@phdthesis{eprints62206,
           title = {PENGARUH FAKTOR SOSIODEMOGRAFI, KARAKTERISTIK FISIK
WILAYAH DAN UPAYA PELAYANAN KESEHATAN TERHADAP
PENYAKIT MALARIA DI KABUPATEN PESAWARAN

PROVINSI LAMPUNG},
          school = {UNIVERITAS LAMPUNG},
          author = {1820011003 Firman},
         address = {UNIVERSITAS LAMPUNG},
       publisher = {FAKULTAS PERTANIAN},
            year = {2021},
             url = {http://digilib.unila.ac.id/62206/},
        abstract = {Malaria merupakan salah satu penyakit menular yang masih menjadi masalah
kesehatan. Tahun 2020 dilaporkan bahwa kasus malaria di dunia sepanjang tahun
2019 sebanyak 229 juta kasus dengan kematian 409 ribu orang meninggal dunia
karena malaria terutama anak Balita. Penyakit malaria tahun 2019 di Indonesia
sebanyak 250.644 kasus, di Provinsi Lampung 1.521 kasus dan di Kabupaten
Pesawaran 888 kasus dengan dengan beberapa kali terjadi kejadian luar biasa
(KLB). Terjadinya kasus malaria dipengaruhi oleh banyak faktor diantaranya
adalah sosiodemografi, karakteristik fisik wilayah dan upaya pelayanan kesehatan.
Penelitian ini bertujuan untuk menentukan pengaruh sosiodemografi, karakteristik
fisik wilayah dan upaya pelayanan kesehatan serta menyusun rekomendasi dalam
upaya pengendalian malaria berbasis model prediksi di Kabupaten Pesawaran.
Penelitian ini adalah penelitian observasional dengan desain penelitian potong
lintang (cross sectional). Sampel sebanyak 436 orang dengan teknik pengumpulan
data menggunakan data sekunder yang bersumber dari e-sismal dan register
laboratorium Puskesmas sedangkan pengambilan data primer dengan panduan
kuesioner untuk mengukur variabel penelitian termasuk koordinat tempat tinggal.
Penelitian ini menggunakan pendekatan hubungan kausal antara variabel (Y) dan
variabel (X). Model dipilih adalah regresi logistik dengan variabel respon
menggunakan skala biner, yaitu malaria versus tidak malaria. Didapatkan model
Ln (p[Malaria]=1])
(1?p[Malaria=1])

= 1.14306 ? 0.0202340 [UMR] + 1.27568 [JKEL] ? 0.527641
[D1\_NLYN/PTMB] - 0.563427 [D1\_PTNI/BRKBN] ? 0.788517
[D1\_PDGG/WRST] ? 1.14061 [D1\_TPAKP] + 0.627210 [D2\_SMP] + 1.61759
[D2\_SMA] + 2.46045 [D2\_PT] + 4.25364 [KRMH] - 4.97580 [MOAN] +
0.0260722 [KTD] + 4.57420 [D3\_JTV5] + 0.883319 [D3JTV10] + 2.11497
[D3\_JTV20 + 2.87183 [D4\_LGN] ? 2.09404 [D4\_RWA] + 0.154395 [D4\_TMBT]
? 1.114470 [D4\_SBTR] + 3.06017 [D4\_SLIG] ? 1.28773 [D4\_KLGL] - 1.83076
[D5\_LVCD] - 2.12563 [D5\_PLMT] ? 2.70970 [D6\_LLINs] - 2.67014 [D6\_IRS] ?
3.22031 [MBS]
Kata kunci : malaria, sosiodemografi, karakteristik fisik wilayah, upaya pelayanan
kesehatan.
Malaria still remains as a public health concern in the world including in Indonesia.
World Health Organization (WHO) estimated 229 million cases of malaria reported
in 2019. Malaria is also known as one of infectious diseases with significant fatality.
Malaria has caused 409,000 deaths occurred in 2019. Under-five children were
identified as the most vulnerable groups in term of death?s occurrence. It was
estimated that 67\% of all malaria deaths alleged with children in the age group.
In Indonesia, there were 250.664 malaria cases reported in 2019. Particularly in
Lampung Province, the public health report showed that there were 1.521 cases of
malaria. Pesawaran is one of endemic areas in Lampung Provinces with high
occurrences of malaria. There were 888 cases reported and it was identified that
malaria outbreaks occurred several times in Puskesmas ?s Area. There are several
factors related to malaria occurrences, including sociodemographic factors,
physical area characteristic, and health service interventions. This study is aimed to
identify the relationship of those factors to malaria occurrences especially in
Pesawaran District. In addition, findings of the study will be able to contribute the
development of malaria controls program based on prediction models in Pesawaran
District. This is an observational study with cross-sectional study design. Primary
and secondary data were collected throughout the study. Standard questionnaires
were used to collect primary data in order to identify study variables including the
domicile coordinate of the study population. Additionally, there were 436 samples
collected from secondary data. The data were taken from e-sismal application and
Puskesmas laboratory?s registers. The study applied variable (Y) and variable (X)
causal model with logistic regression model using binary scales, malaria versus
non-malaria. Get Ln (p[Malaria]=1])
(1?p[Malaria=1])

= 1.14306 ? 0.0202340 [UMR] + 1.27568
[JKEL] ? 0.527641 [D1\_NLYN/PTMB] - 0.563427 [D1\_PTNI/BRKBN] ?
0.788517 [D1\_PDGG/WRST] ? 1.14061 [D1\_TPAKP] + 0.627210 [D2\_SMP] +
1.61759 [D2\_SMA] + 2.46045 [D2\_PT] + 4.25364 [KRMH] - 4.97580 [MOAN]
+ 0.0260722 [KTD] + 4.57420 [D3\_JTV5] + 0.883319 [D3JTV10] + 2.11497
[D3\_JTV20 + 2.87183 [D4\_LGN] ? 2.09404 [D4\_RWA] + 0.154395 [D4\_TMBT]
? 1.114470 [D4\_SBTR] + 3.06017 [D4\_SLIG] ? 1.28773 [D4\_KLGL] - 1.83076
[D5\_LVCD] - 2.12563 [D5\_PLMT] ? 2.70970 [D6\_LLINs] - 2.67014 [D6\_IRS] ?
3.22031 [MBS]
Keyword: malaria, sociodemographic, physical area characteristic, health service
intervensions}
}