Validation of Compiled F1-F8 Data at Provincial Level AIPMNH Documentation of Key Practices
AIPMNH is managed by Coffey on behalf of the Australian Government
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Introduction
The consistency and accuracy of data in the F1-F8 reporting format has been a major concern for Maternal and Neonatal Health (MNH) activities at the Puskesmas level. The District Health Office (DHO) compiles this data from all Puskesmas before sending it to the Provincial Health Office (PHO) for compilation and subsequent transmission to the Ministry of Health (MOH). AIPMNH uses the provincial aggregation of data to monitor the program and to compare results across the NTT districts. The main problem with interpretation of this data has been the inconsistencies in operational definitions of the F1-F8 indicators – this is apparent when data is compared across time and between districts. Another problem is that some district data is completely missing for various periods due to, for example, not handing on the data when staff are transferred. To address these issues, AIPMNH established district and Puskesmas level databases that are updated on a regular basis. This process was developed and initiated in 2012, and has been completed annually in close collaboration with the PHO for the original 14 districts supported by AIPMNH. To further address inconsistencies in operational definitions the PHO developed a pocket reference book titled ‘Operational Definitions of MCH Indicators’ (Buku Saku Pegangan Bidan dalam pencatatan Kesehatan Ibu dan Anak). These are based on the MOH guidelines but provide more detail for District and Puskesmas staff. The overall purpose of this report is to share the experiences and benefits of the data validation process with other regions in Indonesia for potential replication. This report describes the actual problems identified with the provincially compiled F1-F8 data in 2014 and the results from the validation process. A list of F1-F8 data indicators is at Annex 1.
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2 Data Validation Process
The process of data validation consists of the following procedures: The PHO Maternal and Child Health (MCH) section (responsible for the F1-F8 data) conduct data quality self-assessment. This is to check for data completeness, consistency, calculation errors and to discuss any differences in operational definitions for each indicator by telephone with the DHO (operational definitions are as per the MOH PWS KIA guidelines published in 2010). The DHO then verifies with the relevant Puskesmas as to the source of the disputed data. This is to determine if errors in recording or calculation have occurred, or whether there are differences in application of the operational definitions. The PHO staff provide technical guidance and discuss any inconsistencies with the DHO to ensure compliance with the operational definitions as recommended by the MOH (PWS KIA guidelines, 2010). In some instances, it is necessary for the PHO and AIPMNH to visit the Districts for more in-depth discussions to resolve the data issues. The PHO summarises the results and subsequently makes recommendations for strengthening of the F1-F8 data validation process.
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3 Problems in compiling the 2014 F1-F8 data
Four districts have created their own formats rather than the standard formats. This creates differences in recording and reporting F1-F8 data in the four districts using their own formats and creates errors in transcribing and compilation of data. For example, The F1-F8 format used by Sumba Barat is different from the standard format used by Sumba Timur. Differences in data reporting systems. Some districts provide the data in annual totals, while others provided separate sheets for each month. This again can lead to errors in the total calculation. For example, Kabupaten Sikka sent monthly reports for 2014. Differences in understanding of the operational definitions of the F1-F8 indicators. - Differences in application of the definition of ‘facility deliveries' among districts. The operational definition of a facility delivery is delivery in a Puskesmas (in line with the the PHO ‘Revolusi KIA' guidelines). Four districts (Manggarai, Manggarai Barat, Flotim and TTS) include deliveries that take place in the Puskesmas network of lower level facilities i.e. Puskesmas Pembantu, Polindes and Poskesdes.
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The anomaly that managed neonatal complication Manggarai forr 20 2008 0% Th he an a om mal aly ly tth hat a m an nag age ed dn eo onata na n ata tal cco omp plica lilica ati t on o ffigures iggur u es iin nM Ma an ang nggga gara ra ai fo for 2008 08 8 iis % The anomaly that managed neonatal complication figures in Manggarai iss 0% The anomaly that High Risk detection by health provider figures in Belu for 2010 is >100% The Th e an no om mal aly th tthat hat at H iggh Ri R isk isk sk d etecti et ecti ec tion on b on h eal alth th p rro o ovi vid vi vide de er ffi igure igu gu ure res iin nB elu fo el forr 2 20 010 10 iiss >1 100 00% 0% anomaly High Risk detection byy he health provider figures Belu 2010 >100% The that assisted deliveries are higher for the district by Th he anomaly anom anom an maly aly th al that att a ssis ss iste is te ed de elliive veri r ess ffigures ri igur ig urress a ures re ssignificantly re ignifi ig igni fica cca antly ntly nt ly h igghe herr fo or th the e dist d issttri rict ctt ttotals otal ot a s comp al ccompiled co omp pililed ed b ed The anomaly assisted deliveries figures are significantly higher for district totals compiled byy PHO than the compilation of individual Puskesmas by DHO for 2014 P PH HO tth han han n tthe he ccompilation he ompi pila lati lati t on o ndi divi vidu idu dua dual all P uske us kesm smas mass b DHO ffo DHO or 20 2 014 14 PHO than off iin individual Puskesmas byy DH for 2014 recording off M Midwives Trained in APN has midwives who obtained APN TThe Th he re eco ord din ng o of idw id wiive es Tra Tr ra aiine ned in nA P h PN as iincluded as nclu nc ud de ed th tthe he mi m dw dwiv wivvess w ho o ho btai btai bt aine ned ne ed AP A APN PN kn kknowledge now wle ledg edgge -- The recording Midwives Trained APN has included the midwives who obtained knowledge and practicum during their basic training. an nd prac pr rac acti ticu um du d urriing ng ttheir heir b he heir assic ttraining. asic ra ain nin ingg.. and practicum during basic ----
Differences in population figures between districts and province. Differences estimated Differences population figures between districts and province. Differences in the estimated Di D iffffer eren e cce en es in n p op pul u at atio ion io n fi figu gu ures re es be betw tw wee en di issttriictts an nd pr prov rov ovin in ncce e. D ifffe ere enc nces es iin es n th tthe he es sti tima ma m ate t d population figures between BPS Province and BPS Kabupaten. For example, the population p po op pu ula ati tion on n ffigures iggurress b ettween e we w een en B PS P r vi ro vin ncce an a nd B BP PS K Ka abu bupa patte en. n. For Fo or ex xam ampl ple, e,, tthe e he p he opul op u at ul atio tio ion for ffor fo or population between BPS Province and BPS Kabupaten. example, population Manggarai taken from Kabupaten in Figures (BPS) differs from the population estimates from the Ma ang nggga ara rai ta rai ake ken ffr rom m K ab a bup upatte en n iin n Fi F igu igu urre es ((B BPS) PS S) dif d di iffffer erss fr from om m tthe he h e p op pul u at atio io on e es sti tima m te ma t s fr from om m tthe he h e Manggarai taken from Kabupaten Figures (BPS) differs population estimates Provincial BPS used byy tthe PHO. Pr P rovvin rov inci ciial al B al PS P Su sed by sed se b he P he HO. HO HO. Provincial BPS used the PHO.
