 8 shows a binary logistic regression with a dichotomous functional impairment variable as the dependent variable and age, sex, education, Yo’kwekyawa, Okwekubagiza and depression diagnosis by algorithm as the independent variables.  Only age and depression are significantly associated with functional disability, meaning that depression is more closely associated with function than are the self-diagnosed local disorders of Yo’kwekyawa or Okwekubagiza.

 

Table 8: Logistic Regression of age, sex, education, Yo’kwekyawa and Okwekubagiza (both self-diagnosed) and depression vs functional disability for Survey Sample (N=587)

 

Variable

Beta Coefficient

Std. Error

Sig. 

Age

-.047

.006

.000

Sex

-.35

.201

.081

Education

.042

.027

.123

Yo’kwekyawa

.142

.248

.567

Okwekubagiza

.270

.200

.177

Depression

1.056

.247

.000

 

We also examined the relationship between depression and difficulty with specific tasks.  Table 9 compares the proportions of depressed and not depressed respondents who reported difficulty with each task.  Depression was associated with a higher prevalence of difficulty with every task among both men and women. 

 

 

Table 9: Comparison of task-specific functional impairment between depressed and non-depressed.

 

Not depressed

Depressed

Ratio of presence of any dysfunction for depressed: not depressed

% with any dysfunction for task*

% with any dysfunction for task*

Male

Personal Hygiene

7.8

19.6

2.5

Farming

19.3

55.4

2.9

Heading the home

6.6

33.9

5.1

Manual Labor

23.5

58.9

2.5

Planning for the family

9.6

51.8

5.4

Community development activities

12.7

55.4

4.4

Attending meetings

3.0

30.4

10.1

Participating in Burial ceremonies

1.2

25.0

20.1

Socializing

1.8

12.5

6.9

Female

Personal Hygiene

6.5

25.3

3.9

Caring for children

12.7

33.3

2.6

Cooking

12.4

37.9

3.1

Washing clothes/utensils

19.3

45.3

2.3

Cleaning the house

9.8

33.3

3.4

Growing food

32.7

62.1

1.9

Community development activities

16.0

42.5

2.7

Attending meetings

11.6

23.0

2.0

Consoling/assisting the bereaved

5.1

18.4

3.6

* Those who reported any difficulty with the task. 


DISCUSSION

 

Results

 

Validity and Reliability

 

Our conclusions regarding the feasibility of disease concepts and the use of western instruments are similar to those we reached in Rwanda.  The results support the conclusion that depression occurs among this population and is an important problem, even though it is not recognized locally as a distinct syndrome.  The validity and internal reliability of the instrument we adapted to measure depression and function are good and test-retest reliability acceptable.  Therefore, we conclude that the instrument used in this study was appropriate and of acceptable accuracy. 

 

Prevalence of depression

 

The rate of depression is high at 24%.  A study of depression in 10 non-African countries found prevalences ranging between 0.8-5.8% (Weissman, et al, 1996), which was consistent with results from Africa (Bhagwanjee et al, 1998).  Since the salient difference between the Rakai and Masaka districts and other places is the severity of the AIDS epidemic it is most likely that the difference is due to the effects of HIV, both direct and indirect.  Direct effects refer specifically to organic brain syndromes resulting from HIV that manifest with depression.  Indirect effects include depression secondary to losses caused by HIV in both the infected - for example, loss of family, ability to function, lack of acceptance and lack of a future - and the uninfected - such as loss of family and friends, social and material support, and concerns about the future.  The multiple and related losses may explain the high rate of depression.  That men and women showed similar prevalence of depression was a surprise, as other studies have found higher rates among women across a variety of cultures.  We have no explanation for this finding, but believe that it is accurate, given the reliability and validity of the instrument.

 

The finding that 3.7% of the sample reports significant suicidal ideation in just the last week is of major concern.  This rate is very high and warrants rapid intervention for these respondents!

  

Depression and function

 

Depression was highly associated with dysfunction for all the tasks, both in men and women.  Depending on the specific task and sex, those with depression were 2- 20 times more likely to have some degree of dysfunction compared with those not depressed.  Diagnosis of depression by the instrument was more highly associated with dysfunction than were either of the local syndromes.  Therefore, depression is a superior indicator of the impact of this type of mental illness than these syndromes.     

 

The impact of depression on function appeared to be greater among men, both for overall function and for individual tasks.  However, this may have been due to higher levels of dysfunction among non-depressed women.  Among men the impact was greatest on tasks that were social or required thinking.  For women there was no such obvious pattern.   

 

Depression and risk taking

 

The Cronbach’s alpha when the Yo’kwekyawa symptom ‘Feeling like I don’t care what happens to my health’ is removed is comparable to that of the DSM depression symptoms (Table 2).  This symptom is therefore highly correlated with depression symptoms.  Respondents in the ethnographic study described this symptom as a willingness to do reckless behaviors.  Although we did not ask the question with particular reference to HIV, this attitude among the depressed could affect al lack of amenability to behavior change in response to current HIV prevention programs.  These programs assume that knowledge and resources (such as condoms) are sufficient to alter behavior, but this may not be so in the presence of a non-caring attitude to one’s health.  Studies in developed countries support this idea: many have found depression to be highly associated with HIV-related risk-taking behavior (Morrill et al 1996, Nyamathi 1992, Orr et al 1994).  Among population with high rates of depression this could be a significant factor in promoting HIV.  No conclusions are possible, given that we used a single question which did not refer specifically to HIV.  However, these findings suggest that further investigation is warranted as part of future studies.