Differences in numbers and maternal deaths between PHO and DHO. DHO D Di iffffer ere en nce c s iin n tthe he n he um mbe berss of of neonatal ne ne eon ona on onat attal tal al a nd m nd atter a errna nal de na d deat eat aths hs b ettwe e ween en tthe he h eP HO H Oa nd D nd HO H O. TThe O. he D he HO H O ffails aillss ai Differences the numbers of neonatal and maternal deaths between the PHO and DHO. The DHO fails to provide updated data to the PHO when AMP results indicate differences in numbers. For example, the to op rovi ro vide de u pd p dat ated ed dd a a to tthe at he P HO Ow hen A he AM MP resu MP rresults re esu sultts in ndi d ca ate ed i fe if fere renc ncces e iin n nu numb mb berrs. FFor o e or xamp xa mple, le e th the he provide updated data PHO when AMP indicate differences numbers. example, 2014 and neonatal deaths Kota Kupang changed by DHO but 20 014 4 maternal mat ater errna er nal a na an nd ne n eon onat on atal at ta all d eatth ea hs fo ffor or Kota K Ko ota ta K upan up ang ch han nge ged fo ged ffollowing ollow oll llllow owin in ng va vvalidation lliida idati dation da tion ti on b tthe th he DH D DHO HO bu b but ut th tthis iss 2014 maternal and neonatal deaths for Kupang changed following validation byy the this wass no not reported the PHO. was wa n not ott re reported epo ort rted ed tto to o th the e PH P PHO. O.
A few utilise manual a nu number of indicators using A few few districts districts diissttri rict ctts ut u utilise iliisse ma manual anu nuall ccalculations calculations alcu al alcu cula ation tiio on ns fo ffor forr a numb number berr o off in indicators ndica ca atto orss rrather rather atthe at he er th tthan than han an u using ssiing ng tthe the h iinbuilt he inbuilt nbui u lt functions within the spreadsheets. This has resulted in errors in transcribing and calculating data. ffunctions fu unc ncctti tion ion ns wi w within ith thin in tthe he sspreadsheets. he pre pr ea adsshe heetts. s. Th This hiss h has ass rresulted a essu e ullte ted d in ne errors rrrrors ors in ttranscribing ra an nssccrribin ib bin ing an a and nd ca ccalculating alc alc lcul lcul ulat atin ing ng da d data. ata. ta TThe The he he PHO hard copy and needs transcribe and sum all data. For PH PHO HO uses uses usess tthe the h h he hard a d co ar copy opy ffor for orr ccompiling compiling om o mpi pilliingg the the the he data, data, da atta,, a and n ttherefore nd therefore here he ere effo ore n needs eedss tto ee to o transc transcribe sccribe e and d sum um a alllll dat data. ta. Fo F Forr example, detection of high pregnancies were aggregated e ex example, xam a pl p e,, tthe the he ttotal he total ota ot all ffigures figures iggu urress ffor for or d or detection ettec e etec ecti cti t on on o off hi h high gh rrisk risk isk is k pr p preg pregnancies reg egna n nccie na ies we ies w were re e iincorrectly incorrectly ncorrre rect cttly ly a aggregated g re gg rega egga atte e ed d for ffor fo or Kabupaten Kupang Kabupaten Kupang K Ka abu bupa paten te en Ku Kupa pa pang angg
data commonly occur the districts. Submission of incomplete data iss IIncomplete In Incomplete nco comp mple le lete ete te d data ata at a su ssubmissions submissions ubm bm misssi sion ons co commonly omm mmon mon only ly o occur ccur cc ur iin in n th the he d di districts. ist ist stri rict rict cts. s. S Su Submission ubmis ubm bmis bm issi sio ion on o off in incomplete nco com mp ple lete te d data atta is common, as data is progressively compiled for each month, and it is relatively easy to miss out individual ccommon, co mmon mm on,, as on a d data a a is p at progressively ro oggrres essi s ve v lyy ccompiled omp pile l d ffo for or ea or each ach m month, onth th,, an and d it it iiss re relatively elati la atiive vely e easy asssyy to a t m miss isss ou o outt in indi individual d vi di v du dua dual all months, or tto send without months off tthe For example, mont mo nths nt hs,, or hs o o se end d tthe he e ttotal otal ot a ccalculation alcu al lcu cula ula lati tion on nw itho it out ut iincluding ncclu ud diingg tthe he ffinal he inal in al m onth on nth hs of o he yyear. he ea e ar. r. F Fo or exam e xam mpl ple e, tthe e, he h e months, to send the total calculation without including the final months the year. For example, the data 3 sstandard postpartum Manggarai 2010 was incomplete. da ata a on on Kf K (received (re rece ce eivved ed 3 ta and ndar ard d po p sttp pa arttum vvisits) issitts) s ffor or M or an ngg g a arrai a 2 010 01 0w wa as in nco c mp ple lete te e. data on Kf (received standard postpartum visits) for Manggarai 2010 was incomplete.
Utilisation of d different estimated eligible useage. The U Ut Util tililiissat atio ion n of o ifffe iffe if erre ent nt fformulas orrmu o mula las for las ffor fo or es sti tima mate ted el ted elig liigggib ible ibl ib le ccouples oup ou plles p es ((PUS) PUS) PU S)) ffor or ccontraceptive ontr on trraccep pti tive v u se eag age. e TTh e. he Utilisation different formulas estimated eligible couples (PUS) for contraceptive useage. The estimation of the number of eligible couples for the Family Planning (FP) indicator used by the PHO was esti es tima ma m attiio on no the en nu umb mbe err o el liggib ble e ccouples ou o upl ples ess ffor orr tthe he F he amilililyy Pl am P lan anni n ng ((FP) ni FP F P) P) in indi d ca di c to or us se ed db th he PH HO was wa w as estimation off th number off e eligible Family Planning indicator used byy the PHO different from that used example the the while di d iffe iff fffe erren ent fr ro om m tthat ha at us u sed ed b hethe DH D HDHO. O.. Fo O F or For e ex xam m ple th pl he es e sti tima mestimation ati tion ion on b hebyPH P H O issPHO 1 8.6% 8. 8.6% 6is% 18.6% w h le hi e tthe he eD HO H Othe iiss different from used byybytth the DHO. For example the estimation byy tth the PHO 18.6% while DHO estimation isrov 17%. provided data compiled byntPHO 17%. 17 %. The The FP Th Fby data dDHO atta p pr vid i edThe b DFP DH HOdata S Su mba mb a Ti Timu mu urbyan nDHO d th heSumba FP d FP attTimur a co omp mand pilled dthe b PHO PH O is is a lsso di d ffer ff erren . provided byy DHO Sumba Timur and the data compiled byy FP also different. is also different.
D Di iff ffer ffer eren ent nt Fa Fami miily m ly P lann la lann nnin i g (F (FP) P) d P) ata pr ata at p rov ovid ovid ided ed b rrelated ela ela lated lat ted in te iinstitutions. inst nssttit ituttio itut ions n . TThe he ffigures he iggur u es ffor or FFamily amili y Pl am Plan anni an n ngg u ni sers se rs Different Family Planning data provided byy re Planning users data byn figures form Family cDifferent co llec e te ed Family byy tthe hePlanning he mid dw wiife, fe(FP) fe , DH DHO, O, F aprovided am miilly Pl P Plan lan an niirelated ngg O ffic ff ffi icinstitutions. e or W o en om eThe n’s n’s E mpow mp ow wer erme me ent n o fffPlanning ice fr ic req que uusers uent ent ntllyy collected midwife, Family Planning Office Women’s Empowerment office frequently collected byul Planning Office or Empowerment office d di ffer ff ers, s, resulting res esul uthe ltinggmidwife, fro rom different diiff d fDHO, fer eren en nFamily te es sti tima ma ate es a att tthe he d istr istr is ric icWomen’s t le evve el.l. TTh he pr he p rov ovin nci cia all a ggrree eme mfrequently entt o n FP FPdiffer, d atta ata differs, from estimates district level. The provincial agreement on data resulting from different estimates at the district level. The provincial agreement on FP data recommends rrecommends re co om mm men nd dss u se o se datta data a ffrom r m tth ro he Fa ami milyy P lla ann nin ingg Of fffiicce e. use off da the Family Planning Office. use of data from the Family Planning Office.
U Util Ut tiillis lissati attio ion o off d ifffere ffe ere en ntt C ru ude de B ir th ir th R attes a es ((CBR) CBR) CBR) CB R) ffigures. iggur ures res es. V aryi ar ying ng C BR B Ra sed ed b Dist s ri rict c s ha ave ea ffec ff ecte ec ted Utilisation different Crude Birth Rates Varying CBR ass us used byy Di Districts have affected Utilisation different Crude Birth Varying by Districts have affected th the he es esti tima ma m aof tiion n s ffo or pr p egna eg nanc na ncciie es, d eRates el ivverie ie e(CBR) s, e s, tc.figures. tc F orr e xamp xa m ple le e, C CB BCBR R 2014 20as 14 4used K a u ab up pat te en n Kupang: g 1 9 8 an 9. a d estimations for pregnancies, deliveries, etc. For example, CBR Kabupaten 19.8 and the P Pr ro ovvestimations ince ince in ce: 2 23 3.0 0 for pregnancies, deliveries, etc. For example, CBR 2014 Kabupaten Kupang: 19.8 and Province: 23.0 Province: 23.0
A few fe ew dist d di ist s ri rict c sh ha ave ve n ot ssubmitted ot ub bm miitt ittted d vvalidation alid al id da attio ion nd da ata tto o PH P O an and d AI A PM MNH. NH TThe NH he ffinal inal in al vvalidation a id al dat atio ion n forr FF1-F8 1-F8 8 da d tta a districts have not data PHO AIPMNH. data A few have submitted topl and The for has ha s no n odistricts t be een ssent en nt to tnot oP HO Oo to A IP Pvalidation MN NH. H. For Fo odata r ex e xam am pPHO e: The he e ffinal iAIPMNH. in nal vvalidation a id al dat atio io ion on final fo or th thvalidation the e2 20 01 14 4d at a ta F1-F8 for Su fo for S udata m a mb not been PHO orr to AIPMNH. example: for 2014 data Sumba has or B Ba arra anot t ha abeen s no not sent not been been be ntossubmitted uPHO ub b bmi miitt m tted ted edtotto oAIPMNH. ei e ith the err tthe heFor he P Hexample: HO Oo to A IThe IP PMN Nfinal H validation for the 2014 data for Sumba Barat has either PHO orr to AIPMNH Barat has not been submitted to either the PHO or to AIPMNH
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How to resolve data problems
Results of the validation process found that 73% of data errors occurred in: 'DWDFRPSLODWLRQ 8WLOLVDWLRQRIGLIIHUHQWIRUPXODVIRUHVWLPDWLQJHOLJLEOHFRXSOHVDQG ,QFRQVLVWHQF\LQXVHRIWKHRSHUDWLRQDOGHILQLWLRQVRIWKH))LQGLFDWRUV The first step is to prepare a list of the indicators for each district where the DHO compilation differs with the PHO. The next step is for the PHO to discuss with the DHO to identify why the differences have occurred, and reach agreement on the correction. This process should take up to one working day for the PHO and DHO staff to reconcile the data for each district. An example of this validation process is included at Annex 2.
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How to address unresolved problems
If issues remain after completion of the validation process (e.g. the data on managed obstetric complication for Ngada was still >100%) this is usually because the DHO needs to follow-up with the Puskesmas. The process can sometimes take up to two (2) weeks as it takes time for the DHO to clarify with the Puskesmas.
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Results of the data validation process
AIPMNH introduced the F1-F8 data validation process in 2012. In order to sustain the F1-F8 data validation, AIPMNH understood the importance of PHO ownership of the process. AIPMNH funded the validation process for PHO and AIPMNH, for the 14 AIPMNH supported districts in 2014. In 2015 AIPMNH supported the F1-F8 data validation meeting which was attended by the MCH program staff from the 14 districts. The purpose of the meeting was to achieve a common understanding and agreement on the operational definitions for the F1-F8 indicators. As a result of the meeting it was agreed that all districts will use the standard formats, follow the operational definitions as recommended by MOH as well as to strengthen the data validation process.
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Advantage of data validation
The benefit of the validation process is to have a regularly updated and reliable F1-F8 database. This is useful for real time monitoring of progress, provides the capacity for conducting comparative analyses among the districts and for evaluation of the AIPMNH program. Complete and valid data are essential for MNH program management (planning, implementation, intervention, monitoring and evaluation).