 

Limitations in Interpreting Results

 

Since this was a cross-sectional study we cannot assess whether the depression causes the dysfunction, or vice versa or a combination of both.  Also, it is possible that HIV infection may instead cause both depression and dysfunction and therefore be responsible for the relationship between them.  We believe that such an effect is present but unlikely to account for most of the depression - function relationship.  But since we did not identify those who were infected with HIV, we cannot accurately assess its importance.  We did try to form a rough estimate, based on HIV prevalence.  Currently the national HIV prevalence is estimated at 8.3% (UNAIDS, 2000).  We therefore removed the 8.3% of respondents who showed the highest scores for both depression and dysfunction, in effect removing those who showed the strongest association.  This assumes that all of those with HIV demonstrate a strong depression - dysfunction association.  It overestimates the influence of HIV on depression and dysfunction, given that many with HIV will not yet be symptomatic.  Repeat of the binary logistic regression analysis showed a reduction in the depression B coefficient of 27%, from 1.05 to .774, but the association remained highly significant (p = .003).  If the HIV prevalence of 8.3% is accurate for the Rakai and Masaka districts, this suggests that a maximum of 27% of the relationship between depression and dysfunction is due to HIV.  But this is a very rough approximation only and the true prevalence in Rakai and Masaka may be higher than 8.3%.  To determine the true effect of depression on function will require an effective depression intervention with pre and post measurements of depression and corresponding change in function.  

 

Process

 

Overview

 

This study was the second field trial of a method to assess the burden of mental problems across cultures.  In designing this method our major considerations were identical to those of the first field trial in Rwanda, ie to enable NGOs to:

 

            1. Decide whether standard mental health assessment instruments are appropriate (ie, do the diseases they assess occur locally).

            2. Where instruments are appropriate, to adapt them to the local situation.

            3. Test the validity and reliability of the adapted instruments.

            4.  Use these instruments in community based surveys to assess need and subsequently program impact.

 

Existing methods to perform the first 3 vital tasks require time and resources beyond the means of most NGOs, and in many cases the necessary gold standards are just not available.  Our approach, was to understand how local people view mental health so that we could enlist their assistance in answering these questions. 

 

The first trial took place in Rwanda because of our interest in transitional populations, and because World Vision has a psychosocial program there.  The mental health issues assessed in that trial appear to largely be the result of war and genocide.  This second trial reflected the desire of WV to investigate the mental health of other types of disasters, namely HIV.    

 

Details of the Uganda experience

 

The timeline of the study is given in Appendix H.  Briefly, the total study required 20 working days, 5 days less than the Rwanda field trial.  This was due to greater efficiency in organization, the elimination of pile sorts from the ethnographic study, and the use of cluster sampling instead of simple random sampling.  The ethnographic study required 9 days, the pilot and validation studies required 5 days and the main survey 7 days.  The staff consisted of 2 project directors - one from JHU and one from WV, 10 WVU staff who served as ethnographic interviewers and quantitative study supervisors, and 22 quantitative study interviewers[18].  We also had 2-3 drivers and vehicles, and 9 of the supervisors used a motor bike during the main survey.

 

The study proceeded smoothly with one major exception.  We did not organize sufficient transportation for much of the study.  This caused many delays and long days.  In future there should be a vehicle for every 2 teams and each supervisor should have a motorbike for the duration of the validity study and survey.  Otherwise the study went well and reaffirmed our finding in Rwanda - that it is possible to train local staff and conduct a rapid ethnographic and quantitative study in a very short time with resources currently available to many NGOs and other organizations.  These types of studies are best done before commencing programs, even before the interventions have been decided:  Ethnographic methods provide a lot of general information about these communities which can be used for planning, as well as being an effective way to meet local people and build trust.  For example, the first free listing exercise provides information on all the community’s problems that can guide an NGO in setting up all programs, not just programs for mental health.

 

As in Rwanda, local people did not object to identifying others with severe grief.  Nor did those identified object.  This had been a concern prior to both studies.  Using the information from these informants and the respondents themselves to diagnose true cases and true non-cases of grief appeared to work well as a partial measure of validity. 

 

In the Rwanda survey we noticed some reactivity by the end of the survey.  Interviewing in that survey continued for 10 days.  In this survey we interviewed more respondents (587 vs 368) but in only 7 days.  We detected no reactivity, perhaps due to the shorter interviewing period.  We were able to interview more people in a shorter time because of more interviewers (22 vs 17) and because we used the cluster sampling method to find respondents.

 

The instrument functioned well - both interviewers reported few problems, the most common being a reluctance by some respondents to answer the question on sex.  After the Rwanda study we dropped the HSCL question on ‘feeling trapped.’  We also added a question on feeling fidgety which stayed in the instrument until the end of this study (see Appendix A).  However, subjective assessment of psychomotor agitation is not part of the DSM depression criteria.  Therefore this questions was not included in any part of the analysis, including the creation of the depression scores.  This question should be omitted from future study instruments.  

 

In the Rwanda study the HSCL questions on lack of interest and lack of interest in sex performed less well than the other HSCL questions.  In the factor analysis they did not load on the major factor, and internal reliability of the depression questions improved when they were removed.  We were interested to see if the same would occur in Uganda, but here the questions performed well.  So this effect may be peculiar to Rwanda.  More studies in Africa are required to determine the general usefulness of these questions.  In the meantime we plan to retain them in the questionnaire for future studies. 

 

The questions on function performed well in terms of the response categories and the non-verbal response card.  Respondents appeared to understand and be able to use the both correctly.  However, the strategy of asking respondents who complained of dysfunction due to lack of resources to assess their functioning if they had those resources (see Finalizing the English version of the instrument) may have backfired.  Interviewers reported that some respondents appeared to overestimate their functioning if they had those resources.  This may have due to a misunderstanding that World Vision would provide these resources if the expected benefit was great.  If true, the level of dysfunction was underestimated in this survey.  This problem needs to be addressed in future surveys.

 

As in Rwanda, we trained local staff in instrument preparation, data collection and qualitative data analysis.  They did not receive training in quantitative data analysis and interpretation, because of time and resource limitations and because the procedures are still being improved.  In the future, when the procedures are finalized, this training can be provided to selected local staff with computer skills.  Such staff are increasingly common among the NGOs.  Analyses can then be done jointly with JHU faculty or other persons with formal  analytical training.

 

 

CONCLUSIONS AND RECOMMENDATIONS

 

 

1.  The assessment methodology worked well among this population.  As in Rwanda, we believe we have demonstrated that this approach to mental health assessment is feasible for NGOs and other organizations with similar resources, and that it can produce scientifically valid results.

 

2. Improvements based on the Rwanda experience resulted in a much more streamlined and efficient assessment this time around.  However, problems remain and further experience is needed with the more novel aspects of this work (particularly the validity study and function questionnaire) before the method can be confidently recommended for widespread use.  World Vision and Johns Hopkins University should conduct further field trials to resolve these problems while continuing to build a picture of mental illness in Africa and beyond. 

 

3.  WV and JHU should seek additional resources to expand the assessment approach to other illnesses and mental problems, and to develop a parallel approach to child assessment.

 

4.  Assessments both in Rwanda and Uganda have demonstrated high prevalences of depression associated with significant dysfunction.  WV should use these data as the baseline for interventions to address this problem in these areas.  Since interventions currently being used for trauma-based mental health problems have little or no scientific data to support them, WV should provide the most promising interventions and WV and JHU should use this assessment approach to test their impact.  Study protocols should investigate four major issues:

 

                     Finding effective treatment for depression, given limited resources and large group of affected persons.

                     The nature of the cause-effect relationship between depression (and other mental illness) and function.

                     Determining if improvement in depression results in improvement in function and how much.

                     The impact of depression on attitudes and behavior in regard to other health issues, including HIV.

 

Communities should contribute to this process, and particularly to the review of proposals for feasibility, acceptability and implementation.

 

5. As in Rwanda, we found that mental health problems are only one of the issues facing local people as a result of HIV.  WVU should use the data from the free lists about overall perceived community problems to prioritize their interventions for Masaka and Rakai districts; both health and non-health interventions.

 

REFERENCES

 

Aday LA.  Designing and conducting health surveys.  Jossey-Bass publishers, San Francisco and Oxford, 1991.

 

Bhagwanjee A, Parekh A, Paruk Z, Petersen I, Subedar H.  Prevalence of minor psychiatic disorders in an adult African rural community in South Africa.  Psychol Med 1998, Sep;28(5): pp 1137-47.

 

Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. Copyright 1994 by the American Psychiatric Association, Washington DC.

 

Health Measurement Scales: A practical guide to their development and use.  Streiner DL and Norman GR.  Oxford University Press 1995.

 

Morrill AC, Ickovics JR, Golubchikov VV, Beren SE, Rodin J.  Safer sex: social and psychological predictors of behavioral maintenance and change among heterosexual women.  Journal of Consulting and Clinical Psychology.  64(4):819-28, 1996, Aug.

 

Nunn A, Mulder D, Kamali A, Ruberantwari A, Kengeya-Kayondo J, Whitworth J. Mortality associated with HIV-1 infection over five years in a rural Ugandan population: cohort study.  BMJ 315(7111) 27 September 1997 pp 767-771.  

 

Nyamathi A.  Comparative study of factors relating to HIV risk level of black homeless women.  Journal of Acquired Immune Deficiency Syndromes.  5(3):222-8, 1992.

 

Orr, ST, Celentano DD, Santelli J, Burwell L.  Depressive symptoms and risk factors for HIV acquisition among black women attending urban health centers in Baltimore.  AIDS Education and Prevention.  6(3):230-236, June 1994.

 

Report on the global HIV/AIDS epidemic, June 2000.  UNAIDS, Switzerland.

 

Sewankambo NK; Wawer MJ; Gray RH; Serwadda D; Li C; Stallings RY; Musgrave SD; Konde-Lule J.  Demographic impact of HIV infection in rural Rakai district, Uganda: results of a population-based cohort study.  AIDS. 8(12):1707-13, 1994 Dec.

 

Weissman MM, Bland RC, Canino GJ, Faravelli C, Greenwald S, Hwu H, et al.  Cross-National Epidemiology of Major Depression and Bipolar Disorder.  JAMA 1996, Volume 276(4), 24/31 July pp 293-299.

 

 

APPENDIX A: ASSESSMENT INSTRUMENT (ENGLISH VERSION)

 

 

SURVEY OF PEOPLE LIVING IN WORLD VISION PROJECT AREAS IN MASAKA AND RAKAI DISTRICTS, UGANDA.

 

 

Respondent ID # __ __ __

Interviewer ID # __ __ __

Nov-Dec 2000.