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Annex 1: List of F1-F8 indicators maternal deaths neonatal deaths Pregnancies K1 (received standard ANC services for the first time) K4 (received ANC services 4 times) Deliveries Life Birth Assisted Deliveries Received Kf (received 3 standard postpartum services) First neo-natal visit (KN1)- (received standard neo-natal services for the first time) Second neo-natal visits (KN2) - (received standard neo-natal services twice) Facility Deliveries Pregnant women received TT2 Pregnant women received Fe3 Pregnant women received Vitamin A High Risk detection by health provider Maternal high risk cases referral Neonatal high risk cases referral Family Planning users Eligible couples for FP Obstetric Complications Managed (pregnant women with high risk /complication who received management) Neonatal Complications Managed (Neonate with High Risk/Complication Managed ) Puskesmas that provide BEONC (PONED) Pregnant women who have the MCH book P4K (villages that practice birth preparedness) Kemitraan Bidan dan Dukun bayi (traditional birth attendants & midwives partnership) GP in Puskesmas Midwife in Puskesmas Midwife in Village (bidan desa) Nurse in Puskesmas Midwives Trained in APN Midwives trained in PPGDON
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Annex 2: Example of District validation check list: Kabupaten Kupang Kab Kupang: 2014
F1-F8 Kab
F1-F8 Prov
Difference
Verified with DHO 4 Mar 2015
Remarks
Pregnancies Deliveries
8115 7746
8289 7912
174 166
Different CBR for kabupaten 19.8 and Province 23
Different pop between BPS Prov and BPS Kab
K1 Assisted Deliveries
8012 5888
8068 6355
56 467
KN1
6220
6251
31
Facility Deliveries TT2 Vit A High Risk detection by health provider
5464
5495
31
761 6318 1012
762 6349 1026
1 31 14
Neonatal high risk cases referral
10
8
-2
Family Planning users Eligible couples for FP
33286
34563
1277
44944
60939
15995
Obstetric Complications Managed Neonatal Complications Managed
1146
1171
25
569
581
12
Pregnant women who have the MCH book Midwife in Puskesmas Midwives Trained in APN Life Birth
8012
8068
56
95
107
12
148
154
6
6243
6267
24
The correct data from Puskesmas level F1-F8 data sent to AIPMNH
K1 includes RS: 56 recorded by PHO The figures for Facility Deliveries and NonFacility Deliveries recorded as Assisted Deliveries by PHO KN1 includes hospital (RS): 31 recorded by PHO Facility Deliveries recorded by PHO included RS: 31 TT2 includes RS: 1 recorded by PHO Vit A includes RS: 31 recorded by PHO The figures for High Risk detection by health provider recorded by PHO deducted an incorrect figure from the F1-F8: 1026 (due to manual calculation). The correct data: 1016 include RS: 4. The figures for Neonatal high risk cases referral recorded by PHO taken the total from F1-F8 data which was the wrong figures: 8 (due to manual calculation) The staff from DHO will follow up with staff at PHO who compiled the F1-F8 data. DHO use Eligible couples for FP figures collected by bidan while PHO using 18.6% of pop The figures for Obstetric Complications Managed included RS: 25 recorded by PHO
The correct data from Puskesmas level F1-F8 data sent to AIPMNH
The figures for Neonatal Complications Managed recorded by PHO taken the total from F1-F8 data which was the wrong figures: 581 (due to manual calculation). The correct figures: 584 - RS: 15 The figures for Pregnant women who have the MCH book included RS: 56 recorded by PHO The figures for Midwife in Puskesmas included RS: 12 recorded by PHO The figures for Midwives Trained in APN included RS: 6 recorded by PHO The final validation data for Life Birth has been provided during the validation process. The correct figures for Live Birth: 6277.
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Lampiran 2: Contoh daftar validasi kabupaten: Kabupaten Kupang
581
569
1171
1146
Penanganan komplikasi obstetri Penanganan komplikasi neonatus
44944
PUS
33286
Peserta KB aktif
10
Rujukan kasus resiko tinggi neonatus
1012
Deteksi resiko oleh tenaga kesehatan
6318
Vit A
762
761
5495
5464
Persalinan di fasilitas kesehatan TT2
6220
KN1
5888
Persalinan oleh tenaga kesehatan
8012
K1
8115 7746
ibu hamil Ibu bersalin
F1-F8 Kabupaten
Kab Kupang: 2014
Ibu hamil miliki buku KIA Bidan Puskesmas Bidan terlatih APN Lahir Hidup
F1-F8 Provinsi 8289 7912 8068 6355
6251
6349 1026
8
34563
60939
6267
6243
154
148
107
95
8068
8012
Perbedaan 174 166
Perbedaan jumlah penduduk antara BPS Provinsi dan BPS Kabupaten
Perbedaan CBR untuk kabupaten 19,8 dan Provinsi 23,0
Keterangan
Verifikasi dengan Dinkes Kabupaten Kupang 4 Mar 2015
56 467
31 31
1 31
Data yang valid dari data F1-F8 Puskesmas yang dikirim ke AIPMNH
14
-2
1277
15995
25
12
56
Data yang valid dari data F1-F8 Puskesmas yang dikirim ke AIPMNH
12
K1 termasuk RS: 56 yang dicatat oleh Dinkes Provinsi Jumlah persalinan di fasilitas kesehatan dan Non-fasilitas dicatat sebagai Persalinan oleh tenaga kesehatan oleh Dinkes Provinsi KN1 termasuk RS: 31 yang dicatat oleh Dinkes Provinsi Persalinan di fasilitas kesehatan yang dicatat oleh Dinkes Provinsi termasuk RS: 31 TT2 termasuk RS: 1 yang dicatat oleh Dinkes Provinsi Vit A termasuk RS: 31 yang dicatat oleh Dinkes Provinsi Jumlah deteksi resiko oleh tenaga kesehatan dicatat oleh Dinkes Prov mengurangi jumlah yang tidak benar dari F1-F8: 1026 (akibat dari kalkulasi manual). Data yang benar: 1016 termasuk RS: 4. Jumlah rujukan kasus resiko tinggi neonatus dicatat oleh Dinkes Provinsi dengan mengambil total dari data F1-F8 yang merupakah jumlah yang salah: 8 (akibat dari kalkulasi manual) Staf Dinkes Kabupaten akan menindaklanjuti dengan staf Dinkes Provinsi yang merekap data F1-F8. Dinkes Kabupaten menggunakan jumlah PUS yg dikumpulkan oleh bidan sedangkan Dinkes Provinsi menggunakan jumlah estimasi18,6% dari jml penduduk Jumlah penanganan komplikasi obstetriteri termasuk RS: 25 yang dicatat oleh Dinkes Provinsi Jumlah penanganan komplikasi neonatus yang dicatat oleh Dinkes Provinsi dengan mengambil total dari data F1-F8 yang merupakan angka yang salah: 581 (akibat dari kalkulasi manual). Angka yang benar: 584 - RS: 15 Jumlah hamil miliki buku KIA termasuk RS: 56 yang dicatat oleh Dinkes Provinsi Jumlah Bidan Puskesmas termasuk RS: 12 yang dicatat oleh Dinkes Provinsi Jumlah bidan terlatih APN termasuk RS: 6 yang dicatat oleh Dinkes Provinsi Validasi terakhir lahir hidup diberikan saat proses validasi. Jumlah yg benar untuk lahir hidup: 6277.