 

 

Preliminary Instructions.

 

 

Hello.  My name is _______________.  I work for World Vision and we are conducting a study of this community to help us to better serve the people here.  Would you have 30 minutes to answer some questions?

 

If the respondent answers no ask them if you can come back another time and interview them.  If the respondent asks questions answer them, but do not provide information that could affect their answers to the questionnaire.  If the respondent answers yes, then say the following:

 

Before we start I want to read a form to you which explains more about what we want to do. 

 

READ CONSENT FORM HERE.

 

Insist that the interview be conducted in private.  If this is questioned by anyone, explain that this is an important part of our procedure, and that we have found that some people give different answers when there are other people present.                                  

 

 

 

Respondent Name:_______________________________________________                    

 

Address:______________________________________________________________________

 

 

Part A: Assessment of Function

 

There are two versions of this section - one for men and one for women.  If the respondent is a man, use only the version for men.  If the respondent is a woman, use only the version for women. 

 

I am going to read a list of tasks and duties.  These are tasks and duties that other people around here told us were important for people to be able to do.  For each task I am going to ask you how much more difficulty you are having doing it THAN MOST OTHER MEN/WOMEN OF YOUR AGE.  You should tell me whether you are having no more difficulty, a little more, a moderate amount more, or a lot more, or you often cannot do that task.

 

To make it easier to remember I have a card here with pictures.  Each picture represents a different amount of difficulty.

 

Show the respondent the card illustrating levels of difficulty.  Point to each picture as you describe it.

 

The first picture shows someone who has no more difficulty than most other men/women of their age.  The second picture shows someone who has a little more difficulty.  The third picture shows someone who is having a moderate amount more difficulty.  The fourth picture shows someone who is having a lot more difficulty.  The last picture shows someone who has so much more difficulty that they often cannot do the task.  For each task or duty, I will ask you to point to the picture which shows how much more difficulty you are having in doing that task, compared to most other men/women of your age.

 

Now say each task, and after each one say

 

1. Are you having no more difficulty than most other men/women of your age, a little more, a moderate amount more, a lot more, or you often cannot do this task?

 

pointing to each picture as you say it.

 

Record the response by marking the appropriate box next to the symptom in the table below. 

If the respondent indicates no more difficulty in doing a task, go to the next task and ask the same question.  If the respondent indicates at least a little more difficulty (responses 1,2,3, or 4) ask the following question before going on to the next task: 

 

2.  What causes this difficulty?

 

In the table below record the response in the >cause of difficulty= column beside the task.

 

Respondent ID # __ __ __

FOR MALES:

 

 

Task or

activity

 

 

Degree of Difficulty completing task or activity

 

None   Little      Moderate     A lot       Often

                                                       Cannot

 

 

Cause of difficulty

 

 

A01 personal hygiene

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

 

A02 farming

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

 

A03 head the home

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

 

A04 manual labour

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

A05 plan for the family

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

A06 participate in community development activities

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

 

A07 attend meetings

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

A08 participate in burial ceremonies

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

 

A09 socialize

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

 

A10 Other

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

 

            Respondent ID # __ __ __

FOR FEMALES:

 

 

 

Task or activity

Degree of Difficulty completing task or activity

 

None     Little      Moderate     A lot      Often  

                                                        Cannot

 

 

Cause of difficulty

 

 

A11 personal hygiene

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

 

A12 caring for children

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

 

A13 cooking

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

A14 washing clothes/utensils

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

A15 cleaning house and surrounds

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

 

A16 growing food

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

A17 participate in community development activities

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

 

A18 attend meetings

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

A19 console and assist the bereaved

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

 

A20 Other

 

 

0

 

 

1

 

 

2

 

 

3

 

 

4

 

 

 

 

 

 

Part B-Depression in Adults (the Hopkins Symptom Checklist)

 

I am going to read you a list of problems that people can get.  For each one I am going to ask you how much you have experienced each one IN THE LAST WEEK, including today.

 

Say each symptom, and after each one ask how much it has bothered the respondent.  Repeat the categories after each symptom and let the respondent choose one.  Record the response by ticking the appropriate box next to the symptom.

 

Depression Symptoms

Not at all

A little

Quite a bit

Extremely

 

B1.  Feeling low in energy, slowed down

 

0

 

1

 

2

 

3

 

B2.  Blaming yourself for things

 

0

 

1

 

2

 

3

 

B3.  Crying easily

 

0

 

1

 

2

 

3

 

B4.  Feeling fidgety

 

0

 

1

 

2

 

3

 

B5.  Poor appetite

 

0

 

1

 

2

 

3

B6.  Difficulty falling asleep or staying asleep

 

0

 

1

 

2

 

3

 

B7.  Feeling hopeless about the future

 

0

 

1

 

2

 

3

 

B8.  Feeling blue

 

0

 

1

 

2

 

3

 

B9.  Feeling lonely

 

0

 

1

 

2

 

3

 

B10.  Thought of ending your life

 

0

 

1

 

2

 

3

 

B11.  Worrying too much about things

 

0

 

1

 

2

 

3

 

B12.  Feeling no interest in things

 

0

 

1

 

2

 

3

 

B13.  Feeling everything is an effort

 

0

 

1

 

2

 

3

 

B14.  Feeling of worthlessness

 

0

 

1

 

2

 

3

B15.  Loss of sexual interest or sexual pleasure

 

0

 

1

 

2

 

3

B16.  Feeling like I don=t care what happens to my health

 

0

 

1

 

2

 

3

 

 

 

Part C - Self-assessment

 

 

 

C1.  Do you think you have ‘self hate’’?

 

 

Yes (1)

 

 

No (2)

 

 

C2.   Has anyone said that you have ‘self hate’?

 

 

Yes (1)

 

 

No (2)

 

If respondent answers yes to C1, then ask C3:

 

C3.  How long have you had this?                             ____ days                         ___ months___ years

 

 

 

 

 

C4.  Do you think you have ‘self pity’?

 

 

Yes (1)

 

 

No (2)

 

 

C5.   Has anyone said that you have ‘self pity’?

 

 

Yes (1)

 

 

No (2)

 

If respondent answers yes to C4, then ask C6:

 

C6.  How long have you had this?                              ____ days         ____months          ____ years.

 

 

Part D - Demographic information

 

Age:______  years

 

Sex:      M         F

 

Number of years of School and higher education:          ________       

 

 

Final instructions

 

State that this is the end of the questionnaire.  Review the questionnaire while still with the respondent, checking that all questions have been answered and the answers are clear.  If not, review the question with the respondent and insert the missing information.  Once you are satisfied that all questions have been answered and the responses are clear, ask the respondent if it would be OK for you, or your supervisor, to return if they have further questions.  If this is OK, ask if there are any restrictions on when you might return and record these in the space below.  Finally, ask the respondent not to discuss the interview with anyone until after December 12, by which time the study should be over.  This is so that their comments do not affect the answers of others who are yet to be interviewed.

 

Restrictions on Re-visiting      ____________________________________________________________                 ______________________________________________________________________________________________________

 

 

End the interview


APPENDIX B: DSM-IV CRITERIA FOR MAJOR DEPRESSIVE EPISODE

 

A. Five (or more) of the following symptoms have been present during the same 2-week period and represent a change from previous functioning; at least one of the symptoms is either (1) depressed mood or (2) loss of interest or pleasure.

 

Note: Do not include symptoms that are clearly due to a general medical condition, or mood-incongruent delusions or hallucinations.

 


(1)               depressed mood most of the day, nearly every day, as indicated by ­either subjective report (e.g., feels sad or empty) or observation made by others (e.g., appears tearful)

(1)               markedly diminished interest or pleasure in all, or almost all, activities most of the day, nearly every day (as indicated by either subjective account or observation made by others)

 

(1)               significant weight loss when not dieting or weight gain (e.g., a change of more_than 5% of body weight in a month), or decrease or increase in appetite nearly every day.

 

(1)               insomnia or hypersomnia nearly every day

 

(1)               psychomotor agitation or retardation early every day (observable by others, not merely subjective feelings of restlessness or being slowed down)

 

(1)               fatigue or loss of energy nearly every day

 

(1)               feelings of worthlessness or excessive or inappropriate guilt (which may be delusional) nearly every day (not merely self-reproach or guilt about being sick)

 

(1)               diminished ability to think or concentrate, or indecisiveness, nearly every day (either by subjective account or as observed by others)

 

(1)               recurrent thoughts of death (not just fear of dying), recurrent suicidal ideation without a specific plan, or a suicide attempt or a specific plan for committing suicide

 

 

B.  The symptoms do not meet criteria for a Mixed Episode

 

C.  The symptoms cause clinically significant distress or impairment in social, occupational, or other important areas of functioning.

 

D.  The symptoms are not due to the direct physiological effects of a substance (e.g., a drug of abuse, a medication) or a general medical condition (e.g., hypothyroidism).

 

E.  The symptoms are not better accounted for by Bereavement, i.e., after the loss of a loved one, the symptoms persist for longer than 2 months or are characterized by marked functional impairment, morbid preoccupation with worthlessness, suicidal ideation, psychotic symptoms, or psychomotor retardation.


APPENDIX C: SHORT REVIEW OF ETHNOGRAPHIC METHODS USED

 

Ethnographic (or qualitative) methods are structured conversations.  They are methods by which interviewers gather information on a particular topic without leading the respondent.  By a variety of methods the interviewer stimulates the respondent to talk on a particular topic without revealing their own opinions or world views.  Much of the technique involves eliciting relevant responses using as few concepts or language as possible, thereby minimizing the influence of the interviewer’s world view on the response.  In the absence of leads the respondent provides their own views which are recorded as accurately as possible.  The interviewer then uses ethnographic analytic methods to make sense of the data, avoiding interpretations based on their own viewpoint or understanding.  This requires an appreciation of how much the interviewer’s belief system affects their conversation and understanding. 

 

Ethnographic methods are useful whenever workers are dealing with cultures or situations different from their own.  They are particularly useful in the design of survey instruments.  Prior use of ethnographic methods helps ensure appropriate design of questionnaires.  Most questionnaires are necessarily lists of leading questions designed to acquire very specific information.  Without prior ethnographic data these questionnaires may be inappropriate and responses may better reflect the designer’s world view than that of the respondent.  For example, a simple questions like ‘what are the major health problems here?’ may yield different information than that intended.  If the respondent considers mental health to be a spiritual issue rather than a health issue, problems in this area will be omitted.  Prior ethnographic research can provide insight into these differences and produce more appropriately-phrased questions.  Different ethnographic methods exist for different purposes.  Following is a short description of the ethnographic methods we used, and their rationale.