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Lampiran 1: Daftar indikator F1-F8 Penduduk Ibu hamil Ibu hamil yang memiliki buku KIA K1 (mendapat pelayanan ANC pertama kali) K4 (mendapat palayanan ANC ke-4) TT ibu Hamil Ibu hamil mendapat tablet Fe 90 biji Ibu hamil mendapat Vit A Deteksi resiko oleh tenaga kesehatan Rujukan kasus resiko tinggi maternal Rujukan kasus resiko tinggi neonatus Ibu bersalin Ibu nifas Persalinan oleh tenaga kesehatan Persalinan di fasilitas kesehatan Penanganan komplikasi obstetri Penanganan komplikasi neonatus Kunjungan ibu nifas (Kf) Kunjungan neonatus pertama (KN1 Kunjungan neonatus kedua (KN2) Kematian ibu Kematian bayi baru lahir Peserta KB Aktif Pasangan Usia Subur (PUS) Puskesmas PONED P4K (desa yang melaksanakan P4K) Kemitraan bidan dan dukun bayi Dokter Umum di Puskesmas Bidan di Puskesmas Bidan di Desa Perawat di Puskesmas Bidan yang terlatih APN Bidan yang terlatih PPGDON
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Hasil dari proses validasi
AIPMNH telah memperkenalkan proses validasi data F1-F8 pada tahun 2012. Guna terjamin kesinambungan kegiatan validasi data F1-F8, disadari pentingnya keterlibatan mitra Dinkes Provinsi dalam proses validasi data F1-F8. Sehingga pada tahun 2014 AIPMNH mendukung dana proses validasi data F1-F8 bersama Dinkes Provinsi ke 14 kabupaten/kota dukungan AIPMNH. Pada tahun 2015 mendukung dana pertemuan validasi data F1-F8 yang dihadiri oleh staf pengelola program KIA dari 14 Kabupaten/kota dukungan AIPMNH. Tujuan pertemuan adalah untuk memperoleh persamaan pemahaman penggunaan definisi operasional indikator data F1-F8. Hal ini telah disepakati untuk menggunakan format yang baku dan definisi operasional Kemenkes serta menyempurnakan proses validasi data. Definisi operasional disesuaikan dengan yang direkomendasikan oleh Kemenkes yang tercantum dalam buku panduan PWS KIA edisi 2010.
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Manfaat dari validasi data
Manfaat dari proses validasi ini adalah untuk melakukan pemutakhiran database F1-F8 secara berkala. Hal ini sangat bermanfaat untuk melakukan analisa komparatif antar kabupaten dan memantau kemajuan serta evaluasi program AIPMNH. Data yang lengkap dan valid bermanfaat untuk pengelolaan program Kesehatan Ibu dan Bayi Baru Lahir didalam perencanaan, pelaksanaan, intervensi serta permantauan dan evaluasi program.
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Bagaimana menyelesaikan masalah
Hasil dari proses validasi terdapat 72.7% terkait masalah perbedaan dalam hal: .RPSLODVLGDWD 3HQJJXQDDQUXPXV\DQJEHUEHGDXQWXNPHQHWDSNDQHVWLPDVLVDVDUDQ 386 GDQ ,QNRQVLVWHQVLSHPDKDPDQGHILQLVLRSHUDVLRQDOLQGLNDWRU)) Langkah pertama adalah menyiapkan daftar indikator masing-masing kabupaten ketika kompilasi data Dinkes Kabupaten berbeda dengan Dinkes Provinsi. Langkah berikutnya dilakukan pembahasan diantara staf Dinkes Kabupaten dan Dinkes Provinsi untuk mengidentifikasi perbedaaan dan mendapatkan data yang valid. Pengalaman dalam proses validasi diperlukan waktu 1 (satu) hari kerja untuk melakukan diskusi dan validasi data untuk setiap kabupaten. Contoh hasil proses validasi terdapat dalam Lampiran 2.
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Bagaimana mengatasi masalah dalam proses validasi yang tidak terselesaikan
Setelah menyelesaikan proses validasi dan masih terdapat masalah (data penanganan komplikasi obstetri di Ngada masih >100%), maka tindak lanjut dilakukan reverifikasi antara pengelola data F1-F8 Dinkes Kabupaten, Puskesmas dan AIPMNH Proses reverifikasi data dibutuhkan waktu sekitar 2 (dua) minggu, karena Dinkes Kabupaten perlu menghubungi Puskesmas untuk klarifikasi permasalahan dalam proses validasi.