 

Free Listing

 

Here an interviewer asks a primary question designed to elicit a list.  For example, in the Uganda study the primary ethnographic question was:

 

‘What are the main problems that affect the people of this community as a result of HIV?’

 

As with other ethnographic methods, the fewer concepts we introduce while still keeping the respondent on the topic the better.  This question contains two major concepts; the genocide and problems.

 

Having put the primary question to the respondent, the interviewer lists all the responses, recording the exact terms used as much as possible.  The interviewer uses non-leading probes (for example, “is there anything else), until the respondent can no longer think of any more items.  When this happens the interviewer then returns to each item and asks the secondary question.  This usually consists of additional information about the item.  In the Rwanda study we asked for a short description of each item, again recording the exact language used by the respondent as much as possible.

 

Free listing is an efficient way of getting an overview of a topic.  Researchers can then choose the most relevant items in the list for further study. 

 

Key informant interviewing proceeds much like a dialogue between informant and interviewer.  Questions are even usually even more open-ended and the interviewer makes an active effort at building rapport with the informant.  The interviewer can use an interview guide (a general outline of the topics to be discussed), but does not need to follow it exactly.  The interviewer usually explores relevant topics as the informant brings them up during the interview.  In addition, the interviewer usually interviews the same informant several times to discuss certain issues in-depth, and to further develop rapport.  As with other ethnographic methods, the interviewer must keep the interviewee talking around the topic of interest without imposing his own belief system.

 

When key informant interviews are the first (or only) ethnographic method used, interviewers often open the first interview with very general questions which may not even be about the topic (for example, ‘Tell me about your day’).  When the informant mentions something related to the topic of interest, the interviewer then asks him/her to talk more about that topic, referring to it using the local term used by the interviewee.  As the interview (and interviews) progress, the interviewer can use his/her expanding knowledge of the local belief system to focus on the topic of interest.  In the Rwanda study we used free listing first, and used the information from the listing to begin with more specific questions.  We used symptoms mentioned in the free list to create a scenario which was then used to stimulate responses.  We were careful in the scenario to use only symptoms and language that respondents used in the free lists, to avoid injecting our own ideas or views.

 

 

APPENDIX D: VERBAL CONSENT FORM

 

The Johns Hopkins University

School of Hygiene and Public Health

Committee on Human Research

 

Verbal Consent Form for Research Study.

 

Submission Date: _________

 

 

Instructions for the Interviewer:

The following is to be read to the subject prior to the interview.   If the subject then agrees to participate, you must sign on the line marked ‘Witness to Consent Procedures’, at the end of this formAlso mark the date on the appropriate line.

 

Study of health effects due to displacement by war.

 

Purpose of the Study

 

You are being asked to be part of a research study.  We want to find out about the problems affecting people in this area.  This research is being done by Johns Hopkins University and World Vision. 

 

Procedures

 

To obtain this information we are talking with some people in the community who we selected by chance.  This is how we selected you.  If you agree to help us, I will ask you some questions.  These questions are about your health.  We may also want to return and talk with you again later.

 

Risks and Discomforts

 

Each interview will take about 30 minutes.  It is possible that some questions may upset you.  You may refuse to answer these questions, or any questions, if you wish.  You may stop the interview at any time.

 

Benefits

 

This information will help World Vision to provide better programs to improve the health of the people in this area.  However, there may be no direct benefit to you personally.

 

Confidentiality

 

During the interview I will write down the information you tell me.  This is the information we will use for our study.  The record of this information will not have any information which can be used to identify you.  I will also record your name and address, but this will be stored separately from the record, and will be locked in the project director’s office.  Only the project director will have the key.  Only he and the researcher from Johns Hopkins University will be able to see this information.  Every effort will be made to protect the confidentiality of this information as far as is legally possible.    

 

Voluntariness

 

It is your decision whether or not to be in this study.  You can stop being in this study at any time.  This will not affect any assistance you get from World Vision or any other organization.

 

Whom to Contact

 

If you have any questions you can ask Dr. Paul Bolton of the Johns Hopkins University.  He is in charge of the study.  You can also contact Luther Anukur of World Vision.  Both can be contacted through the World Vision Office in Kampala, telephone ______.  In the future if you have any questions about the study, you should ask Luther Anukur.  He and the other researchers will tell you if they learn anything new that they think will affect you.

 

Do you agree to participate in this study?

 

 

_________________________

Witness to Consent Procedures (to be signed by interviewer after subject has verbally consented).

 

_________________________

Signature of Investigator

 

_________________________

Date

 

 

 

APPENDIX E: DSM-BASED ALGORITHM FOR DIAGNOSING DEPRESSION WITH THE HSCL.[19]

 

The original 25 question version of the Hopkins Symptom Checklist (HSCL) measures both anxiety and Depression.  We used only the questions assessing Depression.  We chose the HSCL because of its history of successful use across many situations and cultures.  Since it was created prior to the DSM Depression criteria, it is not entirely consistent with the DSM ‘A’ criteria for Depression.  To improve consistency with DSM we dropped one question from the analysis (‘feeling trapped’) and added a question on psychomotor agitation.  To diagnose Depression with the HSCL we used an algorithm based on the DSM criteria.  This algorithm was developed and tested by the Harvard Program in Refugee Trauma, and is shown in Table 14.

 

Algorithm for Matching HSCL Depression questions to DSM Criteria for Major Depression.

 

DSM-IV Criteria

Comment

Adapted HSCL Question

A1. Depressed mood

Must have at least one of these (and only one counts) or one of A2.