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Th he an anom anomaly om mal aly that aly th hatt m managed akomplikasi an anag nag aged dn neonatal eon eo na ata tal co complication omp mpli liicca ati tion n ffigures iiggurre es s iin nM Ma Manggarai anggga gara raii fo ra ffor or 2 20 2008 00 08 8 iiss 0 0% % - The Jumlah penanganan neonatus di Manggarai tahun 2008 adalah 0% Th he an a anomaly nom nom ma ally ly th tthat hat tH High iggholeh R Ri Risk isk sk tenaga d detection etec et ecti tion ion okesehatan nb byy he health ealltth hdip provider ro ovi vide der de r fi figu figures gu u2010 res in re n >100% B Belu e u fo el fforr 20 2010 10 0 iiss >1 > >100% 100 0% - The Jumlah Deteksi resiko Belu tahun The anomaly Th an nom opersalinan ally th a that a a at assisted ss sis i te ted d de deli deliveries live v kesehatan r ess ffigures ri igur ig urressjauh a are re e ssignificantly iggni nifi f tinggi fi ca ant ntly lyy untuk h higher igghe ertotal fo for or th the e di d district sttri rict ct ttotals oyang ot alls co ccompiled mpililled mp db byy - The Jumlah oleh tenaga lebih kabupaten dikompilasi PHO PHO Dinkes PH tthan th han a tthe he eProvinsi ccompilation om o m mpi pila pi lati adaripada tion ti no off in indi individual nmasing-masing divi di vidu vidu ua all P Puskesmas uske us kesm ke sPuskesmas smas mas b byy DH DHO HOyang ffo for or 20 2 2014 014 014 14 oleh direkap oleh Dinkes Kabupaten untuk - The The re Th recording e2014 cco ord rdin ing of of M Midwives idw id wive wi ivve es Tr Trained ra aiiine ne n ed in nA APN PN h PN has as iincluded as ncclude n nclu lu ude ded d th the he mi m midwives dw d wivve ess w who ho o ho obtained bttaine b ned ed AP A APN PN kn kknowledge owle ow ledg le dgge tahun an a nd pr p practicum ra accti ticu cuBidan m du d during uri riyang ring ng ttheir ng he h eiirr b basic adilatih as siicc ttraining. rra ain inin in ngg.. juga memasukkan data bidan yang mendapat APN sebagai - and Pencatatan telah APN bagian dari kurikulum pendidikan kebidanan. Differences population figures between districts and province. Differences estimated Di D iff ffer ffer eren en e nce c s iin n p op o pul ulat ulat atiio on ffi igu ure res be b etw twee we ee en di d ist strriict cts a an nd pr p ro ovvin incce e. D iffere if iffe fere fe renc n es es iin n th tthe e es sti t ma ate t d population between BPS Province and BPS Kabupaten. example, population p popu po opu pula la lati ati t on n data ffigures igur ig gur ures es b etwe et twe een e B PS S P ro ovi vin nccekabupaten a an nd BP B PS dan Ka Kabu abu bprovinsi. pate pa te en.. Sebagai Forr ex Fo xa am mpl ple, e, tthe he h e p op pulpenduduk lat atio tio on fo ffor or or Perbedaan jumlah penduduk antara contoh, jumlah taken Kabupaten Figures (BPS) differs population estimates Ma M ang ngga g rai ga ra ai yang tta ake k ndiambil ffrom fr om m K ab bupat up pKabupaten aten at n iin n Fi F igu gdalam urre es (B (BPS PS S) d di i(BPS) ffffe errs fr ffrom ro om m tthe he dari he p op pu ul lattiio on es e stima tiim ma ate tess fr ffrom om m BPS tthe he he Manggarai dari Angka berbeda jumlah penduduk dari Provincial BPS used PHO. Pr rov ovin ncial cial ci ayang lB PSdigunakan PS u s d by se by tthe he h eP HDinkes HO. HO O. Provinsi oleh Provinsi. Differences numbers and maternal deaths between PHO and DHO. DHO Di D iff ffer erence ence en esjumlah iin n tthe he h en um mb be ekematian rrss of of neonatal ne n eo on nibu attaldan a a ndbayi nd m atbaru at en er na alahir l de d aths at hss b etwe et w e tthe en he eP HO a nd dD HO O. TThe he h eD HO H O ffails ails ai ls Perbedaan kasus antara Dinkes Provinsi dan Dinkes Kabupaten. provide updated data PHO when results indicate differences numbers. example, tto op rro oviide dProvinsi u pd datttidak ed dd atdiberikan a ata ta tto o tthe he P he HO Ow hen AM he AMP P re resu su ulttshasil iin ndica dicAMP di t d te ifffe f re renc nces es iin es n nu numb mb ber ers. rs. sperbedaan FFor or e xamp xa mp m pjumlah le, le e, th the e Dinkes data terkini, padahal menunjukkan adanya 2014 20 014 14 maternal m ater at ern na al a an and nd ne n neonatal on o nbayi atal at tal al baru d deaths e tth ea hlahir. s fo ffor orSebagai K Ko Kota otta aK Kupang up u p ang ch an chan changed nge ed fo ffollowing llllow ow win nkematian g va vvalidation lilida dati da tion oibub on by y th tthe hebayi DHO DH HO b bu but utlahir tthis th hiss kasus kematian ibu dan contoh, jumlah kasus dan baru was not reported the PHO. wa as no n o2014 t repo re epo ountuk r ted rt ed tto oKota tth h he e PH P HO. O tahun Kupang berubah setelah dilakukan validasi dengan Dinkes Kota Kupang tetapi perubahan ini tidak dilaporkan ke Dinkes Provinsi. A few few districts fe dist di stri st rict ri cts ut cts ct u utilise tiillis ise se ma m manual anu anu nua all ccalculations alcu al lcu cula lati ati tion ons ns fo ffor or a n nu number umb be err o off in iindicators nd diica cato ato torss rrather tors atther he er th tthan ha an n u using sn si ngg tthe he inb inbuilt builtt functions within has errors and data. fu Beberapa unccti t on ns wi w kabupaten thiin th n tthe he h e sspreadsheets. pr p masih reads ea ad dsshe menggunakan ee etts. s Th TThis hiiss h ass perhitungan a rresulted esul es u te ted d in i e rsecara rr ro orrs in ttranscribing manual/paper rans ra nsscrrib n ibiin ng a ng an ndbased ccalculating ca lccu ulluntuk atin at ingg da data sejumlah ata ta. a. TThe he he hard and needs transcribe and alllll dat data. Forr PHO PH indikator HO uses use us ess F1-F8. tthe he h he ardPerhitungan ar ccopy co py ffor py o ccompiling or om o msecara pililing pi ingg tthe he h manual e data, data, atta a,, a untuk nd therefore the hesejumlah re refo effo orre en eeds ee eeds indikator s tto o ttr ran an nsc sF1-F8 scri sc crriibe be a tidak nd d ssum um m menggunakan a a a. Fo F example, e exam ex formula xa am mple, eyang , tthe he h e telah ttotal otall dikembangkan ot ffigures iggu urres res ffor or d or detection eoleh et tec ecti ecti to on Kemenkes n o off h hi high igh g RI. rrisk iissHal k p pr pregnancies rini eggdapat e nanc na nccie emenyebabkan s we w were re e iincorrectly n or nc orrect terjadinya tlyy a aggregated gggre ekesalahan gated ga d fo ffor or Kabupaten Kupang Ka K saat abu b melakukan pate pate pa ten K ten Ku upa pang nperekaman g dan penjumlahan. Sebagai contoh, pelaporan Dinkes Kabupaten Kupang terjadi kesalahan dalam perhitungan jumlah kasus untuk indikator deteksi oleh tenaga kesehatan dan jumlah tinggi neonatus. Incomplete data submissions commonly occur the districts. Submission off in incomplete data Inco In co omp mple lrujukan t d te attakasus su ubm b risiko isssi s io on n s co om mm mo on nlyy o c ur iin cc n th he di dist stri st riict rict cts. s. Su s. S ubm bmis misssi sion on o nco comp omple mple mp lete ete te d ata at a iss common, data progressively compiled each month, relatively easy miss out individual co omm m on o , as a d atta iiss p ro ogr gres gres essi siive vely l com ly om mpiile led fo ffor or ea ach m on o nth t , an and d iitt iiss re ela lati ati tive ve y e vely ve asy as sy to m isss ou ut in ndi divi v du vi dual al al months, Ketidaklengkapan dalam pengiriman without F1-F8 months pada For example, mont mo n hs nt hs,, or o tto o se send nd ddata tthe he e ttotal otal ot a ccalculation alcu al c lati t on o w ittlaporan h ut iincluding ho nclu nc lu ud diing sering tthe he ffinal in nterjadi a m al onth on thss of th o beberapa tthe h yyear. he ear. ea r. kabupaten. Fo or ex e xam xam ampl ple, le, ePada tthe he h e umumnya on (received postpartum tidak Manggarai 2010 dikompilasi was incomplete. da d data ata o n Kf Kfpengiriman (re re ece ce eivve ed d 3 data sstandard ta and ndyang arrd ar d po post ost stpa palengkap rttu rtum um m vvisits) isterjadi is itts) s) ffor orkarena or M an ngg ggar gdata arai ar arai ai 2 010 w 01 wa as iin nco cpermp mbulan, le ete e. sehingga pada bulan-bulan tertentu data mudah hilang atau mengirimkan hanya jumlah total data tanpa memasukkan bulan-bulan dari Sebagai setelah dilakukan validasi (kunjungan Utilisation different for estimated eligible useage. The U Ut tiillis isat atiio on of ofterakhir d ifffe ere ren nt t fformulas ortahun o rmu ula asberjalan. ffo or e es sttiim ma ate ed el e elig lcontoh, iggib igib ible ble le ccouples ou o uples ple pl ess ((PUS) PU US) S) ffor or ccontraceptive or on o ntr trac acep acep epti pdata tive Kf ti u seag se age. ag e. TTh he ibu nifas) 2010 tidak lengkap. estimation off th number offManggarai eligible couples Family Planning indicator byy th PHO was e es t ma ti attiion nuntuk o the ekabupaten n nu umb ber e o e el elig ligib ble cou oupl ou uternyata les ffor or tahun tthe he F he amil am ililyy Pl P lan anni n ng ni ((FP) FP P) in indi d ca di ato torr us used ed e db the e PH P Ow wa as different used byy th the DHO. For example estimation byy th the 18.6% while DHO diff di ffer ff erren ent fr ffrom ro om m tthat ha at us sed ed b he D DH HO O.. F Fo or ex xamp plle th the e es esti tima ti ma m ati t on b he PH PHO O iss 1 8.6% 8.6% 8. %w hille hi e tthe he h eD HO O iiss 17%. data provided byy DH DHO Sumba the data compiled byy PH also different. 17 7%. % The Th he FP FP dat ata ta p pr rovvid ded d b D O Su S um mb ba Ti TTimur im mu ursasaran and an nd tth hePasangan FP F Pd atta co comp oUsia mp m piled ililed b PHO O iiss a lsso di lso d f eren ff ntt.. Perbedaan rumus dalam menetapkan estimasi Subur (PUS). Estimasi jumlah PUS untuk indikator KB yang digunakan oleh Dinkes Provinsi berbeda dari yang digunakan oleh Dinkes Kabupaten sasaran PUS dari xmjumlah penduduk Different Family Planning data provided by institutions. Planning users D Di f eren ff erren entt Fa F akarena mily mi llyy P larumus la nn n nin ing (F ((FP) Festimasi FP) P) d P) ata p at pr rovid ovid ov ided ded ed b y re rrelated ela late ate tedDinkes inst in nssttit tit itut utProvinsi utio ions io ions ns. TThe hadalah he ffigures igur ig gur ures ures es18,6% ffor orr FFamily o am a iilly ly Pl P anni an ning ngg u sers se rs sedangkan Kabupaen yang diberikan collected midwife, DHO, Family Planning Office Women’s Empowerment office frequently coll co llec ecte te ed byDinkes tthe he h e m id dwiife e, DH D HO, O17%. F am a mSebagai ililyy Pl P lan an nni ncontoh, ngg O ffic ff ice cdata e or sasaran W o en om en’ssPUS E mpow mp ower ow werm me ent o ent ffic ff icceoleh ffr req qDinkes ue u ent ntly lyy Kabupaten Sumba data oleh untuk Timur berbeda. differs, estimates district level. The provincial agreement on data d di iff ffer ers, s, resulting res e ul ulti lttiingg from fro oTimur m different d ff di fdan eren er ent en t es e esti stPUS mate ma tes te s at atDinkes tthe h d he isProvinsi tricct le tr leve vel. ve l. TTh hkabupaten e pr p o in ov inci cial ci aSumba al a ggrree eeme ement m me entt o n FP FP d ata at recommends reco re eco comm om mm me en nds nds ds u use se o se off da d data a ata ta ffrom ta ro om tth the he F Fa Family ami millyy P Planning lla ann nnin nnin ing Of O Office. ffi fice ice ce. Perbedaan data Keluarga Berencana (KB) diantara institusi pengelola program KB. Jumlah peserta KB Aktif dikompilasi oleh Telah Utilisation different Crude Birth Rates Varying CBR ass us used byy D Districts have affected Ut U tilillis issat ayang tio on of fd iifffe fere en ntt C rru ude de bidan, B iirr th R aDinkes at e ((CBR) es CB C BKabupaten R) ffigures. R) iggu urres. es. dan es V a yi ar yiKantor ngg C BR BKKBN BR a u sed ed seringkali b Di ist strriict stri ctsberbeda. h ha ave ea ffffec ecte ted te disepakati tingkat provinsi Aktif adalah data dari Kantor estimations pregnancies, deliveries, etc. For example, CBR 2014 Kabupaten Kupang: 19.8 and tthe th h he e es e sti tima mattipada ions ons fo on ffor or p pr reggna n anc n iie es, s data d elliv i peserta erie ess,, e ttcKB c. F orr e xa amp mple l , menggunakan C CB BR 20 2 14 K abup ab upat up a en nK u an up ng: g BKKBN. 1 9 8 an 9. a nd Province: Pr ovvin ince e: 23 2 23.0 3.0 0 Penggunaan Angka Kelahiran Kasar (CBR) sangat bervariasi antar kabupaten dan provinsi. Penggunaan CBR bervariasi terhadap bersalin ibu A few few yang d di districts stri st rict ct ts ha h have vve en not otberpengaruh ot ssubmitted ub bmi m tt tted e vvalidation ed alid al idat atio at on da data aestimasi ta tto o PH P PHO Osasaran a an and nd AI A AIPMNH. IPM Mibu NH.hamil, NH . The he e ffinal in nibu all vvalidation a alid al idat id a io ondan forr F fo F1 F1-F8 1 -F F8nifas. d da data t ta Sebagai contoh, 2014 19,8 Dinkes has h ha sn no not ot be been n ssent ent en nCBR t to t P PHO HO o HO orrKabupaten tto oA AIPMNH. IP PMN MNH. HKupang: . Fo Forr e ex example: xam mpl p e:sedangkan TThe he ffinal he iin nal a vvalidation alid dat a io oProvinsi: n fo for or th the he 23.0 2014 20 14 4d data a a fo at ffor or Su S Sumba umb mba Barat B Ba ra at ha has as not no ot be b been een n ssubmitted u m ub miitt tte ed d tto o ei e either ith ther er tthe he h eP PHO HO o HO orr to A AIPMNH IP PMN MNH Beberapa kabupaten tidak mengirim data F1-F8 yang valid dan terkini ke Dinkes Provinsi dan AIPMNH. Sebagai contoh: Data F1-F8 yang valid terkini Kabupaten Sumba Barat tahun 2014 tidak dikirim ke Dinkes Provinsi dan AIPMNH.