B3 crying easily

B7 feeling hopeless

B8 feeling blue

B9 feeling lonely

A2. Diminished interest or pleasure

Must have at least one of these (and only one counts) or one of A1.

B13 loss of interest

B19 loss of sexual pleasure or interest

A3. Significant weight loss or change in appetite

B5 poor appetite

A4. Insomnia or hypersomnia

B6 difficulty sleeping

A5. Psychomotor agitation

B4. feeling fidgety

 A6. Fatigue or loss of energy

Count only one of these

B1 feeling low in energy

B14 feeling everything is an effort

A7. Feeling worthless or guilty

Count only one of these

B2 blaming yourself for things

B15 feeling worthless

A8. Diminished ability to think or concentrate

B12 worrying too much

A9. Recurrent thoughts of death

B10 thought of suicide

B.  Do not meet criteria for a mixed episode

C. Clinically significant distress or functional impairment

C1 presence of severe grief

significant functional disability

D. Symptoms not due to substance abuse or medical condition

E. Symptoms not due to bereavement, last more than 2 months, or involve marked functional impairment, feeling worthless, suicidal or psychotic symptoms or psychomotor retardation.

C2 duration of severe grief

significant functional disability

B15 feeling worthless

B10 thoughts of suicide

 

Under DSM criteria a Depression diagnosis requires the presence of 5 or more of the A criteria during the same 2 week period and represent a change from previous functioning.  At least one of the symptoms must be A1 or A2.  Criteria for a mixed episode (B) were not assessed, nor were substance abuse (apart from alcohol) or medical conditions which might cause these symptoms (D).  Significant functional disability was assessed using a dichotomous ‘function’ variable (see Analysis in the Methods section for how this variable was created).  


APPENDIX F: TESTING CONSTRUCT VALIDITY USING FACTOR ANALYSIS OF SURVEY DATA.

 

We studied the construct validity of the depression questions using factor analysis with principal components extraction and varimax rotation.  Scree plots were used to determine the number of factors to extract.  The scree plot is shown in Figure 7. 

 

Figure 7: Scree plot of factor analysis of Depression questions with principal factor extraction.

 

Only one factor is above the elbow of the plot, suggesting that a single factor underlies much of the variation in the depression questions.  In fact, this factor accounts for 32.5% of the total variance of the 15 depression questions, compared with 7% for the second most significant factor.  Having determined from the plot how many factors to retain, we then reran the analysis, specifying a single factor.  The results of this analysis are shown in table 10.

 

 

Table 10: Principal component matrix of depression Questions (N=587).

 

Correlation between question and factor

Lack of energy   

.60

Blaming self for things    

.30

Crying easily

.59

Feeling fidgety

.57

Poor appetite 

.49

Poor sleeping    

.60

Lack of hope

.59

Feeling sad    

.68

Feeling lonely 

.63

Thoughts of suicide

.34

Worrying too much  

.61

Loss of interest in things

.63

Everything takes too much effort   

.69

Feeling worthless

.63

Loss of interest in sex/sexual pleasure

.45

 

Given the standard cut-off of 0.3 to decide whether a question loads on the factor, all questions load on the single factor which suggests that there is a single major concept being measured by the depression questions.  This supports the hypothesis that depression exists in this population, and the construct validity of the questionnaire. 

 

 

APPENDIX G:  VALIDITY TESTING OF FUNCTION ASSESSMENT

 
 
Figure 8: Function scores according to respondent own assessments (funscore) vs scores according to assessments by cohabiting adults (othefunc) (N=37)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 9: Function scores according to respondent own assessments (funscore) vs scores according to assessments by cohabiting adults (othefunc): non-depressed respondents only(N=24)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 10: Function scores according to respondent own assessments (funscore) vs scores according to assessments by cohabiting adults (othefunc): depressed respondents only (N=13)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

APPENDIX H:  TIMELINE FOR UGANDA FIELD TRIAL

 

 

Day 1: Meet with WV and local authorities.  Review project logistics and training facilities.

 

Ethnographic Study

 

Day 2: Didactic training of WV staff.

 

Day 3: Free listing.   

 

Day 4: Group analysis of free list data

 

Day 5: Complete free list analysis and training for key informant interviews.  Conduct 1st key informant interview.

 

Day 6: Review 1st key informant interview.  Conduct 2nd interview and repeat male function free lists. 

 

Day 7: Review 2nd key informant interview.  Conduct 3rd interview. 

 

Day 8:  Review 3rd key informant interview and repeat of male function free lists.  Translation of instrument.

 

Pilot and Validity Studies

 

Day 9: Discussion of ethnographic results, including identification of depression-like local illnesses.  Preliminary training for validity study supervisors.  Identification and appointments made for interviewing validity study respondents the following week

 

Completion of instrument translation.  Analysis of function free lists (investigator only) and selection of tasks for inclusion in the instrument, based on function free lists.

 

Day 10: Interview training for supervisors and interviewers.  Group review and correction of draft instrument translation.

 

Weekend:  investigator chose 30 study villages by systematic random sampling. Finalized the questionnaire. 

 

Day 11: Study villages mapped.  Pilot interviews completed and discussed and changes made to instrument and procedures.  Data entry methods tested.

 

Day 12: Reviewed changes to instrument and procedures and continuing interview problems.  Instrument finalized.  Explanation of validity study.  Validity study interviews.  Review of interviews.  Data entry commenced.

 

Day 13: Completed validity study interviews and repeated some village maps (that were inadequate).  Random selection of sectors from each village for main survey. 

 

Main Survey

 

Day 14:  Didactic training on mapping of sectors and selecting respondents.  Distribution of villages among interviewing teams.  Mapping and interviewing.

 

Day 15-19:  Morning review meeting. Mapping and interviewing.  Data entry

 

Day20:  Completion of interviewing and exit meeting with WV staff and local authorities.

 

Remaining tasks (completed later)

 

  • Completion of data entry

  • Data cleaning and analysis

  • Write up of study report.

 

 


[1] We defined these illnesses according to their diagnostic criteria published in the Diagnostic and Statistical Manual, 4th edition (DSM-IV) of the American Psychiatric Association.  These criteria are listed in Appendix B.

 

[2]We also intend 2 further stages in future: 7. Using the ethnographic and survey data to design appropriate interventions; and 8. Repeating measurement after the intervention to assess impact.

[3] Brief explanations of the ethnographic methods used are provided in Appendix C.

[4] In other situations we have used this approach to confirm the presence of physical illnesses.  For example in Angola we found that people describe the symptoms of malaria and pneumonia in areas where these occur, yet do not combine these symptoms into these diseases.  The presence of the symptoms is stronger evidence for the occurrence of a disease than whether local people have arrived at the same classification that we use.

[5] To assist those who are illiterate respondents are also shown a ‘non-verbal’ response card.  This has pictures corresponding to each category of difficulty, with increasing difficulty represented by a person carrying an increasingly heavy load.

[6] The function questionnaire template also includes a section on the causes of difficulty for each task (see Appendix A).  Interviewers asked this question if the respondent reported at least mild difficulty.  If the respondent listed the cause(s) as a lack of some external physical resource not related to health (such as assistance or money) the interviewer asked the respondent to reconsider their response as if they had those resources.  Interviewers recorded the level of difficulty for a task only when the causes given were related to physical, mental or emotional problems.  We asked about function in this way because we were interested only in dysfunction due to health problems.  A simpler method would be to ask the respondent up front to consider only dysfunction due to poor health, as is done in most existing function assessment instruments.  However, across (and even within) cultures the meaning of ‘health’ varies widely and it can be difficult to determine its precise meaning.  Therefore, asking respondents to exclude ‘health’-related issues may result in the false inclusion of non-health related causes and the false exclusion of those that are health related.  By having the respondent explicitly state each reason, the trained interviewer (rather than the respondent) can judge what should be included and excluded.  As a check on the interviewer’s judgement we asked them to record all the causes they accepted.  These were reviewed by the supervisor and again during the data analysis.  This was done in the pilot and validity studies and during the main survey, and remedial interviewer training provided when necessary.

 

[7] See Survey Sampling below.

[8]The study also provided a test of the changes recommended after the pilot study, as well as further training for the interviewers. 

[9] Where respondents answered ‘yes’ they were also asked how long they had had this problem.  The intent of asking for the duration was to eliminate positive responses if the duration was less than one week (to try to eliminate simple mood swings) and so ensure that we had only true cases.  One week was chosen as the cut-off because this was also the duration of symptoms asked for in the HSCL.  However, no respondents in the validity study claimed to have either local illness for less than a week, so this was not an issue.

[10] Dichotomous means that the data values can only be yes or no, rather than a number on a scale as, for example, with temperature.

[11]384 interviews is derived from the formula for sample size for estimating proportions:  n=z2p(1-p)/(0.5d)2.  n is the required sample size; p is the best estimate of the proportion prior to the study; d is the width of the confidence interval and z is the level of confidence that the estimate lies within that interval.  For this study we do not have an estimate for p (the proportion of the population who are depressed) so, to be conservative, we use p=0.5 since this gives the largest sample size.  We wanted to be 95% confident that our measured estimate would be within 5 percentage points of the true value (ie, confidence interval=10 percentage points).  Therefore sample size= 1.962[0.5*(1-0.5)]/(0.5*0.1)2 = 384.16 for a simple random sample.  This number is then multiplied by a standard design effect of 1.3, to allow for the cluster element in sample selection, and twice by 1.1 to allow for the expected refusal and failure rates.

[12]Statistical Package for the Social Sciences

[13]Above 0.9 suggests that the questionnaires has too many questions and some could be eliminated (Streiner et al, 1995).

[14] See validity section below for creation of the function and depression scores.

[15] Excluding the final question about other tasks since not all respondents answered this.  Also some interviewees did not give responses for some tasks because they were not responsible for those tasks.  For example, some women did not have children and so did not care for them.  In these cases we assigned a value to this task which was the mean of the respondent’s answers for the other tasks.

[16] Since the distribution of these function scores was not normal, we could not use them in a linear regression.  Instead a dichotomous variable was created - those with function scores of 0 were said to have no dysfunction and those with scores >0 were said to have dysfunction.

 

[17] We had also planned to explore the presence of PTSD, but none of the characteristic symptoms emerged in the free lists.  Therefore only depression symptoms could be used in the key informant interviews.  Here we expected that some characteristic symptoms of PTSD might still emerge, associated with depression.  But this did not happen.  We concluded that PTSD-like illness was either not an important issue with regards to HIV or did not occur locally, and plans to explore it were dropped. 

[18]20 interviewers are required but 22 were hired in case of dropouts.

[19] This algorithm was developed by Richard Mollica of the Harvard Program for Refugee Trauma.