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3 Masalahmasalah dalam proses validasi data F1-F8 tahun 2014 Permasalahan yang ditemukan dalam proses validasi data F1-F8 tahun 2014 sbb: Empat kabupaten mengembangkan format F1-F8 yang berbeda dari format baku. Perbedaan format pencatatan dan pelaporan data F1-F8 pada empat kabupaten yang menggunakan format mereka sendiri bisa menyebabkan kesalahan dalam perekaman dan kompilasi data. Sebagai contoh, format F1-F8 yang digunakan oleh Kabupaten Sumba Barat berbeda dengan format baku yang digunakan oleh Sumba Timur. Perbedaan dalam sistem pelaporan. Beberapa kabupaten menyajikan data total tahunan, sedangkan kabupaten lain menyajikan lembaran terpisah untuk data bulanan. Ini juga bisa menyebabkan kesalahan dalam penjumlahan. Sebagai contoh, Kabupaten Sikka mengirim laporan bulanan untuk data tahun 2014. Perbedaan pemahaman definisi operasional indikator F1-F8. - Perbedaan penerapan definisi ‘persalinan fasilitas' antara kabupaten. Persalinan Fasilitas adalah persalinan di fasilitas kesehatan sesuai dengan definisi operasional yang direkomendasikan oleh Dinkes Provinsi pada buku pedoman Revolusi KIA. Meskipun kebanyakan kabupaten mendefinisikan ‘persalinan fasilitas’ sebagai persalinan yang terjadi di Puskesmas Memadai, namun empat kabupaten (Manggarai, Manggarai Barat, Flotim dan TTS) memasukkan juga persalinan yang terjadi di Puskesmas Pembantu, Polindes dan Poskesdes.
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2 Proses validasi data
Proses validasi data terdiri dari prosedur dibawah ini: Pada Dinkes Provinsi, Seksi Kesehatan Ibu dan Anak (KIA) yang menangani data F1-F8 melaksanakan validasi data secara mandiri dan komunikasi melalui telepon terutama berkaitan dengan kelengkapan data, inkonsistensi, verifikasi perhitungan dan definisi operasional (definisi operasional didasarkan pada buku panduan PWS KIA yang dipublikasi oleh Kemenkes tahun 2010). Pada Dinkes Kabupaten, proses validasi data dilaksanakan oleh Dinkes Provinsi, staf yang mengelola data F1-F8 pada Dinkes Kabupaten dan AIPMNH. Proses validasi dilakukan dengan metode desk untuk mengkonfirmasi masalah hasil validasi yang ditemukan pada Dinas Kesehatan Provinsi. Dinkes Kabupaten perlu melaksanakan verifikasi khususnya Puskesmas yang bermasalah dalam hal permasalahan pencatatan, kalkulasi data serta perbedaan pemahaman definisi operasional. Selanjutnya Dinkes Provinsi memberikan bimbingan teknis dan mendiskusikan bersama Dinkes Kabupaten terkait pemahaman definisi operasional sesuai dengan yang direkomendasikan oleh Kemenkes pada buku panduan PWS KIA edisi 2010. Dinkes Provinsi dan AIPMNH perlu melaksanakan kunjungan ke Dinkes Kabupaten untuk berdiskusi secara mendalam dan melakukan validasi data. Pada Dinkes Provinsi, merangkum hasil validasi dan selanjutnya membuat rekomendasi untuk pemantapan proses validasi data F1-F8.
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Pendahuluan
Konsistensi dan akurasi data dalam format pelaporan F1-F8 telah menjadi perhatian utama bagi kegiatan kesehatan ibu dan bayi baru lahir di tingkat Puskesmas. Dinas Kesehatan Kabupaten (Dinkes Kabupaten) melakukan kompilasi data dari semua Puskesmas sebelum dikirim ke Dinas Kesehatan Provinsi (Dinkes Provinsi) untuk direkap dan selanjutnya dikirim ke Kementerian Kesehatan (Kemenkes). AIPMNH menggunakan data F1-F8 yang dikompilasi oleh Dinkes Provinsi untuk memantau kemajuan program dan melakukan analisa komparatif antar kabupaten di provinsi NTT. Masalah utama dalam proses validasi data adalah perbedaan pemahaman definisi operasional indikator F1-F8, inkonsistensi, dan ketidaksesuaian saat membandingkan data dengan tahun sebelumnya, atau data antara kabupaten. Masalah lainnya adalah ada data kabupaten yang hilang seluruhnya untuk periode tertentu karena proses mutasi staf adakalanya data tidak diserahkan. Untuk mengatasi masalah-masalah ini, AIPMNH mengembangkan database F1-F8 untuk Dinas Kesehatan Kabupaten dan Puskesmas kemudian dilakukan pemutakhiran secara berkala. Proses validasi data F1-F8 dimulai pada tahun 2012, dan telah dilakukan setiap tahun bekerja sama dengan Dinkes Provinsi di 14 kabupaten/kota dukungan AIPMNH. Selanjutnya dalam mengatasi permasalahan pemahaman definisi operasional Dinkes Provinsi telah mengembangkan buku saku definisi operasional indikator KIA, ‘Buku Saku Pegangan Bidan dalam pencatatan Kesehatan Ibu dan Anak’. Hal ini didasarkan pada pedoman Kemenkes tetapi memberikan definisi operasional yang lebih rinci bagi staf kabupaten dan Puskesmas. Tujuan laporan ini adalah untuk berbagi pengalaman mengenai proses validasi data dan diharapkan dapat direplikasi di wilayah lain di Indonesia. Dokumen ini menyajikan uraian mengenai masalah-masalah aktual yang ditemukan dalam proses validasi data F1-F8 pada tahun 2014. Daftar indikator data F1-F8 terdapat pada Lampiran 1.
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Validasi Kompilasi Data F1-F8 di Tingkat Provinsi Dokumentasi AIPMNH mengenai Praktik Cerdas
AIPMNH is managed by Coffey on behalf of the Australian Government
